System

Abstract

An object of a system according to an embodiment is to enable a student to quickly obtain an answer to a question.SOLUTION: A system includes a question analysis part, an answer generation part, a lesson generation part, a progress tracking part, and a feedback provision part. The question analysis unit analyzes a question of a student. The answer generation unit generates an answer to the question analyzed by the question analysis unit. The lesson generation unit generates the individual lesson based on the answer generated by the answer generation unit. The progress tracking unit tracks the progress of the lesson generated by the lesson generation unit. The feedback providing unit may provide feedback based on the progress tracked by the progress tracking unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have made it difficult for students to quickly obtain answers to their questions in online learning.

[0005] The system according to the embodiment aims to enable students to quickly obtain answers to their questions. [Means for solving the problem]

[0006] The system according to the embodiment includes a question analysis unit, an answer generation unit, a lesson generation unit, a progress tracking unit, and a feedback providing unit. The question analysis unit analyzes a student's question. The answer generation unit generates an answer to the question analyzed by the question analysis unit. The lesson generation unit generates an individual lesson based on the answer generated by the answer generation unit. The progress tracking unit tracks progress of the lesson generated by the lesson generation unit. The feedback providing unit provides feedback based on the progress tracked by the progress tracking unit. [Effects of the Invention]

[0007] The system according to the embodiment allows students to get answers to their questions quickly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The interactive learning assistant system according to an embodiment of the present invention is a system that responds promptly and appropriately to students' questions, provides individual lessons, tracks learning progress, and provides feedback, thereby supporting students' learning and providing prompt and appropriate answers.

[0029] An interactive learning assistant system according to an embodiment includes a question analysis unit, an answer generation unit, a lesson generation unit, a progress tracking unit, and a feedback provision unit. The question analysis unit analyzes a student's question. For example, the question analysis unit analyzes the content of the question using natural language processing technology. The question analysis unit can also extract important parts of the question using keyword extraction technology. The question analysis unit can also analyze background information of the question using context analysis technology. The answer generation unit generates an answer to the question analyzed by the question analysis unit. For example, the answer generation unit generates an answer by referring to a database. The answer generation unit can also generate an answer using a generation AI (e.g., a text generation AI or a multimodal generation AI). The answer generation unit can also generate a template-based answer. The lesson generation unit generates an individual lesson based on the answer generated by the answer generation unit. For example, the lesson generation unit customizes a curriculum based on a student's learning progress. The lesson generation unit can also set learning goals and generate lessons based on the goals. The lesson generation unit can also adjust the lesson content according to the student's level of understanding. The progress tracking unit tracks the progress of the lesson generated by the lesson generation unit. For example, the progress tracking unit records the study time. The progress tracking unit can also evaluate the degree of achievement. The progress tracking unit can also analyze the learning history. The feedback providing unit provides feedback based on the progress tracked by the progress tracking unit. For example, the feedback providing unit provides evaluation comments. The feedback providing unit can also suggest areas for improvement. The feedback providing unit can also suggest next steps. This allows the interactive learning assistant system according to the embodiment to support students' learning and provide prompt and appropriate answers. For example, the output unit displays the feedback to students and teachers via a web application or a mobile application. If students or teachers desire feedback on paper, the results are printed using a printer.Email delivery provides immediate feedback by sending results directly to students and parents.

[0030] The question analysis unit generates multiple answer candidates based on the content of the question, allowing students to select the answer that is easiest to understand. For example, when a student inputs a question, the question analysis unit generates multiple answer candidates for that question. For example, for the question, "I don't know how to solve a quadratic equation," the generation AI presents three candidate solutions: factorization, completing the square, and using a formula. The question analysis unit also analyzes the content of the question and generates answers from different perspectives. For example, for the question, "What are some important historical events?", the generation AI presents answers from political, economic, and cultural perspectives. The question analysis unit also provides concise explanations for each candidate answer to help students select the answer that is easiest to understand. For example, for the question, "What are some types of chemical reactions?", the generation AI presents three candidate solutions: oxidation-reduction reactions, acid-base reactions, and precipitation reactions, with a brief explanation of the characteristics of each reaction. This allows students to select the answer that is easiest to understand, thereby improving learning effectiveness.

[0031] The question analysis unit analyzes the background information of the question and references related past questions and answers to generate more accurate answers. For example, when a student inputs a question, the question analysis unit allows the generation AI to analyze the background information of the question and, if similar questions have been asked in the past, reference those answers to generate a more accurate answer. For example, in response to a question such as "I don't understand the basic concept of differentiation," the generation AI provides a detailed explanation based on answers to similar questions in the past. The question analysis unit also generates more specific answers by analyzing the background information of the question and referencing related past questions and answers. For example, in response to a question such as "I don't understand how to use the present perfect tense in English grammar," the generation AI references past questions and answers and provides an explanation using specific example sentences. In addition, when analyzing the background information of the question, the generation AI also takes into account the student's learning history and generates an answer based on what the student has learned in the past. For example, in response to a question such as "I don't understand the equations of motion in physics," the generation AI references what the student has learned in the past to provide an easy-to-understand answer. By referencing past questions and answers, more accurate answers can be provided.

[0032] The answer generation unit can suggest related video tutorials and interactive simulations when responding to a question. For example, when a student inputs a question, the answer generation unit causes the generation AI to suggest video tutorials related to the question. For example, for a question such as "I don't understand the reaction rate in chemistry," a video tutorial on reaction rate is provided. The answer generation unit also causes the generation AI to suggest interactive simulations depending on the content of the question. For example, for a question such as "I don't understand the composition of forces in physics," a simulation that allows students to visually understand the composition of forces is provided. The answer generation unit also provides learning resources that are visually easy for students to understand by having the generation AI suggest related video tutorials and simulations when responding to a question. For example, for a question such as "I don't understand how to calculate vectors in mathematics," a video or simulation that visually shows how vectors are calculated is provided. In this way, by suggesting video tutorials and simulations, it is possible to provide learning resources that are visually easy to understand.

[0033] The answer generation unit can promote community-based learning by referencing questions and answers from other students depending on the content of the question. For example, when a student inputs a question, the generation AI references questions and answers from other students in response to that question, promoting community-based learning. For example, in response to the question, "Tell me about important events in history," it provides answers to similar questions asked by other students. In addition, in response to the question, the generation AI references questions and answers from other students and suggests related discussions. For example, in response to the question, "I can't solve a programming error," it provides discussions in which other students have solved similar problems. In addition, when responding to a question, the answer generation unit references questions and answers from other students, promoting community-based learning and supporting knowledge sharing among students. For example, in response to the question, "I don't understand the process of biological evolution," it provides answers and discussions in which other students have asked similar questions. In this way, by referencing questions and answers from other students, community-based learning is promoted and knowledge sharing among students is supported.

[0034] The lesson generation unit can analyze a student's learning history and generate an optimal lesson plan based on their past learning patterns. For example, the lesson generation unit uses a generation AI to analyze a student's learning history and generate an optimal lesson plan based on their past learning patterns. For example, it provides a plan that focuses on reviewing units that the student struggled with in the past. When analyzing a student's learning history, the generation AI also takes past grades and study time into consideration to generate an optimal lesson plan. For example, it provides a plan that incorporates methods that have been effective in studying in a short amount of time in the past. The lesson generation unit also uses a generation AI to analyze a student's past learning patterns based on their learning history and generate an optimal lesson plan. For example, it provides a plan that incorporates study methods that have been successful in the past. In this way, by providing an optimal lesson plan based on past learning patterns, effective learning is supported.

