System

Abstract

A system according to an embodiment aims to provide a customized teaching approach tailored to the individual needs of the learner.SOLUTION: A system includes an interaction unit, an analysis unit, and a customization unit. The dialogue unit performs a dialogue with the learner. The analysis unit analyzes the dialogue content acquired by the dialogue unit. The customizing unit provides a customized educational approach based on the result analyzed by the analyzing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have difficulty providing educational approaches tailored to the individual needs of learners, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a customized educational approach to the individual needs of the learner. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, an analysis unit, and a customization unit. The dialogue unit engages in dialogue with a learner. The analysis unit analyzes the dialogue content acquired by the dialogue unit. The customization unit provides a customized educational approach based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] Systems according to embodiments can provide a customized educational approach tailored to the individual needs of 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) Smart Learning Companion, an embodiment of the present invention, is a system that automatically reads answers written by learners, summarizes them using a generation AI, calculates the similarity to model answers, and assigns a score. This allows Smart Learning Companion to provide an educational approach tailored to the individual needs of learners, improving the quality of education.

[0029] The Smart Learning Companion according to the embodiment includes a dialogue unit, an analysis unit, and a customization unit. The dialogue unit dialogues with a learner. For example, when a learner asks a question, the dialogue unit receives the question and sends it to the generation AI. The dialogue unit can also analyze the learner's tone of voice and speaking style to estimate their emotional state. The analysis unit analyzes the dialogue content acquired by the dialogue unit. For example, the generation AI analyzes the content of the learner's question and generates an appropriate answer or explanation. The analysis unit can also analyze the dialogue history to grasp the learner's thinking pattern and level of understanding. The customization unit provides a customized educational approach based on the results of the analysis by the analysis unit. For example, the generation AI provides individual feedback and advice based on the learner's progress and level of understanding. The customization unit can also provide learner progress data to teachers in real time, enabling immediate adjustments to lesson content. As a result, the Smart Learning Companion according to the embodiment can provide an educational approach tailored to the individual needs of learners and improve the quality of education.

[0030] The analysis unit can predict the next question by analyzing the dialogue history and grasping the learner's thinking pattern or level of understanding. The analysis unit, for example, analyzes the dialogue history and grasps the learner's thinking pattern. For example, it predicts the most likely next question based on the content of past questions. The analysis unit also analyzes the content of past dialogues to grasp the learner's level of understanding. For example, it can identify parts that the learner does not understand and predict questions related to those parts. The analysis unit can also comprehensively analyze the learner's thinking pattern and level of understanding and predict the next question. This makes it possible to predict the next question based on the learner's thinking pattern and level of understanding.

[0031] The customization unit can provide individual feedback or advice based on the learner's progress or level of understanding. The customization unit, for example, analyzes the learner's progress and provides individual feedback. For example, if the learner is making progress, it can provide praising feedback. The customization unit can also analyze the learner's level of understanding and provide individual advice. For example, it can suggest that the learner re-study parts that the learner did not understand. The customization unit can also comprehensively analyze the learner's progress and level of understanding and provide individual feedback and advice. This makes it possible to provide individual feedback and advice based on the learner's progress and level of understanding.

[0032] The analysis unit can automatically summarize the content of the dialogue so that the learner can review it later. For example, the analysis unit can automatically summarize the content of the dialogue so that the learner can review it later. For example, the analysis unit can display important points in bullet points. The analysis unit can also automatically summarize the content of the dialogue so that the learner can review it later. For example, the analysis unit can save the main points of the dialogue as text. The analysis unit can also automatically summarize the content of the dialogue so that the learner can review it later. For example, the analysis unit can also save the content of the dialogue as audio. This allows the content of the dialogue to be automatically summarized so that the learner can review it later.

[0033] The analysis unit can provide additional information related to topics in which the learner showed interest during the dialogue. For example, the analysis unit can provide additional information related to topics in which the learner showed interest during the dialogue. For example, it can suggest related articles or videos. The analysis unit can also provide additional information related to topics in which the learner showed interest during the dialogue. For example, it can suggest related books or materials. The analysis unit can also provide additional information related to topics in which the learner showed interest during the dialogue. For example, it can suggest related websites or online courses. This makes it possible to provide additional information related to topics in which the learner showed interest.

[0034] The customization unit can analyze the learning style of a learner and provide learning materials based on that. The customization unit, for example, analyzes the learning style of a learner and provides learning materials based on that. For example, for a visual learner, learning materials that make extensive use of diagrams and graphs are provided. The customization unit can also analyze the learning style of a learner and provide learning materials based on that. For example, for an auditory learner, learning materials that make extensive use of audio and video are provided. The customization unit can also analyze the learning style of a learner and provide learning materials based on that. For example, for a tactile learner, learning materials that make extensive use of experiments and practical training are provided. In this way, learning materials based on the learning style of a learner can be provided.

[0035] The customization unit can automatically generate an optimal study schedule using the learner's past study data. The customization unit, for example, automatically generates an optimal study schedule using the learner's past study data. For example, more time is allocated to areas where the learner is weak. The customization unit also automatically generates an optimal study schedule using the learner's past study data. For example, less time is allocated to areas where the learner is strong. The customization unit also automatically generates an optimal study schedule using the learner's past study data. For example, a schedule that matches the learner's daily rhythm is proposed. In this way, an optimal study schedule can be automatically generated based on the learner's past study data.

