System

Abstract

An object of a system according to an embodiment is to provide appropriate learning support in accordance with individual levels of understanding of students.SOLUTION: A system according to an embodiment includes a vocabulary comprehension analysis unit, a sentence presentation unit, a question answering unit, a text image use unit, and a multimodal support unit. The vocabulary comprehension analysis unit analyzes the vocabulary and comprehension of the student. The sentence presentation unit presents a sentence of an appropriate level based on the result analyzed by the vocabulary comprehension analysis unit. The question answering unit answers the question of the student. A text image using part supports the understanding of the student by using the text and the image. The multimodal support portion provides visual and linguistic support.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to provide appropriate learning support that is tailored to each student's individual level of understanding.

[0005] The system according to the embodiment aims to provide appropriate learning support according to the individual level of understanding of each student. [Means for solving the problem]

[0006] The system according to the embodiment includes a vocabulary and comprehension analysis unit, a sentence presentation unit, a question and answer unit, a text image utilization unit, and a multimodal support unit. The vocabulary and comprehension analysis unit analyzes a student's vocabulary and comprehension. The sentence presentation unit presents sentences at an appropriate level based on the results of the analysis by the vocabulary and comprehension analysis unit. The question and answer unit responds to students' questions. The text image utilization unit uses text and images to support students' understanding. The multimodal support unit provides visual and linguistic support. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate learning support according to the individual level of understanding of each student. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A self-learning educational tool according to an embodiment of the present invention is a system in which a generation AI analyzes a student's vocabulary and level of comprehension, presents sentences at an appropriate level, responds to questions, uses text and images to assist comprehension, and provides visual and linguistic support. This allows the self-learning educational tool to provide learning support tailored to the student's level of comprehension and enrich the learning experience.

[0029] A self-study educational tool according to an embodiment includes a vocabulary and comprehension analysis unit, a sentence presentation unit, a question-and-answer unit, a text image utilization unit, and a multimodal support unit. The vocabulary and comprehension analysis unit analyzes a student's vocabulary and comprehension. For example, the generation AI analyzes text and answers entered by the student and evaluates their vocabulary and comprehension. The generation AI can also generate answers at an appropriate level based on the student's past answer history. The sentence presentation unit presents sentences at an appropriate level based on the results of the analysis by the vocabulary and comprehension analysis unit. For example, the generation AI presents simple sentences to students with low vocabulary and more complex sentences to students with high vocabulary. The question-and-answer unit responds to students' questions. For example, if a student asks, "Why is the sky blue?", the generation AI provides a scientific explanation for the question. The text image utilization unit uses text and images to support students' understanding. For example, when learning about animals, the generation AI provides an explanation of the animal along with an image of the animal. The multimodal support unit provides visual and linguistic support. For example, in a history class, the generative AI provides explanations of historical events along with images and videos of those events. This allows the self-learning educational tool according to the embodiment to provide learning support tailored to each student's level of understanding and enrich the learning experience. For example, even if students with different vocabulary abilities use the same learning materials, content tailored to each student's level of understanding is provided, reducing the chance of differences in learning progress. Furthermore, combining visual and textual information can deepen students' understanding.

[0030] The vocabulary and comprehension analysis unit can analyze students' pronunciation and intonation and provide feedback on areas for pronunciation improvement. For example, the vocabulary and comprehension analysis unit analyzes the pronunciation and intonation of students when they read aloud, and the generation AI uses that data to provide feedback on areas for pronunciation improvement. For example, if the pronunciation of a particular word is unclear, the AI ​​can emphasize that part and have the student practice it. The vocabulary and comprehension analysis unit can also evaluate the accuracy and rhythm of pronunciation and suggest specific areas for improvement. This allows students to improve their pronunciation.

[0031] The vocabulary and comprehension analysis unit can analyze a student's handwriting and evaluate their vocabulary and comprehension from the handwriting. For example, the vocabulary and comprehension analysis unit scans a student's handwritten text, and the generation AI analyzes the handwriting. For example, it evaluates the shape of the characters and the order of strokes to estimate vocabulary and comprehension. The vocabulary and comprehension analysis unit can also comprehensively evaluate the writer's characteristics based on data on pen pressure and stroke order. This makes it possible to evaluate a student's vocabulary and comprehension from their handwriting.