[0035] The lesson generation unit can evaluate a student's level of understanding in real time and dynamically adjust the lesson content as needed. In the lesson generation unit, for example, the generation AI evaluates a student's level of understanding in real time and dynamically adjusts the lesson content as needed. For example, if the level of understanding is low, more detailed explanations are added. In addition, when the lesson generation unit evaluates a student's level of understanding in real time, the generation AI measures the level of understanding using quizzes and tests and adjusts the lesson content. For example, if the level of understanding is high, the student moves on to the next unit. In addition, the lesson generation unit evaluates a student's level of understanding in real time and dynamically adjusts the lesson content as needed. For example, if the level of understanding is low, supplementary materials or additional practice questions are provided. In this way, dynamic adjustment of the lesson content according to the student's level of understanding supports effective learning.

[0036] The progress tracking unit analyzes learning progress in detail and can suggest specific areas for improvement and next steps to students. For example, in the progress tracking unit, the generation AI analyzes a student's learning progress in detail and suggests specific areas for improvement. For example, based on the analysis result that "the student makes many calculation errors when solving math problems," the generation AI suggests calculation practice. In addition, when analyzing learning progress, the generation AI identifies the student's weaknesses and suggests the next step. For example, based on the analysis result that "the student's English listening ability is lacking," the generation AI suggests listening practice. In addition, in the progress tracking unit, the generation AI analyzes a student's learning progress in detail and suggests specific areas for improvement and next steps. For example, based on the analysis result that "the student has difficulty remembering historical dates," the generation AI suggests flash cards for memorizing dates. In this way, by analyzing learning progress in detail and suggesting specific areas for improvement and next steps, learning effectiveness is improved.

[0037] The progress tracking unit can stimulate a competitive spirit by comparing a student's learning progress with other students and providing a relative evaluation. In the progress tracking unit, for example, the generation AI compares a student's learning progress with other students and provides a relative evaluation. For example, it provides feedback such as, "Your grades are higher than the class average." In addition, when comparing learning progress with other students, the generation AI provides a relative evaluation in the form of a ranking. For example, it provides feedback such as, "Your grades are third in the class." In addition, the progress tracking unit stimulates a competitive spirit by comparing a student's learning progress with other students and providing a relative evaluation. For example, it provides feedback such as, "Your progress is slower than other students," thereby increasing motivation to learn. In this way, by comparing learning progress with other students and providing a relative evaluation, a competitive spirit is stimulated and motivation to learn is increased.

[0038] The feedback providing unit can automatically share the tracking results of learning progress with parents and teachers to promote support. For example, the generation AI automatically shares the tracking results of learning progress with parents to promote support. For example, the feedback providing unit provides a report saying, "Your child's learning progress is going well." The feedback providing unit also promotes support by having the generation AI automatically share the tracking results of learning progress with teachers. For example, the feedback providing unit sends a notification saying, "Please check your student's progress." The feedback providing unit also promotes support by having the generation AI automatically share the tracking results of learning progress with parents and teachers. For example, the feedback providing unit provides a report saying, "Your child is struggling with a particular unit" and suggests additional support. In this way, sharing the tracking results of learning progress with parents and teachers promotes support and assists students' learning.

[0039] The feedback providing unit can suggest appropriate rewards and incentives to students according to their learning progress. For example, the generation AI in the feedback providing unit suggests appropriate rewards to students according to their learning progress. For example, it may suggest, "Now that you've achieved your goal, take some time to read your favorite book." The feedback providing unit also suggests incentives to students according to their learning progress. For example, it may suggest, "If you get a high score on the next test, spend more time playing your favorite game." The feedback providing unit also suggests appropriate rewards and incentives to students according to their learning progress. For example, it may suggest, "Once you've completed this unit, take some time to hang out with your friends." In this way, by suggesting appropriate rewards and incentives according to their learning progress, students' motivation to learn is increased.

[0040] The lesson generation unit can automatically share the content of individual lessons with other students to promote collaborative learning. For example, the generation AI of the lesson generation unit automatically shares the content of individual lessons with other students to promote collaborative learning. For example, students studying the same unit share the lesson content and hold discussions. The lesson generation unit also promotes collaborative learning between students by having the generation AI automatically share the content of individual lessons. For example, students working on the same assignment share their answers and provide feedback to each other. The lesson generation unit also promotes collaborative learning by having the generation AI automatically share the content of individual lessons with other students. For example, students interested in the same topic share the lesson content and work together on a project. In this way, sharing the content of individual lessons with other students promotes collaborative learning and supports knowledge sharing between students.

[0041] The lesson generation unit can suggest appropriate break times and refreshment methods to students depending on the progress of the lesson. For example, the generation AI of the lesson generation unit suggests appropriate break times to students depending on the progress of the lesson. For example, it may advise students to take a short break after studying for a certain period of time. The lesson generation unit also analyzes the progress of the lesson, and the generation AI suggests refreshment methods to students. For example, if the students feel tired, it may suggest eye stretches. The lesson generation unit also suggests appropriate break times and refreshment methods to students depending on the progress of the lesson. For example, if the students' concentration is declining, it may suggest light exercise. In this way, by suggesting appropriate break times and refreshment methods depending on the progress of the lesson, the students' concentration can be maintained.

[0042] The feedback providing unit can automatically share the tracking results of learning progress with parents and teachers to promote support. For example, the generation AI automatically shares the tracking results of learning progress with parents to promote support. For example, the feedback providing unit provides a report saying, "Your child's learning progress is going well." The feedback providing unit also promotes support by having the generation AI automatically share the tracking results of learning progress with teachers. For example, the feedback providing unit sends a notification saying, "Please check your student's progress." The feedback providing unit also promotes support by having the generation AI automatically share the tracking results of learning progress with parents and teachers. For example, the feedback providing unit provides a report saying, "Your child is struggling with a particular unit" and suggests additional support. In this way, sharing the tracking results of learning progress with parents and teachers promotes support and assists students' learning.

[0043] The feedback providing unit can suggest appropriate rewards and incentives to students according to their learning progress. For example, the generation AI in the feedback providing unit suggests appropriate rewards to students according to their learning progress. For example, it may suggest, "Now that you've achieved your goal, take some time to read your favorite book." The feedback providing unit also suggests incentives to students according to their learning progress. For example, it may suggest, "If you get a high score on the next test, spend more time playing your favorite game." The feedback providing unit also suggests appropriate rewards and incentives to students according to their learning progress. For example, it may suggest, "Once you've completed this unit, take some time to hang out with your friends." In this way, by suggesting appropriate rewards and incentives according to their learning progress, students' motivation to learn is increased.

[0044] The lesson generation unit can automatically share the content of individual lessons with other students to promote collaborative learning. For example, the generation AI of the lesson generation unit automatically shares the content of individual lessons with other students to promote collaborative learning. For example, students studying the same unit share the lesson content and hold discussions. The lesson generation unit also promotes collaborative learning between students by having the generation AI automatically share the content of individual lessons. For example, students working on the same assignment share their answers and provide feedback to each other. The lesson generation unit also promotes collaborative learning by having the generation AI automatically share the content of individual lessons with other students. For example, students interested in the same topic share the lesson content and work together on a project. In this way, sharing the content of individual lessons with other students promotes collaborative learning and supports knowledge sharing between students.

[0045] The lesson generation unit can suggest appropriate break times and refreshment methods to students depending on the progress of the lesson. For example, the generation AI of the lesson generation unit suggests appropriate break times to students depending on the progress of the lesson. For example, it may advise students to take a short break after studying for a certain period of time. The lesson generation unit also analyzes the progress of the lesson, and the generation AI suggests refreshment methods to students. For example, if the students feel tired, it may suggest eye stretches. The lesson generation unit also suggests appropriate break times and refreshment methods to students depending on the progress of the lesson. For example, if the students' concentration is declining, it may suggest light exercise. In this way, by suggesting appropriate break times and refreshment methods depending on the progress of the lesson, the students' concentration can be maintained.

