System

Abstract

An object of a system according to an embodiment is to provide educational content that meets individual needs of students and learners.SOLUTION: A system includes a user profile creation part, a learning plan creation part, and a multimedia teaching material provision part. A user profile creation part collects information on a learning style, a learning target, and an interesting field to create a user profile. The learning plan creating section creates an optimum learning plan based on the user profile information created by the user profile creating section. The multimedia teaching material providing unit provides at least one multimedia teaching material among a text, a video, a voice, and an interactive quiz.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 had the problem of making it difficult to efficiently provide educational content tailored to the individual needs of students and learners.

[0005] The system according to the embodiment aims to provide educational content that is tailored to the individual needs of students or learners. [Means for solving the problem]

[0006] The system according to the embodiment includes a user profile creation unit, a study plan creation unit, and a multimedia learning material provision unit. The user profile creation unit collects information on learning styles, study goals, and areas of interest to create a user profile. The study plan creation unit generates an optimal study plan based on the user profile information created by the user profile creation unit. The multimedia learning material provision unit provides at least one of multimedia learning materials including text, video, audio, and interactive quizzes. [Effects of the Invention]

[0007] The system according to the embodiment can provide educational content tailored to the individual needs of students or learners. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An educational content provision platform according to an embodiment of the present invention is a system that provides educational content tailored to the individual needs of students and learners. This system includes user profile creation, learning plan creation using generative AI, multimedia learning materials provision, real-time progress monitoring, and a community with educators. As a result, the educational content provision platform can provide an optimal learning environment tailored to the individual needs of learners.

[0029] An educational content delivery platform according to an embodiment includes a user profile creation unit, a study plan creation unit, and a multimedia learning material provision unit. The user profile creation unit collects information about a user's learning style, learning goals, and areas of interest to create a user profile. For example, if a user inputs information such as "I'm good at math and interested in science," a profile is created based on that information. The user profile creation unit can also incorporate the learner's past learning history and grade data to create a more detailed profile. For example, the learning history may be integrated into the profile by linking with other learning platforms or school grade databases. The study plan creation unit generates an optimal study plan based on the user profile information created by the user profile creation unit. The generation AI generates an optimal study plan based on the user's profile information. For example, the generation AI may propose a specific schedule such as "study math three times a week for one hour each." The generation AI receives inputs including the user's profile information and learning goals, and generates a study plan based on the prompts. The multimedia learning material provision unit provides at least one multimedia learning material from the following: text, video, audio, and interactive quizzes. For example, the educational content may include video tutorials for solving math problems, interactive quizzes for understanding science concepts, etc. This allows learners to choose learning materials that suit their learning style, allowing the educational content providing platform according to the embodiment to provide educational content tailored to the individual needs of learners.

[0030] The user profile creation unit can import a learner's past learning history or grade data to create a more detailed profile. For example, the user profile creation unit adds a function to automatically import a learner's past learning history. For example, it links with other learning platforms or school grade databases to integrate the learning history into the profile. The user profile creation unit also builds a system that imports grade data from tests and exams that a learner has taken in the past into the profile. For example, it provides a function to upload a report card and reflects that data in the profile. The user profile creation unit also provides a form that allows a learner to manually enter a learner's past learning history and grade data to create a detailed profile. For example, the learner can enter their past grades and learning content themselves and add them to their profile. This makes it possible to create a detailed profile based on a learner's past learning history and grade data.

[0031] The user profile creation unit can record a learner's preferred learning environment and adjust the learning plan based on that. For example, the user profile creation unit provides a form for inputting information about the learner's preferred learning environment and reflects that data in the learner's profile. For example, if a learner inputs that they "like to study in quiet places," the learning plan is adjusted based on that information. The user profile creation unit also conducts a survey to record the learner's preferred learning environment and reflects the results in the learner's profile. For example, the unit periodically asks questions such as "Do you like to study while listening to music?" and adds the answers to the learner's profile. The user profile creation unit also builds a system that automatically detects a learner's preferred learning environment. For example, the unit analyzes the learner's behavior and environmental sounds during learning and updates the learner's profile based on that data. This makes it possible to adjust the learning plan based on the learner's preferred learning environment.

[0032] The user profile creation unit can record a learner's hobbies or special skills and provide related learning materials and assignments. For example, the user profile creation unit provides a form for inputting information about the learner's hobbies and special skills and reflects that data in the profile. For example, if the learner inputs "I like music," music-related learning materials and assignments are provided. The user profile creation unit also conducts a survey to record the learner's hobbies and special skills and reflects the results in the profile. For example, the unit periodically asks questions such as "Are you good at sports?" and adds the answers to the profile. The user profile creation unit also builds a system that automatically detects a learner's hobbies and special skills. For example, the unit analyzes the learner's social media accounts and online activity and updates the profile based on that data. This makes it possible to provide learning materials and assignments related to the learner's hobbies and special skills.

[0033] The user profile creation unit can match learners based on profile information and provide opportunities for collaborative learning. The user profile creation unit, for example, builds a system that matches learners based on profile information. For example, it automatically matches learners with the same interests and goals and provides opportunities for collaborative learning. The user profile creation unit also adds a function that analyzes learner profile information and recommends compatible learning partners. For example, it recommends learners with similar learning styles and goals. The user profile creation unit also develops a system that automatically generates groups of learners based on profile information and provides a place for collaborative learning. For example, it groups learners with the same interests and conducts collaborative learning online. This makes it possible to provide opportunities for collaborative learning by matching learners.

[0034] The learning plan creation unit can clearly set a learner's short-term and long-term goals and provide a specific action plan for each. For example, the learning plan creation unit provides a form for inputting a learner's short-term and long-term goals, and builds a system that automatically generates a specific action plan based on the data. For example, it suggests daily learning tasks for short-term goals. The learning plan creation unit also provides guidelines to support the learner in setting goals, and develops a system that creates a specific action plan based on the guidelines. For example, it provides a step-by-step plan for achieving the goals. The learning plan creation unit also builds a system that monitors the learner's short-term and long-term goals in real time and dynamically adjusts the action plan based on the data. For example, it updates the action plan according to progress in goal achievement. This makes it possible to provide a specific action plan for the learner's short-term and long-term goals.