[0032] The vocabulary and comprehension analysis unit can analyze students' physical movements and gestures to assess their level of comprehension. For example, the vocabulary and comprehension analysis unit uses a camera to record the physical movements and gestures that students make while studying, and the generation AI analyzes the data. For example, it evaluates whether students are concentrating and estimates their level of comprehension. The vocabulary and comprehension analysis unit can also comprehensively assess students' level of comprehension based on the type and frequency of movements. This makes it possible to assess students' level of comprehension from their physical movements and gestures.

[0033] The vocabulary and comprehension analysis unit can analyze a student's voice input and evaluate their pronunciation and language fluency. For example, the vocabulary and comprehension analysis unit analyzes the content input by the student's voice, and the generation AI evaluates the pronunciation and language fluency. For example, the evaluation is based on the accuracy of pronunciation and the flow of words. The vocabulary and comprehension analysis unit can also use voice recognition technology to specifically suggest areas for improvement in the student's pronunciation. This makes it possible to evaluate the student's pronunciation and language fluency from the student's voice input.

[0034] The text presentation unit can present text related to past learning content based on the student's learning history. For example, the text presentation unit analyzes the student's learning history, and the generation AI presents text related to previously learned content. For example, it provides new text related to a topic previously studied. The text presentation unit can also present text in stages based on the student's learning history according to their level of understanding. This makes it possible to present related text based on the student's learning history.

[0035] The text presentation unit can generate personalized text based on the student's interests. For example, the text presentation unit analyzes the student's interests and the generation AI generates personalized text. For example, it can provide text related to a student's favorite animal or hobby. The text presentation unit can also customize learning content according to the student's interests and concerns. This allows personalized text to be generated based on the student's interests and concerns.

[0036] The text presentation unit can present text according to different learning styles (visual, auditory, tactile). For example, the text presentation unit analyzes the student's learning style, and the generation AI presents text according to the visual, auditory, or tactile senses. For example, visual learners can be provided with text that includes diagrams and illustrations. The text presentation unit can also provide audio guides to auditory learners and interactive learning materials to tactile learners. This makes it possible to present text according to different learning styles.

[0037] The text presentation unit can adjust the difficulty level in stages according to the student's learning progress. For example, the text presentation unit analyzes the student's learning progress, and the generation AI presents texts with gradually adjusted difficulty levels. For example, the difficulty level increases from beginner to intermediate to advanced. The text presentation unit can also adjust the learning content according to the student's level of comprehension. This allows the difficulty level to be adjusted in stages according to the student's learning progress.

[0038] The question answering unit can provide answers from multiple perspectives to students' questions. For example, when a student inputs a question, the generative AI provides answers from multiple perspectives. For example, it may present answers from a scientific perspective, a historical perspective, or a cultural perspective. The question answering unit can also adjust the content of the answer depending on the student's level of understanding. This makes it possible to provide answers from multiple perspectives to students' questions.

[0039] The question answering unit can automatically generate related questions based on the student's question history. For example, the question answering unit analyzes the student's past question history, and the generation AI automatically generates related questions. For example, it presents a new question related to the previous question. The question answering unit can also adjust the content of the question according to the student's interests and concerns. This makes it possible to automatically generate related questions based on the student's question history.

[0040] The question answering unit can also accommodate voice input or handwriting input when students input questions. The question answering unit builds a system that also accommodates voice input or handwriting input when students input questions. For example, it allows questions to be input using a microphone or tablet. The question answering unit can also accurately analyze students' questions using voice recognition technology or handwriting recognition technology. This allows students to input questions using voice input or handwriting input.

[0041] The question answering unit can provide videos and images related to students' questions. For example, when a student inputs a question, the question answering unit uses a generative AI to provide related videos and images. For example, an experiment video can be presented in response to a scientific question. The question answering unit can also use image recognition technology and video analysis technology to provide visual information related to the student's question. This makes it possible to provide videos and images related to the student's question.

[0042] The text image utilization unit can automatically generate images related to the text entered by the student. For example, the text image utilization unit analyzes the text entered by the student, and the generation AI automatically generates images related to the content. For example, it provides images of animals for text about animals. The text image utilization unit can also use an image generation algorithm to provide visual information related to the student's text. This makes it possible to automatically generate images related to the text entered by the student.