[0046] The learning resource providing unit can analyze a student's learning history and provide optimal learning resources that are individually customized. In the learning resource providing unit, for example, the generation AI analyzes a student's learning history and provides optimal learning resources that are individually customized. For example, it provides learning materials related to units that the student struggled with in the past. Furthermore, when analyzing a student's learning history, the generation AI takes past grades and study time into consideration and provides optimal learning resources. For example, it provides learning materials that incorporate methods that have been used to effectively study in a short amount of time in the past. Furthermore, the learning resource providing unit can provide optimal learning resources that are individually customized based on the student's learning history. For example, it provides learning materials that incorporate learning methods that have been successful in the past. In this way, effective learning is supported by analyzing a student's learning history and providing optimal learning resources that are individually customized.

[0047] The learning resource providing unit can track the usage of learning resources and prioritize suggest effective resources. In the learning resource providing unit, for example, the generation AI tracks the usage of learning resources and prioritizes suggesting effective resources. For example, it prioritizes providing teaching materials that have received high ratings in the past. Furthermore, when analyzing the usage of learning resources, the generation AI takes student feedback into consideration and suggests effective resources. For example, it prioritizes providing teaching materials that have been used by many students in the past. Furthermore, in the learning resource providing unit, the generation AI tracks the usage of learning resources and prioritizes suggesting effective resources. For example, it prioritizes providing teaching materials that have received high ratings in the past. In this way, by tracking the usage of learning resources and preferentially suggesting effective resources, learning effectiveness is improved.

[0048] The learning resource providing unit can automatically share learning resources with other students to promote collaborative learning. In the learning resource providing unit, for example, the generating AI automatically shares learning resources with other students to promote collaborative learning. For example, students studying the same unit share resources and hold discussions. The learning resource providing unit also promotes collaborative learning between students by having the generating AI automatically share learning resources. For example, students working on the same assignment share resources and provide feedback to each other. The learning resource providing unit also promotes collaborative learning by having the generating AI automatically share learning resources with other students. For example, students interested in the same topic share resources and work on a project together. In this way, sharing learning resources with other students promotes collaborative learning and supports knowledge sharing between students.

[0049] The learning resource providing unit can propose an appropriate study schedule to the student when providing the learning resources. For example, the learning resource providing unit proposes an appropriate study schedule to the student when the generation AI provides the learning resources. For example, it provides a schedule for studying a specific unit. Furthermore, the learning resource providing unit proposes an appropriate study schedule to the student when providing the learning resources. For example, it provides a schedule for studying efficiently before an exam. Furthermore, the learning resource providing unit proposes an appropriate study schedule to the student when the generation AI provides the learning resources. For example, it provides a schedule for making a long-term study plan. In this way, by proposing an appropriate study schedule when providing the learning resources, effective learning is supported.

[0050] The interactive interface providing unit can increase motivation to learn by displaying a student's learning history and progress status in real time through the interactive interface. In the interactive interface providing unit, for example, the generation AI displays a student's learning history and progress status in real time through the interactive interface, thereby increasing motivation to learn. For example, the learning progress is visually displayed in graphs and charts. In addition, the interactive interface providing unit can increase motivation to learn by displaying a student's learning history and progress status in real time through the interactive interface. For example, it displays achieved goals and next steps. In addition, the interactive interface providing unit can increase motivation to learn by displaying a student's learning history and progress status in real time through the interactive interface. For example, it visually displays learning progress, giving a sense of accomplishment. In this way, displaying learning history and progress status in real time increases motivation to learn.

[0051] The interactive interface providing unit can provide appropriate study advice and hints to students according to the content of the dialogue. In the interactive interface providing unit, for example, the generation AI provides appropriate study advice and hints to students according to the content of the dialogue. For example, in response to a dialogue such as "I can't solve the math problem," a specific solution hint is provided. In addition, the interactive interface providing unit provides appropriate study advice to students according to the content of the dialogue. For example, in response to a dialogue such as "I don't understand English grammar," the rules of grammar are explained. In addition, the interactive interface providing unit provides appropriate study advice and hints to students according to the content of the dialogue. For example, in response to a dialogue such as "I can't remember historical dates," tips on remembering dates are provided. In this way, appropriate study advice and hints are provided according to the content of the dialogue, thereby improving learning effectiveness.

[0052] The interactive interface providing unit can set appropriate learning goals for students through the interactive interface and support their achievement. In the interactive interface providing unit, for example, the generation AI sets appropriate learning goals for students through the interactive interface and supports their achievement. For example, it sets a goal such as "memorize 50 English words in one week" and tracks progress. The interactive interface providing unit also allows the generation AI to set appropriate learning goals for students through the interactive interface and supports their achievement. For example, it sets a goal such as "master the basics of mathematics in one month" and tracks progress. The interactive interface providing unit also allows the generation AI to set appropriate learning goals for students through the interactive interface and supports their achievement. For example, it sets a goal such as "memorize important historical events in three months" and tracks progress. In this way, setting appropriate learning goals for students and supporting their achievement increases their motivation to learn.

[0053] The interactive interface providing unit can suggest appropriate learning resources and teaching materials to students according to the content of the dialogue. In the interactive interface providing unit, for example, the generation AI suggests appropriate learning resources and teaching materials to students according to the content of the dialogue. For example, in response to a dialogue such as "I can't solve math problems," it suggests related teaching materials and reference books. In addition, the interactive interface providing unit suggests appropriate learning resources to students according to the content of the dialogue. For example, in response to a dialogue such as "I don't understand English grammar," it suggests teaching materials that explain the rules of grammar. In addition, the interactive interface providing unit suggests appropriate learning resources and teaching materials to students according to the content of the dialogue. For example, in response to a dialogue such as "I can't remember historical dates," it suggests teaching materials for memorizing dates. In this way, by suggesting appropriate learning resources and teaching materials according to the content of the dialogue, learning effectiveness is improved.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The interactive learning assistant system can also analyze students' learning styles and suggest optimal learning methods. For example, for students who prefer visual learning, it can provide learning materials that make extensive use of diagrams and graphs. For students who prefer auditory learning, it can provide learning materials with audio commentary. Furthermore, it can suggest interactive simulations and experiments for students who prefer hands-on learning. This can improve learning effectiveness by providing optimal learning methods that suit each student's learning style.

[0056] The interactive learning assistant system can also analyze a student's learning environment and suggest the optimal learning environment. For example, for a student who is better able to concentrate in a quiet environment, it can suggest using noise-canceling headphones. For a student who learns better in natural light, it can suggest studying by a window. For a student who learns better in a relaxed environment, it can suggest studying while listening to relaxing music. In this way, learning effectiveness can be improved by providing the optimal learning environment according to the student's learning environment.

[0057] The interactive learning assistant system can also set learning goals for students and track their progress. For example, a short-term goal can be set as "memorize 50 English words in one week" and progress toward that goal can be tracked. A medium-term goal can be set as "master the basics of mathematics in one month" and progress toward that goal can be tracked. A long-term goal can be set as "memorize important historical events in three months" and progress toward that goal can be tracked. In this way, setting students' learning goals and tracking their progress can increase their motivation to learn.

