System

Abstract

An object of a system according to an embodiment is to analyze the progress and understanding level of a learner and provide appropriate learning content.SOLUTION: A system includes a learning content providing unit, a progress analysis unit, and a comprehension degree analysis unit. The learning content providing unit provides learning content through smart glasses. The progress analysis unit analyzes the progress of the learner based on the learning content provided by the learning content providing unit. The comprehension degree analysis unit analyzes the comprehension degree of the learner based on the progress data analyzed by the progress analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to adequately analyze learners' progress and level of understanding in real time and provide appropriate learning content.

[0005] The system according to the embodiment aims to analyze the progress and level of understanding of a learner and provide appropriate learning content. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning content provider, a progress analysis unit, and a comprehension analysis unit. The learning content provider provides learning content through smart glasses. The progress analysis unit analyzes the learner's progress based on the learning content provided by the learning content provider. The comprehension analysis unit analyzes the learner's comprehension based on the progress data analyzed by the progress analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the progress and level of understanding of the learner and provide appropriate learning content. [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) A learning system according to an embodiment of the present invention is a system that combines generative AI and e-learning using smart glasses. This system provides appropriate learning content to learners in real time and analyzes their learning progress and level of understanding. This allows the learning system to provide appropriate learning content to learners in real time and analyze their learning progress and level of understanding.

[0029] A learning system according to an embodiment includes a learning content provider, a progress analysis unit, and a comprehension analysis unit. The learning content provider provides learning content through smart glasses. For example, the learning content provider displays questions and explanations on the smart glasses' display. The learning content provider can also use a generation AI to provide questions of appropriate difficulty and additional explanations based on the learner's progress and level of comprehension. For example, the generation AI adjusts the next learning content to be provided based on the learner's progress data. The progress analysis unit analyzes the learner's progress based on the learning content provided by the learning content provider. For example, the progress analysis unit analyzes the learner's progress by analyzing the correct answer rate and answer time of the questions solved by the learner to evaluate the learner's progress. The progress analysis unit can also analyze learning trends based on the learner's learning history. The comprehension analysis unit analyzes the learner's level of comprehension based on the progress data analyzed by the progress analysis unit. For example, the comprehension analysis unit evaluates the learner's level of comprehension based on the learner's correct answer rate and answer time. The comprehension analysis unit can also adjust the next learning content to be provided based on the learner's level of comprehension. As a result, the learning system according to the embodiment can provide appropriate learning content to learners in real time and analyze their learning progress and level of understanding.

[0030] The learning content providing unit can use the smart glasses' eye-tracking function to identify the part the learner is focusing on and provide additional explanations related to that part. For example, the learning content providing unit can use the smart glasses' eye-tracking function to identify the part the learner is focusing on and display additional explanations related to that part in real time. For example, if a learner is focusing on a specific formula while solving a math problem, an explanation of that formula can be displayed. The learning content providing unit can also identify parts that the learner finds difficult to understand based on the eye-tracking data and provide detailed explanations and example problems for those parts. For example, if a learner is focusing on a specific diagram while learning a physics concept, a detailed explanation of that diagram can be displayed. The learning content providing unit can also analyze the learner's eye-tracking data and provide interactive quizzes and questions for parts that attract high attention. For example, if a learner is focusing on a specific date in a history class, a quiz related to that date can be displayed. This can improve learning effectiveness by providing appropriate explanations for the parts the learner is focusing on.

[0031] The learning content provider is equipped with a learner's facial expression recognition function, which can estimate the learner's level of comprehension and concentration from the learner's facial expression and provide content accordingly. The learning content provider, for example, uses a camera mounted on smart glasses to analyze the learner's facial expressions in real time to estimate the learner's level of comprehension and concentration. For example, if the learner looks confused, the learning content provider can provide additional explanation for that part. The learning content provider can also use facial expression recognition technology to measure the learner's concentration level and, if the learner's concentration level decreases, suggest a short break or exercise to refresh themselves. For example, if the learner's concentration level decreases during long periods of study, the learning content provider can suggest a short stretch. The learning content provider can also provide more detailed explanations and visual aids for parts of the learning where the learner's comprehension is estimated to be low, based on the learner's facial expression data. For example, if the learner looks confused while learning the procedures of a chemistry experiment, a video explanation of the procedure can be displayed. This improves learning effectiveness by providing content tailored to the learner's level of comprehension and concentration.

[0032] The learning content providing unit can provide learning content related to a real object or environment through the smart glasses when the learner interacts with the object or environment using the generation AI. For example, when the learner observes a real object through the smart glasses, the learning content providing unit displays learning content related to the object. For example, when observing a plant, the learning content providing unit displays the plant's ecology and characteristics. The learning content providing unit can also provide learning content related to a specific environment when the learner is in that environment. For example, when visiting a historical building, the learning content providing unit displays an explanation of the building's history and architectural style. The learning content providing unit can also provide a function in which the generation AI answers questions in real time when the learner interacts with the real object or environment using the smart glasses. For example, when viewing an exhibit in a museum, the generation AI answers questions about the exhibit. This can improve learning effectiveness by providing relevant learning content when the learner interacts with the real object or environment.