[0035] The learning plan creation unit can share the learning plan with the learner's family or friends to strengthen the support system. The learning plan creation unit, for example, provides a function for sharing the learning plan with family and friends, and builds a system to strengthen the support system. For example, a function for sharing the learning plan via email or social media is added. The learning plan creation unit also develops a dedicated app for sharing the learning plan, allowing family and friends to check the learner's progress in real time. For example, the app displays the progress of the learning plan. The learning plan creation unit also provides an online platform for sharing the learning plan, allowing family and friends to support the learner. For example, a function is added that allows family and friends to post comments and advice on the learning plan. This makes it possible to strengthen the support system by sharing the learning plan with the learner's family and friends.

[0036] The learning plan creation unit can incorporate the topics that interest the learner or the latest research results to increase the learner's motivation to learn. The learning plan creation unit, for example, provides a form for the learner to input topics of interest and builds a system that automatically generates a learning plan based on that data. For example, if a learner inputs "I'm interested in space science," the system provides learning materials related to that topic. The learning plan creation unit also builds a database for incorporating the latest research results into the learning plan and develops a system that adjusts the learning plan based on that data. For example, the latest scientific research and technological trends are reflected in the learning plan. The learning plan creation unit also builds a system that collects topics that interest the learner and the latest research results in real time and dynamically adjusts the learning plan based on that data. For example, the learning plan is updated when new research results are published. This makes it possible to increase the learner's motivation to learn by incorporating the learner's interests and the latest research results.

[0037] The multimedia teaching material providing unit can automatically provide the additional explanation or supplementary material according to the learner's level of understanding. The multimedia teaching material providing unit, for example, analyzes the learner's level of understanding in real time and builds a system that provides additional explanations or supplementary material based on the data. For example, if a learner does not understand a particular concept, an additional video tutorial is provided. The multimedia teaching material providing unit also develops a system that automatically provides supplementary material according to the learner's level of understanding based on the learner's progress data. For example, if a learner is struggling with a particular problem, additional practice problems are provided. The multimedia teaching material providing unit also builds a system that dynamically adjusts the content of the teaching material based on the learner's level of understanding. For example, if a learner understands a particular topic, materials for moving on to the next topic are provided. This makes it possible to provide additional explanations and supplementary material according to the learner's level of understanding.

[0038] The multimedia teaching material providing unit can provide the feedback according to the learner's progress in real time, thereby maintaining the motivation for learning. The multimedia teaching material providing unit, for example, analyzes the learner's progress data in real time and builds a system that provides feedback based on the data. For example, when the learner completes a specific task, an encouraging message is displayed. The multimedia teaching material providing unit also develops a system that automatically provides feedback according to the learner's progress. For example, when the learner achieves a goal, a reward or badge is provided. The multimedia teaching material providing unit also builds a system that dynamically adjusts the content of the feedback based on the learner's progress data. For example, if the learner is falling behind, additional support is provided. This makes it possible to maintain the motivation for learning by providing feedback according to the learner's progress.

[0039] The multimedia teaching material providing unit can customize the teaching materials based on the learner's interests and provide the personalized learning experience. For example, the multimedia teaching material providing unit provides a form for inputting information about the learner's interests and builds a system that customizes teaching materials based on the data. For example, if a learner inputs "I'm interested in history," history-related teaching materials are provided. The multimedia teaching material providing unit also develops a data collection system for analyzing the learner's interests and customizes teaching materials based on the data. For example, the multimedia teaching material providing unit analyzes the learner's online activities and provides teaching materials based on the results. The multimedia teaching material providing unit also builds a system that dynamically adjusts the content of teaching materials based on the learner's interests and interests. For example, if a learner is interested in a particular topic, teaching materials related to that topic are provided. This makes it possible to provide a personalized learning experience based on the learner's interests and interests.

[0040] The multimedia teaching material providing unit can support a variety of learning methods, including the content corresponding to different learning styles. The multimedia teaching material providing unit, for example, builds a system for providing content corresponding to visual, auditory, and tactile senses. For example, it provides teaching materials including video, audio, and interactive simulations. The multimedia teaching material providing unit also develops a system that automatically provides content according to a learner's learning style. For example, it provides visual notes to visual learners and audiobooks to auditory learners. The multimedia teaching material providing unit also builds a system that analyzes a learner's learning style and dynamically adjusts the content of the teaching materials based on the data. For example, if a learner prefers tactile learning, it provides interactive simulations. This makes it possible to support a variety of learning methods corresponding to different learning styles.

[0041] The real-time progress monitoring unit can quantitatively evaluate the effectiveness of the learning based on the learner's progress data and suggest specific areas for improvement. The real-time progress monitoring unit, for example, analyzes the learner's progress data in real time and builds a system that quantitatively evaluates the effectiveness of the learning based on that data. For example, the evaluation is made based on the learner's test results and the completion status of assignments. The real-time progress monitoring unit also develops a system that suggests specific areas for improvement based on the learner's progress data. For example, if a learner is struggling with a particular topic, additional practice questions are provided. The real-time progress monitoring unit also builds a system that quantitatively evaluates the effectiveness of the learning based on the learner's progress data and provides feedback based on the results. For example, specific areas for improvement are suggested depending on the learner's progress. This makes it possible to quantitatively evaluate the learning effectiveness and suggest specific areas for improvement based on the learner's progress data.