[0043] The text image utilization unit can automatically generate text related to an image when a student selects the image. For example, the text image utilization unit analyzes an image selected by a student, and the generation AI automatically generates text related to the content. For example, for an image of an animal, it provides a description of the animal. The text image utilization unit can also use a text generation algorithm to provide sentences related to the student's image. This allows text related to the image to be automatically generated when a student selects the image.

[0044] The text image utilization unit can provide 3D models and animations related to the text entered by the student. For example, the text image utilization unit analyzes the text entered by the student, and the generation AI provides 3D models and animations related to the content. For example, it provides 3D models of animals for text about animals. The text image utilization unit can also provide visual information related to the student's text using 3D modeling technology and animation generation technology. This makes it possible to provide 3D models and animations related to the text entered by the student.

[0045] The text image utilization unit can provide an audio explanation related to an image when a student selects the image. For example, the text image utilization unit analyzes an image selected by a student, and a generation AI provides an audio explanation related to the content. For example, for an image of an animal, an audio description of the animal is provided. The text image utilization unit can also use speech synthesis technology to provide an audio guide related to the student's image. This allows an audio explanation related to the image to be provided when a student selects the image.

[0046] The multimodal support unit can automatically generate optimal multimodal teaching materials based on the student's learning history. For example, the multimodal support unit analyzes the student's learning history and the generation AI automatically generates optimal multimodal teaching materials. For example, it provides teaching materials that combine text, images, and videos based on past learning content. The multimodal support unit can also customize the content of the teaching materials according to the student's learning history. This makes it possible to automatically generate optimal multimodal teaching materials based on the student's learning history.

[0047] The multimodal support unit can customize the way multimodal teaching materials are presented according to the student's learning style. For example, the multimodal support unit analyzes the student's learning style, and the generative AI customizes the way multimodal teaching materials are presented. For example, visual learners can be provided with teaching materials that make extensive use of diagrams and illustrations. The multimodal support unit can also provide audio guides to auditory learners and interactive teaching materials to tactile learners. This makes it possible to customize the way multimodal teaching materials are presented according to the student's learning style.

[0048] The multimodal support unit can provide students with multimodal learning materials that utilize AR (augmented reality) and VR (virtual reality) when they are studying. For example, the multimodal support unit uses generative AI to provide students with multimodal learning materials that utilize AR and VR when they are studying. For example, it can provide learning materials that allow students to experience historical events in VR. The multimodal support unit can also use AR and VR technology to make students' learning experiences more realistic. This makes it possible to provide students with multimodal learning materials that utilize AR and VR when they are studying.

[0049] The multimodal support unit can provide multimodal learning materials that incorporate interactive game elements when students are studying. For example, the multimodal support unit provides multimodal learning materials that incorporate interactive game elements when students are studying by using a generation AI. For example, it provides learning materials in the form of a quiz. The multimodal support unit can also increase students' motivation to learn through game elements. This makes it possible to provide multimodal learning materials that incorporate interactive game elements when students are studying.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] Self-paced learning tools can also be customized to accommodate students' learning styles. For example, visual learners can be provided with diagrams and illustrations, auditory learners can be provided with audio guides, and tactile learners can be provided with interactive materials. This allows for optimal learning experiences tailored to each student's learning style.

[0052] Self-learning educational tools can also be equipped with a function that allows for the level of difficulty to be adjusted in stages according to the student's learning progress. For example, the difficulty level can be increased from beginner to intermediate to advanced. The learning content can also be adjusted according to the student's level of understanding. This makes it possible to provide optimal learning support according to the student's learning progress.

[0053] Self-learning educational tools can also be equipped with a function to provide answers to students' questions from multiple perspectives. For example, answers can be presented from a scientific perspective, a historical perspective, or a cultural perspective. They can also adjust the content of the answers depending on the student's level of understanding. This allows them to provide answers to students' questions from multiple perspectives.

[0054] The self-learning educational tool can also have a function to present texts related to past learning content based on the student's learning history. For example, it can present new texts related to a topic previously studied. It can also present learning content in stages according to the student's level of understanding. This makes it possible to present related texts based on the student's learning history.