[0033] The learning content providing unit can provide a collaborative learning function using smart glasses that allows a learner to advance learning while collaborating with other learners in real time. The learning content providing unit can, for example, provide a video call function using smart glasses that allows a learner to advance learning while collaborating with other learners in real time. For example, this function is used for group discussions or collaborative projects. The learning content providing unit can also provide a chat function that allows a learner to advance learning while sharing information with other learners in real time through smart glasses. For example, sharing answers and explanations to questions. The learning content providing unit can also provide a collaborative editing function using smart glasses that allows a learner to advance learning while collaborating with other learners in real time. For example, creating documents or presentations together. This allows a learner to advance learning while collaborating with other learners, thereby improving learning effectiveness.

[0034] The progress analysis unit can analyze a learner's past learning history in detail and generate a learning path optimized for each individual learner. The progress analysis unit, for example, analyzes a learner's past learning history in detail and generates a learning path optimized for each individual learner. For example, it can provide a path that focuses on reviewing topics that the learner was weak at in the past. The progress analysis unit can also evaluate the learner's learning progress and level of understanding based on the learner's learning history data and suggest an optimal learning path. For example, it can provide a step-by-step learning path for acquiring a specific skill. The progress analysis unit can also analyze a learner's past learning history, identify learning trends and patterns, and generate an optimal learning path based on that. For example, it can provide a path that allows the learner to learn a new topic by utilizing topics in which the learner is strong. In this way, learning effectiveness can be improved by providing an optimized learning path based on the learner's past learning history.

[0035] The progress analysis unit can analyze a learner's answer patterns, identify the causes of incorrect answers, and provide specific countermeasures to address those causes. For example, the progress analysis unit can analyze a learner's answer patterns in detail and identify the causes of incorrect answers. For example, if there are many incorrect answers for a particular question format or topic, the cause can be analyzed. After identifying the causes of incorrect answers, the progress analysis unit can also provide specific countermeasures to address those causes. For example, if a particular concept is not understood, additional explanations or practice questions for that concept can be provided. The progress analysis unit can also analyze a learner's answer patterns and provide feedback to identify the causes of incorrect answers. For example, a video explanation can be provided that shows the pattern of incorrect answers and explains the causes. In this way, the learning effect can be improved by identifying the causes of the learner's incorrect answers and providing specific countermeasures.

[0036] The progress analysis unit can stimulate a competitive spirit by comparing the learner's progress data with other learners and indicating the learner's relative position. The progress analysis unit, for example, builds a system that stimulates a competitive spirit by comparing the learner's progress data with other learners and indicating the learner's relative position. For example, it can display the ranking within a class. The progress analysis unit can also provide feedback based on the learner's progress data, comparing the learner's progress with other learners. For example, it can display the average correct answer rate of other learners for the same topic. The progress analysis unit can also introduce a reward system that compares the learner's progress data with other learners and stimulates a competitive spirit. For example, it can award badges or points to top-ranked learners. This can stimulate the learner's competitive spirit and improve learning effectiveness.

[0037] The progress analysis unit can share the learner's progress data with parents and teachers, enabling them to receive learning support. The progress analysis unit, for example, builds a system that allows the learner's progress data to be shared with parents and teachers, enabling them to receive learning support. For example, the progress analysis unit may periodically send reports on the learner's grades and progress. The progress analysis unit can also provide a dashboard that allows parents and teachers to check the learner's progress data in real time. For example, the learner's answer history and level of understanding may be displayed in graphs. The progress analysis unit can also enable parents and teachers to provide appropriate support to the learner based on the learner's progress data. For example, the progress analysis unit may suggest additional learning materials or advice for a specific topic. In this way, the learner's progress data can be shared with parents and teachers, enabling them to receive learning support.

[0038] The comprehension analysis unit can perform a detailed analysis of the learner's answer time and the consistency of their answers, and track fluctuations in their comprehension in real time. The comprehension analysis unit, for example, can analyze the learner's answer time in detail and build a system that tracks fluctuations in their comprehension in real time. For example, it can provide additional explanations for questions that take a long time to answer. The comprehension analysis unit can also analyze the consistency of the learner's answers and track fluctuations in their comprehension in real time. For example, it can evaluate the consistency of answers to the same type of question and estimate their comprehension. The comprehension analysis unit can also track fluctuations in the learner's comprehension in real time based on data on answer time and consistency, and provide appropriate feedback. For example, it can suggest review if their comprehension is declining. In this way, tracking fluctuations in the learner's comprehension in real time can improve learning effectiveness.

[0039] The comprehension analysis unit can provide instant feedback on the learner's answers and promote improvement in comprehension. The comprehension analysis unit, for example, builds a system that provides instant feedback on the learner's answers. For example, explanations for correct and incorrect answers are displayed in real time. The comprehension analysis unit can also provide instant additional practice questions and explanations to promote improvement in comprehension based on the learner's answers. For example, additional practice questions are provided for topics with many incorrect answers. The comprehension analysis unit can also provide instant feedback on the learner's answers and provide interactive quizzes and games to promote improvement in comprehension. For example, an interactive quiz is provided for questions with a low correct answer rate. In this way, instant feedback on the learner's answers can promote improvement in comprehension.

