System

Abstract

An object of a system according to an embodiment is to improve a learning effect and reduce a load on a teacher by digitizing a teaching material and customizing the teaching material in accordance with a level of a learner.SOLUTION: A system includes a digital teaching material generation part, a learner level analysis part, a feedback reflection part, and a teacher support part. The digital teaching material generation unit digitizes the teaching material. The learner level analysis unit customizes the digital teaching materials generated by the digital teaching material generation unit in accordance with the level of the learner. The feedback reflection unit analyzes the learner data based on the content customized by the learner level analysis unit and reflects the feedback. The teacher support unit provides the feedback generated by the feedback reflection unit to the teacher to reduce the load on the teacher.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 technology did not adequately digitize teaching materials or customize them to suit the level of learners, which resulted in a heavy workload for teachers.

[0005] The system according to the embodiment aims to improve learning effectiveness and reduce the burden on teachers by digitizing teaching materials and customizing them to suit the level of the learners. [Means for solving the problem]

[0006] The system according to the embodiment includes a digital teaching material generation unit, a learner level analysis unit, a feedback reflection unit, and a teacher support unit. The digital teaching material generation unit digitizes teaching materials. The learner level analysis unit customizes the digital teaching materials generated by the digital teaching material generation unit to suit the level of the learner. The feedback reflection unit analyzes learner data based on the content customized by the learner level analysis unit and reflects feedback. The teacher support unit provides the feedback generated by the feedback reflection unit to the teacher, thereby reducing the teacher's workload. [Effects of the Invention]

[0007] The system according to the embodiment can improve learning effectiveness and reduce the burden on teachers by digitizing teaching materials and customizing them to suit the level of the learners. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The educational support system according to an embodiment of the present invention digitizes teaching materials used in schools, automatically customizes the content to suit the learner's level using a generation AI, and analyzes learner data and incorporates feedback. This allows the educational support system to provide optimal learning content according to the learner's level, improving learning effectiveness and reducing the burden on teachers.

[0029] An educational support system according to an embodiment includes a digital learning material generation unit, a learner level analysis unit, a feedback reflection unit, and a teacher support unit. The digital learning material generation unit digitizes learning materials. For example, it digitizes the content of textbooks and adds interactive quizzes and video explanations. The digital learning material generation unit also provides learning materials in various formats, such as text, images, videos, and audio. For example, it digitizes the content of textbooks and adds interactive quizzes and video explanations. The learner level analysis unit customizes the digital learning materials generated by the digital learning material generation unit to suit the learner's level. For example, the generation AI analyzes the learner's past learning data and current level of understanding and provides optimal learning content based on that. The generation AI adjusts the difficulty of questions according to the learner's level of understanding using a text generation AI (e.g., GPT-3) or a multimodal generation AI. The feedback reflection unit analyzes learner data based on the content customized by the learner level analysis unit and reflects feedback. For example, the generation AI analyzes the learner's answer data and learning progress to identify where the learner is struggling. Based on the results, the generation AI provides the learner with specific advice and additional learning content. The teacher support unit provides the teacher with the feedback generated by the feedback reflection unit, thereby reducing the teacher's workload. For example, the generation AI monitors the learner's progress in real time and provides the teacher with the necessary information. This allows the education support system according to the embodiment to provide optimal learning content according to the learner's level, improving learning effectiveness and reducing the teacher's workload.

[0030] The feedback reflecting unit can provide a real-time feedback function that is automatically updated according to the learner's progress. The feedback reflecting unit, for example, adds a real-time feedback function to digital learning materials and provides instant feedback according to the learner's answer. For example, the difficulty level of the next question is adjusted according to whether the answer is correct or incorrect. The feedback reflecting unit also provides a real-time feedback function that is automatically updated according to the learner's progress. For example, the feedback is provided instantaneously according to the learner's answer. The feedback reflecting unit also provides a real-time feedback function that is automatically updated according to the learner's progress. For example, the feedback is provided instantaneously according to the learner's answer. This makes it possible to improve learning effectiveness by providing real-time feedback according to the learner's progress.

[0031] The learner level analysis unit can automatically provide supplementary materials and links customized based on the learner's interests and concerns. The learner level analysis unit, for example, analyzes the learner's interests and concerns and provides customized supplementary materials based on the interests and concerns. For example, if the learner is interested in history, related historical videos and articles are displayed. The learner level analysis unit also analyzes the learner's interests and concerns and provides customized supplementary materials based on the interests and concerns. For example, if the learner is interested in history, related historical videos and articles are displayed. The learner level analysis unit also analyzes the learner's interests and concerns and provides customized supplementary materials based on the interests and concerns. For example, if the learner is interested in history, related historical videos and articles are displayed. This makes it possible to increase the depth of learning by providing supplementary materials and links based on the learner's interests and concerns.

[0032] The digital teaching material generation unit can provide digital teaching materials in a multimodal format that accommodates different learning styles. For example, the digital teaching material generation unit provides the digital teaching materials in a multimodal format that accommodates visual, auditory, and tactile senses. For example, text, audio commentary, and interactive simulations can be combined. The digital teaching material generation unit also provides the digital teaching materials in a multimodal format that accommodates visual, auditory, and tactile senses. For example, text, audio commentary, and interactive simulations can be combined. The digital teaching material generation unit also provides the digital teaching materials in a multimodal format that accommodates visual, auditory, and tactile senses. For example, text, audio commentary, and interactive simulations can be combined. This makes it possible to accommodate different learning styles and improve the learner's understanding.