[0036] The customization unit can automatically generate questions of different difficulty levels according to the learner's needs. The customization unit, for example, automatically generates questions of different difficulty levels according to the learner's needs. For example, the difficulty of the questions is adjusted according to the learner's level of understanding. The customization unit also automatically generates questions of different difficulty levels according to the learner's needs. For example, the difficulty of the questions is adjusted according to the learner's progress. The customization unit also automatically generates questions of different difficulty levels according to the learner's needs. For example, high-difficulty questions are provided for areas in which the learner is strong, and low-difficulty questions are provided for areas in which the learner is weak. In this way, questions of different difficulty levels can be automatically generated according to the learner's needs.

[0037] The customization unit can adjust the learning plan in real time according to the learner's progress. The customization unit, for example, adjusts the learning plan in real time according to the learner's progress. For example, if progress is slow, the learning time is increased. The customization unit also adjusts the learning plan in real time according to the learner's progress. For example, if progress is fast, the learner moves on to the next step. The customization unit also adjusts the learning plan in real time according to the learner's progress. For example, if progress is slow, supplementary learning materials are provided. In this way, the learning plan can be adjusted in real time according to the learner's progress.

[0038] The dialogue unit can provide related videos or animations in real time in response to a learner's questions. For example, the dialogue unit provides related videos in real time in response to a learner's questions. For example, a related documentary is provided in response to a history question. The dialogue unit also provides related animations in real time in response to a learner's questions. For example, a related animation is provided in response to a science question. The dialogue unit also provides related videos or animations in real time in response to a learner's questions. For example, a related explanatory video is provided in response to a math question. In this way, related videos or animations can be provided in real time in response to a learner's questions.

[0039] The analysis unit can evaluate the learner's level of understanding in real time during the dialogue and provide supplementary explanations as necessary. The analysis unit, for example, evaluates the learner's level of understanding in real time during the dialogue and provides supplementary explanations as necessary. For example, it identifies parts where the learner has a shallow understanding and provides supplementary explanations. The analysis unit can also evaluate the learner's level of understanding in real time during the dialogue and provide supplementary explanations as necessary. For example, it can provide additional explanations for parts that the learner does not understand. The analysis unit can also evaluate the learner's level of understanding in real time during the dialogue and provide supplementary explanations as necessary. For example, it can provide related materials for the content of a question asked by the learner. This makes it possible to evaluate the learner's level of understanding in real time and provide supplementary explanations as necessary.

[0040] The analysis unit can automatically record the content of the dialogue so that the learner can review it later. The analysis unit, for example, automatically records the content of the dialogue so that the learner can review it later. For example, the main points of the dialogue are saved as text. The analysis unit also automatically records the content of the dialogue so that the learner can review it later. For example, the content of the dialogue is saved as audio. The analysis unit also automatically records the content of the dialogue so that the learner can review it later. For example, the content of the dialogue is saved as video. In this way, the content of the dialogue is automatically recorded so that the learner can review it later.

[0041] The analysis unit can provide related additional learning materials based on the interests shown by the learner during the dialogue. The analysis unit, for example, provides related additional learning materials based on the interests shown by the learner during the dialogue. For example, articles related to the topic of interest are provided. The analysis unit also provides related additional learning materials based on the interests shown by the learner during the dialogue. For example, videos related to the topic of interest are provided. The analysis unit also provides related additional learning materials based on the interests shown by the learner during the dialogue. For example, books related to the topic of interest are provided. In this way, related additional learning materials can be provided based on the interests shown by the learner.

[0042] The customization unit can analyze the learner's test results in detail and suggest specific areas for improvement. The customization unit, for example, analyzes the learner's test results in detail and suggests specific areas for improvement. For example, it provides explanations for questions that were answered incorrectly. The customization unit can also analyze the learner's test results in detail and suggest specific areas for improvement. For example, it can suggest the next task that the learner should work on. The customization unit can also analyze the learner's test results in detail and suggest specific areas for improvement. For example, it can suggest areas for improvement in learning methods. In this way, the learner's test results can be analyzed in detail and suggest specific areas for improvement.

[0043] The customization unit can suggest what content to learn next based on the learner's learning history. The customization unit, for example, suggests what content to learn next based on the learner's learning history. For example, it identifies the topic to learn next from past learning content. The customization unit also suggests what content to learn next based on the learner's learning history. For example, it suggests what content to learn next based on the learner's areas of strength. The customization unit also suggests what content to learn next based on the learner's learning history. For example, it suggests what content to learn next based on the learner's areas of weakness. In this way, it is possible to suggest what content to learn next based on the learner's learning history.

[0044] The customization unit can visually display the feedback to make it easier for the learner to understand. The customization unit, for example, visually displays the feedback to make it easier for the learner to understand. For example, the customization unit displays the feedback using a graph or chart. The customization unit also visually displays the feedback to make it easier for the learner to understand. For example, the customization unit displays the feedback using an illustration. The customization unit also visually displays the feedback to make it easier for the learner to understand. For example, the customization unit displays the feedback using a video. In this way, the feedback is visually displayed to make it easier for the learner to understand.

[0045] The customization unit can adjust the content of the feedback in real time according to the learner's progress. The customization unit adjusts the content of the feedback in real time according to the learner's progress, for example. For example, if progress is slow, it sends an encouraging message. The customization unit also adjusts the content of the feedback in real time according to the learner's progress, for example, urging the learner to move on to the next step if progress is fast. The customization unit also adjusts the content of the feedback in real time according to the learner's progress, for example, providing supplementary learning materials if progress is slow. In this way, the content of the feedback can be adjusted in real time according to the learner's progress.