[0040] The comprehension analysis unit can share a learner's comprehension data with other learners and provide opportunities for them to work together to solve problems. For example, the comprehension analysis unit can provide a platform for sharing a learner's comprehension data with other learners and working together to solve problems. For example, it can utilize an online forum or discussion board. The comprehension analysis unit can also suggest group work for working together to solve problems with other learners based on the learner's comprehension data. For example, it can pair learners with high and low comprehension levels on the same topic. The comprehension analysis unit can also share learners' comprehension data and provide project-based learning activities for working together to solve problems. For example, it can conduct group projects or collaborative research. This allows learners to work together to solve problems, thereby improving learning effectiveness.

[0041] The comprehension analysis unit can pair learners based on the learners' comprehension data and improve their comprehension by having them teach each other. The comprehension analysis unit, for example, builds a system in which learners are paired based on the learners' comprehension data and have them teach each other to improve their comprehension. For example, it pairs learners with high comprehension with learners with low comprehension. The comprehension analysis unit can also provide an online platform for pairing learners and having them teach each other. For example, it can utilize video calls or chat functions. The comprehension analysis unit can also pair learners based on the learners' comprehension data and suggest collaborative learning activities for them to teach each other. For example, it can conduct pair work or group discussions. This allows learners to teach each other and improve their comprehension.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The learning content provider can analyze the learner's voice input and respond in real time to questions and comments made by the learner. For example, if a learner asks a question about a specific issue, the provider can provide an explanation for that question. The learning content provider can also evaluate the learner's pronunciation and language skills based on the voice input and provide appropriate feedback. For example, when practicing English pronunciation, the provider can evaluate the accuracy of the pronunciation and point out areas for improvement. The learning content provider can also analyze the voice input and provide relevant learning content based on the learner's interests. For example, if a learner is talking about a specific topic, the provider can provide additional materials or videos related to that topic. This makes it possible to utilize the learner's voice input to provide a more interactive and personalized learning experience.

[0044] The learning content provider can recognize a learner's gestures and provide learning content in response to the gestures. For example, if a learner raises their hand, the learning content provider can accept questions in response to that gesture. The learning content provider can also use gesture recognition technology to provide explanations and feedback related to a specific action when a learner performs that action. For example, when performing a scientific experiment, if the learner performs the action according to the experimental procedure, an explanation for that action can be displayed. The learning content provider can also analyze the learner's actions based on the gesture recognition data and provide appropriate learning content. For example, when training for sports, the learning content provider can provide instruction on correct form and technique. This makes it possible to utilize the learner's gestures to provide a more interactive and effective learning experience.

[0045] The learning content provider can monitor the learner's biometric information and provide learning content tailored to the learner's health condition. For example, it can monitor the learner's heart rate and blood pressure and provide relaxation content if stress levels are high. The learning content provider can also evaluate the learner's concentration and fatigue level based on the biometric information and suggest appropriate breaks or exercises. For example, if fatigue accumulates after a long period of study, it can suggest a short stretch or meditation. The learning content provider can also analyze the learner's biometric information and adjust the learning schedule according to the learner's health condition. For example, if the learner is not feeling well, it can provide lighter learning content. This can improve learning effectiveness by providing learning content tailored to the learner's health condition.

[0046] The learning content provider can provide learning content related to real objects and environments through the smart glasses when the learner interacts with those objects or environments using the generation AI. For example, when observing a plant, the ecology and characteristics of that plant can be displayed. The learning content provider can also provide learning content related to a specific environment when the learner is in that environment. For example, when visiting a historical building, the generation AI can display an explanation of the building's history and architectural style. The learning content provider can also provide a function where the generation AI answers questions in real time when the learner interacts with real objects and environments using the smart glasses. For example, when viewing an exhibit in a museum, the generation AI can answer questions about the exhibit. This can improve learning effectiveness by providing relevant learning content when the learner interacts with real objects and environments.

[0047] The progress analysis unit can analyze a learner's past learning history in detail and generate a learning path optimized for each individual learner. For example, the progress analysis unit can analyze a learner's past learning history in detail and generate a learning path optimized for each individual learner. For example, it can provide a path that focuses on reviewing topics that the learner was weak at in the past. The progress analysis unit can also evaluate the learner's learning progress and level of understanding based on the learner's learning history data and suggest an optimal learning path. For example, it can provide a step-by-step learning path for mastering a specific skill. The progress analysis unit can also analyze a learner's past learning history, identify learning trends and patterns, and generate an optimal learning path based on that. For example, it can provide a path that allows the learner to learn a new topic by utilizing topics in which the learner is strong. In this way, learning effectiveness can be improved by providing an optimized learning path based on the learner's past learning history.