[0042] The real-time progress monitoring unit can provide a function for comparing the learner's progress data with other learners and grasping the relative position. The real-time progress monitoring unit, for example, builds a system for comparing the learner's progress data with other learners. For example, it displays the learner's relative position based on the learner's test results and assignment completion status. The real-time progress monitoring unit also develops a system for providing the comparison results with other learners in real time based on the learner's progress data. For example, it displays the learner's progress in graph or ranking format. The real-time progress monitoring unit also builds a system for comparing the learner's progress data with other learners and providing feedback based on the results. For example, if the learner is lagging behind other learners, additional support is provided. This makes it possible to grasp the learner's relative position by comparing the learner's progress data with other learners.

[0043] The real-time progress monitoring unit can individually adjust the learning pace or the learning method based on the learner's progress data, thereby providing an optimal learning environment. The real-time progress monitoring unit, for example, analyzes the learner's progress data in real time and builds a system that individually adjusts the learning pace or method based on that data. For example, if the learner is falling behind, the learning pace is slowed. The real-time progress monitoring unit also develops a system that dynamically adjusts the learning pace or method based on the learner's progress data. For example, if the learner is progressing quickly, more difficult tasks are added. The real-time progress monitoring unit also builds a system that provides an optimal learning environment based on the learner's progress data. For example, the learning time or location is adjusted to provide an environment where the learner can concentrate. This makes it possible to individually adjust the learning pace and method based on the learner's progress data.

[0044] The real-time progress monitoring unit can share the learner's progress data with the guardian or the educator to strengthen the support system. The real-time progress monitoring unit, for example, builds a system for sharing the learner's progress data with guardians and educators. For example, it adds a function for sharing the learner's progress via email or social media. The real-time progress monitoring unit also develops a dedicated app for sharing the learner's progress data with guardians and educators in real time. For example, it displays the learner's progress in the app so that guardians and educators can provide support. The real-time progress monitoring unit also builds a system for sharing the learner's progress data on an online platform so that guardians and educators can support the learner. For example, it adds a function for posting comments and advice on the learning plan. This makes it possible to strengthen the support system by sharing the learner's progress data with guardians and educators.

[0045] The real-time progress monitoring unit can link the learner's progress data with different learning platforms and manage the comprehensive learning history. The real-time progress monitoring unit, for example, builds a system for linking the learner's progress data with different learning platforms. For example, it links with other online learning platforms or school databases to manage a comprehensive learning history. The real-time progress monitoring unit also develops a system for sharing the learner's progress data with different platforms and managing a comprehensive learning history based on that data. For example, it provides a dashboard for centrally managing the learner's progress. The real-time progress monitoring unit also builds a system for linking the learner's progress data with different learning platforms and managing a comprehensive learning history based on that data. For example, it updates the learner's progress in real time and provides a comprehensive learning history. This makes it possible to link the learner's progress data with different learning platforms and manage a comprehensive learning history.

[0046] The educator community unit can analyze the communication history between the educator and the learner and propose an effective teaching method. The educator community unit, for example, automatically analyzes the communication history between the educator and the learner and builds a system that proposes an effective teaching method based on the data. For example, it proposes an optimal teaching method based on the content of past communication. The educator community unit also develops a system that proposes improvements to the teaching method based on the communication history between the educator and the learner. For example, if a particular teaching method is effective, it proposes that method to other educators. The educator community unit also builds a system that analyzes the communication history between the educator and the learner in real time and dynamically proposes effective teaching methods based on the data. For example, it adjusts the teaching method according to the learner's response. This makes it possible to analyze the communication history between the educator and the learner and propose effective teaching methods.

[0047] The community with educators department can add a function that allows educators to grasp the progress of the learner in real time and provide the support at the appropriate time. For example, the community with educators department builds a system that allows educators to grasp the progress of the learner in real time. For example, the community with educators displays learner progress data in real time, allowing educators to provide support at the appropriate time. The community with educators department also adds an alert function that allows educators to provide support at the appropriate time based on the learner's progress. For example, if the learner is falling behind, a notification is sent to the educator. The community with educators department also develops a system that allows educators to grasp the learner's progress in real time and provide support based on that data. For example, if the learner is struggling with a particular task, the educator can provide immediate support. This enables educators to grasp the learner's progress in real time and provide support at the appropriate time.

[0048] The community with educators section enables educators to collaborate with the learner's parents to strengthen the learning support at home. The community with educators section, for example, builds a system that enables educators to collaborate with the learner's parents to strengthen learning support at home. For example, the learner's progress can be shared with parents to promote support at home. The community with educators section also develops a dedicated app for educators to collaborate with parents and adds a function to strengthen learning support at home. For example, the app provides an app that allows educators to share learning plans and progress with parents. The community with educators section also provides an online platform for educators to collaborate with parents to strengthen learning support at home. For example, the platform provides a platform that allows parents to check the learner's progress and communicate with educators. This makes it possible for educators to collaborate with the learner's parents to strengthen learning support at home.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The user profile creation unit can record the learner's health condition and adjust the study plan based on that. For example, if the learner reports feeling unwell, the study load for that day can be reduced. The user profile creation unit can also incorporate the learner's sleep data and adjust the study plan if the learner is sleep deprived. Furthermore, the user profile creation unit can record the learner's exercise habits and provide a study plan that takes into account the time spent refreshing after exercise. This makes it possible to provide a flexible study plan that suits the learner's health condition.

[0051] The user profile creation unit can record the learner's cultural background and customize learning content based on that. For example, if the learner belongs to a particular culture, the unit can provide learning materials that include examples and cases related to that culture. The user profile creation unit can also take into account the learner's language background and provide learning materials that include explanations in the learner's native language. Furthermore, the unit can take into account the learner's religious background and provide learning plans that take religious holidays and events into consideration. This makes it possible to provide a personalized learning experience that is tailored to the learner's cultural background.

[0052] The user profile creation unit can suggest learning methods that match the learner's learning style. For example, visual notes can be provided to visual learners, and audiobooks to auditory learners. Also, interactive simulations can be provided to tactile learners. Furthermore, the learning plan can be customized according to the learner's learning style. This makes it possible to provide the optimal learning method that matches the learner's learning style.