[0046] The customization unit can provide the teacher with learner progress data in real time, enabling immediate adjustment of lesson content. The customization unit, for example, provides the teacher with learner progress data in real time, enabling immediate adjustment of lesson content. For example, providing supplementary lessons for learners who are lagging behind. The customization unit also provides the teacher with learner progress data in real time, enabling immediate adjustment of lesson content. For example, providing additional assignments for learners who are progressing quickly. The customization unit also provides the teacher with learner progress data in real time, enabling immediate adjustment of lesson content. For example, adjusting the progress of lessons based on the learner's progress. This allows the teacher to provide the teacher with learner progress data in real time, enabling immediate adjustment of lesson content.

[0047] The customization unit allows a teacher to use the generation AI to automatically generate individual advice for a learner. For example, the customization unit allows a teacher to use the generation AI to automatically generate individual advice for a learner. For example, the advice is generated based on the learner's progress data. The customization unit also allows a teacher to use the generation AI to automatically generate individual advice for a learner. For example, the advice is generated based on the learner's level of understanding. The customization unit also allows a teacher to use the generation AI to automatically generate individual advice for a learner. For example, the advice is generated based on the learner's learning history. This allows a teacher to use the generation AI to automatically generate individual advice for a learner.

[0048] The customization unit can record communication between a teacher and a learner so that it can be referenced later. For example, the customization unit records communication between a teacher and a learner so that it can be referenced later. For example, it saves email and chat history. The customization unit also records communication between a teacher and a learner so that it can be referenced later. For example, it saves voice call history. The customization unit also records communication between a teacher and a learner so that it can be referenced later. For example, it saves video call history. In this way, the communication between a teacher and a learner is recorded so that it can be referenced later.

[0049] The customization unit allows a teacher to use the generation AI to create customized teaching materials based on a learner's progress. For example, the customization unit allows a teacher to use the generation AI to create customized teaching materials based on a learner's progress. For example, supplementary teaching materials are created for learners whose progress is lagging behind. The customization unit also allows a teacher to use the generation AI to create customized teaching materials based on a learner's progress. For example, additional assignments are created for learners whose progress is fast. The customization unit also allows a teacher to use the generation AI to create customized teaching materials based on a learner's progress. For example, teaching materials are created based on the learner's level of understanding. This allows a teacher to use the generation AI to create customized teaching materials based on a learner's progress.

[0050] The customization unit enables the generation AI to provide the optimal answer based on the learning content of that day when a learner asks a question at night. For example, when a learner asks a question at night, the customization unit enables the generation AI to provide the optimal answer based on the learning content of that day. For example, detailed explanations are provided for questions related to the content of the lesson that day. Furthermore, when a learner asks a question at night, the customization unit enables the generation AI to provide the optimal answer based on the learning content of that day. For example, additional explanations are provided for parts that the learner does not understand. Furthermore, when a learner asks a question at night, the customization unit enables the generation AI to provide the optimal answer based on the learning content of that day. For example, materials related to the content asked by the learner are provided. In this way, when a learner asks a question at night, the optimal answer can be provided based on the learning content of that day.

[0051] The customization unit can analyze the learner's learning history and suggest what content should be learned next. The customization unit, for example, analyzes the learner's learning history and suggests what content should be learned next. For example, it identifies the next topic to be learned from past learning content. The customization unit can also analyze the learner's learning history and suggest what content should be learned next. For example, it can suggest what content should be learned next based on the learner's areas of strength. The customization unit can also analyze the learner's learning history and suggest what content should be learned next. For example, it can suggest what content should be learned next based on the learner's areas of weakness. In this way, the learner's learning history can be analyzed and what content should be learned next can be suggested.

[0052] The customization unit can provide an online forum that learners can access 24 hours a day. The customization unit, for example, provides an online forum that learners can access 24 hours a day. For example, a bulletin board where learners can ask questions and exchange opinions can be set up. The customization unit also provides an online forum that learners can access 24 hours a day. For example, a chat room where learners can interact with each other can be set up. The customization unit also provides an online forum that learners can access 24 hours a day. For example, a Q&A section where learners can post questions and other learners or teachers can answer them. In this way, an online forum that learners can access 24 hours a day can be provided.

[0053] The customization unit can automatically record the content of questions asked by the learner so that they can be referenced later. For example, the customization unit automatically records the content of questions asked by the learner so that they can be referenced later. For example, the customization unit saves the questions and answers as text. The customization unit also automatically records the content of questions asked by the learner so that they can be referenced later. For example, the questions and answers are saved as audio. The customization unit also automatically records the content of questions asked by the learner so that they can be referenced later. For example, the questions and answers are saved as video. In this way, the content of questions asked by the learner is automatically recorded so that they can be referenced later.

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

[0055] Smart Learning Companion can suggest the optimal learning environment based on the learner's learning style. For example, it can recommend a quiet environment for visual learners, provide appropriate music for auditory learners, and suggest an environment with hands-on activities for tactile learners. This allows learners to learn in the most effective environment.

[0056] Smart Learning Companion can automatically set learning goals according to the learner's progress. For example, if progress is slow, it sets short-term goals, and if progress is fast, it sets long-term goals. It can also adjust goals according to the learner's level of understanding. This allows it to provide appropriate learning goals according to the learner's progress.

[0057] Smart Learning Companion can provide learning content that incorporates game elements to attract learners' interest. For example, it can provide quiz-style questions, allowing learners to earn points for each correct answer. It can also introduce a system that allows learners to level up as they progress. This can increase learners' motivation.

[0058] Smart Learning Companion can evaluate the effectiveness of learning based on learner progress data. For example, it can record how long it takes a learner to solve a problem and analyze the results. It can also evaluate the learner's level of understanding and suggest the next task they should tackle. This allows for effective learning assessment based on learner progress data.