[0048] The progress analysis unit can analyze a learner's answer patterns, identify the causes of incorrect answers, and provide specific countermeasures to address those causes. For example, the progress analysis unit can analyze a learner's answer patterns in detail and identify the causes of incorrect answers. For example, if there are many incorrect answers for a particular question format or topic, the cause can be analyzed. After identifying the causes of incorrect answers, the progress analysis unit can also provide specific countermeasures to address those causes. For example, if a particular concept is not understood, additional explanations or practice questions for that concept can be provided. The progress analysis unit can also analyze a learner's answer patterns and provide feedback to identify the causes of incorrect answers. For example, a video explanation can be provided that shows the pattern of incorrect answers and explains the causes. In this way, the learning effect can be improved by identifying the causes of the learner's incorrect answers and providing specific countermeasures.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The learning content provider provides learning content through the smart glasses. For example, the learning content provider displays questions and explanations on the smart glasses' display. The learning content provider can also use a generation AI to provide questions of appropriate difficulty and additional explanations based on the learner's progress and level of understanding. The generation AI adjusts the learning content to be provided next based on the learner's progress data. Step 2: The progress analysis unit analyzes the learner's progress based on the learning content provided by the learning content provider. For example, the progress analysis unit analyzes the correct answer rate and answer time of the questions solved by the learner to evaluate the learner's progress. The progress analysis unit can also analyze the learner's learning trends based on the learner's learning history. Step 3: The comprehension analysis unit analyzes the learner's level of comprehension based on the progress data analyzed by the progress analysis unit. For example, the comprehension analysis unit evaluates the learner's level of comprehension based on the learner's correct answer rate and answer time. The comprehension analysis unit can also adjust the next learning content to be provided based on the learner's level of comprehension.

[0051] (Example 2) A learning system according to an embodiment of the present invention is a system that combines generative AI and e-learning using smart glasses. This system provides appropriate learning content to learners in real time and analyzes their learning progress and level of understanding. This allows the learning system to provide appropriate learning content to learners in real time and analyze their learning progress and level of understanding.

[0052] A learning system according to an embodiment includes a learning content provider, a progress analysis unit, and a comprehension analysis unit. The learning content provider provides learning content through smart glasses. For example, the learning content provider displays questions and explanations on the smart glasses' display. The learning content provider can also use a generation AI to provide questions of appropriate difficulty and additional explanations based on the learner's progress and level of comprehension. For example, the generation AI adjusts the next learning content to be provided based on the learner's progress data. The progress analysis unit analyzes the learner's progress based on the learning content provided by the learning content provider. For example, the progress analysis unit analyzes the learner's progress by analyzing the correct answer rate and answer time of the questions solved by the learner to evaluate the learner's progress. The progress analysis unit can also analyze learning trends based on the learner's learning history. The comprehension analysis unit analyzes the learner's level of comprehension based on the progress data analyzed by the progress analysis unit. For example, the comprehension analysis unit evaluates the learner's level of comprehension based on the learner's correct answer rate and answer time. The comprehension analysis unit can also adjust the next learning content to be provided based on the learner's level of comprehension. As a result, the learning system according to the embodiment can provide appropriate learning content to learners in real time and analyze their learning progress and level of understanding.

[0053] The learning content providing unit can use the smart glasses' eye-tracking function to identify the part the learner is focusing on and provide additional explanations related to that part. For example, the learning content providing unit can use the smart glasses' eye-tracking function to identify the part the learner is focusing on and display additional explanations related to that part in real time. For example, if a learner is focusing on a specific formula while solving a math problem, an explanation of that formula can be displayed. The learning content providing unit can also identify parts that the learner finds difficult to understand based on the eye-tracking data and provide detailed explanations and example problems for those parts. For example, if a learner is focusing on a specific diagram while learning a physics concept, a detailed explanation of that diagram can be displayed. The learning content providing unit can also analyze the learner's eye-tracking data and provide interactive quizzes and questions for parts that attract high attention. For example, if a learner is focusing on a specific date in a history class, a quiz related to that date can be displayed. This can improve learning effectiveness by providing appropriate explanations for the parts the learner is focusing on.

[0054] The learning content provider is equipped with a learner's facial expression recognition function, which can estimate the learner's level of comprehension and concentration from the learner's facial expression and provide content accordingly. The learning content provider, for example, uses a camera mounted on smart glasses to analyze the learner's facial expressions in real time to estimate the learner's level of comprehension and concentration. For example, if the learner looks confused, the learning content provider can provide additional explanation for that part. The learning content provider can also use facial expression recognition technology to measure the learner's concentration level and, if the learner's concentration level decreases, suggest a short break or exercise to refresh themselves. For example, if the learner's concentration level decreases during long periods of study, the learning content provider can suggest a short stretch. The learning content provider can also provide more detailed explanations and visual aids for parts of the learning where the learner's comprehension is estimated to be low, based on the learner's facial expression data. For example, if the learner looks confused while learning the procedures of a chemistry experiment, a video explanation of the procedure can be displayed. This improves learning effectiveness by providing content tailored to the learner's level of comprehension and concentration.

[0055] The learning content providing unit can use the emotion estimation function to analyze the learner's emotional state in real time and provide content that elicits positive emotions. The learning content providing unit can, for example, use sensors installed in smart glasses to analyze the learner's emotional state in real time and provide content that elicits positive emotions. For example, if the learner is feeling stressed, the learning content providing unit can play relaxation music. The learning content providing unit can also preferentially provide content that evokes positive emotions in the learner based on the emotion estimation data. For example, if it is estimated that the learner is enjoying themselves, the learning content providing unit can provide an interactive game related to that topic. The learning content providing unit can also analyze the learner's emotional state and provide feedback to alleviate negative emotions. For example, if the learner is feeling anxious, the learning content providing unit can display encouraging messages or success stories. This can improve learning effectiveness by providing content that matches the learner's emotional state.