[0053] The learning plan creation unit can introduce a reward system according to the learner's learning goals. For example, if a learner achieves a specific goal, badges or points can be awarded. Also, if a learner achieves a long-term goal, special rewards can be provided. Furthermore, rewards can be provided in stages according to the learner's progress. This makes it possible to maintain the learner's motivation and increase their willingness to learn.

[0054] The study plan creation unit can provide a study plan that focuses on reviewing topics that the learner previously struggled with, based on the learner's study history. For example, additional practice questions and supplementary materials can be provided for topics that the learner previously struggled with. Also, it can provide additional challenges for topics that the learner previously excelled at. Furthermore, the study plan can be customized based on the learner's study history. This makes it possible to provide the optimal study plan according to the learner's study history.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The user profile creation unit collects information about a user's learning style, learning goals, and areas of interest, and creates a user profile. For example, if a user enters information such as "I'm good at math and interested in science," a profile is created based on that information. The user profile creation unit can also incorporate the learner's past learning history and grade data to create a more detailed profile. For example, it can link with other learning platforms or school grade databases to integrate learning history into the profile. Step 2: The study plan creation unit generates an optimal study plan based on the user profile information created by the user profile creation unit. The generation AI generates an optimal study plan based on the user's profile information. For example, it suggests a specific schedule such as "study math three times a week for one hour each." The input to the generation AI is a prompt containing the user's profile information and study goals, and the generation AI generates a study plan based on the prompt. Step 3: The multimedia learning material provider provides at least one of the following multimedia learning materials: text, video, audio, and interactive quizzes. For example, the multimedia learning materials may include video tutorials for solving math problems and interactive quizzes for understanding science concepts. This allows learners to choose learning materials that suit their learning style.

[0057] (Example 2) An educational content provision platform according to an embodiment of the present invention is a system that provides educational content tailored to the individual needs of students and learners. This system includes user profile creation, learning plan creation using generative AI, multimedia learning materials provision, real-time progress monitoring, and a community with educators. As a result, the educational content provision platform can provide an optimal learning environment tailored to the individual needs of learners.

[0058] An educational content delivery platform according to an embodiment includes a user profile creation unit, a study plan creation unit, and a multimedia learning material provision unit. The user profile creation unit collects information about a user's learning style, learning goals, and areas of interest to create a user profile. For example, if a user inputs information such as "I'm good at math and interested in science," a profile is created based on that information. The user profile creation unit can also incorporate the learner's past learning history and grade data to create a more detailed profile. For example, the learning history may be integrated into the profile by linking with other learning platforms or school grade databases. The study plan creation unit generates an optimal study plan based on the user profile information created by the user profile creation unit. The generation AI generates an optimal study plan based on the user's profile information. For example, the generation AI may propose a specific schedule such as "study math three times a week for one hour each." The generation AI receives inputs including the user's profile information and learning goals, and generates a study plan based on the prompts. The multimedia learning material provision unit provides at least one multimedia learning material from the following: text, video, audio, and interactive quizzes. For example, the educational content may include video tutorials for solving math problems, interactive quizzes for understanding science concepts, etc. This allows learners to choose learning materials that suit their learning style, allowing the educational content providing platform according to the embodiment to provide educational content tailored to the individual needs of learners.

[0059] The user profile creation unit can periodically record the learner's emotional state and update the profile according to changes in emotion. For example, the user profile creation unit adds a function to periodically record the learner's emotional state when the learner uses the platform. For example, a form for entering the learner's emotional state may be displayed at the end of each day's study, and the data may be reflected in the learner's profile. The user profile creation unit may also incorporate technology to analyze the learner's facial expressions and voice to record the learner's emotional state. For example, the learner's emotions may be analyzed in real time using a camera or microphone, and the results may be automatically updated in the learner's profile. The user profile creation unit may also periodically conduct a survey to record the learner's emotional state and reflect the results in the learner's profile. For example, the user profile creation unit may send the learner questions about their emotional state once a week, and add the answers to the profile. This makes it possible to update the learner's profile according to their emotional state.

[0060] The user profile creation unit can import a learner's past learning history or grade data to create a more detailed profile. For example, the user profile creation unit adds a function to automatically import a learner's past learning history. For example, it links with other learning platforms or school grade databases to integrate the learning history into the profile. The user profile creation unit also builds a system that imports grade data from tests and exams that a learner has taken in the past into the profile. For example, it provides a function to upload a report card and reflects that data in the profile. The user profile creation unit also provides a form that allows a learner to manually enter a learner's past learning history and grade data to create a detailed profile. For example, the learner can enter their past grades and learning content themselves and add them to their profile. This makes it possible to create a detailed profile based on a learner's past learning history and grade data.

[0061] The user profile creation unit can record a learner's preferred learning environment and adjust the learning plan based on that. For example, the user profile creation unit provides a form for inputting information about the learner's preferred learning environment and reflects that data in the learner's profile. For example, if a learner inputs that they "like to study in quiet places," the learning plan is adjusted based on that information. The user profile creation unit also conducts a survey to record the learner's preferred learning environment and reflects the results in the learner's profile. For example, the unit periodically asks questions such as "Do you like to study while listening to music?" and adds the answers to the learner's profile. The user profile creation unit also builds a system that automatically detects a learner's preferred learning environment. For example, the unit analyzes the learner's behavior and environmental sounds during learning and updates the learner's profile based on that data. This makes it possible to adjust the learning plan based on the learner's preferred learning environment.