[0033] The digital teaching material generation unit can incorporate content from different cultures and regions to promote learning from a global perspective. The digital teaching material generation unit, for example, incorporates content from different cultures and regions into the digital teaching materials to promote learning from a global perspective. For example, it provides videos and articles about the history and culture of each country. The digital teaching material generation unit can also incorporate content from different cultures and regions into the digital teaching materials to promote learning from a global perspective. For example, it provides videos and articles about the history and culture of each country. The digital teaching material generation unit can also incorporate content from different cultures and regions into the digital teaching materials to promote learning from a global perspective. For example, it provides videos and articles about the history and culture of each country. In this way, the incorporation of content from different cultures and regions can broaden the learner's horizons.

[0034] The learner level analysis unit analyzes not only the learner's past learning data but also real-time biometric information to provide optimal learning content. The learner level analysis unit, for example, analyzes the learner's past learning data and real-time biometric information to build a system that provides optimal learning content. For example, the learning progress is adjusted based on heart rate and facial expression data. The learner level analysis unit also analyzes the learner's past learning data and real-time biometric information to build a system that provides optimal learning content. For example, the learning progress is adjusted based on heart rate and facial expression data. The learner level analysis unit also analyzes the learner's past learning data and real-time biometric information to build a system that provides optimal learning content. For example, the learning progress is adjusted based on heart rate and facial expression data. This makes it possible to improve learning effectiveness by providing optimal learning content that takes the learner's biometric information into consideration.

[0035] The learner level analysis unit can predict future learning performance based on the learner's learning history and provide a customized learning plan based on that. The learner level analysis unit, for example, analyzes the learner's past learning history and builds a system that predicts future learning performance. For example, it predicts learning progress based on past grades and answer history. The learner level analysis unit also analyzes the learner's past learning history and builds a system that predicts future learning performance. For example, it predicts learning progress based on past grades and answer history. The learner level analysis unit also analyzes the learner's past learning history and builds a system that predicts future learning performance. For example, it predicts learning progress based on past grades and answer history. In this way, it is possible to predict future learning performance based on the learner's learning history and provide an optimal learning plan, thereby improving learning effectiveness.

[0036] The learner level analysis unit can enable content tailored to a learner's level to be used seamlessly across different devices. The learner level analysis unit, for example, builds a system that enables content tailored to a learner's level to be used seamlessly across different devices, such as smartphones, tablets, and VR headsets. For example, it synchronizes learning progress between devices. The learner level analysis unit also builds a system that enables content tailored to a learner's level to be used seamlessly across different devices, such as smartphones, tablets, and VR headsets. For example, it synchronizes learning progress between devices. The learner level analysis unit also builds a system that enables content tailored to a learner's level to be used seamlessly across different devices, such as smartphones, tablets, and VR headsets. For example, it synchronizes learning progress between devices. This allows learning content to be used seamlessly across different devices, thereby improving learning convenience.

[0037] The learner level analysis unit can provide content appropriate to the learner's level in the form of group learning or collaborative learning, and promote interaction with other learners. The learner level analysis unit, for example, builds a system that provides content appropriate to the learner's level in the form of group learning or collaborative learning. For example, learners of the same level are divided into groups and work on assignments together. The learner level analysis unit also builds a system that provides content appropriate to the learner's level in the form of group learning or collaborative learning. For example, learners of the same level are divided into groups and work on assignments together. The learner level analysis unit also builds a system that provides content appropriate to the learner's level in the form of group learning or collaborative learning. For example, learners of the same level are divided into groups and work on assignments together. This can promote interaction between learners through group learning or collaborative learning, and improve learning effectiveness.

[0038] The feedback reflection unit can integrate different data sources in the analysis of learner data to provide more comprehensive feedback. For example, the feedback reflection unit integrates data from social media and online forums in the analysis of learner data to build a system for providing comprehensive feedback. For example, the learner's online activities are analyzed to provide information related to learning progress. The feedback reflection unit also integrates data from social media and online forums in the analysis of learner data to build a system for providing comprehensive feedback. For example, the learner's online activities are analyzed to provide information related to learning progress. The feedback reflection unit also integrates data from social media and online forums in the analysis of learner data to build a system for providing comprehensive feedback. For example, the learner's online activities are analyzed to provide information related to learning progress. In this way, by integrating different data sources, more comprehensive feedback can be provided and learning effectiveness can be improved.

[0039] The feedback reflection unit can analyze learner data, identify the optimal learning pattern for each individual learner, and customize feedback based on that. The feedback reflection unit, for example, analyzes learner data and builds a system that identifies the optimal learning pattern for each individual learner. For example, it proposes the optimal learning pattern based on the learner's past grades and answer history. The feedback reflection unit also analyzes learner data and builds a system that identifies the optimal learning pattern for each individual learner. For example, it proposes the optimal learning pattern based on the learner's past grades and answer history. The feedback reflection unit also analyzes learner data and builds a system that identifies the optimal learning pattern for each learner. For example, it proposes the optimal learning pattern based on the learner's past grades and answer history. In this way, by analyzing learner data and identifying the optimal learning pattern, it is possible to provide personalized feedback and improve learning effectiveness.