[0055] The self-learning educational tool can further have a function to generate personalized sentences based on the student's interests. For example, sentences related to a student's favorite animal or hobby can be provided. The learning content can also be customized according to the student's interests. This makes it possible to generate personalized sentences based on the student's interests.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The vocabulary and comprehension analysis unit analyzes the student's vocabulary and comprehension. For example, the generation AI analyzes the text and answers entered by the student and evaluates their vocabulary and comprehension. The generation AI can also generate answers at an appropriate level based on the student's past answer history. Step 2: The sentence presentation unit presents sentences of an appropriate level based on the results of the analysis by the vocabulary and comprehension analysis unit. For example, the generation AI presents simple sentences to students with low vocabulary skills and more complex sentences to students with high vocabulary skills. Step 3: The question answering unit responds to the student's question. For example, if a student asks, "Why is the sky blue?", the generative AI will provide a scientific explanation for the question. Step 4: The text-image utilization unit uses text and images to help students understand. For example, when learning about animals, the generative AI provides images of the animals along with their descriptions. Step 5: Multimodal support provides visual and linguistic support. For example, in a history lesson, a generative AI might provide images and videos of historical events along with descriptions of them.

[0058] (Example 2) A self-learning educational tool according to an embodiment of the present invention is a system in which a generation AI analyzes a student's vocabulary and level of comprehension, presents sentences at an appropriate level, responds to questions, uses text and images to assist comprehension, and provides visual and linguistic support. This allows the self-learning educational tool to provide learning support tailored to the student's level of comprehension and enrich the learning experience.

[0059] A self-study educational tool according to an embodiment includes a vocabulary and comprehension analysis unit, a sentence presentation unit, a question-and-answer unit, a text image utilization unit, and a multimodal support unit. The vocabulary and comprehension analysis unit analyzes a student's vocabulary and comprehension. For example, the generation AI analyzes text and answers entered by the student and evaluates their vocabulary and comprehension. The generation AI can also generate answers at an appropriate level based on the student's past answer history. The sentence presentation unit presents sentences at an appropriate level based on the results of the analysis by the vocabulary and comprehension analysis unit. For example, the generation AI presents simple sentences to students with low vocabulary and more complex sentences to students with high vocabulary. The question-and-answer unit responds to students' questions. For example, if a student asks, "Why is the sky blue?", the generation AI provides a scientific explanation for the question. The text image utilization unit uses text and images to support students' understanding. For example, when learning about animals, the generation AI provides an explanation of the animal along with an image of the animal. The multimodal support unit provides visual and linguistic support. For example, in a history class, the generative AI provides explanations of historical events along with images and videos of those events. This allows the self-learning educational tool according to the embodiment to provide learning support tailored to each student's level of understanding and enrich the learning experience. For example, even if students with different vocabulary abilities use the same learning materials, content tailored to each student's level of understanding is provided, reducing the chance of differences in learning progress. Furthermore, combining visual and textual information can deepen students' understanding.

[0060] The vocabulary and comprehension analysis unit can analyze students' pronunciation and intonation and provide feedback on areas for pronunciation improvement. For example, the vocabulary and comprehension analysis unit analyzes the pronunciation and intonation of students when they read aloud, and the generation AI uses that data to provide feedback on areas for pronunciation improvement. For example, if the pronunciation of a particular word is unclear, the AI ​​can emphasize that part and have the student practice it. The vocabulary and comprehension analysis unit can also evaluate the accuracy and rhythm of pronunciation and suggest specific areas for improvement. This allows students to improve their pronunciation.

[0061] The vocabulary and comprehension analysis unit can analyze a student's handwriting and evaluate their vocabulary and comprehension from the handwriting. For example, the vocabulary and comprehension analysis unit scans a student's handwritten text, and the generation AI analyzes the handwriting. For example, it evaluates the shape of the characters and the order of strokes to estimate vocabulary and comprehension. The vocabulary and comprehension analysis unit can also comprehensively evaluate the writer's characteristics based on data on pen pressure and stroke order. This makes it possible to evaluate a student's vocabulary and comprehension from their handwriting.