[0062] The user profile creation unit can record a learner's hobbies or special skills and provide related learning materials and assignments. For example, the user profile creation unit provides a form for inputting information about the learner's hobbies and special skills and reflects that data in the profile. For example, if the learner inputs "I like music," music-related learning materials and assignments are provided. The user profile creation unit also conducts a survey to record the learner's hobbies and special skills and reflects the results in the profile. For example, the unit periodically asks questions such as "Are you good at sports?" and adds the answers to the profile. The user profile creation unit also builds a system that automatically detects a learner's hobbies and special skills. For example, the unit analyzes the learner's social media accounts and online activity and updates the profile based on that data. This makes it possible to provide learning materials and assignments related to the learner's hobbies and special skills.

[0063] The user profile creation unit can match learners based on profile information and provide opportunities for collaborative learning. The user profile creation unit, for example, builds a system that matches learners based on profile information. For example, it automatically matches learners with the same interests and goals and provides opportunities for collaborative learning. The user profile creation unit also adds a function that analyzes learner profile information and recommends compatible learning partners. For example, it recommends learners with similar learning styles and goals. The user profile creation unit also develops a system that automatically generates groups of learners based on profile information and provides a place for collaborative learning. For example, it groups learners with the same interests and conducts collaborative learning online. This makes it possible to provide opportunities for collaborative learning by matching learners.

[0064] The user profile creation unit can use the emotion estimation function to analyze the emotions of learners when they enter their profile information and provide an interface for eliciting positive emotions. The user profile creation unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of learners when they enter their profile information in real time. For example, it uses a camera or microphone to analyze the learner's emotions and provides an interface for eliciting positive emotions. The user profile creation unit also develops a system that provides real-time feedback based on emotion estimation data when a learner enters their profile information. For example, it displays encouraging messages or success stories to elicit positive emotions. The user profile creation unit also uses the emotion estimation function to analyze the emotions of learners when they enter their profile information and builds a system that dynamically adjusts the interface based on the results. For example, if negative emotions are detected, it provides advice for eliciting positive emotions. This makes it possible to provide an interface that suits the learner's emotions.

[0065] The learning plan creation unit can assign more difficult tasks to periods when the learner's emotions are stable based on the learner's emotional state. The learning plan creation unit, for example, builds a system that periodically records the learner's emotional state and adjusts the learning plan based on that data. For example, it assigns more difficult tasks to periods when the learner's emotions are stable. The learning plan creation unit also develops a system that uses an emotion estimation function to analyze the learner's emotional state in real time and dynamically adjusts the learning plan based on the results. For example, it adds more difficult tasks to periods when the learner's emotions are stable. The learning plan creation unit also builds a system that automatically generates a learning plan that takes the learner's emotional state into consideration. For example, it assigns more difficult tasks to periods when the learner's emotions are stable, and provides relaxing tasks to periods when the learner's emotions are unstable. This makes it possible to assign tasks according to the learner's emotional state.

[0066] The learning plan creation unit can clearly set a learner's short-term and long-term goals and provide a specific action plan for each. For example, the learning plan creation unit provides a form for inputting a learner's short-term and long-term goals, and builds a system that automatically generates a specific action plan based on the data. For example, it suggests daily learning tasks for short-term goals. The learning plan creation unit also provides guidelines to support the learner in setting goals, and develops a system that creates a specific action plan based on the guidelines. For example, it provides a step-by-step plan for achieving the goals. The learning plan creation unit also builds a system that monitors the learner's short-term and long-term goals in real time and dynamically adjusts the action plan based on the data. For example, it updates the action plan according to progress in goal achievement. This makes it possible to provide a specific action plan for the learner's short-term and long-term goals.

[0067] The learning plan creation unit can share the learning plan with the learner's family or friends to strengthen the support system. The learning plan creation unit, for example, provides a function for sharing the learning plan with family and friends, and builds a system to strengthen the support system. For example, a function for sharing the learning plan via email or social media is added. The learning plan creation unit also develops a dedicated app for sharing the learning plan, allowing family and friends to check the learner's progress in real time. For example, the app displays the progress of the learning plan. The learning plan creation unit also provides an online platform for sharing the learning plan, allowing family and friends to support the learner. For example, a function is added that allows family and friends to post comments and advice on the learning plan. This makes it possible to strengthen the support system by sharing the learning plan with the learner's family and friends.

[0068] The learning plan creation unit can incorporate the topics that interest the learner or the latest research results to increase the learner's motivation to learn. The learning plan creation unit, for example, provides a form for the learner to input topics of interest and builds a system that automatically generates a learning plan based on that data. For example, if a learner inputs "I'm interested in space science," the system provides learning materials related to that topic. The learning plan creation unit also builds a database for incorporating the latest research results into the learning plan and develops a system that adjusts the learning plan based on that data. For example, the latest scientific research and technological trends are reflected in the learning plan. The learning plan creation unit also builds a system that collects topics that interest the learner and the latest research results in real time and dynamically adjusts the learning plan based on that data. For example, the learning plan is updated when new research results are published. This makes it possible to increase the learner's motivation to learn by incorporating the learner's interests and the latest research results.

[0069] The learning plan creation unit uses the emotion estimation function to monitor the learner's emotions while the learning plan is in progress, and can flexibly adjust the plan as needed. The learning plan creation unit, for example, uses the emotion estimation function to build a system that monitors the learner's emotions in real time while the learning plan is in progress. For example, it analyzes the learner's facial expressions and voice to detect changes in emotion. The learning plan creation unit also develops a system that dynamically adjusts the learning plan based on the learner's emotional state. For example, if the learner is feeling stressed, it adds tasks that help them relax. The learning plan creation unit also builds a system that provides feedback while the learning plan is in progress based on the emotion estimation data. For example, if the learner's emotions are positive, it adds more difficult tasks. This makes it possible to flexibly adjust the learning plan according to the learner's emotions.