[0040] The feedback reflection unit can share the analysis results of the learner data with parents and educational institutions, thereby strengthening the support system for learners. The feedback reflection unit, for example, shares the analysis results of the learner data with parents, thereby building a system to strengthen the support system for learners. For example, it reports the learner's progress and level of understanding to the parents. The feedback reflection unit also shares the analysis results of the learner data with parents, thereby building a system to strengthen the support system for learners. For example, it reports the learner's progress and level of understanding to the parents. The feedback reflection unit also shares the analysis results of the learner data with parents, thereby building a system to strengthen the support system for learners. For example, it reports the learner's progress and level of understanding to the parents. In this way, by sharing the analysis results of the learner data, the support system for learners can be strengthened and learning effectiveness can be improved.

[0041] The feedback reflection unit can share the analysis results of the learner data with teachers of different grades or departments, and provide comprehensive learning support to the learner. The feedback reflection unit, for example, shares the analysis results of the learner data with teachers of different grades or departments, and builds a system that provides comprehensive learning support. For example, the learner's grades and progress data are shared with multiple teachers. The feedback reflection unit also shares the analysis results of the learner data with teachers of different grades or departments, and builds a system that provides comprehensive learning support. For example, the learner's grades and progress data are shared with multiple teachers. The feedback reflection unit also shares the analysis results of the learner data with teachers of different grades or departments, and builds a system that provides comprehensive learning support. For example, the learner's grades and progress data are shared with multiple teachers. In this way, by sharing the analysis results of the learner data, comprehensive learning support can be provided to the learner, and learning effectiveness can be improved.

[0042] The teacher support unit uses generative AI to automatically generate lesson plans for teachers, thereby reducing the burden on teachers. The teacher support unit, for example, uses generative AI to build a system that automatically generates lesson plans for teachers. For example, it automatically generates lesson content and a progress schedule based on a curriculum. The teacher support unit also uses generative AI to build a system that automatically generates lesson plans for teachers. For example, it automatically generates lesson content and a progress schedule based on a curriculum. The teacher support unit also uses generative AI to build a system that automatically generates lesson plans for teachers. For example, it automatically generates lesson content and a progress schedule based on a curriculum. In this way, by automatically generating lesson plans for teachers, it is possible to reduce the burden on teachers and improve the quality of education.

[0043] The teacher support department uses generative AI to automate teacher evaluation tasks, thereby improving the fairness and efficiency of evaluation. The teacher support department, for example, uses generative AI to build a system that automates teacher evaluation tasks. For example, it analyzes learners' answer data and automatically performs evaluations. The teacher support department also uses generative AI to build a system that automates teacher evaluation tasks. For example, it analyzes learners' answer data and automatically performs evaluations. The teacher support department also uses generative AI to build a system that automates teacher evaluation tasks. For example, it analyzes learners' answer data and automatically performs evaluations. In this way, automating teacher evaluation tasks can improve fairness and efficiency.

[0044] The teacher support unit can use generative AI to analyze the teacher's lesson content in real time and suggest areas for improvement. The teacher support unit, for example, uses generative AI to build a system that analyzes the teacher's lesson content in real time and suggests areas for improvement. For example, it analyzes the progress of the lesson and the responses of the students and suggests areas for improvement. The teacher support unit also uses generative AI to build a system that analyzes the teacher's lesson content in real time and suggests areas for improvement. For example, it analyzes the progress of the lesson and the responses of the students and suggests areas for improvement. The teacher support unit also uses generative AI to build a system that analyzes the teacher's lesson content in real time and suggests areas for improvement. For example, it analyzes the progress of the lesson and the responses of the students and suggests areas for improvement. In this way, the quality of lessons can be improved by analyzing the teacher's lesson content in real time and suggesting areas for improvement.

[0045] The teacher support unit uses generative AI to answer questions and doubts that teachers have during class in real time, thereby reducing the burden on teachers. For example, the teacher support unit uses generative AI to build a system that answers questions and doubts that teachers have during class in real time. For example, it provides instant answers to questions from learners. The teacher support unit also uses generative AI to build a system that answers questions and doubts that teachers have during class in real time. For example, it provides instant answers to questions from learners. The teacher support unit also uses generative AI to build a system that answers questions and doubts that teachers have during class in real time. For example, it provides instant answers to questions from learners. In this way, by answering questions and doubts that teachers have during class in real time, it is possible to reduce the burden on teachers and improve the quality of lessons.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The education support system can also be equipped with a health management unit that monitors the learner's health condition. For example, it can measure the learner's heart rate and stress level and encourage them to take a break if they are under excessive stress. The health management unit can also analyze the learner's sleep patterns and suggest appropriate study times. Furthermore, the health management unit can record the learner's dietary and exercise habits and provide advice to support a balanced lifestyle. This can improve learning effectiveness by providing learning support that takes the learner's health condition into consideration.

[0048] The educational support system can also be equipped with a learning style analysis unit that analyzes the learner's learning style and suggests the optimal learning method. For example, a learner who prefers visual learning can be provided with learning materials that make extensive use of diagrams and graphs. A learner who prefers auditory learning can be provided with learning materials in the form of audio commentary or podcasts. Furthermore, a learner who prefers tactile learning can be provided with interactive simulations and experiments. This can improve learning effectiveness by providing the optimal learning method that matches the learner's learning style.