[0062] The vocabulary and comprehension analysis unit uses an emotion estimation function to analyze the emotions expressed when students input text and can provide feedback according to those emotions. For example, when a student inputs text, the generation AI analyzes their facial expressions and vocal tone to estimate their emotions. For example, if negative emotions are detected, an encouraging message is displayed. The vocabulary and comprehension analysis unit can also provide specific feedback according to the student's emotions based on the emotion score. This makes it possible to provide feedback according to the student's emotions.

[0063] The vocabulary and comprehension analysis unit can analyze students' physical movements and gestures to assess their level of comprehension. For example, the vocabulary and comprehension analysis unit uses a camera to record the physical movements and gestures that students make while studying, and the generation AI analyzes the data. For example, it evaluates whether students are concentrating and estimates their level of comprehension. The vocabulary and comprehension analysis unit can also comprehensively assess students' level of comprehension based on the type and frequency of movements. This makes it possible to assess students' level of comprehension from their physical movements and gestures.

[0064] The vocabulary and comprehension analysis unit can analyze a student's voice input and evaluate their pronunciation and language fluency. For example, the vocabulary and comprehension analysis unit analyzes the content input by the student's voice, and the generation AI evaluates the pronunciation and language fluency. For example, the evaluation is based on the accuracy of pronunciation and the flow of words. The vocabulary and comprehension analysis unit can also use voice recognition technology to specifically suggest areas for improvement in the student's pronunciation. This makes it possible to evaluate the student's pronunciation and language fluency from the student's voice input.

[0065] The vocabulary and comprehension analysis unit uses an emotion estimation function to monitor students' emotions in real time as they type, and can make suggestions that elicit positive emotions. For example, when a student types, the generative AI analyzes their facial expressions and vocal tone to monitor their emotions in real time. For example, if negative emotions are detected, positive suggestions are made. The vocabulary and comprehension analysis unit can also provide specific suggestions based on the student's emotions, based on the emotion score. This makes it possible to monitor students' emotions in real time and make suggestions that elicit positive emotions.

[0066] The text presentation unit can present text related to past learning content based on the student's learning history. For example, the text presentation unit analyzes the student's learning history, and the generation AI presents text related to previously learned content. For example, it provides new text related to a topic previously studied. The text presentation unit can also present text in stages based on the student's learning history according to their level of understanding. This makes it possible to present related text based on the student's learning history.

[0067] The text presentation unit can generate personalized text based on the student's interests. For example, the text presentation unit analyzes the student's interests and the generation AI generates personalized text. For example, it can provide text related to a student's favorite animal or hobby. The text presentation unit can also customize learning content according to the student's interests and concerns. This allows personalized text to be generated based on the student's interests and concerns.

[0068] The text presentation unit can use the emotion estimation function to present text related to topics that students are most interested in. For example, the text presentation unit uses the emotion estimation function to identify topics that students are most interested in, and the generation AI presents text related to those topics. For example, it provides text related to topics with high emotion scores. The text presentation unit can also adjust the learning content according to the student's emotions. This makes it possible to present text related to topics that students are most interested in.

[0069] The text presentation unit can present text according to different learning styles (visual, auditory, tactile). For example, the text presentation unit analyzes the student's learning style, and the generation AI presents text according to the visual, auditory, or tactile senses. For example, visual learners can be provided with text that includes diagrams and illustrations. The text presentation unit can also provide audio guides to auditory learners and interactive learning materials to tactile learners. This makes it possible to present text according to different learning styles.

[0070] The text presentation unit can adjust the difficulty level in stages according to the student's learning progress. For example, the text presentation unit analyzes the student's learning progress, and the generation AI presents texts with gradually adjusted difficulty levels. For example, the difficulty level increases from beginner to intermediate to advanced. The text presentation unit can also adjust the learning content according to the student's level of comprehension. This allows the difficulty level to be adjusted in stages according to the student's learning progress.

[0071] The sentence presentation unit can use the emotion estimation function to present sentences that evoke the most positive emotions in students. For example, the sentence presentation unit uses the emotion estimation function to identify sentences that evoke the most positive emotions in students, and the generation AI presents those sentences. For example, sentences with high emotion scores are provided preferentially. The sentence presentation unit can also adjust the learning content according to the student's emotions. This makes it possible to present sentences that evoke the most positive emotions in students.