[0058] The interactive learning assistant system can also compare a student's learning progress with other students and provide a relative evaluation. For example, it can provide feedback such as, "Your grades are higher than the class average." It can also provide feedback such as, "Your grades are third in the class." It can also provide feedback such as, "Your progress is lagging behind other students," which can increase motivation to learn. In this way, by comparing a student's learning progress with other students and providing a relative evaluation, it can stimulate a competitive spirit and increase motivation to learn.

[0059] The interactive learning assistant system can also analyze a student's learning history and generate an optimal lesson plan based on their past learning patterns. For example, it can provide a plan that focuses on reviewing units that students struggled with in the past. It can also provide a plan that incorporates methods that have been effective in learning in a short amount of time in the past. It can also provide a plan that incorporates learning methods that have been successful in the past. This makes it possible to support effective learning by providing optimal lesson plans based on students' past learning patterns.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The question analysis unit analyzes the student's question. For example, the question analysis unit may use natural language processing technology to analyze the content of the question, keyword extraction technology to extract important parts of the question, and context analysis technology to analyze the background information of the question. Step 2: The answer generation unit generates an answer to the question analyzed by the question analysis unit. For example, the answer generation unit can generate an answer by referring to a database, generate an answer using a generation AI (e.g., a text generation AI or a multimodal generation AI), or generate a template-based answer. Step 3: The lesson generation unit generates individual lessons based on the answers generated by the answer generation unit. For example, the lesson generation unit customizes a curriculum based on the student's learning progress, sets learning goals, generates lessons based on the goals, and adjusts the lesson content according to the student's level of understanding. Step 4: The progress tracking unit tracks the progress of the lesson generated by the lesson generation unit, for example, by recording study time, evaluating achievement, and analyzing study history. Step 5: The feedback providing unit provides feedback based on the progress tracked by the progress tracking unit. For example, the feedback providing unit provides evaluation comments, suggests improvements, and suggests next steps.

[0062] (Example 2) The interactive learning assistant system according to an embodiment of the present invention is a system that responds promptly and appropriately to students' questions, provides individual lessons, tracks learning progress, and provides feedback, thereby supporting students' learning and providing prompt and appropriate answers.

[0063] An interactive learning assistant system according to an embodiment includes a question analysis unit, an answer generation unit, a lesson generation unit, a progress tracking unit, and a feedback provision unit. The question analysis unit analyzes a student's question. For example, the question analysis unit analyzes the content of the question using natural language processing technology. The question analysis unit can also extract important parts of the question using keyword extraction technology. The question analysis unit can also analyze background information of the question using context analysis technology. The answer generation unit generates an answer to the question analyzed by the question analysis unit. For example, the answer generation unit generates an answer by referring to a database. The answer generation unit can also generate an answer using a generation AI (e.g., a text generation AI or a multimodal generation AI). The answer generation unit can also generate a template-based answer. The lesson generation unit generates an individual lesson based on the answer generated by the answer generation unit. For example, the lesson generation unit customizes a curriculum based on a student's learning progress. The lesson generation unit can also set learning goals and generate lessons based on the goals. The lesson generation unit can also adjust the lesson content according to the student's level of understanding. The progress tracking unit tracks the progress of the lesson generated by the lesson generation unit. For example, the progress tracking unit records the study time. The progress tracking unit can also evaluate the degree of achievement. The progress tracking unit can also analyze the learning history. The feedback providing unit provides feedback based on the progress tracked by the progress tracking unit. For example, the feedback providing unit provides evaluation comments. The feedback providing unit can also suggest areas for improvement. The feedback providing unit can also suggest next steps. This allows the interactive learning assistant system according to the embodiment to support students' learning and provide prompt and appropriate answers. For example, the output unit displays the feedback to students and teachers via a web application or a mobile application. If students or teachers desire feedback on paper, the results are printed using a printer.Email delivery provides immediate feedback by sending results directly to students and parents.

[0064] The question analysis unit generates multiple answer candidates based on the content of the question, allowing students to select the answer that is easiest to understand. For example, when a student inputs a question, the question analysis unit generates multiple answer candidates for that question. For example, for the question, "I don't know how to solve a quadratic equation," the generation AI presents three candidate solutions: factorization, completing the square, and using a formula. The question analysis unit also analyzes the content of the question and generates answers from different perspectives. For example, for the question, "What are some important historical events?", the generation AI presents answers from political, economic, and cultural perspectives. The question analysis unit also provides concise explanations for each candidate answer to help students select the answer that is easiest to understand. For example, for the question, "What are some types of chemical reactions?", the generation AI presents three candidate solutions: oxidation-reduction reactions, acid-base reactions, and precipitation reactions, with a brief explanation of the characteristics of each reaction. This allows students to select the answer that is easiest to understand, thereby improving learning effectiveness.

[0065] The question analysis unit analyzes the background information of the question and references related past questions and answers to generate more accurate answers. For example, when a student inputs a question, the question analysis unit allows the generation AI to analyze the background information of the question and, if similar questions have been asked in the past, reference those answers to generate a more accurate answer. For example, in response to a question such as "I don't understand the basic concept of differentiation," the generation AI provides a detailed explanation based on answers to similar questions in the past. The question analysis unit also generates more specific answers by analyzing the background information of the question and referencing related past questions and answers. For example, in response to a question such as "I don't understand how to use the present perfect tense in English grammar," the generation AI references past questions and answers and provides an explanation using specific example sentences. In addition, when analyzing the background information of the question, the generation AI also takes into account the student's learning history and generates an answer based on what the student has learned in the past. For example, in response to a question such as "I don't understand the equations of motion in physics," the generation AI references what the student has learned in the past to provide an easy-to-understand answer. By referencing past questions and answers, more accurate answers can be provided.

[0066] The question analysis unit can use the emotion estimation function to analyze a student's emotions when asking a question and generate an answer that reduces stress and anxiety. For example, when a student inputs a question, the question analysis unit uses the emotion estimation function to analyze the student's emotions and generate an answer that reduces stress and anxiety. For example, in response to a question such as, "I can't solve any math problems," an answer with words of encouragement is provided. The question analysis unit also uses the emotion estimation function to analyze a student's emotions in real time when asking a question and generates an answer that elicits positive emotions. For example, in response to a question such as, "I'm not good at speaking English," advice that builds confidence is provided. The question analysis unit also analyzes a student's emotions and generates an answer that helps the generation AI relax in order to reduce stress and anxiety. For example, in response to a question such as, "I'm nervous before the exam," relaxation techniques and words of encouragement are provided. In this way, by providing an answer that takes the student's emotions into consideration, stress and anxiety are reduced.

[0067] The answer generation unit can suggest related video tutorials and interactive simulations when responding to a question. For example, when a student inputs a question, the answer generation unit causes the generation AI to suggest video tutorials related to the question. For example, for a question such as "I don't understand the reaction rate in chemistry," a video tutorial on reaction rate is provided. The answer generation unit also causes the generation AI to suggest interactive simulations depending on the content of the question. For example, for a question such as "I don't understand the composition of forces in physics," a simulation that allows students to visually understand the composition of forces is provided. The answer generation unit also provides learning resources that are visually easy for students to understand by having the generation AI suggest related video tutorials and simulations when responding to a question. For example, for a question such as "I don't understand how to calculate vectors in mathematics," a video or simulation that visually shows how vectors are calculated is provided. In this way, by suggesting video tutorials and simulations, it is possible to provide learning resources that are visually easy to understand.