[0049] The educational support system can further include a predictive analysis unit that analyzes the learner's learning history and predicts future learning performance. For example, it can present predicted scores for the next test based on past grades and answer history. The predictive analysis unit can also identify the learner's weak points and suggest areas that should be focused on. Furthermore, the predictive analysis unit can monitor the learner's progress in real time and adjust the learning plan as needed. This makes it possible to predict the learner's future learning performance and provide an optimal learning plan, thereby improving learning effectiveness.

[0050] The educational support system can further include a collaborative learning unit that analyzes learners' learning data and promotes cooperation among learners. For example, learners with similar interests can be divided into groups and work on tasks together. The collaborative learning unit can also provide opportunities for peer learning, where learners teach each other. Furthermore, the collaborative learning unit can provide a chat function or forum to promote communication among learners. This can promote cooperation among learners and improve learning effectiveness.

[0051] The education support system may further include an engagement unit that analyzes learners' learning data and stimulates their interest. For example, it may provide quizzes or games related to topics that interest the learner. The engagement unit may also provide interactive content so that learners can learn while having fun. Furthermore, the engagement unit may provide feedback that visualizes the learner's progress so that the learner feels a sense of accomplishment. This may stimulate the learner's interest and improve the learning effect.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The digital learning material generator digitizes learning materials. For example, it digitizes textbook content and adds interactive quizzes and video explanations. It also provides learning materials in various formats, such as text, images, videos, and audio. Step 2: The learner level analysis unit customizes the digital learning materials generated by the digital learning material generation unit to suit the learner's level. For example, the generation AI analyzes the learner's past learning data and current level of understanding and provides optimal learning content based on that. The generation AI uses text generation AI (e.g., GPT-3) and multimodal generation AI to adjust the difficulty of questions according to the learner's level of understanding. Step 3: The feedback reflection unit analyzes the learner's data based on the content customized by the learner level analysis unit and reflects feedback. For example, the generation AI analyzes the learner's answer data and learning progress to identify where the learner is struggling. Based on the results, the generation AI provides the learner with specific advice and additional learning content. Step 4: The teacher support unit provides the feedback generated by the feedback reflection unit to the teacher, reducing the teacher's workload. For example, the generation AI monitors the learner's progress in real time and provides the teacher with the necessary information.

[0054] (Example 2) The educational support system according to an embodiment of the present invention digitizes teaching materials used in schools, automatically customizes the content to suit the learner's level using a generation AI, and analyzes learner data and incorporates feedback. This allows the educational support system to provide optimal learning content according to the learner's level, improving learning effectiveness and reducing the burden on teachers.

[0055] An educational support system according to an embodiment includes a digital learning material generation unit, a learner level analysis unit, a feedback reflection unit, and a teacher support unit. The digital learning material generation unit digitizes learning materials. For example, it digitizes the content of textbooks and adds interactive quizzes and video explanations. The digital learning material generation unit also provides learning materials in various formats, such as text, images, videos, and audio. For example, it digitizes the content of textbooks and adds interactive quizzes and video explanations. The learner level analysis unit customizes the digital learning materials generated by the digital learning material generation unit to suit the learner's level. For example, the generation AI analyzes the learner's past learning data and current level of understanding and provides optimal learning content based on that. The generation AI adjusts the difficulty of questions according to the learner's level of understanding using a text generation AI (e.g., GPT-3) or a multimodal generation AI. The feedback reflection unit analyzes learner data based on the content customized by the learner level analysis unit and reflects feedback. For example, the generation AI analyzes the learner's answer data and learning progress to identify where the learner is struggling. Based on the results, the generation AI provides the learner with specific advice and additional learning content. The teacher support unit provides the teacher with the feedback generated by the feedback reflection unit, thereby reducing the teacher's workload. For example, the generation AI monitors the learner's progress in real time and provides the teacher with the necessary information. This allows the education support system according to the embodiment to provide optimal learning content according to the learner's level, improving learning effectiveness and reducing the teacher's workload.

[0056] The digital teaching material generation unit can use a generative AI to estimate a learner's emotions and add interactive elements to elicit positive emotions. For example, the generative AI in the digital teaching material generation unit analyzes the learner's facial expressions and voice to estimate emotions in real time. For example, if the learner is confused, it displays an encouraging message or hint. The digital teaching material generation unit also analyzes a learner's emotions and adds interactive elements to elicit positive emotions. For example, it displays a message of praise when the learner gets the answer correct. The digital teaching material generation unit also analyzes a learner's emotions and adds interactive elements to elicit positive emotions. For example, it displays a message of praise when the learner gets the answer correct. In this way, by adding interactive elements that take the learner's emotions into consideration, it is possible to improve learning motivation.

[0057] The feedback reflecting unit can provide a real-time feedback function that is automatically updated according to the learner's progress. The feedback reflecting unit, for example, adds a real-time feedback function to digital learning materials and provides instant feedback according to the learner's answer. For example, the difficulty level of the next question is adjusted according to whether the answer is correct or incorrect. The feedback reflecting unit also provides a real-time feedback function that is automatically updated according to the learner's progress. For example, the feedback is provided instantaneously according to the learner's answer. The feedback reflecting unit also provides a real-time feedback function that is automatically updated according to the learner's progress. For example, the feedback is provided instantaneously according to the learner's answer. This makes it possible to improve learning effectiveness by providing real-time feedback according to the learner's progress.