[0072] The question answering unit can provide answers from multiple perspectives to students' questions. For example, when a student inputs a question, the generative AI provides answers from multiple perspectives. For example, it may present answers from a scientific perspective, a historical perspective, or a cultural perspective. The question answering unit can also adjust the content of the answer depending on the student's level of understanding. This makes it possible to provide answers from multiple perspectives to students' questions.

[0073] The question answering unit can automatically generate related questions based on the student's question history. For example, the question answering unit analyzes the student's past question history, and the generation AI automatically generates related questions. For example, it presents a new question related to the previous question. The question answering unit can also adjust the content of the question according to the student's interests and concerns. This makes it possible to automatically generate related questions based on the student's question history.

[0074] The question answering unit uses the emotion estimation function to analyze students' emotional responses to questions and can provide answers that correspond to their emotions. For example, when a student inputs a question, the question answering unit uses the generation AI to analyze their facial expressions and voice tone to estimate their emotions. For example, if a negative emotion is detected, the answer is provided in a gentle tone. The question answering unit can also provide specific answers that correspond to the student's emotions based on the emotion score. This makes it possible to analyze students' emotional responses to questions and provide answers that correspond to their emotions.

[0075] The question answering unit can also accommodate voice input or handwriting input when students input questions. The question answering unit builds a system that also accommodates voice input or handwriting input when students input questions. For example, it allows questions to be input using a microphone or tablet. The question answering unit can also accurately analyze students' questions using voice recognition technology or handwriting recognition technology. This allows students to input questions using voice input or handwriting input.

[0076] The question answering unit can provide videos and images related to students' questions. For example, when a student inputs a question, the question answering unit uses a generative AI to provide related videos and images. For example, an experiment video can be presented in response to a scientific question. The question answering unit can also use image recognition technology and video analysis technology to provide visual information related to the student's question. This makes it possible to provide videos and images related to the student's question.

[0077] The question answering unit uses an emotion estimation function to monitor the emotions of students when they ask questions in real time and make suggestions that elicit positive emotions. For example, when a student enters a question, the question answering unit uses the generation AI to analyze facial expressions and voice tone to monitor emotions in real time. For example, if negative emotions are detected, a positive suggestion is made. The question answering unit can also provide specific suggestions based on the student's emotions based on the emotion score. This makes it possible to monitor the emotions of students when they ask questions in real time and make suggestions that elicit positive emotions.

[0078] The text image utilization unit can automatically generate images related to the text entered by the student. For example, the text image utilization unit analyzes the text entered by the student, and the generation AI automatically generates images related to the content. For example, it provides images of animals for text about animals. The text image utilization unit can also use an image generation algorithm to provide visual information related to the student's text. This makes it possible to automatically generate images related to the text entered by the student.

[0079] The text image utilization unit can automatically generate text related to an image when a student selects the image. For example, the text image utilization unit analyzes an image selected by a student, and the generation AI automatically generates text related to the content. For example, for an image of an animal, it provides a description of the animal. The text image utilization unit can also use a text generation algorithm to provide sentences related to the student's image. This allows text related to the image to be automatically generated when a student selects the image.

[0080] The text image utilization unit can use the emotion estimation function to present the image that students find most interesting and provide text related to that image. For example, the text image utilization unit can use the emotion estimation function to identify the image that students find most interesting, and the generation AI can present that image. For example, images with a high emotion score can be provided preferentially. The text image utilization unit can also deepen students' understanding by providing text related to the image. This allows the unit to present the image that students find most interesting and provide text related to that image.

[0081] The text image utilization unit can provide 3D models and animations related to the text entered by the student. For example, the text image utilization unit analyzes the text entered by the student, and the generation AI provides 3D models and animations related to the content. For example, it provides 3D models of animals for text about animals. The text image utilization unit can also provide visual information related to the student's text using 3D modeling technology and animation generation technology. This makes it possible to provide 3D models and animations related to the text entered by the student.

[0082] The text image utilization unit can provide an audio explanation related to an image when a student selects the image. For example, the text image utilization unit analyzes an image selected by a student, and a generation AI provides an audio explanation related to the content. For example, for an image of an animal, an audio description of the animal is provided. The text image utilization unit can also use speech synthesis technology to provide an audio guide related to the student's image. This allows an audio explanation related to the image to be provided when a student selects the image.