[0056] The learning content providing unit can provide learning content related to a real object or environment through the smart glasses when the learner interacts with the object or environment using the generation AI. For example, when the learner observes a real object through the smart glasses, the learning content providing unit displays learning content related to the object. For example, when observing a plant, the learning content providing unit displays the plant's ecology and characteristics. The learning content providing unit can also provide learning content related to a specific environment when the learner is in that environment. For example, when visiting a historical building, the learning content providing unit displays an explanation of the building's history and architectural style. The learning content providing unit can also provide a function in which the generation AI answers questions in real time when the learner interacts with the real object or environment using the smart glasses. For example, when viewing an exhibit in a museum, the generation AI answers questions about the exhibit. This can improve learning effectiveness by providing relevant learning content when the learner interacts with the real object or environment.

[0057] The learning content providing unit can provide a collaborative learning function using smart glasses that allows a learner to advance learning while collaborating with other learners in real time. The learning content providing unit can, for example, provide a video call function using smart glasses that allows a learner to advance learning while collaborating with other learners in real time. For example, this function is used for group discussions or collaborative projects. The learning content providing unit can also provide a chat function that allows a learner to advance learning while sharing information with other learners in real time through smart glasses. For example, sharing answers and explanations to questions. The learning content providing unit can also provide a collaborative editing function using smart glasses that allows a learner to advance learning while collaborating with other learners in real time. For example, creating documents or presentations together. This allows a learner to advance learning while collaborating with other learners, thereby improving learning effectiveness.

[0058] The learning content providing unit can use the emotion estimation function to provide relaxation content to reduce stress and anxiety felt by the learner while studying. For example, when the emotion estimation function is used to estimate that the learner is feeling stressed or anxious, the learning content providing unit plays relaxation music or natural sounds. For example, if the learner is tense, it provides relaxing music. The learning content providing unit can also analyze the learner's emotional state and provide short meditation or breathing exercise guides to reduce stress and anxiety. For example, if the learner is lacking concentration, it can suggest a short meditation session. The learning content providing unit can also provide visual content to help the learner relax based on the emotion estimation data. For example, if the learner is tired, it can display beautiful scenery or artwork. This can reduce the stress and anxiety felt by the learner while studying, thereby improving learning effectiveness.

[0059] The progress analysis unit can analyze a learner's past learning history in detail and generate a learning path optimized for each individual learner. The progress analysis unit, for example, analyzes a learner's past learning history in detail and generates a learning path optimized for each individual learner. For example, it can provide a path that focuses on reviewing topics that the learner was weak at in the past. The progress analysis unit can also evaluate the learner's learning progress and level of understanding based on the learner's learning history data and suggest an optimal learning path. For example, it can provide a step-by-step learning path for acquiring a specific skill. The progress analysis unit can also analyze a learner's past learning history, identify learning trends and patterns, and generate an optimal learning path based on that. For example, it can provide a path that allows the learner to learn a new topic by utilizing topics in which the learner is strong. In this way, learning effectiveness can be improved by providing an optimized learning path based on the learner's past learning history.

[0060] The progress analysis unit can analyze a learner's answer patterns, identify the causes of incorrect answers, and provide specific countermeasures to address those causes. For example, the progress analysis unit can analyze a learner's answer patterns in detail and identify the causes of incorrect answers. For example, if there are many incorrect answers for a particular question format or topic, the cause can be analyzed. After identifying the causes of incorrect answers, the progress analysis unit can also provide specific countermeasures to address those causes. For example, if a particular concept is not understood, additional explanations or practice questions for that concept can be provided. The progress analysis unit can also analyze a learner's answer patterns and provide feedback to identify the causes of incorrect answers. For example, a video explanation can be provided that shows the pattern of incorrect answers and explains the causes. In this way, the learning effect can be improved by identifying the causes of the learner's incorrect answers and providing specific countermeasures.

[0061] The progress analysis unit can use the emotion estimation function to track changes in the learner's emotions and provide learning content that corresponds to the changes in emotions. For example, the progress analysis unit can use the emotion estimation function to track changes in the learner's emotions in real time and provide learning content that corresponds to the changes in emotions. For example, if the learner is tired, the progress analysis unit can suggest a short break to refresh the learner. The progress analysis unit can also customize learning content that corresponds to changes in emotions based on the learner's emotion data. For example, if the learner is excited, the progress analysis unit can provide challenging questions. The progress analysis unit can also analyze the emotion estimation data and provide feedback that corresponds to changes in the learner's emotions. For example, if the learner is feeling anxious, the progress analysis unit can display encouraging messages or success stories. In this way, learning content that corresponds to changes in the learner's emotions can be provided, thereby improving learning effectiveness.

[0062] The progress analysis unit can stimulate a competitive spirit by comparing the learner's progress data with other learners and indicating the learner's relative position. The progress analysis unit, for example, builds a system that stimulates a competitive spirit by comparing the learner's progress data with other learners and indicating the learner's relative position. For example, it can display the ranking within a class. The progress analysis unit can also provide feedback based on the learner's progress data, comparing the learner's progress with other learners. For example, it can display the average correct answer rate of other learners for the same topic. The progress analysis unit can also introduce a reward system that compares the learner's progress data with other learners and stimulates a competitive spirit. For example, it can award badges or points to top-ranked learners. This can stimulate the learner's competitive spirit and improve learning effectiveness.