[0058] The learner level analysis unit can automatically provide supplementary materials and links customized based on the learner's interests and concerns. The learner level analysis unit, for example, analyzes the learner's interests and concerns and provides customized supplementary materials based on the interests and concerns. For example, if the learner is interested in history, related historical videos and articles are displayed. The learner level analysis unit also analyzes the learner's interests and concerns and provides customized supplementary materials based on the interests and concerns. For example, if the learner is interested in history, related historical videos and articles are displayed. The learner level analysis unit also analyzes the learner's interests and concerns and provides customized supplementary materials based on the interests and concerns. For example, if the learner is interested in history, related historical videos and articles are displayed. This makes it possible to increase the depth of learning by providing supplementary materials and links based on the learner's interests and concerns.

[0059] The digital teaching material generation unit can provide digital teaching materials in a multimodal format that accommodates different learning styles. For example, the digital teaching material generation unit provides the digital teaching materials in a multimodal format that accommodates visual, auditory, and tactile senses. For example, text, audio commentary, and interactive simulations can be combined. The digital teaching material generation unit also provides the digital teaching materials in a multimodal format that accommodates visual, auditory, and tactile senses. For example, text, audio commentary, and interactive simulations can be combined. The digital teaching material generation unit also provides the digital teaching materials in a multimodal format that accommodates visual, auditory, and tactile senses. For example, text, audio commentary, and interactive simulations can be combined. This makes it possible to accommodate different learning styles and improve the learner's understanding.

[0060] The digital teaching material generation unit can incorporate content from different cultures and regions to promote learning from a global perspective. The digital teaching material generation unit, for example, incorporates content from different cultures and regions into the digital teaching materials to promote learning from a global perspective. For example, it provides videos and articles about the history and culture of each country. The digital teaching material generation unit can also incorporate content from different cultures and regions into the digital teaching materials to promote learning from a global perspective. For example, it provides videos and articles about the history and culture of each country. The digital teaching material generation unit can also incorporate content from different cultures and regions into the digital teaching materials to promote learning from a global perspective. For example, it provides videos and articles about the history and culture of each country. In this way, the incorporation of content from different cultures and regions can broaden the learner's horizons.

[0061] The feedback reflection unit can use the emotion estimation function to monitor in real time how the learner feels about the learning materials and make adjustments to reduce negative emotions. The feedback reflection unit, for example, uses the emotion estimation function to monitor in real time how the learner feels about the learning materials. For example, it analyzes the learner's facial expressions and voice and calculates an emotion score. The feedback reflection unit also uses the emotion estimation function to monitor in real time how the learner feels about the learning materials. For example, it analyzes the learner's facial expressions and voice and calculates an emotion score. The feedback reflection unit also uses the emotion estimation function to monitor in real time how the learner feels about the learning materials. For example, it analyzes the learner's facial expressions and voice and calculates an emotion score. In this way, the learner's emotions can be monitored in real time and negative emotions can be reduced, thereby improving the effectiveness of learning.

[0062] The learner-level analysis unit can use the generation AI to estimate the learner's emotions and automatically generate content to elicit positive emotions. The learner-level analysis unit, for example, uses the generation AI to analyze the learner's emotions in real time and automatically generate content to elicit positive emotions. For example, it displays words of praise and encouraging messages that make the learner feel happy. The learner-level analysis unit also uses the generation AI to analyze the learner's emotions in real time and automatically generate content to elicit positive emotions. For example, it displays words of praise and encouraging messages that make the learner feel happy. The learner-level analysis unit also uses the generation AI to analyze the learner's emotions in real time and automatically generate content to elicit positive emotions. For example, it displays words of praise and encouraging messages that make the learner feel happy. In this way, by automatically generating content that takes the learner's emotions into consideration, it is possible to improve learning motivation.

[0063] The learner level analysis unit analyzes not only the learner's past learning data but also real-time biometric information to provide optimal learning content. The learner level analysis unit, for example, analyzes the learner's past learning data and real-time biometric information to build a system that provides optimal learning content. For example, the learning progress is adjusted based on heart rate and facial expression data. The learner level analysis unit also analyzes the learner's past learning data and real-time biometric information to build a system that provides optimal learning content. For example, the learning progress is adjusted based on heart rate and facial expression data. The learner level analysis unit also analyzes the learner's past learning data and real-time biometric information to build a system that provides optimal learning content. For example, the learning progress is adjusted based on heart rate and facial expression data. This makes it possible to improve learning effectiveness by providing optimal learning content that takes the learner's biometric information into consideration.

[0064] The learner level analysis unit can predict future learning performance based on the learner's learning history and provide a customized learning plan based on that. The learner level analysis unit, for example, analyzes the learner's past learning history and builds a system that predicts future learning performance. For example, it predicts learning progress based on past grades and answer history. The learner level analysis unit also analyzes the learner's past learning history and builds a system that predicts future learning performance. For example, it predicts learning progress based on past grades and answer history. The learner level analysis unit also analyzes the learner's past learning history and builds a system that predicts future learning performance. For example, it predicts learning progress based on past grades and answer history. In this way, it is possible to predict future learning performance based on the learner's learning history and provide an optimal learning plan, thereby improving learning effectiveness.