[0068] The answer generation unit can promote community-based learning by referencing questions and answers from other students depending on the content of the question. For example, when a student inputs a question, the generation AI references questions and answers from other students in response to that question, promoting community-based learning. For example, in response to the question, "Tell me about important events in history," it provides answers to similar questions asked by other students. In addition, in response to the question, the generation AI references questions and answers from other students and suggests related discussions. For example, in response to the question, "I can't solve a programming error," it provides discussions in which other students have solved similar problems. In addition, when responding to a question, the answer generation unit references questions and answers from other students, promoting community-based learning and supporting knowledge sharing among students. For example, in response to the question, "I don't understand the process of biological evolution," it provides answers and discussions in which other students have asked similar questions. In this way, by referencing questions and answers from other students, community-based learning is promoted and knowledge sharing among students is supported.

[0069] The answer generation unit can use the emotion estimation function to analyze the emotions of students when they input questions in real time, and provide an interface that elicits positive emotions. For example, when a student inputs a question, the generation AI in the answer generation unit uses the emotion estimation function to analyze the student's emotions in real time, and provides an interface that elicits positive emotions. For example, an encouraging message is displayed as the question is being entered. The answer generation unit also uses the emotion estimation function to analyze the emotions of students when they input questions, and provides an interface that elicits positive emotions. For example, positive feedback is displayed as the question is being entered. The answer generation unit also uses the emotion estimation function to analyze the emotions of students when they input questions, and provides an interface that elicits positive emotions. For example, animations or sounds that elicit positive emotions are played as the question is being entered. In this way, the student's emotions are analyzed in real time, and positive emotions are elicited, thereby increasing their motivation to learn.

[0070] The lesson generation unit can analyze a student's learning history and generate an optimal lesson plan based on their past learning patterns. For example, the lesson generation unit uses a generation AI to analyze a student's learning history and generate an optimal lesson plan based on their past learning patterns. For example, it provides a plan that focuses on reviewing units that the student struggled with in the past. When analyzing a student's learning history, the generation AI also takes past grades and study time into consideration to generate an optimal lesson plan. For example, it provides a plan that incorporates methods that have been effective in studying in a short amount of time in the past. The lesson generation unit also uses a generation AI to analyze a student's past learning patterns based on their learning history and generate an optimal lesson plan. For example, it provides a plan that incorporates study methods that have been successful in the past. In this way, by providing an optimal lesson plan based on past learning patterns, effective learning is supported.

[0071] The lesson generation unit can evaluate a student's level of understanding in real time and dynamically adjust the lesson content as needed. In the lesson generation unit, for example, the generation AI evaluates a student's level of understanding in real time and dynamically adjusts the lesson content as needed. For example, if the level of understanding is low, more detailed explanations are added. In addition, when the lesson generation unit evaluates a student's level of understanding in real time, the generation AI measures the level of understanding using quizzes and tests and adjusts the lesson content. For example, if the level of understanding is high, the student moves on to the next unit. In addition, the lesson generation unit evaluates a student's level of understanding in real time and dynamically adjusts the lesson content as needed. For example, if the level of understanding is low, supplementary materials or additional practice questions are provided. In this way, dynamic adjustment of the lesson content according to the student's level of understanding supports effective learning.

[0072] The lesson generation unit can use the emotion estimation function to analyze a student's emotions while studying and provide lesson content to maintain motivation. For example, the lesson generation unit uses the emotion estimation function to analyze a student's emotions while studying and provide lesson content to maintain motivation. For example, if a student feels tired while studying, the lesson generation unit suggests taking a break to refresh. The lesson generation unit also uses the emotion estimation function to analyze a student's emotions while studying in real time and provide lesson content to maintain motivation. For example, if a student feels stressed while studying, the lesson generation unit displays an encouraging message. The lesson generation unit also uses the emotion estimation function to analyze a student's emotions while studying and provide lesson content to maintain motivation. For example, interactive elements are added to elicit positive emotions while studying. This improves learning effectiveness by analyzing a student's emotions and providing lesson content to maintain motivation.

[0073] The progress tracking unit analyzes learning progress in detail and can suggest specific areas for improvement and next steps to students. For example, in the progress tracking unit, the generation AI analyzes a student's learning progress in detail and suggests specific areas for improvement. For example, based on the analysis result that "the student makes many calculation errors when solving math problems," the generation AI suggests calculation practice. In addition, when analyzing learning progress, the generation AI identifies the student's weaknesses and suggests the next step. For example, based on the analysis result that "the student's English listening ability is lacking," the generation AI suggests listening practice. In addition, in the progress tracking unit, the generation AI analyzes a student's learning progress in detail and suggests specific areas for improvement and next steps. For example, based on the analysis result that "the student has difficulty remembering historical dates," the generation AI suggests flash cards for memorizing dates. In this way, by analyzing learning progress in detail and suggesting specific areas for improvement and next steps, learning effectiveness is improved.

[0074] The progress tracking unit can stimulate a competitive spirit by comparing a student's learning progress with other students and providing a relative evaluation. In the progress tracking unit, for example, the generation AI compares a student's learning progress with other students and provides a relative evaluation. For example, it provides feedback such as, "Your grades are higher than the class average." In addition, when comparing learning progress with other students, the generation AI provides a relative evaluation in the form of a ranking. For example, it provides feedback such as, "Your grades are third in the class." In addition, the progress tracking unit stimulates a competitive spirit by comparing a student's learning progress with other students and providing a relative evaluation. For example, it provides feedback such as, "Your progress is slower than other students," thereby increasing motivation to learn. In this way, by comparing learning progress with other students and providing a relative evaluation, a competitive spirit is stimulated and motivation to learn is increased.

[0075] The progress tracking unit can use the emotion estimation function to analyze a student's emotions regarding their learning progress and provide positive feedback. For example, the progress tracking unit uses the emotion estimation function to analyze a student's emotions regarding their learning progress and provide positive feedback. For example, it can provide feedback such as, "Your attitude of trying hard is admirable." The progress tracking unit can also use the emotion estimation function to analyze a student's emotions regarding their learning progress in real time and provide positive feedback. For example, it can provide feedback such as, "You're improving little by little." The progress tracking unit can also use the emotion estimation function to analyze a student's emotions regarding their learning progress and provide positive feedback. For example, it can provide feedback such as, "Your efforts are paying off." In this way, analyzing a student's emotions and providing positive feedback increases their motivation to learn.

[0076] The feedback providing unit can automatically share the tracking results of learning progress with parents and teachers to promote support. For example, the generation AI automatically shares the tracking results of learning progress with parents to promote support. For example, the feedback providing unit provides a report saying, "Your child's learning progress is going well." The feedback providing unit also promotes support by having the generation AI automatically share the tracking results of learning progress with teachers. For example, the feedback providing unit sends a notification saying, "Please check your student's progress." The feedback providing unit also promotes support by having the generation AI automatically share the tracking results of learning progress with parents and teachers. For example, the feedback providing unit provides a report saying, "Your child is struggling with a particular unit" and suggests additional support. In this way, sharing the tracking results of learning progress with parents and teachers promotes support and assists students' learning.

[0077] The feedback providing unit can suggest appropriate rewards and incentives to students according to their learning progress. For example, the generation AI in the feedback providing unit suggests appropriate rewards to students according to their learning progress. For example, it may suggest, "Now that you've achieved your goal, take some time to read your favorite book." The feedback providing unit also suggests incentives to students according to their learning progress. For example, it may suggest, "If you get a high score on the next test, spend more time playing your favorite game." The feedback providing unit also suggests appropriate rewards and incentives to students according to their learning progress. For example, it may suggest, "Once you've completed this unit, take some time to hang out with your friends." In this way, by suggesting appropriate rewards and incentives according to their learning progress, students' motivation to learn is increased.