[0059] Smart Learning Companion can suggest what a learner should study next based on their learning history. For example, it can identify the next topic to learn from past learning content and suggest what to study next based on the learner's areas of strength. It can also suggest what to study next based on the learner's areas of weakness. This allows it to suggest what to study next based on the learner's learning history.

[0060] Smart Learning Companion can provide advice to maximize the effectiveness of learning based on learners' progress data. For example, it can record how long it takes a learner to solve a problem and analyze the results. It can also evaluate the learner's level of understanding and suggest the next task they should tackle. This allows it to provide effective learning advice based on learners' progress data.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The dialogue unit engages in a dialogue with the learner. For example, when the learner asks a question, the dialogue unit receives the question and sends it to the generation AI. The dialogue unit can also analyze the learner's tone of voice and speaking style to estimate their emotional state. Step 2: The analysis unit analyzes the dialogue content acquired by the dialogue unit. For example, the generation AI analyzes the content of the learner's question and generates appropriate answers and explanations. The analysis unit can also analyze the dialogue history to grasp the learner's thought patterns and level of understanding. Step 3: The customization unit provides a customized educational approach based on the results analyzed by the analysis unit. For example, the generative AI provides individual feedback and advice based on the learner's progress and level of understanding. The customization unit can also provide learner progress data to teachers in real time, enabling them to immediately adjust lesson content.

[0063] (Example 2) Smart Learning Companion, an embodiment of the present invention, is a system that automatically reads answers written by learners, summarizes them using a generation AI, calculates the similarity to model answers, and assigns a score. This allows Smart Learning Companion to provide an educational approach tailored to the individual needs of learners, improving the quality of education.

[0064] The Smart Learning Companion according to the embodiment includes a dialogue unit, an analysis unit, and a customization unit. The dialogue unit dialogues with a learner. For example, when a learner asks a question, the dialogue unit receives the question and sends it to the generation AI. The dialogue unit can also analyze the learner's tone of voice and speaking style to estimate their emotional state. The analysis unit analyzes the dialogue content acquired by the dialogue unit. For example, the generation AI analyzes the content of the learner's question and generates an appropriate answer or explanation. The analysis unit can also analyze the dialogue history to grasp the learner's thinking pattern and level of understanding. The customization unit provides a customized educational approach based on the results of the analysis by the analysis unit. For example, the generation AI provides individual feedback and advice based on the learner's progress and level of understanding. The customization unit can also provide learner progress data to teachers in real time, enabling immediate adjustments to lesson content. As a result, the Smart Learning Companion according to the embodiment can provide an educational approach tailored to the individual needs of learners and improve the quality of education.

[0065] The dialogue unit can analyze the tone of voice or speaking style of the learner, infer the learner's emotional state, and generate a response. For example, the dialogue unit analyzes the tone of voice or speaking style when the learner asks a question to infer the learner's emotional state. For example, if the learner is nervous, the dialogue unit can generate a response that relaxes the learner. If the learner is excited, the dialogue unit can also generate a response that calms the learner. If the learner is depressed, the dialogue unit can also generate a response that encourages the learner. In this way, it is possible to generate an appropriate response according to the learner's emotional state.

[0066] The analysis unit can predict the next question by analyzing the dialogue history and grasping the learner's thinking pattern or level of understanding. The analysis unit, for example, analyzes the dialogue history and grasps the learner's thinking pattern. For example, it predicts the most likely next question based on the content of past questions. The analysis unit also analyzes the content of past dialogues to grasp the learner's level of understanding. For example, it can identify parts that the learner does not understand and predict questions related to those parts. The analysis unit can also comprehensively analyze the learner's thinking pattern and level of understanding and predict the next question. This makes it possible to predict the next question based on the learner's thinking pattern and level of understanding.

[0067] The customization unit can provide individual feedback or advice based on the learner's progress or level of understanding. The customization unit, for example, analyzes the learner's progress and provides individual feedback. For example, if the learner is making progress, it can provide praising feedback. The customization unit can also analyze the learner's level of understanding and provide individual advice. For example, it can suggest that the learner re-study parts that the learner did not understand. The customization unit can also comprehensively analyze the learner's progress and level of understanding and provide individual feedback and advice. This makes it possible to provide individual feedback and advice based on the learner's progress and level of understanding.

[0068] The dialogue unit can analyze the tone of voice or speaking style of the learner, estimate the emotional state, and provide dialogue to relax the learner. For example, the dialogue unit can provide dialogue to relax the learner when the learner is feeling stressed, for example, by suggesting relaxing music. Furthermore, the dialogue unit can provide dialogue to relax the learner when the learner is tense, for example, by encouraging the learner to take a deep breath. Furthermore, the dialogue unit can provide dialogue to relax the learner when the learner is impatient, for example, by encouraging the learner to speak more slowly. In this way, dialogue to relax the learner when the learner is feeling stressed can be provided.

[0069] The analysis unit can automatically summarize the content of the dialogue so that the learner can review it later. For example, the analysis unit can automatically summarize the content of the dialogue so that the learner can review it later. For example, the analysis unit can display important points in bullet points. The analysis unit can also automatically summarize the content of the dialogue so that the learner can review it later. For example, the analysis unit can save the main points of the dialogue as text. The analysis unit can also automatically summarize the content of the dialogue so that the learner can review it later. For example, the analysis unit can also save the content of the dialogue as audio. This allows the content of the dialogue to be automatically summarized so that the learner can review it later.