[0083] The text image utilization unit can use the emotion estimation function to present the image that evokes the most positive emotion in the student and provide text related to that image. For example, the text image utilization unit can use the emotion estimation function to identify the image that evokes the most positive emotion in the student, and the generation AI can present that image. For example, images with a high emotion score can be provided preferentially. The text image utilization unit can also deepen the student's understanding by providing text related to the image. This allows the unit to present the image that evokes the most positive emotion in the student and provide text related to that image.

[0084] The multimodal support unit can automatically generate optimal multimodal teaching materials based on the student's learning history. For example, the multimodal support unit analyzes the student's learning history and the generation AI automatically generates optimal multimodal teaching materials. For example, it provides teaching materials that combine text, images, and videos based on past learning content. The multimodal support unit can also customize the content of the teaching materials according to the student's learning history. This makes it possible to automatically generate optimal multimodal teaching materials based on the student's learning history.

[0085] The multimodal support unit can customize the way multimodal teaching materials are presented according to the student's learning style. For example, the multimodal support unit analyzes the student's learning style, and the generative AI customizes the way multimodal teaching materials are presented. For example, visual learners can be provided with teaching materials that make extensive use of diagrams and illustrations. The multimodal support unit can also provide audio guides to auditory learners and interactive teaching materials to tactile learners. This makes it possible to customize the way multimodal teaching materials are presented according to the student's learning style.

[0086] The multimodal support unit can use the emotion estimation function to provide multimodal learning materials that students are most interested in. For example, the multimodal support unit uses the emotion estimation function to identify the multimodal learning materials that students are most interested in, and the generation AI provides those learning materials. For example, learning materials with high emotion scores are provided preferentially. The multimodal support unit can also adjust the content of the learning materials according to the student's emotions. This makes it possible to provide multimodal learning materials that students are most interested in.

[0087] The multimodal support unit can provide students with multimodal learning materials that utilize AR (augmented reality) and VR (virtual reality) when they are studying. For example, the multimodal support unit uses generative AI to provide students with multimodal learning materials that utilize AR and VR when they are studying. For example, it can provide learning materials that allow students to experience historical events in VR. The multimodal support unit can also use AR and VR technology to make students' learning experiences more realistic. This makes it possible to provide students with multimodal learning materials that utilize AR and VR when they are studying.

[0088] The multimodal support unit can provide multimodal learning materials that incorporate interactive game elements when students are studying. For example, the multimodal support unit provides multimodal learning materials that incorporate interactive game elements when students are studying by using a generation AI. For example, it provides learning materials in the form of a quiz. The multimodal support unit can also increase students' motivation to learn through game elements. This makes it possible to provide multimodal learning materials that incorporate interactive game elements when students are studying.

[0089] The multimodal support unit can use the emotion estimation function to provide multimodal learning materials that evoke the most positive emotions in students. For example, the multimodal support unit uses the emotion estimation function to identify multimodal learning materials that evoke the most positive emotions in students, and the generation AI provides those learning materials. For example, learning materials with high emotion scores are provided preferentially. The multimodal support unit can also adjust the content of the learning materials according to the student's emotions. This makes it possible to provide multimodal learning materials that evoke the most positive emotions in students.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] Self-paced learning tools can also be customized to accommodate students' learning styles. For example, visual learners can be provided with diagrams and illustrations, auditory learners can be provided with audio guides, and tactile learners can be provided with interactive materials. This allows for optimal learning experiences tailored to each student's learning style.

[0092] Self-learning educational tools can also be equipped with a function that allows for the level of difficulty to be adjusted in stages according to the student's learning progress. For example, the difficulty level can be increased from beginner to intermediate to advanced. The learning content can also be adjusted according to the student's level of understanding. This makes it possible to provide optimal learning support according to the student's learning progress.

[0093] Self-learning educational tools can also be equipped with a function to provide answers to students' questions from multiple perspectives. For example, answers can be presented from a scientific perspective, a historical perspective, or a cultural perspective. They can also adjust the content of the answers depending on the student's level of understanding. This allows them to provide answers to students' questions from multiple perspectives.