[0078] The feedback providing unit can use the emotion estimation function to analyze students' emotions regarding their learning progress in real time and provide feedback that elicits positive emotions. For example, the feedback providing unit uses the emotion estimation function to analyze students' emotions regarding their learning progress in real time and provide feedback that elicits positive emotions. For example, the feedback providing unit provides feedback such as, "Your efforts are paying off." The feedback providing unit can also use the emotion estimation function to analyze students' emotions regarding their learning progress in real time and provide feedback that elicits positive emotions. For example, the feedback providing unit provides feedback such as, "You are improving little by little." The feedback providing unit can also use the emotion estimation function to analyze students' emotions regarding their learning progress in real time and provide feedback that elicits positive emotions. For example, the feedback providing unit provides feedback such as, "Your efforts are paying off." In this way, the generation AI can analyze students' emotions in real time and provide positive feedback, thereby increasing their motivation to learn.

[0079] The lesson generation unit can automatically share the content of individual lessons with other students to promote collaborative learning. For example, the generation AI of the lesson generation unit automatically shares the content of individual lessons with other students to promote collaborative learning. For example, students studying the same unit share the lesson content and hold discussions. The lesson generation unit also promotes collaborative learning between students by having the generation AI automatically share the content of individual lessons. For example, students working on the same assignment share their answers and provide feedback to each other. The lesson generation unit also promotes collaborative learning by having the generation AI automatically share the content of individual lessons with other students. For example, students interested in the same topic share the lesson content and work together on a project. In this way, sharing the content of individual lessons with other students promotes collaborative learning and supports knowledge sharing between students.

[0080] The lesson generation unit can suggest appropriate break times and refreshment methods to students depending on the progress of the lesson. For example, the generation AI of the lesson generation unit suggests appropriate break times to students depending on the progress of the lesson. For example, it may advise students to take a short break after studying for a certain period of time. The lesson generation unit also analyzes the progress of the lesson, and the generation AI suggests refreshment methods to students. For example, if the students feel tired, it may suggest eye stretches. The lesson generation unit also suggests appropriate break times and refreshment methods to students depending on the progress of the lesson. For example, if the students' concentration is declining, it may suggest light exercise. In this way, by suggesting appropriate break times and refreshment methods depending on the progress of the lesson, the students' concentration can be maintained.

[0081] The lesson generation unit can use the emotion estimation function to analyze students' emotions while studying in real time and provide lesson content that elicits positive emotions. For example, the lesson generation unit uses the emotion estimation function to analyze students' emotions while studying in real time and provide lesson content that elicits positive emotions. For example, interactive elements are added to elicit positive emotions while studying. The lesson generation unit also uses the emotion estimation function to analyze students' emotions while studying in real time and provide lesson content that elicits positive emotions. For example, if a student is feeling stressed while studying, an encouraging message is displayed. The lesson generation unit also uses the emotion estimation function to analyze students' emotions while studying in real time and provide lesson content that elicits positive emotions. For example, if a student is feeling tired while studying, the lesson generation unit suggests taking a break to refresh. In this way, the generation AI analyzes students' emotions in real time and provides lesson content that elicits positive emotions, thereby increasing their motivation to learn.

[0082] The feedback providing unit can automatically share the tracking results of learning progress with parents and teachers to promote support. For example, the generation AI automatically shares the tracking results of learning progress with parents to promote support. For example, the feedback providing unit provides a report saying, "Your child's learning progress is going well." The feedback providing unit also promotes support by having the generation AI automatically share the tracking results of learning progress with teachers. For example, the feedback providing unit sends a notification saying, "Please check your student's progress." The feedback providing unit also promotes support by having the generation AI automatically share the tracking results of learning progress with parents and teachers. For example, the feedback providing unit provides a report saying, "Your child is struggling with a particular unit" and suggests additional support. In this way, sharing the tracking results of learning progress with parents and teachers promotes support and assists students' learning.

[0083] The feedback providing unit can suggest appropriate rewards and incentives to students according to their learning progress. For example, the generation AI in the feedback providing unit suggests appropriate rewards to students according to their learning progress. For example, it may suggest, "Now that you've achieved your goal, take some time to read your favorite book." The feedback providing unit also suggests incentives to students according to their learning progress. For example, it may suggest, "If you get a high score on the next test, spend more time playing your favorite game." The feedback providing unit also suggests appropriate rewards and incentives to students according to their learning progress. For example, it may suggest, "Once you've completed this unit, take some time to hang out with your friends." In this way, by suggesting appropriate rewards and incentives according to their learning progress, students' motivation to learn is increased.

[0084] The feedback providing unit can use the emotion estimation function to analyze students' emotions regarding their learning progress in real time and provide feedback that elicits positive emotions. For example, the feedback providing unit uses the emotion estimation function to analyze students' emotions regarding their learning progress in real time and provide feedback that elicits positive emotions. For example, the feedback providing unit provides feedback such as, "Your efforts are paying off." The feedback providing unit can also use the emotion estimation function to analyze students' emotions regarding their learning progress in real time and provide feedback that elicits positive emotions. For example, the feedback providing unit provides feedback such as, "You are improving little by little." The feedback providing unit can also use the emotion estimation function to analyze students' emotions regarding their learning progress in real time and provide feedback that elicits positive emotions. For example, the feedback providing unit provides feedback such as, "Your efforts are paying off." In this way, the generation AI can analyze students' emotions in real time and provide positive feedback, thereby increasing their motivation to learn.

[0085] The lesson generation unit can automatically share the content of individual lessons with other students to promote collaborative learning. For example, the generation AI of the lesson generation unit automatically shares the content of individual lessons with other students to promote collaborative learning. For example, students studying the same unit share the lesson content and hold discussions. The lesson generation unit also promotes collaborative learning between students by having the generation AI automatically share the content of individual lessons. For example, students working on the same assignment share their answers and provide feedback to each other. The lesson generation unit also promotes collaborative learning by having the generation AI automatically share the content of individual lessons with other students. For example, students interested in the same topic share the lesson content and work together on a project. In this way, sharing the content of individual lessons with other students promotes collaborative learning and supports knowledge sharing between students.

[0086] The lesson generation unit can suggest appropriate break times and refreshment methods to students depending on the progress of the lesson. For example, the generation AI of the lesson generation unit suggests appropriate break times to students depending on the progress of the lesson. For example, it may advise students to take a short break after studying for a certain period of time. The lesson generation unit also analyzes the progress of the lesson, and the generation AI suggests refreshment methods to students. For example, if the students feel tired, it may suggest eye stretches. The lesson generation unit also suggests appropriate break times and refreshment methods to students depending on the progress of the lesson. For example, if the students' concentration is declining, it may suggest light exercise. In this way, by suggesting appropriate break times and refreshment methods depending on the progress of the lesson, the students' concentration can be maintained.

[0087] The lesson generation unit can use the emotion estimation function to analyze students' emotions while studying in real time and provide lesson content that elicits positive emotions. For example, the lesson generation unit uses the emotion estimation function to analyze students' emotions while studying in real time and provide lesson content that elicits positive emotions. For example, interactive elements are added to elicit positive emotions while studying. The lesson generation unit also uses the emotion estimation function to analyze students' emotions while studying in real time and provide lesson content that elicits positive emotions. For example, if a student is feeling stressed while studying, an encouraging message is displayed. The lesson generation unit also uses the emotion estimation function to analyze students' emotions while studying in real time and provide lesson content that elicits positive emotions. For example, if a student is feeling tired while studying, the lesson generation unit suggests taking a break to refresh. In this way, the generation AI analyzes students' emotions in real time and provides lesson content that elicits positive emotions, thereby increasing their motivation to learn.