[0070] The multimedia teaching material providing unit can add the interactive elements according to the learner's emotional state to enhance the learning effect. The multimedia teaching material providing unit, for example, analyzes the learner's emotional state in real time and builds a system that adds interactive elements based on the data. For example, if the learner is interested, additional quizzes or games are provided. The multimedia teaching material providing unit also develops a system that uses an emotion estimation function to provide feedback according to the learner's emotional state. For example, if the learner is tired, it provides content that helps the learner relax. The multimedia teaching material providing unit also builds a system that dynamically adjusts the content of the multimedia teaching materials based on the learner's emotional state. For example, if the learner has positive emotions, it adds a more difficult task. This makes it possible to enhance learning effect by adding interactive elements according to the learner's emotional state.

[0071] The multimedia teaching material providing unit can automatically provide the additional explanation or supplementary material according to the learner's level of understanding. The multimedia teaching material providing unit, for example, analyzes the learner's level of understanding in real time and builds a system that provides additional explanations or supplementary material based on the data. For example, if a learner does not understand a particular concept, an additional video tutorial is provided. The multimedia teaching material providing unit also develops a system that automatically provides supplementary material according to the learner's level of understanding based on the learner's progress data. For example, if a learner is struggling with a particular problem, additional practice problems are provided. The multimedia teaching material providing unit also builds a system that dynamically adjusts the content of the teaching material based on the learner's level of understanding. For example, if a learner understands a particular topic, materials for moving on to the next topic are provided. This makes it possible to provide additional explanations and supplementary material according to the learner's level of understanding.

[0072] The multimedia teaching material providing unit can provide the feedback according to the learner's progress in real time, thereby maintaining the motivation for learning. The multimedia teaching material providing unit, for example, analyzes the learner's progress data in real time and builds a system that provides feedback based on the data. For example, when the learner completes a specific task, an encouraging message is displayed. The multimedia teaching material providing unit also develops a system that automatically provides feedback according to the learner's progress. For example, when the learner achieves a goal, a reward or badge is provided. The multimedia teaching material providing unit also builds a system that dynamically adjusts the content of the feedback based on the learner's progress data. For example, if the learner is falling behind, additional support is provided. This makes it possible to maintain the motivation for learning by providing feedback according to the learner's progress.

[0073] The multimedia teaching material providing unit can customize the teaching materials based on the learner's interests and provide the personalized learning experience. For example, the multimedia teaching material providing unit provides a form for inputting information about the learner's interests and builds a system that customizes teaching materials based on the data. For example, if a learner inputs "I'm interested in history," history-related teaching materials are provided. The multimedia teaching material providing unit also develops a data collection system for analyzing the learner's interests and customizes teaching materials based on the data. For example, the multimedia teaching material providing unit analyzes the learner's online activities and provides teaching materials based on the results. The multimedia teaching material providing unit also builds a system that dynamically adjusts the content of teaching materials based on the learner's interests and interests. For example, if a learner is interested in a particular topic, teaching materials related to that topic are provided. This makes it possible to provide a personalized learning experience based on the learner's interests and interests.

[0074] The multimedia teaching material providing unit can support a variety of learning methods, including the content corresponding to different learning styles. The multimedia teaching material providing unit, for example, builds a system for providing content corresponding to visual, auditory, and tactile senses. For example, it provides teaching materials including video, audio, and interactive simulations. The multimedia teaching material providing unit also develops a system that automatically provides content according to a learner's learning style. For example, it provides visual notes to visual learners and audiobooks to auditory learners. The multimedia teaching material providing unit also builds a system that analyzes a learner's learning style and dynamically adjusts the content of the teaching materials based on the data. For example, if a learner prefers tactile learning, it provides interactive simulations. This makes it possible to support a variety of learning methods corresponding to different learning styles.

[0075] The real-time progress monitoring unit can quantitatively evaluate the effectiveness of the learning based on the learner's progress data and suggest specific areas for improvement. The real-time progress monitoring unit, for example, analyzes the learner's progress data in real time and builds a system that quantitatively evaluates the effectiveness of the learning based on that data. For example, the evaluation is made based on the learner's test results and the completion status of assignments. The real-time progress monitoring unit also develops a system that suggests specific areas for improvement based on the learner's progress data. For example, if a learner is struggling with a particular topic, additional practice questions are provided. The real-time progress monitoring unit also builds a system that quantitatively evaluates the effectiveness of the learning based on the learner's progress data and provides feedback based on the results. For example, specific areas for improvement are suggested depending on the learner's progress. This makes it possible to quantitatively evaluate the learning effectiveness and suggest specific areas for improvement based on the learner's progress data.

[0076] The real-time progress monitoring unit can provide a function for comparing the learner's progress data with other learners and grasping the relative position. The real-time progress monitoring unit, for example, builds a system for comparing the learner's progress data with other learners. For example, it displays the learner's relative position based on the learner's test results and assignment completion status. The real-time progress monitoring unit also develops a system for providing the comparison results with other learners in real time based on the learner's progress data. For example, it displays the learner's progress in graph or ranking format. The real-time progress monitoring unit also builds a system for comparing the learner's progress data with other learners and providing feedback based on the results. For example, if the learner is lagging behind other learners, additional support is provided. This makes it possible to grasp the learner's relative position by comparing the learner's progress data with other learners.

[0077] The real-time progress monitoring unit can individually adjust the learning pace or the learning method based on the learner's progress data, thereby providing an optimal learning environment. The real-time progress monitoring unit, for example, analyzes the learner's progress data in real time and builds a system that individually adjusts the learning pace or method based on that data. For example, if the learner is falling behind, the learning pace is slowed. The real-time progress monitoring unit also develops a system that dynamically adjusts the learning pace or method based on the learner's progress data. For example, if the learner is progressing quickly, more difficult tasks are added. The real-time progress monitoring unit also builds a system that provides an optimal learning environment based on the learner's progress data. For example, the learning time or location is adjusted to provide an environment where the learner can concentrate. This makes it possible to individually adjust the learning pace and method based on the learner's progress data.