[0070] The analysis unit can provide additional information related to topics in which the learner showed interest during the dialogue. For example, the analysis unit can provide additional information related to topics in which the learner showed interest during the dialogue. For example, it can suggest related articles or videos. The analysis unit can also provide additional information related to topics in which the learner showed interest during the dialogue. For example, it can suggest related books or materials. The analysis unit can also provide additional information related to topics in which the learner showed interest during the dialogue. For example, it can suggest related websites or online courses. This makes it possible to provide additional information related to topics in which the learner showed interest.

[0071] The analysis unit can use the emotion estimation function to suggest topics that are likely to interest the learner. The analysis unit, for example, uses the emotion estimation function to suggest topics that are likely to interest the learner. For example, it identifies topics that are likely to interest the learner from past dialogue history. The analysis unit also uses the emotion estimation function to suggest topics that are likely to interest the learner. For example, it analyzes the emotional state of the learner and identifies topics that are likely to interest the learner. The analysis unit also uses the emotion estimation function to suggest topics that are likely to interest the learner. For example, it analyzes the learner's past learning data and identifies topics that are likely to interest the learner. This makes it possible to suggest topics that are likely to interest the learner.

[0072] The customization unit can analyze the learning style of a learner and provide learning materials based on that. The customization unit, for example, analyzes the learning style of a learner and provides learning materials based on that. For example, for a visual learner, learning materials that make extensive use of diagrams and graphs are provided. The customization unit can also analyze the learning style of a learner and provide learning materials based on that. For example, for an auditory learner, learning materials that make extensive use of audio and video are provided. The customization unit can also analyze the learning style of a learner and provide learning materials based on that. For example, for a tactile learner, learning materials that make extensive use of experiments and practical training are provided. In this way, learning materials based on the learning style of a learner can be provided.

[0073] The customization unit can automatically generate an optimal study schedule using the learner's past study data. The customization unit, for example, automatically generates an optimal study schedule using the learner's past study data. For example, more time is allocated to areas where the learner is weak. The customization unit also automatically generates an optimal study schedule using the learner's past study data. For example, less time is allocated to areas where the learner is strong. The customization unit also automatically generates an optimal study schedule using the learner's past study data. For example, a schedule that matches the learner's daily rhythm is proposed. In this way, an optimal study schedule can be automatically generated based on the learner's past study data.

[0074] The customization unit can use the emotion estimation function to send encouraging messages at appropriate times so that the learner does not lose motivation. The customization unit, for example, uses the emotion estimation function to send encouraging messages at appropriate times so that the learner does not lose motivation. For example, sending an encouraging message when the learner is not making progress in their studies. The customization unit can also use the emotion estimation function to send encouraging messages at appropriate times so that the learner does not lose motivation. For example, sending an encouraging message when the learner is tired. The customization unit can also use the emotion estimation function to send encouraging messages at appropriate times so that the learner does not lose motivation. For example, sending an encouraging message when the learner is feeling down. This makes it possible to send encouraging messages at appropriate times so that the learner does not lose motivation.

[0075] The customization unit can automatically generate questions of different difficulty levels according to the learner's needs. The customization unit, for example, automatically generates questions of different difficulty levels according to the learner's needs. For example, the difficulty of the questions is adjusted according to the learner's level of understanding. The customization unit also automatically generates questions of different difficulty levels according to the learner's needs. For example, the difficulty of the questions is adjusted according to the learner's progress. The customization unit also automatically generates questions of different difficulty levels according to the learner's needs. For example, high-difficulty questions are provided for areas in which the learner is strong, and low-difficulty questions are provided for areas in which the learner is weak. In this way, questions of different difficulty levels can be automatically generated according to the learner's needs.

[0076] The customization unit can adjust the learning plan in real time according to the learner's progress. The customization unit, for example, adjusts the learning plan in real time according to the learner's progress. For example, if progress is slow, the learning time is increased. The customization unit also adjusts the learning plan in real time according to the learner's progress. For example, if progress is fast, the learner moves on to the next step. The customization unit also adjusts the learning plan in real time according to the learner's progress. For example, if progress is slow, supplementary learning materials are provided. In this way, the learning plan can be adjusted in real time according to the learner's progress.

[0077] The customization unit can use the emotion estimation function to identify a time period when the learner can most concentrate and encourage the learner to study during that time period. The customization unit, for example, uses the emotion estimation function to identify a time period when the learner can most concentrate and encourage the learner to study during that time period. For example, the customization unit recommends studying during a time period when the learner is most able to concentrate. The customization unit can also use the emotion estimation function to identify a time period when the learner can most concentrate and encourage the learner to study during that time period. For example, the customization unit recommends studying based on the learner's physiological rhythm. The customization unit can also use the emotion estimation function to identify a time period when the learner can most concentrate and encourage the learner to study during that time period. For example, the customization unit analyzes past study data to identify a time period when the learner is most able to concentrate. This makes it possible to encourage the learner to study during a time period when the learner is most able to concentrate.

[0078] The dialogue unit can provide related videos or animations in real time in response to a learner's questions. For example, the dialogue unit provides related videos in real time in response to a learner's questions. For example, a related documentary is provided in response to a history question. The dialogue unit also provides related animations in real time in response to a learner's questions. For example, a related animation is provided in response to a science question. The dialogue unit also provides related videos or animations in real time in response to a learner's questions. For example, a related explanatory video is provided in response to a math question. In this way, related videos or animations can be provided in real time in response to a learner's questions.