[0088] The learning resource providing unit can analyze a student's learning history and provide optimal learning resources that are individually customized. In the learning resource providing unit, for example, the generation AI analyzes a student's learning history and provides optimal learning resources that are individually customized. For example, it provides learning materials related to units that the student struggled with in the past. Furthermore, when analyzing a student's learning history, the generation AI takes past grades and study time into consideration and provides optimal learning resources. For example, it provides learning materials that incorporate methods that have been used to effectively study in a short amount of time in the past. Furthermore, the learning resource providing unit can provide optimal learning resources that are individually customized based on the student's learning history. For example, it provides learning materials that incorporate learning methods that have been successful in the past. In this way, effective learning is supported by analyzing a student's learning history and providing optimal learning resources that are individually customized.

[0089] The learning resource providing unit can track the usage of learning resources and prioritize suggest effective resources. In the learning resource providing unit, for example, the generation AI tracks the usage of learning resources and prioritizes suggesting effective resources. For example, it prioritizes providing teaching materials that have received high ratings in the past. Furthermore, when analyzing the usage of learning resources, the generation AI takes student feedback into consideration and suggests effective resources. For example, it prioritizes providing teaching materials that have been used by many students in the past. Furthermore, in the learning resource providing unit, the generation AI tracks the usage of learning resources and prioritizes suggesting effective resources. For example, it prioritizes providing teaching materials that have received high ratings in the past. In this way, by tracking the usage of learning resources and preferentially suggesting effective resources, learning effectiveness is improved.

[0090] The learning resource providing unit can use the emotion estimation function to provide resources according to the students' learning needs, thereby increasing their motivation to learn. For example, the generation AI in the learning resource providing unit uses the emotion estimation function to provide resources according to the students' learning needs, thereby increasing their motivation to learn. For example, interactive learning materials are provided to elicit positive emotions while learning. The learning resource providing unit also uses the emotion estimation function to provide resources according to the students' learning needs in real time, thereby increasing their motivation to learn. For example, if the student is feeling stressed while learning, learning materials that help them relax are provided. The learning resource providing unit also uses the emotion estimation function to provide resources according to the students' learning needs, thereby increasing their motivation to learn. For example, interactive learning materials are provided to elicit positive emotions while learning. In this way, resources according to the students' learning needs are provided, thereby increasing their motivation to learn.

[0091] The learning resource providing unit can automatically share learning resources with other students to promote collaborative learning. In the learning resource providing unit, for example, the generating AI automatically shares learning resources with other students to promote collaborative learning. For example, students studying the same unit share resources and hold discussions. The learning resource providing unit also promotes collaborative learning between students by having the generating AI automatically share learning resources. For example, students working on the same assignment share resources and provide feedback to each other. The learning resource providing unit also promotes collaborative learning by having the generating AI automatically share learning resources with other students. For example, students interested in the same topic share resources and work on a project together. In this way, sharing learning resources with other students promotes collaborative learning and supports knowledge sharing between students.

[0092] The learning resource providing unit can propose an appropriate study schedule to the student when providing the learning resources. For example, the learning resource providing unit proposes an appropriate study schedule to the student when the generation AI provides the learning resources. For example, it provides a schedule for studying a specific unit. Furthermore, the learning resource providing unit proposes an appropriate study schedule to the student when providing the learning resources. For example, it provides a schedule for studying efficiently before an exam. Furthermore, the learning resource providing unit proposes an appropriate study schedule to the student when the generation AI provides the learning resources. For example, it provides a schedule for making a long-term study plan. In this way, by proposing an appropriate study schedule when providing the learning resources, effective learning is supported.

[0093] The learning resource providing unit uses the emotion estimation function to provide resources according to the student's learning needs in real time, thereby eliciting positive emotions. For example, the learning resource providing unit uses the emotion estimation function to provide resources according to the student's learning needs in real time, thereby eliciting positive emotions. For example, interactive learning materials are provided to elicit positive emotions while studying. The learning resource providing unit also uses the emotion estimation function to provide resources according to the student's learning needs in real time, thereby eliciting positive emotions. For example, if the student is feeling stressed while studying, learning materials that help them relax are provided. The learning resource providing unit also uses the emotion estimation function to provide resources according to the student's learning needs in real time, thereby eliciting positive emotions. For example, interactive learning materials are provided to elicit positive emotions while studying. In this way, by providing resources according to the student's learning needs in real time, positive emotions are elicited and motivation to learn is increased.

[0094] The interactive interface providing unit can increase motivation to learn by displaying a student's learning history and progress status in real time through the interactive interface. In the interactive interface providing unit, for example, the generation AI displays a student's learning history and progress status in real time through the interactive interface, thereby increasing motivation to learn. For example, the learning progress is visually displayed in graphs and charts. In addition, the interactive interface providing unit can increase motivation to learn by displaying a student's learning history and progress status in real time through the interactive interface. For example, it displays achieved goals and next steps. In addition, the interactive interface providing unit can increase motivation to learn by displaying a student's learning history and progress status in real time through the interactive interface. For example, it visually displays learning progress, giving a sense of accomplishment. In this way, displaying learning history and progress status in real time increases motivation to learn.

[0095] The interactive interface providing unit can provide appropriate study advice and hints to students according to the content of the dialogue. In the interactive interface providing unit, for example, the generation AI provides appropriate study advice and hints to students according to the content of the dialogue. For example, in response to a dialogue such as "I can't solve the math problem," a specific solution hint is provided. In addition, the interactive interface providing unit provides appropriate study advice to students according to the content of the dialogue. For example, in response to a dialogue such as "I don't understand English grammar," the rules of grammar are explained. In addition, the interactive interface providing unit provides appropriate study advice and hints to students according to the content of the dialogue. For example, in response to a dialogue such as "I can't remember historical dates," tips on remembering dates are provided. In this way, appropriate study advice and hints are provided according to the content of the dialogue, thereby improving learning effectiveness.

[0096] The conversational interface providing unit can use the emotion estimation function to analyze students' emotions during conversations and promote positive conversations. For example, the conversational interface providing unit uses the emotion estimation function to analyze students' emotions during conversations and promote positive conversations. For example, it provides encouraging words to draw out positive emotions during conversations. The conversational interface providing unit also uses the emotion estimation function to analyze students' emotions during conversations in real time and promote positive conversations. For example, if a student is feeling stressed during a conversation, it provides advice to help them relax. The conversational interface providing unit also uses the emotion estimation function to analyze students' emotions during conversations and promote positive conversations. For example, it adds interactive elements to draw out positive emotions during conversations. In this way, students' emotions during conversations are analyzed and positive conversations are promoted, thereby increasing their motivation to learn.

[0097] The interactive interface providing unit can set appropriate learning goals for students through the interactive interface and support their achievement. In the interactive interface providing unit, for example, the generation AI sets appropriate learning goals for students through the interactive interface and supports their achievement. For example, it sets a goal such as "memorize 50 English words in one week" and tracks progress. The interactive interface providing unit also allows the generation AI to set appropriate learning goals for students through the interactive interface and supports their achievement. For example, it sets a goal such as "master the basics of mathematics in one month" and tracks progress. The interactive interface providing unit also allows the generation AI to set appropriate learning goals for students through the interactive interface and supports their achievement. For example, it sets a goal such as "memorize important historical events in three months" and tracks progress. In this way, setting appropriate learning goals for students and supporting their achievement increases their motivation to learn.