[0063] The progress analysis unit can share the learner's progress data with parents and teachers, enabling them to receive learning support. The progress analysis unit, for example, builds a system that allows the learner's progress data to be shared with parents and teachers, enabling them to receive learning support. For example, the progress analysis unit may periodically send reports on the learner's grades and progress. The progress analysis unit can also provide a dashboard that allows parents and teachers to check the learner's progress data in real time. For example, the learner's answer history and level of understanding may be displayed in graphs. The progress analysis unit can also enable parents and teachers to provide appropriate support to the learner based on the learner's progress data. For example, the progress analysis unit may suggest additional learning materials or advice for a specific topic. In this way, the learner's progress data can be shared with parents and teachers, enabling them to receive learning support.

[0064] The progress analysis unit can use the emotion estimation function to analyze the learner's interest in specific content and customize the learning content based on that. The progress analysis unit, for example, uses the emotion estimation function to analyze the learner's interest in specific content in real time and customize the learning content based on that. For example, it can provide additional materials related to topics in which the learner has shown interest. The progress analysis unit can also build a system that prioritizes providing content that is of high interest to the learner based on the learner's emotion data. For example, if it is estimated that the learner is enjoying a content, it can provide an interactive game related to that topic. The progress analysis unit can also analyze the emotion estimation data and provide feedback according to the learner's interests. For example, it can display detailed explanations or videos on topics in which the learner is interested. This makes it possible to improve learning effectiveness by customizing learning content based on the learner's interests.

[0065] The comprehension analysis unit can perform a detailed analysis of the learner's answer time and the consistency of their answers, and track fluctuations in their comprehension in real time. The comprehension analysis unit, for example, can analyze the learner's answer time in detail and build a system that tracks fluctuations in their comprehension in real time. For example, it can provide additional explanations for questions that take a long time to answer. The comprehension analysis unit can also analyze the consistency of the learner's answers and track fluctuations in their comprehension in real time. For example, it can evaluate the consistency of answers to the same type of question and estimate their comprehension. The comprehension analysis unit can also track fluctuations in the learner's comprehension in real time based on data on answer time and consistency, and provide appropriate feedback. For example, it can suggest review if their comprehension is declining. In this way, tracking fluctuations in the learner's comprehension in real time can improve learning effectiveness.

[0066] The comprehension analysis unit can provide instant feedback on the learner's answers and promote improvement in comprehension. The comprehension analysis unit, for example, builds a system that provides instant feedback on the learner's answers. For example, explanations for correct and incorrect answers are displayed in real time. The comprehension analysis unit can also provide instant additional practice questions and explanations to promote improvement in comprehension based on the learner's answers. For example, additional practice questions are provided for topics with many incorrect answers. The comprehension analysis unit can also provide instant feedback on the learner's answers and provide interactive quizzes and games to promote improvement in comprehension. For example, an interactive quiz is provided for questions with a low correct answer rate. In this way, instant feedback on the learner's answers can promote improvement in comprehension.

[0067] The comprehension analysis unit uses the emotion estimation function to analyze the learner's emotional state, and if a decline in comprehension is due to emotion, it can identify the cause and provide countermeasures. The comprehension analysis unit, for example, uses the emotion estimation function to analyze the learner's emotional state in real time, and if a decline in comprehension is due to emotion, it can identify the cause. For example, if the learner is feeling stressed, it can identify the cause. Furthermore, if a decline in comprehension is due to emotion based on the learner's emotion data, the comprehension analysis unit can also provide specific countermeasures for the cause. For example, it can provide relaxation content to reduce stress. Furthermore, the comprehension analysis unit can analyze the emotion estimation data, and if a decline in comprehension is due to emotion, it can identify the cause and provide appropriate feedback to the learner. For example, if the learner is feeling anxious, it can display an encouraging message or a success story. In this way, the learning effect can be improved by identifying the cause of a decline in comprehension based on the learner's emotional state and providing countermeasures.

[0068] The comprehension analysis unit can share a learner's comprehension data with other learners and provide opportunities for them to work together to solve problems. For example, the comprehension analysis unit can provide a platform for sharing a learner's comprehension data with other learners and working together to solve problems. For example, it can utilize an online forum or discussion board. The comprehension analysis unit can also suggest group work for working together to solve problems with other learners based on the learner's comprehension data. For example, it can pair learners with high and low comprehension levels on the same topic. The comprehension analysis unit can also share learners' comprehension data and provide project-based learning activities for working together to solve problems. For example, it can conduct group projects or collaborative research. This allows learners to work together to solve problems, thereby improving learning effectiveness.

[0069] The comprehension analysis unit can pair learners based on the learners' comprehension data and improve their comprehension by having them teach each other. The comprehension analysis unit, for example, builds a system in which learners are paired based on the learners' comprehension data and have them teach each other to improve their comprehension. For example, it pairs learners with high comprehension with learners with low comprehension. The comprehension analysis unit can also provide an online platform for pairing learners and having them teach each other. For example, it can utilize video calls or chat functions. The comprehension analysis unit can also pair learners based on the learners' comprehension data and suggest collaborative learning activities for them to teach each other. For example, it can conduct pair work or group discussions. This allows learners to teach each other and improve their comprehension.