[0078] The real-time progress monitoring unit can share the learner's progress data with the guardian or the educator to strengthen the support system. The real-time progress monitoring unit, for example, builds a system for sharing the learner's progress data with guardians and educators. For example, it adds a function for sharing the learner's progress via email or social media. The real-time progress monitoring unit also develops a dedicated app for sharing the learner's progress data with guardians and educators in real time. For example, it displays the learner's progress in the app so that guardians and educators can provide support. The real-time progress monitoring unit also builds a system for sharing the learner's progress data on an online platform so that guardians and educators can support the learner. For example, it adds a function for posting comments and advice on the learning plan. This makes it possible to strengthen the support system by sharing the learner's progress data with guardians and educators.

[0079] The real-time progress monitoring unit can link the learner's progress data with different learning platforms and manage the comprehensive learning history. The real-time progress monitoring unit, for example, builds a system for linking the learner's progress data with different learning platforms. For example, it links with other online learning platforms or school databases to manage a comprehensive learning history. The real-time progress monitoring unit also develops a system for sharing the learner's progress data with different platforms and managing a comprehensive learning history based on that data. For example, it provides a dashboard for centrally managing the learner's progress. The real-time progress monitoring unit also builds a system for linking the learner's progress data with different learning platforms and managing a comprehensive learning history based on that data. For example, it updates the learner's progress in real time and provides a comprehensive learning history. This makes it possible to link the learner's progress data with different learning platforms and manage a comprehensive learning history.

[0080] The real-time progress monitoring unit can use an emotion estimation function to analyze the learner's emotional response to the progress and provide the feedback to elicit the positive emotion. The real-time progress monitoring unit, for example, uses the emotion estimation function to build a system that analyzes the learner's emotional response to the progress in real time. For example, it analyzes the learner's facial expressions and voice to detect changes in emotion. The real-time progress monitoring unit also develops a system that provides feedback to elicit positive emotion based on the learner's emotional response. For example, it provides an encouraging message or a reward when the learner succeeds. The real-time progress monitoring unit also builds a system that dynamically adjusts feedback on the learner's progress based on the emotion estimation data. For example, if the learner has negative emotions, it provides advice to elicit positive emotions. This makes it possible to analyze the learner's emotional response to the progress and provide feedback to elicit positive emotions.

[0081] The educator community unit can analyze the communication history between the educator and the learner and propose an effective teaching method. The educator community unit, for example, automatically analyzes the communication history between the educator and the learner and builds a system that proposes an effective teaching method based on the data. For example, it proposes an optimal teaching method based on the content of past communication. The educator community unit also develops a system that proposes improvements to the teaching method based on the communication history between the educator and the learner. For example, if a particular teaching method is effective, it proposes that method to other educators. The educator community unit also builds a system that analyzes the communication history between the educator and the learner in real time and dynamically proposes effective teaching methods based on the data. For example, it adjusts the teaching method according to the learner's response. This makes it possible to analyze the communication history between the educator and the learner and propose effective teaching methods.

[0082] The community with educators department can add a function that allows educators to grasp the progress of the learner in real time and provide the support at the appropriate time. For example, the community with educators department builds a system that allows educators to grasp the progress of the learner in real time. For example, the community with educators displays learner progress data in real time, allowing educators to provide support at the appropriate time. The community with educators department also adds an alert function that allows educators to provide support at the appropriate time based on the learner's progress. For example, if the learner is falling behind, a notification is sent to the educator. The community with educators department also develops a system that allows educators to grasp the learner's progress in real time and provide support based on that data. For example, if the learner is struggling with a particular task, the educator can provide immediate support. This enables educators to grasp the learner's progress in real time and provide support at the appropriate time.

[0083] The educator community unit enables educators to grasp the emotional state of the learner and select the teaching method according to the emotion. The educator community unit, for example, builds a system that enables educators to grasp the emotional state of the learner in real time. For example, it analyzes the learner's facial expressions and voice to detect changes in emotion. The educator community unit also develops a system that enables educators to select a teaching method according to the learner's emotion based on the learner's emotional state. For example, if the learner is feeling stressed, it suggests a teaching method that helps the learner relax. The educator community unit also builds a system that enables educators to grasp the learner's emotional state based on emotion estimation data and dynamically adjusts the teaching method based on the data. For example, if the learner is feeling positive emotion, it provides a more difficult task. This enables educators to grasp the learner's emotional state and select a teaching method according to the emotion.

[0084] The community with educators section enables educators to collaborate with the learner's parents to strengthen the learning support at home. The community with educators section, for example, builds a system that enables educators to collaborate with the learner's parents to strengthen learning support at home. For example, the learner's progress can be shared with parents to promote support at home. The community with educators section also develops a dedicated app for educators to collaborate with parents and adds a function to strengthen learning support at home. For example, the app provides an app that allows educators to share learning plans and progress with parents. The community with educators section also provides an online platform for educators to collaborate with parents to strengthen learning support at home. For example, the platform provides a platform that allows parents to check the learner's progress and communicate with educators. This makes it possible for educators to collaborate with the learner's parents to strengthen learning support at home.

[0085] The educator community unit uses an emotion estimation function to enable the educator to grasp the emotional state of the learner in real time and provide the appropriate support. The educator community unit, for example, uses the emotion estimation function to build a system that allows the educator to grasp the emotional state of the learner in real time. For example, the emotion estimation function is used to analyze the learner's facial expressions and voice to detect changes in emotion. The educator community unit also develops a system that allows the educator to provide appropriate support based on the learner's emotional state. For example, if the learner is feeling stressed, support to help the learner relax is provided. The educator community unit also builds a system that allows the educator to grasp the learner's emotional state based on emotion estimation data and dynamically adjust support based on the data. For example, if the learner is feeling positive emotions, a more difficult task is provided. This makes it possible for the educator to grasp the learner's emotional state in real time using the emotion estimation function and provide appropriate support.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The user profile creation unit can record the learner's health condition and adjust the study plan based on that. For example, if the learner reports feeling unwell, the study load for that day can be reduced. The user profile creation unit can also incorporate the learner's sleep data and adjust the study plan if the learner is sleep deprived. Furthermore, the user profile creation unit can record the learner's exercise habits and provide a study plan that takes into account the time spent refreshing after exercise. This makes it possible to provide a flexible study plan that suits the learner's health condition.