[0098] The interactive interface providing unit can suggest appropriate learning resources and teaching materials to students according to the content of the dialogue. In the interactive interface providing unit, for example, the generation AI suggests appropriate learning resources and teaching materials to students according to the content of the dialogue. For example, in response to a dialogue such as "I can't solve math problems," it suggests related teaching materials and reference books. In addition, the interactive interface providing unit suggests appropriate learning resources to students according to the content of the dialogue. For example, in response to a dialogue such as "I don't understand English grammar," it suggests teaching materials that explain the rules of grammar. In addition, the interactive interface providing unit suggests appropriate learning resources and teaching materials to students according to the content of the dialogue. For example, in response to a dialogue such as "I can't remember historical dates," it suggests teaching materials for memorizing dates. In this way, by suggesting appropriate learning resources and teaching materials according to the content of the dialogue, learning effectiveness is improved.

[0099] The conversational interface providing unit can use the emotion estimation function to analyze students' emotions during a conversation in real time and provide a dialogue that elicits positive emotions. For example, the conversational interface providing unit uses the emotion estimation function to analyze students' emotions during a conversation in real time and provide a dialogue that elicits positive emotions. For example, it provides words of encouragement that elicit positive emotions during a conversation. The conversational interface providing unit also uses the emotion estimation function to analyze students' emotions during a conversation in real time and provide a dialogue that elicits positive emotions. For example, if a student is feeling stressed during a conversation, it provides advice to help them relax. The conversational interface providing unit also uses the emotion estimation function to analyze students' emotions during a conversation in real time and provide a dialogue that elicits positive emotions. For example, it adds interactive elements that elicit positive emotions during a conversation. In this way, students' emotions during a conversation are analyzed in real time and a dialogue that elicits positive emotions is provided, thereby increasing their motivation to learn.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The interactive learning assistant system can also analyze students' learning styles and suggest optimal learning methods. For example, for students who prefer visual learning, it can provide learning materials that make extensive use of diagrams and graphs. For students who prefer auditory learning, it can provide learning materials with audio commentary. Furthermore, it can suggest interactive simulations and experiments for students who prefer hands-on learning. This can improve learning effectiveness by providing optimal learning methods that suit each student's learning style.

[0102] The interactive learning assistant system can also analyze a student's learning environment and suggest the optimal learning environment. For example, for a student who is better able to concentrate in a quiet environment, it can suggest using noise-canceling headphones. For a student who learns better in natural light, it can suggest studying by a window. For a student who learns better in a relaxed environment, it can suggest studying while listening to relaxing music. In this way, learning effectiveness can be improved by providing the optimal learning environment according to the student's learning environment.

[0103] The interactive learning assistant system can also set learning goals for students and track their progress. For example, a short-term goal can be set as "memorize 50 English words in one week" and progress toward that goal can be tracked. A medium-term goal can be set as "master the basics of mathematics in one month" and progress toward that goal can be tracked. A long-term goal can be set as "memorize important historical events in three months" and progress toward that goal can be tracked. In this way, setting students' learning goals and tracking their progress can increase their motivation to learn.

[0104] The interactive learning assistant system can also compare a student's learning progress with other students and provide a relative evaluation. For example, it can provide feedback such as, "Your grades are higher than the class average." It can also provide feedback such as, "Your grades are third in the class." It can also provide feedback such as, "Your progress is lagging behind other students," which can increase motivation to learn. In this way, by comparing a student's learning progress with other students and providing a relative evaluation, it can stimulate a competitive spirit and increase motivation to learn.

[0105] The interactive learning assistant system can also analyze a student's learning history and generate an optimal lesson plan based on their past learning patterns. For example, it can provide a plan that focuses on reviewing units that students struggled with in the past. It can also provide a plan that incorporates methods that have been effective in learning in a short amount of time in the past. It can also provide a plan that incorporates learning methods that have been successful in the past. This makes it possible to support effective learning by providing optimal lesson plans based on students' past learning patterns.

[0106] The interactive learning assistant system can also estimate a student's emotions and adjust the learning content based on the estimated emotions. For example, if a student is feeling stressed, it can provide relaxing learning content. If a student is tired, it can suggest a break. Furthermore, if a student is feeling positive, it can provide challenging tasks. This can improve learning effectiveness by providing learning content that takes students' emotions into consideration.

[0107] The interactive learning assistant system can further estimate the student's emotions and provide feedback based on the estimated emotions. For example, if a student shows an attitude of making an effort, the system can provide feedback such as "Your attitude of trying hard is wonderful." If the student is gradually improving, the system can provide feedback such as "You are improving little by little." Furthermore, if the student's efforts are paying off, the system can provide feedback such as "Your efforts are paying off." In this way, providing feedback that takes the student's emotions into consideration can increase their motivation to learn.

[0108] The interactive learning assistant system can further estimate the student's emotions and adjust the interface based on the estimated emotions. For example, if the student is feeling stressed, it can provide an interface that encourages relaxation. If the student is tired, it can provide an interface that suggests taking a break. Furthermore, if the student is feeling positive, it can provide an interface that offers challenging tasks. In this way, by providing an interface that takes the student's emotions into consideration, it is possible to improve learning effectiveness.

[0109] The interactive learning assistant system can further estimate the student's emotions and provide learning resources based on the estimated emotions. For example, if a student is feeling stressed, it can provide learning materials that help the student relax. If a student is tired, it can provide learning materials that help the student refresh. Furthermore, if a student has positive emotions, it can provide interactive learning materials. In this way, providing learning resources that take the student's emotions into consideration can increase their motivation to learn.

[0110] The interactive learning assistant system can also estimate students' emotions and set learning goals based on the estimated emotions. For example, if a student is feeling positive, it can set challenging goals. If a student is feeling stressed, it can set easier goals. If a student is tired, it can set short-term goals. In this way, setting learning goals that take students' emotions into consideration can increase their motivation to learn.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The question analysis unit analyzes the student's question. For example, the question analysis unit may use natural language processing technology to analyze the content of the question, keyword extraction technology to extract important parts of the question, and context analysis technology to analyze the background information of the question. Step 2: The answer generation unit generates an answer to the question analyzed by the question analysis unit. For example, the answer generation unit can generate an answer by referring to a database, generate an answer using a generation AI (e.g., a text generation AI or a multimodal generation AI), or generate a template-based answer. Step 3: The lesson generation unit generates individual lessons based on the answers generated by the answer generation unit. For example, the lesson generation unit customizes a curriculum based on the student's learning progress, sets learning goals, generates lessons based on the goals, and adjusts the lesson content according to the student's level of understanding. Step 4: The progress tracking unit tracks the progress of the lesson generated by the lesson generation unit, for example, by recording study time, evaluating achievement, and analyzing study history. Step 5: The feedback providing unit provides feedback based on the progress tracked by the progress tracking unit. For example, the feedback providing unit provides evaluation comments, suggests improvements, and suggests next steps.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] 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.

[0172] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a question analysis unit that analyzes students' questions; an answer generation unit that generates an answer to the question analyzed by the question analysis unit; a lesson generation unit that generates an individual lesson based on the answer generated by the answer generation unit; a progress tracking unit that tracks the progress of the lesson generated by the lesson generation unit; a feedback providing unit that provides feedback based on the progress tracked by the progress tracking unit. A system characterized by:

2. The question analysis unit Generate multiple answer candidates according to the content of the question, and allow the student to select the answer that is easiest to understand 2. The system of claim 1.

3. The question analysis unit Analyzes the context of a question and references related previous questions and answers to generate more accurate answers 2. The system of claim 1.

4. The question analysis unit Analyze the student's emotions when asking a question and generate answers that reduce stress and anxiety 2. The system of claim 1.

5. The answer generation unit Suggest relevant video tutorials and interactive simulations when answering questions 2. The system of claim 1.

6. The answer generation unit Depending on the question, students can browse other students' questions and answers to promote community-based learning.

2. The system of claim 1.

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