[0065] The learner level analysis unit can enable content tailored to a learner's level to be used seamlessly across different devices. The learner level analysis unit, for example, builds a system that enables content tailored to a learner's level to be used seamlessly across different devices, such as smartphones, tablets, and VR headsets. For example, it synchronizes learning progress between devices. The learner level analysis unit also builds a system that enables content tailored to a learner's level to be used seamlessly across different devices, such as smartphones, tablets, and VR headsets. For example, it synchronizes learning progress between devices. The learner level analysis unit also builds a system that enables content tailored to a learner's level to be used seamlessly across different devices, such as smartphones, tablets, and VR headsets. For example, it synchronizes learning progress between devices. This allows learning content to be used seamlessly across different devices, thereby improving learning convenience.

[0066] The learner level analysis unit can provide content appropriate to the learner's level in the form of group learning or collaborative learning, and promote interaction with other learners. The learner level analysis unit, for example, builds a system that provides content appropriate to the learner's level in the form of group learning or collaborative learning. For example, learners of the same level are divided into groups and work on assignments together. The learner level analysis unit also builds a system that provides content appropriate to the learner's level in the form of group learning or collaborative learning. For example, learners of the same level are divided into groups and work on assignments together. The learner level analysis unit also builds a system that provides content appropriate to the learner's level in the form of group learning or collaborative learning. For example, learners of the same level are divided into groups and work on assignments together. This can promote interaction between learners through group learning or collaborative learning, and improve learning effectiveness.

[0067] The feedback reflection unit can use the generation AI to estimate the learner's emotions and provide feedback to elicit positive emotions. The feedback reflection unit, for example, uses the generation AI to analyze the learner's emotions in real time and provide feedback to elicit positive emotions. For example, it displays a message of praise when the learner gets the answer correct. The feedback reflection unit can also use the generation AI to analyze the learner's emotions in real time and provide feedback to elicit positive emotions. For example, it displays a message of praise when the learner gets the answer correct. The feedback reflection unit can also use the generation AI to analyze the learner's emotions in real time and provide feedback to elicit positive emotions. For example, it displays a message of praise when the learner gets the answer correct. In this way, by providing feedback that takes the learner's emotions into consideration, it is possible to improve learning motivation.

[0068] The feedback reflection unit can integrate different data sources in the analysis of learner data to provide more comprehensive feedback. For example, the feedback reflection unit integrates data from social media and online forums in the analysis of learner data to build a system for providing comprehensive feedback. For example, the learner's online activities are analyzed to provide information related to learning progress. The feedback reflection unit also integrates data from social media and online forums in the analysis of learner data to build a system for providing comprehensive feedback. For example, the learner's online activities are analyzed to provide information related to learning progress. The feedback reflection unit also integrates data from social media and online forums in the analysis of learner data to build a system for providing comprehensive feedback. For example, the learner's online activities are analyzed to provide information related to learning progress. In this way, by integrating different data sources, more comprehensive feedback can be provided and learning effectiveness can be improved.

[0069] The feedback reflection unit can analyze learner data, identify the optimal learning pattern for each individual learner, and customize feedback based on that. The feedback reflection unit, for example, analyzes learner data and builds a system that identifies the optimal learning pattern for each individual learner. For example, it proposes the optimal learning pattern based on the learner's past grades and answer history. The feedback reflection unit also analyzes learner data and builds a system that identifies the optimal learning pattern for each individual learner. For example, it proposes the optimal learning pattern based on the learner's past grades and answer history. The feedback reflection unit also analyzes learner data and builds a system that identifies the optimal learning pattern for each learner. For example, it proposes the optimal learning pattern based on the learner's past grades and answer history. In this way, by analyzing learner data and identifying the optimal learning pattern, it is possible to provide personalized feedback and improve learning effectiveness.

[0070] The feedback reflection unit can share the analysis results of the learner data with parents and educational institutions, thereby strengthening the support system for learners. The feedback reflection unit, for example, shares the analysis results of the learner data with parents, thereby building a system to strengthen the support system for learners. For example, it reports the learner's progress and level of understanding to the parents. The feedback reflection unit also shares the analysis results of the learner data with parents, thereby building a system to strengthen the support system for learners. For example, it reports the learner's progress and level of understanding to the parents. The feedback reflection unit also shares the analysis results of the learner data with parents, thereby building a system to strengthen the support system for learners. For example, it reports the learner's progress and level of understanding to the parents. In this way, by sharing the analysis results of the learner data, the support system for learners can be strengthened and learning effectiveness can be improved.

[0071] The feedback reflection unit can share the analysis results of the learner data with teachers of different grades or departments, and provide comprehensive learning support to the learner. The feedback reflection unit, for example, shares the analysis results of the learner data with teachers of different grades or departments, and builds a system that provides comprehensive learning support. For example, the learner's grades and progress data are shared with multiple teachers. The feedback reflection unit also shares the analysis results of the learner data with teachers of different grades or departments, and builds a system that provides comprehensive learning support. For example, the learner's grades and progress data are shared with multiple teachers. The feedback reflection unit also shares the analysis results of the learner data with teachers of different grades or departments, and builds a system that provides comprehensive learning support. For example, the learner's grades and progress data are shared with multiple teachers. In this way, by sharing the analysis results of the learner data, comprehensive learning support can be provided to the learner, and learning effectiveness can be improved.

[0072] The feedback reflection unit can use the emotion estimation function to analyze how the learner feels about the feedback and adjust the content of the feedback. The feedback reflection unit, for example, uses the emotion estimation function to build a system that analyzes in real time how the learner feels about the feedback. For example, it analyzes the learner's facial expressions and voice and calculates an emotion score. The feedback reflection unit also uses the emotion estimation function to build a system that analyzes in real time how the learner feels about the feedback. For example, it analyzes the learner's facial expressions and voice and calculates an emotion score. The feedback reflection unit also uses the emotion estimation function to build a system that analyzes in real time how the learner feels about the feedback. For example, it analyzes the learner's facial expressions and voice and calculates an emotion score. In this way, feedback that takes the learner's emotions into consideration can be provided, thereby improving learning motivation.