[0070] The comprehension analysis unit can use the emotion estimation function to suggest breaks or exercises to refresh the learner when the learner feels a decline in comprehension. For example, the comprehension analysis unit uses the emotion estimation function to build a system that suggests breaks or exercises to refresh the learner when the learner feels a decline in comprehension. For example, it can suggest short stretches or meditation. The comprehension analysis unit can also provide visual content to refresh the learner when the learner feels a decline in comprehension based on the learner's emotion data. For example, it can display beautiful scenery or artwork. The comprehension analysis unit can also analyze the emotion estimation data and play music or natural sounds to refresh the learner when the learner feels a decline in comprehension. For example, it can provide relaxing music. This can improve learning effectiveness by suggesting breaks or exercises to refresh the learner when the learner feels a decline in comprehension.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The learning content provider can analyze the learner's voice input and respond in real time to questions and comments made by the learner. For example, if a learner asks a question about a specific issue, the provider can provide an explanation for that question. The learning content provider can also evaluate the learner's pronunciation and language skills based on the voice input and provide appropriate feedback. For example, when practicing English pronunciation, the provider can evaluate the accuracy of the pronunciation and point out areas for improvement. The learning content provider can also analyze the voice input and provide relevant learning content based on the learner's interests. For example, if a learner is talking about a specific topic, the provider can provide additional materials or videos related to that topic. This makes it possible to utilize the learner's voice input to provide a more interactive and personalized learning experience.

[0073] The learning content provider can recognize a learner's gestures and provide learning content in response to the gestures. For example, if a learner raises their hand, the learning content provider can accept questions in response to that gesture. The learning content provider can also use gesture recognition technology to provide explanations and feedback related to a specific action when a learner performs that action. For example, when performing a scientific experiment, if the learner performs the action according to the experimental procedure, an explanation for that action can be displayed. The learning content provider can also analyze the learner's actions based on the gesture recognition data and provide appropriate learning content. For example, when training for sports, the learning content provider can provide instruction on correct form and technique. This makes it possible to utilize the learner's gestures to provide a more interactive and effective learning experience.

[0074] The learning content provider can monitor the learner's biometric information and provide learning content tailored to the learner's health condition. For example, it can monitor the learner's heart rate and blood pressure and provide relaxation content if stress levels are high. The learning content provider can also evaluate the learner's concentration and fatigue level based on the biometric information and suggest appropriate breaks or exercises. For example, if fatigue accumulates after a long period of study, it can suggest a short stretch or meditation. The learning content provider can also analyze the learner's biometric information and adjust the learning schedule according to the learner's health condition. For example, if the learner is not feeling well, it can provide lighter learning content. This can improve learning effectiveness by providing learning content tailored to the learner's health condition.

[0075] The learning content provider can use the emotion estimation function to analyze the learner's emotional state in real time and provide content that elicits positive emotions. For example, if the learner is feeling stressed, it can play relaxation music. The learning content provider can also prioritize providing content that evokes positive emotions in the learner based on the emotion estimation data. For example, if it is estimated that the learner is having fun, it can provide an interactive game related to that topic. The learning content provider can also analyze the learner's emotional state and provide feedback to alleviate negative emotions. For example, if the learner is feeling anxious, it can display encouraging messages or success stories. This can improve learning effectiveness by providing content that matches the learner's emotional state.

[0076] The learning content provider can provide learning content related to real objects and environments through the smart glasses when the learner interacts with those objects or environments using the generation AI. For example, when observing a plant, the ecology and characteristics of that plant can be displayed. The learning content provider can also provide learning content related to a specific environment when the learner is in that environment. For example, when visiting a historical building, the generation AI can display an explanation of the building's history and architectural style. The learning content provider can also provide a function where the generation AI answers questions in real time when the learner interacts with real objects and environments using the smart glasses. For example, when viewing an exhibit in a museum, the generation AI can answer questions about the exhibit. This can improve learning effectiveness by providing relevant learning content when the learner interacts with real objects and environments.

[0077] The learning content providing unit can use the emotion estimation function to provide relaxation content to reduce the stress and anxiety that a learner feels while studying. For example, if the emotion estimation function is used to estimate that a learner is feeling stressed or anxious, the unit can play relaxation music or natural sounds. For example, if a learner is tense, the unit can provide relaxing music. The learning content providing unit can also analyze the learner's emotional state and provide short meditation or breathing exercise guides to reduce stress and anxiety. For example, if a learner is lacking concentration, the unit can suggest a short meditation session. The learning content providing unit can also provide visual content that helps the learner relax based on the emotion estimation data. For example, if a learner is tired, the unit can display beautiful scenery or artwork. This can reduce the stress and anxiety that a learner feels while studying, thereby improving learning effectiveness.