[0088] The user profile creation unit can provide relaxing content to learners if they are feeling stressed based on the learner's emotional state. For example, if it is estimated that the learner is feeling stressed, it can provide relaxing music or a meditation guide. Also, if the learner is feeling positive, it can provide more difficult tasks. Furthermore, it can send encouraging messages to the learner depending on the learner's emotional state. This makes it possible to provide flexible learning support according to the learner's emotional state.

[0089] The user profile creation unit can record the learner's cultural background and customize learning content based on that. For example, if the learner belongs to a particular culture, the unit can provide learning materials that include examples and cases related to that culture. The user profile creation unit can also take into account the learner's language background and provide learning materials that include explanations in the learner's native language. Furthermore, the unit can take into account the learner's religious background and provide learning plans that take religious holidays and events into consideration. This makes it possible to provide a personalized learning experience that is tailored to the learner's cultural background.

[0090] The user profile creation unit can suggest a break if the learner is tired based on the learner's emotional state. For example, if it is estimated that the learner is tired, it can suggest that the learner take a short break. Also, if the learner is lacking concentration, it can provide activities to improve concentration. Furthermore, it can provide content that can refresh the learner depending on the learner's emotional state. This makes it possible to provide flexible learning support that suits the learner's emotional state.

[0091] The user profile creation unit can suggest learning methods that match the learner's learning style. For example, visual notes can be provided to visual learners, and audiobooks to auditory learners. Also, interactive simulations can be provided to tactile learners. Furthermore, the learning plan can be customized according to the learner's learning style. This makes it possible to provide the optimal learning method that matches the learner's learning style.

[0092] The user profile creation unit can send encouraging messages to learners who are losing motivation based on their emotional state. For example, if it is estimated that a learner is losing motivation, encouraging messages and success stories can be displayed. Also, if the learner has positive emotions, further challenges can be provided. Furthermore, activities that will refresh the learner can be suggested according to the learner's emotional state. This enables flexible learning support that is tailored to the learner's emotional state.

[0093] The learning plan creation unit can introduce a reward system according to the learner's learning goals. For example, if a learner achieves a specific goal, badges or points can be awarded. Also, if a learner achieves a long-term goal, special rewards can be provided. Furthermore, rewards can be provided in stages according to the learner's progress. This makes it possible to maintain the learner's motivation and increase their willingness to learn.

[0094] The learning plan creation unit can provide relaxing tasks to a learner if the learner is feeling stressed based on the learner's emotional state. For example, if it is estimated that the learner is feeling stressed, it can provide relaxing activities or light tasks. Also, if the learner is feeling positive, it can provide more difficult tasks. Furthermore, it can dynamically adjust the learning plan according to the learner's emotional state. This makes it possible to provide a flexible learning plan that corresponds to the learner's emotional state.

[0095] The study plan creation unit can provide a study plan that focuses on reviewing topics that the learner previously struggled with, based on the learner's study history. For example, additional practice questions and supplementary materials can be provided for topics that the learner previously struggled with. Also, it can provide additional challenges for topics that the learner previously excelled at. Furthermore, the study plan can be customized based on the learner's study history. This makes it possible to provide the optimal study plan according to the learner's study history.

[0096] The learning plan creation unit can provide challenging tasks to the learner if the learner is feeling positive based on the learner's emotional state. For example, if it is estimated that the learner is feeling positive, it can provide difficult tasks or new topics. On the other hand, if the learner is feeling negative, it can provide relaxing tasks or light activities. Furthermore, it can dynamically adjust the learning plan according to the learner's emotional state. This makes it possible to provide a flexible learning plan that suits the learner's emotional state.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The user profile creation unit collects information about a user's learning style, learning goals, and areas of interest, and creates a user profile. For example, if a user enters information such as "I'm good at math and interested in science," a profile is created based on that information. The user profile creation unit can also incorporate the learner's past learning history and grade data to create a more detailed profile. For example, it can link with other learning platforms or school grade databases to integrate learning history into the profile. Step 2: The study plan creation unit generates an optimal study plan based on the user profile information created by the user profile creation unit. The generation AI generates an optimal study plan based on the user's profile information. For example, it suggests a specific schedule such as "study math three times a week for one hour each." The input to the generation AI is a prompt containing the user's profile information and study goals, and the generation AI generates a study plan based on the prompt. Step 3: The multimedia learning material provider provides at least one of the following multimedia learning materials: text, video, audio, and interactive quizzes. For example, the multimedia learning materials may include video tutorials for solving math problems and interactive quizzes for understanding science concepts. This allows learners to choose learning materials that suit their learning style.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 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 system comprising: a user profile creation unit that collects information on learning style, learning goals, and areas of interest and creates a user profile; a study plan creation unit that generates an optimal study plan based on the user profile information created by the user profile creation unit; and a multimedia learning material provision unit that provides at least one of multimedia learning materials selected from text, video, audio, and interactive quizzes.

2. 2. The system according to claim 1, wherein the user profile creation unit creates a more detailed profile by incorporating the learner's past learning history or performance data.

3. 2. The system according to claim 1, wherein the learning plan creation unit clearly sets short-term and long-term goals for the learner and provides a specific action plan for each of the goals.

4. 2. The system according to claim 1, wherein the multimedia teaching material providing unit adds interactive elements according to the emotional state of the learner to enhance learning effectiveness.

5. 10. The system of claim 1, wherein the real-time progress monitor analyzes the learner's emotional response to their progress and provides feedback to elicit positive emotions.

6. 2. The system according to claim 1, wherein the community unit with educators enables the educators to grasp the emotional state of the learners and select teaching methods according to the emotions.

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