[0073] The teacher support unit can use the generative AI to estimate the teacher's emotions and provide support to bring out positive emotions. The teacher support unit, for example, uses the generative AI to analyze the teacher's emotions in real time and provide support to bring out positive emotions. For example, if the teacher is feeling stressed, it provides content that will help them relax. The teacher support unit can also use the generative AI to analyze the teacher's emotions in real time and provide support to bring out positive emotions. For example, if the teacher is feeling stressed, it provides content that will help them relax. The teacher support unit can also use the generative AI to analyze the teacher's emotions in real time and provide support to bring out positive emotions. For example, if the teacher is feeling stressed, it provides content that will help them relax. In this way, by providing support that takes the teacher's emotions into consideration, it is possible to reduce teacher stress and improve the quality of education.

[0074] The teacher support unit uses generative AI to automatically generate lesson plans for teachers, thereby reducing the burden on teachers. The teacher support unit, for example, uses generative AI to build a system that automatically generates lesson plans for teachers. For example, it automatically generates lesson content and a progress schedule based on a curriculum. The teacher support unit also uses generative AI to build a system that automatically generates lesson plans for teachers. For example, it automatically generates lesson content and a progress schedule based on a curriculum. The teacher support unit also uses generative AI to build a system that automatically generates lesson plans for teachers. For example, it automatically generates lesson content and a progress schedule based on a curriculum. In this way, by automatically generating lesson plans for teachers, it is possible to reduce the burden on teachers and improve the quality of education.

[0075] The teacher support department uses generative AI to automate teacher evaluation tasks, thereby improving the fairness and efficiency of evaluation. The teacher support department, for example, uses generative AI to build a system that automates teacher evaluation tasks. For example, it analyzes learners' answer data and automatically performs evaluations. The teacher support department also uses generative AI to build a system that automates teacher evaluation tasks. For example, it analyzes learners' answer data and automatically performs evaluations. The teacher support department also uses generative AI to build a system that automates teacher evaluation tasks. For example, it analyzes learners' answer data and automatically performs evaluations. In this way, automating teacher evaluation tasks can improve fairness and efficiency.

[0076] The teacher support unit can use generative AI to analyze the teacher's lesson content in real time and suggest areas for improvement. The teacher support unit, for example, uses generative AI to build a system that analyzes the teacher's lesson content in real time and suggests areas for improvement. For example, it analyzes the progress of the lesson and the responses of the students and suggests areas for improvement. The teacher support unit also uses generative AI to build a system that analyzes the teacher's lesson content in real time and suggests areas for improvement. For example, it analyzes the progress of the lesson and the responses of the students and suggests areas for improvement. The teacher support unit also uses generative AI to build a system that analyzes the teacher's lesson content in real time and suggests areas for improvement. For example, it analyzes the progress of the lesson and the responses of the students and suggests areas for improvement. In this way, the quality of lessons can be improved by analyzing the teacher's lesson content in real time and suggesting areas for improvement.

[0077] The teacher support unit uses generative AI to answer questions and doubts that teachers have during class in real time, thereby reducing the burden on teachers. For example, the teacher support unit uses generative AI to build a system that answers questions and doubts that teachers have during class in real time. For example, it provides instant answers to questions from learners. The teacher support unit also uses generative AI to build a system that answers questions and doubts that teachers have during class in real time. For example, it provides instant answers to questions from learners. The teacher support unit also uses generative AI to build a system that answers questions and doubts that teachers have during class in real time. For example, it provides instant answers to questions from learners. In this way, by answering questions and doubts that teachers have during class in real time, it is possible to reduce the burden on teachers and improve the quality of lessons.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The education support system can also be equipped with a health management unit that monitors the learner's health condition. For example, it can measure the learner's heart rate and stress level and encourage them to take a break if they are under excessive stress. The health management unit can also analyze the learner's sleep patterns and suggest appropriate study times. Furthermore, the health management unit can record the learner's dietary and exercise habits and provide advice to support a balanced lifestyle. This can improve learning effectiveness by providing learning support that takes the learner's health condition into consideration.

[0080] The education support system may further include an environment adjustment unit that estimates the learner's emotions and adjusts the learning environment based on the estimated emotions. For example, if the learner is lacking concentration, the environment adjustment unit may change the background music or adjust the lighting. The environment adjustment unit may also display natural scenery and sounds to help the learner relax. Furthermore, if the learner is feeling stressed, it may provide content that helps the learner relax. In this way, the learning effect can be improved by providing a learning environment that takes the learner's emotions into consideration.

[0081] The educational support system can also be equipped with a learning style analysis unit that analyzes the learner's learning style and suggests the optimal learning method. For example, a learner who prefers visual learning can be provided with learning materials that make extensive use of diagrams and graphs. A learner who prefers auditory learning can be provided with learning materials in the form of audio commentary or podcasts. Furthermore, a learner who prefers tactile learning can be provided with interactive simulations and experiments. This can improve learning effectiveness by providing the optimal learning method that matches the learner's learning style.