[0079] The analysis unit can evaluate the learner's level of understanding in real time during the dialogue and provide supplementary explanations as necessary. The analysis unit, for example, evaluates the learner's level of understanding in real time during the dialogue and provides supplementary explanations as necessary. For example, it identifies parts where the learner has a shallow understanding and provides supplementary explanations. The analysis unit can also evaluate the learner's level of understanding in real time during the dialogue and provide supplementary explanations as necessary. For example, it can provide additional explanations for parts that the learner does not understand. The analysis unit can also evaluate the learner's level of understanding in real time during the dialogue and provide supplementary explanations as necessary. For example, it can provide related materials for the content of a question asked by the learner. This makes it possible to evaluate the learner's level of understanding in real time and provide supplementary explanations as necessary.

[0080] The analysis unit can use the emotion estimation function to provide a simpler explanation when a learner is having difficulty understanding. The analysis unit, for example, uses the emotion estimation function to provide a simpler explanation when a learner is having difficulty understanding. For example, a difficult concept is explained in simple terms. The analysis unit can also use the emotion estimation function to provide a simpler explanation when a learner is having difficulty understanding. For example, an explanation is made using an illustration. The analysis unit can also use the emotion estimation function to provide a simpler explanation when a learner is having difficulty understanding. For example, an explanation is made using a specific example. This makes it possible to provide a simpler explanation when a learner is having difficulty understanding.

[0081] The analysis unit can automatically record the content of the dialogue so that the learner can review it later. The analysis unit, for example, automatically records the content of the dialogue so that the learner can review it later. For example, the main points of the dialogue are saved as text. The analysis unit also automatically records the content of the dialogue so that the learner can review it later. For example, the content of the dialogue is saved as audio. The analysis unit also automatically records the content of the dialogue so that the learner can review it later. For example, the content of the dialogue is saved as video. In this way, the content of the dialogue is automatically recorded so that the learner can review it later.

[0082] The analysis unit can provide related additional learning materials based on the interests shown by the learner during the dialogue. The analysis unit, for example, provides related additional learning materials based on the interests shown by the learner during the dialogue. For example, articles related to the topic of interest are provided. The analysis unit also provides related additional learning materials based on the interests shown by the learner during the dialogue. For example, videos related to the topic of interest are provided. The analysis unit also provides related additional learning materials based on the interests shown by the learner during the dialogue. For example, books related to the topic of interest are provided. In this way, related additional learning materials can be provided based on the interests shown by the learner.

[0083] The analysis unit can use the emotion estimation function to suggest topics that are likely to interest the learner. The analysis unit, for example, uses the emotion estimation function to suggest topics that are likely to interest the learner. For example, it identifies topics that are likely to interest the learner from past dialogue history. The analysis unit also uses the emotion estimation function to suggest topics that are likely to interest the learner. For example, it analyzes the emotional state of the learner and identifies topics that are likely to interest the learner. The analysis unit also uses the emotion estimation function to suggest topics that are likely to interest the learner. For example, it analyzes the learner's past learning data and identifies topics that are likely to interest the learner. This makes it possible to suggest topics that are likely to interest the learner.

[0084] The customization unit can analyze the learner's test results in detail and suggest specific areas for improvement. The customization unit, for example, analyzes the learner's test results in detail and suggests specific areas for improvement. For example, it provides explanations for questions that were answered incorrectly. The customization unit can also analyze the learner's test results in detail and suggest specific areas for improvement. For example, it can suggest the next task that the learner should work on. The customization unit can also analyze the learner's test results in detail and suggest specific areas for improvement. For example, it can suggest areas for improvement in learning methods. In this way, the learner's test results can be analyzed in detail and suggest specific areas for improvement.

[0085] The customization unit can suggest what content to learn next based on the learner's learning history. The customization unit, for example, suggests what content to learn next based on the learner's learning history. For example, it identifies the topic to learn next from past learning content. The customization unit also suggests what content to learn next based on the learner's learning history. For example, it suggests what content to learn next based on the learner's areas of strength. The customization unit also suggests what content to learn next based on the learner's learning history. For example, it suggests what content to learn next based on the learner's areas of weakness. In this way, it is possible to suggest what content to learn next based on the learner's learning history.

[0086] The customization unit can use the emotion estimation function to provide feedback at a timing when it is easy for the learner to accept the feedback. The customization unit, for example, uses the emotion estimation function to provide feedback at a timing when it is easy for the learner to accept the feedback. For example, feedback is provided when the learner is relaxed. The customization unit also uses the emotion estimation function to provide feedback at a timing when it is easy for the learner to accept the feedback. For example, feedback is provided when the learner is concentrating. The customization unit also uses the emotion estimation function to provide feedback at a timing when it is easy for the learner to accept the feedback. For example, feedback is provided when the learner is motivated. This makes it possible to provide feedback at a timing when it is easy for the learner to accept the feedback.

[0087] The customization unit can visually display the feedback to make it easier for the learner to understand. The customization unit, for example, visually displays the feedback to make it easier for the learner to understand. For example, the customization unit displays the feedback using a graph or chart. The customization unit also visually displays the feedback to make it easier for the learner to understand. For example, the customization unit displays the feedback using an illustration. The customization unit also visually displays the feedback to make it easier for the learner to understand. For example, the customization unit displays the feedback using a video. In this way, the feedback is visually displayed to make it easier for the learner to understand.

[0088] The customization unit can adjust the content of the feedback in real time according to the learner's progress. The customization unit adjusts the content of the feedback in real time according to the learner's progress, for example. For example, if progress is slow, it sends an encouraging message. The customization unit also adjusts the content of the feedback in real time according to the learner's progress, for example, urging the learner to move on to the next step if progress is fast. The customization unit also adjusts the content of the feedback in real time according to the learner's progress, for example, providing supplementary learning materials if progress is slow. In this way, the content of the feedback can be adjusted in real time according to the learner's progress.