[0094] The self-learning educational tool can also have a function to present texts related to past learning content based on the student's learning history. For example, it can present new texts related to a topic previously studied. It can also present learning content in stages according to the student's level of understanding. This makes it possible to present related texts based on the student's learning history.

[0095] The self-learning educational tool can further have a function to generate personalized sentences based on the student's interests. For example, sentences related to a student's favorite animal or hobby can be provided. The learning content can also be customized according to the student's interests. This makes it possible to generate personalized sentences based on the student's interests.

[0096] The self-learning educational tool can further use the emotion estimation function to present texts related to the topics that students are most interested in. For example, it can provide texts related to topics with high emotion scores. It can also adjust the learning content according to the student's emotions. This allows it to present texts related to the topics that students are most interested in.

[0097] The self-learning educational tool can further use the emotion estimation function to present sentences that evoke the most positive emotions in students. For example, sentences with high emotion scores can be provided preferentially. It can also adjust the learning content according to the student's emotions. This allows it to present sentences that evoke the most positive emotions in students.

[0098] The self-learning educational tool also uses an emotion estimation function to monitor students' emotions in real time as they input and make suggestions to elicit positive emotions. For example, if negative emotions are detected, positive suggestions will be made. It can also provide specific suggestions based on the student's emotions. This allows the tool to monitor students' emotions in real time and make suggestions to elicit positive emotions.

[0099] The self-learning educational tool also uses an emotion estimation function to monitor students' emotions in real time when they ask questions and make suggestions that will elicit positive emotions. For example, if negative emotions are detected, positive suggestions will be made. It can also provide specific suggestions based on the student's emotions. This allows the tool to monitor students' emotions in real time when they ask questions and make suggestions that will elicit positive emotions.

[0100] The self-learning teaching tool can further use emotion estimation to analyze students' emotional responses to questions and provide answers that correspond to their emotions. For example, if negative emotions are detected, the tool can provide answers in a gentler tone. It can also provide specific answers depending on the student's emotions. This allows the tool to analyze students' emotional responses to questions and provide answers that correspond to their emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The vocabulary and comprehension analysis unit analyzes the student's vocabulary and comprehension. For example, the generation AI analyzes the text and answers entered by the student and evaluates their vocabulary and comprehension. The generation AI can also generate answers at an appropriate level based on the student's past answer history. Step 2: The sentence presentation unit presents sentences of an appropriate level based on the results of the analysis by the vocabulary and comprehension analysis unit. For example, the generation AI presents simple sentences to students with low vocabulary skills and more complex sentences to students with high vocabulary skills. Step 3: The question answering unit responds to the student's question. For example, if a student asks, "Why is the sky blue?", the generative AI will provide a scientific explanation for the question. Step 4: The text-image utilization unit uses text and images to help students understand. For example, when learning about animals, the generative AI provides images of the animals along with their descriptions. Step 5: Multimodal support provides visual and linguistic support. For example, in a history lesson, a generative AI might provide images and videos of historical events along with descriptions of them.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] 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]

[0170] 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 vocabulary and comprehension analysis department that analyzes students' vocabulary and comprehension, a sentence presenting unit that presents sentences at an appropriate level based on the results of the analysis by the vocabulary ability comprehension analysis unit; A question and answer section that answers questions from students; The Text and Image Utilization Department uses text and images to help students understand the material. a multimodal support unit that provides visual and linguistic support; A system characterized by:

2. The vocabulary comprehension analysis unit Analyze students' handwriting and assess their vocabulary and comprehension based on their handwriting 2. The system of claim 1.

3. The sentence presentation unit Based on the student's learning history, present texts related to past learning content 2. The system of claim 1.

4. The question answering unit Provide multiple perspectives on answers to student questions 2. The system of claim 1.

5. The text image utilization unit Automatically generate images related to student-entered text 2. The system of claim 1.

6. The multimodal support section includes: Automatically generating optimal multimodal learning materials based on students' learning histories 2. The system of claim 1.

7. The vocabulary comprehension analysis unit Analyzes students' emotions when they type and provides feedback based on their emotions 2. The system of claim 1.

8. The sentence presentation unit Present texts related to topics that interest students most 2. The system of claim 1.

Images