[0082] The education support system can further include a goal setting unit that estimates the learner's emotions and sets learning goals based on the estimated emotions. For example, if the learner feels motivated, a challenging goal can be set. On the other hand, if the learner feels anxious, an easy-to-achieve goal can be set. Furthermore, a message praising the learner when they achieve their goal can be displayed so that they feel a sense of accomplishment. In this way, learning goals that take the learner's emotions into consideration can be set, thereby improving their motivation to learn.

[0083] The educational support system can further include a predictive analysis unit that analyzes the learner's learning history and predicts future learning performance. For example, it can present predicted scores for the next test based on past grades and answer history. The predictive analysis unit can also identify the learner's weak points and suggest areas that should be focused on. Furthermore, the predictive analysis unit can monitor the learner's progress in real time and adjust the learning plan as needed. This makes it possible to predict the learner's future learning performance and provide an optimal learning plan, thereby improving learning effectiveness.

[0084] The education support system can further include a content adjustment unit that estimates the learner's emotions and adjusts the learning content based on the estimated emotions. For example, if the learner is excited, it can provide more difficult questions. If the learner is tired, it can provide easier questions or content that helps the learner relax. It can also provide content related to areas in which the learner is interested preferentially. This allows for improved learning effectiveness by providing learning content that takes the learner's emotions into consideration.

[0085] The educational support system can further include a collaborative learning unit that analyzes learners' learning data and promotes cooperation among learners. For example, learners with similar interests can be divided into groups and work on tasks together. The collaborative learning unit can also provide opportunities for peer learning, where learners teach each other. Furthermore, the collaborative learning unit can provide a chat function or forum to promote communication among learners. This can promote cooperation among learners and improve learning effectiveness.

[0086] The education support system can further include a progress adjustment unit that estimates the learner's emotions and adjusts the learning progress based on the estimated emotions. For example, if the learner is feeling impatient, the learning pace can be slowed down. Also, if the learner is feeling bored, the learning pace can be increased. Furthermore, feedback that visualizes the progress can be provided so that the learner feels a sense of accomplishment. In this way, the learning progress can be adjusted taking the learner's emotions into consideration, thereby improving the learning effect.

[0087] The education support system may further include an engagement unit that analyzes learners' learning data and stimulates their interest. For example, it may provide quizzes or games related to topics that interest the learner. The engagement unit may also provide interactive content so that learners can learn while having fun. Furthermore, the engagement unit may provide feedback that visualizes the learner's progress so that the learner feels a sense of accomplishment. This may stimulate the learner's interest and improve the learning effect.

[0088] The educational support system can further include a motivation maintenance unit that estimates the learner's emotions and maintains their motivation to learn based on the estimated emotions. For example, if the learner is losing motivation, it can provide encouraging messages and advice on how to achieve their goals. It can also provide feedback that visualizes the learner's progress so that the learner feels a sense of accomplishment. It can also provide content that incorporates game elements so that the learner can learn while having fun. This can improve learning effectiveness by maintaining motivation while taking the learner's emotions into consideration.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The digital learning material generator digitizes learning materials. For example, it digitizes textbook content and adds interactive quizzes and video explanations. It also provides learning materials in various formats, such as text, images, videos, and audio. Step 2: The learner level analysis unit customizes the digital learning materials generated by the digital learning material generation unit to suit the learner's level. For example, the generation AI analyzes the learner's past learning data and current level of understanding and provides optimal learning content based on that. The generation AI uses text generation AI (e.g., GPT-3) and multimodal generation AI to adjust the difficulty of questions according to the learner's level of understanding. Step 3: The feedback reflection unit analyzes the learner's data based on the content customized by the learner level analysis unit and reflects feedback. For example, the generation AI analyzes the learner's answer data and learning progress to identify where the learner is struggling. Based on the results, the generation AI provides the learner with specific advice and additional learning content. Step 4: The teacher support unit provides the feedback generated by the feedback reflection unit to the teacher, reducing the teacher's workload. For example, the generation AI monitors the learner's progress in real time and provides the teacher with the necessary information.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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]

[0158] 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 digital teaching material generation unit that digitizes teaching materials; a learner level analysis unit that customizes the digital teaching materials generated by the digital teaching material generation unit according to the level of the learner; a feedback reflecting unit that analyzes learner data based on the content customized by the learner level analyzing unit and reflects feedback; a teacher support unit that provides the teacher with the feedback generated by the feedback reflection unit and reduces the teacher's burden. A system characterized by:

2. The digital teaching material generation unit The generative AI is used to estimate the learner's emotions and add interactive elements to elicit positive emotions.

2. The system of claim 1.

3. The feedback reflection unit Provide real-time feedback that is automatically updated according to the learner's progress 2. The system of claim 1.

4. The learner level analysis unit Automatically provide supplemental materials and links customized based on the learner's interests 2. The system of claim 1.

5. The digital teaching material generation unit Present the digital learning materials in a multimodal format that accommodates different learning styles 2. The system of claim 1.

6. The learner level analysis unit The generation AI is used to estimate the learner's emotions and automatically generate the content to elicit positive emotions.

2. The system of claim 1.

7. The feedback reflection unit The generative AI is used to estimate the learner's emotions and provide the feedback to elicit positive emotions.

2. The system of claim 1.

8. The teacher support department The generative AI is used to estimate the teacher's emotions and provide support to elicit positive emotions.

2. The system of claim 1.

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