[0089] The customization unit can provide the teacher with learner progress data in real time, enabling immediate adjustment of lesson content. The customization unit, for example, provides the teacher with learner progress data in real time, enabling immediate adjustment of lesson content. For example, providing supplementary lessons for learners who are lagging behind. The customization unit also provides the teacher with learner progress data in real time, enabling immediate adjustment of lesson content. For example, providing additional assignments for learners who are progressing quickly. The customization unit also provides the teacher with learner progress data in real time, enabling immediate adjustment of lesson content. For example, adjusting the progress of lessons based on the learner's progress. This allows the teacher to provide the teacher with learner progress data in real time, enabling immediate adjustment of lesson content.

[0090] The customization unit allows a teacher to use the generation AI to automatically generate individual advice for a learner. For example, the customization unit allows a teacher to use the generation AI to automatically generate individual advice for a learner. For example, the advice is generated based on the learner's progress data. The customization unit also allows a teacher to use the generation AI to automatically generate individual advice for a learner. For example, the advice is generated based on the learner's level of understanding. The customization unit also allows a teacher to use the generation AI to automatically generate individual advice for a learner. For example, the advice is generated based on the learner's learning history. This allows a teacher to use the generation AI to automatically generate individual advice for a learner.

[0091] The customization unit can use the emotion estimation function to notify the teacher of the learner's emotional state and encourage the teacher to take an appropriate action. For example, the customization unit can use the emotion estimation function to notify the teacher of the learner's emotional state and encourage the teacher to take an appropriate action. For example, if the learner is feeling stressed, the customization unit can encourage the teacher to take a relaxing action. The customization unit can also use the emotion estimation function to notify the teacher of the learner's emotional state and encourage the teacher to take an appropriate action. For example, if the learner is feeling depressed, the customization unit can encourage the teacher to take an encouraging action. The customization unit can also use the emotion estimation function to notify the teacher of the learner's emotional state and encourage the teacher to take an appropriate action. For example, if the learner is excited, the customization unit can encourage the teacher to calm down. In this way, the teacher can be notified of the learner's emotional state and encourage the teacher to take an appropriate action.

[0092] The customization unit can record communication between a teacher and a learner so that it can be referenced later. For example, the customization unit records communication between a teacher and a learner so that it can be referenced later. For example, it saves email and chat history. The customization unit also records communication between a teacher and a learner so that it can be referenced later. For example, it saves voice call history. The customization unit also records communication between a teacher and a learner so that it can be referenced later. For example, it saves video call history. In this way, the communication between a teacher and a learner is recorded so that it can be referenced later.

[0093] The customization unit allows a teacher to use the generation AI to create customized teaching materials based on a learner's progress. For example, the customization unit allows a teacher to use the generation AI to create customized teaching materials based on a learner's progress. For example, supplementary teaching materials are created for learners whose progress is lagging behind. The customization unit also allows a teacher to use the generation AI to create customized teaching materials based on a learner's progress. For example, additional assignments are created for learners whose progress is fast. The customization unit also allows a teacher to use the generation AI to create customized teaching materials based on a learner's progress. For example, teaching materials are created based on the learner's level of understanding. This allows a teacher to use the generation AI to create customized teaching materials based on a learner's progress.

[0094] The customization unit can use the emotion estimation function to suggest an environment in which the learner can learn most effectively. The customization unit, for example, uses the emotion estimation function to suggest an environment in which the learner can learn most effectively. For example, it recommends studying in a quiet environment. The customization unit also uses the emotion estimation function to suggest an environment in which the learner can learn most effectively. For example, it recommends studying in appropriate lighting. The customization unit also uses the emotion estimation function to suggest an environment in which the learner can learn most effectively. For example, it recommends studying at a comfortable temperature. In this way, it is possible to suggest an environment in which the learner can learn most effectively.

[0095] The customization unit enables the generation AI to provide the optimal answer based on the learning content of that day when a learner asks a question at night. For example, when a learner asks a question at night, the customization unit enables the generation AI to provide the optimal answer based on the learning content of that day. For example, detailed explanations are provided for questions related to the content of the lesson that day. Furthermore, when a learner asks a question at night, the customization unit enables the generation AI to provide the optimal answer based on the learning content of that day. For example, additional explanations are provided for parts that the learner does not understand. Furthermore, when a learner asks a question at night, the customization unit enables the generation AI to provide the optimal answer based on the learning content of that day. For example, materials related to the content asked by the learner are provided. In this way, when a learner asks a question at night, the optimal answer can be provided based on the learning content of that day.

[0096] The customization unit can analyze the learner's learning history and suggest what content should be learned next. The customization unit, for example, analyzes the learner's learning history and suggests what content should be learned next. For example, it identifies the next topic to be learned from past learning content. The customization unit can also analyze the learner's learning history and suggest what content should be learned next. For example, it can suggest what content should be learned next based on the learner's areas of strength. The customization unit can also analyze the learner's learning history and suggest what content should be learned next. For example, it can suggest what content should be learned next based on the learner's areas of weakness. In this way, the learner's learning history can be analyzed and what content should be learned next can be suggested.

[0097] The customization unit can use the emotion estimation function to encourage the learner to study during a time period when they can concentrate best. The customization unit, for example, uses the emotion estimation function to encourage the learner to study during a time period when they can concentrate best. For example, it recommends studying during a time period when they are most able to concentrate best. The customization unit also uses the emotion estimation function to encourage the learner to study during a time period when they can concentrate best. For example, it recommends studying based on the learner's physiological rhythm. The customization unit also uses the emotion estimation function to encourage the learner to study during a time period when they can concentrate best. For example, it analyzes past study data to identify a time period when they are most able to concentrate best. This makes it possible to encourage the learner to study during a time period when they can concentrate best.