[0078] The progress analysis unit can analyze a learner's past learning history in detail and generate a learning path optimized for each individual learner. For example, the progress analysis unit can analyze a learner's past learning history in detail and generate a learning path optimized for each individual learner. For example, it can provide a path that focuses on reviewing topics that the learner was weak at in the past. The progress analysis unit can also evaluate the learner's learning progress and level of understanding based on the learner's learning history data and suggest an optimal learning path. For example, it can provide a step-by-step learning path for mastering a specific skill. The progress analysis unit can also analyze a learner's past learning history, identify learning trends and patterns, and generate an optimal learning path based on that. For example, it can provide a path that allows the learner to learn a new topic by utilizing topics in which the learner is strong. In this way, learning effectiveness can be improved by providing an optimized learning path based on the learner's past learning history.

[0079] The progress analysis unit can use the emotion estimation function to track changes in the learner's emotions and provide learning content that corresponds to the changes in emotions. For example, the emotion estimation function can be used to track changes in the learner's emotions in real time and provide learning content that corresponds to the changes in emotions. For example, if the learner is tired, the progress analysis unit can suggest a short break to refresh the learner. The progress analysis unit can also customize learning content that corresponds to changes in emotions based on the learner's emotion data. For example, if the learner is excited, the progress analysis unit can provide challenging questions. The progress analysis unit can also analyze the emotion estimation data and provide feedback that corresponds to changes in the learner's emotions. For example, if the learner is feeling anxious, the progress analysis unit can display encouraging messages or success stories. In this way, learning content that corresponds to changes in the learner's emotions can be provided, thereby improving learning effectiveness.

[0080] The progress analysis unit can analyze a learner's answer patterns, identify the causes of incorrect answers, and provide specific countermeasures to address those causes. For example, the progress analysis unit can analyze a learner's answer patterns in detail and identify the causes of incorrect answers. For example, if there are many incorrect answers for a particular question format or topic, the cause can be analyzed. After identifying the causes of incorrect answers, the progress analysis unit can also provide specific countermeasures to address those causes. For example, if a particular concept is not understood, additional explanations or practice questions for that concept can be provided. The progress analysis unit can also analyze a learner's answer patterns and provide feedback to identify the causes of incorrect answers. For example, a video explanation can be provided that shows the pattern of incorrect answers and explains the causes. In this way, the learning effect can be improved by identifying the causes of the learner's incorrect answers and providing specific countermeasures.

[0081] The comprehension analysis unit uses the emotion estimation function to analyze the learner's emotional state, and if a decline in comprehension is due to emotion, it can identify the cause and provide countermeasures. For example, the emotion estimation function can be used to analyze the learner's emotional state in real time, and if a decline in comprehension is due to emotion, it can identify the cause. For example, if the learner is feeling stressed, it can identify the cause. Furthermore, if a decline in comprehension is due to emotion based on the learner's emotional data, the comprehension analysis unit can also provide specific countermeasures for the cause. For example, it can provide relaxation content to reduce stress. Furthermore, the comprehension analysis unit can analyze the emotion estimation data, and if a decline in comprehension is due to emotion, it can identify the cause and provide appropriate feedback to the learner. For example, if the learner is feeling anxious, it can display an encouraging message or a success story. In this way, the learning effect can be improved by identifying the cause of a decline in comprehension based on the learner's emotional state and providing countermeasures.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The learning content provider provides learning content through the smart glasses. For example, the learning content provider displays questions and explanations on the smart glasses' display. The learning content provider can also use a generation AI to provide questions of appropriate difficulty and additional explanations based on the learner's progress and level of understanding. The generation AI adjusts the learning content to be provided next based on the learner's progress data. Step 2: The progress analysis unit analyzes the learner's progress based on the learning content provided by the learning content provider. For example, the progress analysis unit analyzes the correct answer rate and answer time of the questions solved by the learner to evaluate the learner's progress. The progress analysis unit can also analyze the learner's learning trends based on the learner's learning history. Step 3: The comprehension analysis unit analyzes the learner's level of comprehension based on the progress data analyzed by the progress analysis unit. For example, the comprehension analysis unit evaluates the learner's level of comprehension based on the learner's correct answer rate and answer time. The comprehension analysis unit can also adjust the next learning content to be provided based on the learner's level of comprehension.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[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 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.

[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 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).

[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] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 learning content provider that provides learning content through smart glasses; a progress analysis unit that analyzes the learner's progress based on the learning content provided by the learning content provider; a comprehension analysis unit that analyzes the learner's comprehension level based on the progress data analyzed by the progress analysis unit. A system characterized by:

2. The learning content providing unit Through the smart glasses, the learner interacts with real-world objects and environments, and the generative AI provides learning content relevant to those objects and environments.

2. The system of claim 1.

3. The progress analysis unit A detailed analysis of the learner's past learning history is conducted to generate a learning path optimized for each individual learner.

2. The system of claim 1.

4. The understanding level analysis unit The learner's response time and consistency are analyzed in detail, and fluctuations in the learner's level of understanding are tracked in real time.

2. The system of claim 1.

5. The learning content providing unit Analyze the learner's emotional state in real time and provide content that elicits positive emotions.

2. The system of claim 1.

6. The progress analysis unit Tracking changes in the learner's emotions and providing learning content in response to those changes 2. The system of claim 1.

7. The understanding level analysis unit Analyzing the emotional state of the learner, and if the decline in comprehension is due to emotions, identifying the cause and providing countermeasures.

2. The system of claim 1.

8. The learning content providing unit Provide relaxation content to reduce the stress and anxiety that the learner feels while studying.

2. The system of claim 1.

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