[0098] The customization unit can provide an online forum that learners can access 24 hours a day. The customization unit, for example, provides an online forum that learners can access 24 hours a day. For example, a bulletin board where learners can ask questions and exchange opinions can be set up. The customization unit also provides an online forum that learners can access 24 hours a day. For example, a chat room where learners can interact with each other can be set up. The customization unit also provides an online forum that learners can access 24 hours a day. For example, a Q&A section where learners can post questions and other learners or teachers can answer them. In this way, an online forum that learners can access 24 hours a day can be provided.

[0099] The customization unit can automatically record the content of questions asked by the learner so that they can be referenced later. For example, the customization unit automatically records the content of questions asked by the learner so that they can be referenced later. For example, the customization unit saves the questions and answers as text. The customization unit also automatically records the content of questions asked by the learner so that they can be referenced later. For example, the questions and answers are saved as audio. The customization unit also automatically records the content of questions asked by the learner so that they can be referenced later. For example, the questions and answers are saved as video. In this way, the content of questions asked by the learner is automatically recorded so that they can be referenced later.

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

[0101] Smart Learning Companion can suggest the optimal learning environment based on the learner's learning style. For example, it can recommend a quiet environment for visual learners, provide appropriate music for auditory learners, and suggest an environment with hands-on activities for tactile learners. This allows learners to learn in the most effective environment.

[0102] Smart Learning Companion can automatically set learning goals according to the learner's progress. For example, if progress is slow, it sets short-term goals, and if progress is fast, it sets long-term goals. It can also adjust goals according to the learner's level of understanding. This allows it to provide appropriate learning goals according to the learner's progress.

[0103] Smart Learning Companion can adjust learning progress based on the learner's emotional state. For example, it can suggest a break if the learner is tired, or provide more difficult questions if the learner is highly focused. It can also suggest relaxing activities if the learner is feeling stressed. This allows it to provide an appropriate learning progress according to the learner's emotional state.

[0104] Smart Learning Companion can provide learning content that incorporates game elements to attract learners' interest. For example, it can provide quiz-style questions, allowing learners to earn points for each correct answer. It can also introduce a system that allows learners to level up as they progress. This can increase learners' motivation.

[0105] Smart Learning Companion can adjust the timing of learning based on the learner's emotional state. For example, it can suggest starting learning when the learner is relaxed, and suggest taking a break when the learner is stressed. It can also provide more difficult questions when the learner is concentrating. This allows it to provide the optimal learning timing according to the learner's emotional state.

[0106] Smart Learning Companion can evaluate the effectiveness of learning based on learner progress data. For example, it can record how long it takes a learner to solve a problem and analyze the results. It can also evaluate the learner's level of understanding and suggest the next task they should tackle. This allows for effective learning assessment based on learner progress data.

[0107] Smart Learning Companion can adjust learning progress in real time based on the learner's emotional state. For example, if the learner is excited, it generates a calming response, and if the learner is depressed, it generates an encouraging response. It can also suggest that the learner continue learning if they are relaxed. This allows it to provide appropriate learning progress in real time according to the learner's emotional state.

[0108] Smart Learning Companion can suggest what a learner should study next based on their learning history. For example, it can identify the next topic to learn from past learning content and suggest what to study next based on the learner's areas of strength. It can also suggest what to study next based on the learner's areas of weakness. This allows it to suggest what to study next based on the learner's learning history.

[0109] Smart Learning Companion can support the progress of a learner's learning based on their emotional state. For example, if a learner is losing motivation, it can send encouraging messages. If a learner is concentrating, it can provide more difficult questions. This allows it to provide appropriate learning support according to the learner's emotional state.

[0110] Smart Learning Companion can provide advice to maximize the effectiveness of learning based on learners' progress data. For example, it can record how long it takes a learner to solve a problem and analyze the results. It can also evaluate the learner's level of understanding and suggest the next task they should tackle. This allows it to provide effective learning advice based on learners' progress data.

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

[0112] Step 1: The dialogue unit engages in a dialogue with the learner. For example, when the learner asks a question, the dialogue unit receives the question and sends it to the generation AI. The dialogue unit can also analyze the learner's tone of voice and speaking style to estimate their emotional state. Step 2: The analysis unit analyzes the dialogue content acquired by the dialogue unit. For example, the generation AI analyzes the content of the learner's question and generates appropriate answers and explanations. The analysis unit can also analyze the dialogue history to grasp the learner's thought patterns and level of understanding. Step 3: The customization unit provides a customized educational approach based on the results analyzed by the analysis unit. For example, the generative AI provides individual feedback and advice based on the learner's progress and level of understanding. The customization unit can also provide learner progress data to teachers in real time, enabling them to immediately adjust lesson content.

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

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

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

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

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

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

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

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

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

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

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

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

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0141] 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.

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

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

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

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

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

[0147] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0157] 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

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

Claims

1. A dialogue section that engages in dialogue with learners; an analysis unit that analyzes the dialogue content acquired by the dialogue unit; a customization unit that provides a customized educational approach based on the results analyzed by the analysis unit. A system characterized by:

2. The analysis unit The content of the dialogue is automatically summarized so that the learner can review it later.

2. The system of claim 1.

3. The customization unit Analyzing the learning style of the learner and providing learning materials based on that 2. The system of claim 1.

4. The dialogue unit Providing relevant videos or animations in real time in response to the learner's questions 2. The system of claim 1.

5. The customization unit Inform the teacher of the learner's emotional state and prompt appropriate action 2. The system of claim 1.

Images