system

Abstract

The system according to the embodiment aims to promote active learning and deepen understanding. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a presentation unit, and an explanation unit. The reception unit receives questions from a user. The generation unit generates learning content based on the questions received by the reception unit. The presentation unit presents the learning content generated by the generation unit using AR. The explanation unit works in conjunction with the presentation unit to automatically provide explanations and commentary on what a child is viewing.

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] With conventional technology, paper textbooks and tablet textbooks had the problem of making it difficult for students to actively learn and understand.

[0005] The system according to the embodiment aims to promote active learning and deepen understanding. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a presentation unit, and an explanation unit. The reception unit receives questions from a user. The generation unit generates learning content based on the questions received by the reception unit. The presentation unit presents the learning content generated by the generation unit using AR. The explanation unit works in conjunction with the presentation unit to automatically provide explanations and commentary on what the child is viewing. [Effects of the Invention]

[0007] The system according to the embodiment can promote active learning and deepen understanding. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) An embodiment of the present invention provides an educational support system that receives questions from users, generates learning content using a generative AI, and presents it using augmented reality (AR). This educational support system receives questions from users, generates learning content in response to those questions, and presents it to the user using AR. Furthermore, the educational support system utilizes multimodal generative AI to automatically provide explanations and commentary on what the child is looking at. For example, if a user inputs a question about a textbook into the interactive AI, the generative AI generates learning content in response to that question and presents it to the user using AR. For instance, if the user asks "Tell me about World War II" in a history textbook, the generative AI generates content including an overview of the war and important events, and displays battlefield scenes and 3D models of important figures using AR. In addition, the educational support system utilizes multimodal generative AI to automatically provide explanations and commentary on what the child is looking at. For example, if a child is looking at a diagram in a textbook, the generative AI analyzes information related to the diagram and displays supplementary explanations using AR. This makes it easier for children to understand visually and promotes active learning. This new textbook overcomes the shortcomings of conventional textbooks and enables more effective learning. For example, if a student asks "Explain Newton's laws of motion" in a physics textbook, the generative AI will generate an explanation of the laws of motion and display experimental scenes and force vectors using augmented reality (AR). This allows for a visual understanding of abstract concepts. In this way, the educational support system can provide learning content using generative AI and AR in response to user questions, and promote active learning by automatically providing explanations and interpretations of what the child is seeing.

[0029] The education support system according to the embodiment includes a reception unit, a generation unit, a presentation unit, and an explanation unit. The reception unit receives a question from a user. The question from the user may be in, for example, text format, audio format, or image format, but is not limited to these examples. The reception unit receives, for example, a text format question. The reception unit can also receive audio format questions. The reception unit can also receive image format questions. For example, the reception unit analyzes text entered by the user to understand the question. Audio format questions are converted into text using speech recognition technology. Image format questions are analyzed using image analysis technology. The generation unit uses a generation AI to generate learning content based on the question received by the reception unit. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The generation unit generates an answer to the question using, for example, the text generation AI. The generation unit can also generate visual content for the question using the multimodal generation AI. For example, in the generation unit, the text generation AI generates text including an overview of the war and important events in response to the question, "Tell me about World War II." The multimodal generation AI generates 3D models of battlefield scenes and important figures. The presentation unit presents the learning content generated by the generation unit using AR. The presentation unit displays the generated content to the user, for example, using an AR device. For example, the presentation unit displays the generated battlefield scenes and 3D models of important figures using AR glasses. The presentation unit can also display the generated content using a smartphone or tablet. For example, the presentation unit displays the battlefield scenes and 3D models of important figures on a smartphone screen. The commentary unit uses the multimodal generation AI to work in conjunction with the presentation unit to automatically provide explanations and commentary on what a child is looking at. For example, when a child is looking at a diagram in a textbook, the generation AI analyzes information related to the diagram and displays supplemental explanations using AR. For example, the commentary unit analyzes information related to a diagram in a textbook and displays supplemental explanations using AR. The commentary unit can also provide audio explanations and commentary on what the child is looking at.For example, the explanation section provides audio explanations of information related to the diagrams in the textbook. This allows the educational support system, according to this embodiment, to provide learning content using generative AI and AR in response to user questions, and to automatically provide explanations and commentary on what the child is seeing, thereby promoting active learning.

[0030] The reception desk can analyze the user's past question history and select an appropriate question reception method. For example, the reception desk can prioritize topics that the user has frequently asked about in the past. It can also prioritize suggesting question formats (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can accept questions at specific time periods based on the user's past question history. In this way, by analyzing the user's past question history, the optimal question reception method can be provided. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past question history data into a generating AI and have the generating AI select the optimal question reception method.

[0031] The reception unit can filter questions based on the user's current learning progress and areas of interest. For example, the reception unit can prioritize questions related to the topic the user is currently studying. It can also filter relevant questions based on the user's areas of interest. Furthermore, the reception unit can accept questions of appropriate difficulty according to the user's learning progress. This allows the reception unit to receive more relevant questions by filtering them based on the user's learning progress and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's learning progress data into a generating AI and have the generating AI perform the question filtering.

[0032] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving questions related to the home. In this way, by taking the user's geographical location information into account, highly relevant questions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant questions.

[0033] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. The reception unit can receive related questions based on, for example, information shared by the user on social media. The reception unit can also receive related questions based on topics the user follows on social media. The reception unit can also receive related questions based on groups the user participates in on social media. In this way, related questions can be received by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to select related questions.

[0034] When generating the learning content, the generation unit can adjust the level of detail of the generated learning content based on the importance of the question. For example, the generation unit generates learning content that includes detailed explanations for important questions. The generation unit can also generate learning content that includes concise explanations for general questions. The generation unit can also generate short learning content that focuses on the main points for simple questions. In this way, by adjusting the level of detail of the learning content based on the importance of the question, an appropriate amount of information can be provided. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input question importance data into the generation AI and have the generation AI adjust the level of detail of the learning content.

[0035] When generating learning content, the generation unit can apply different generation algorithms depending on the question category. For example, the generation unit can apply an algorithm that provides chronological explanations to questions about history. The generation unit can also apply an algorithm that emphasizes experimental results to questions about science. The generation unit can also apply an algorithm that provides step-by-step explanations to questions about mathematics. In this way, by applying a generation algorithm depending on the question category, more appropriate learning content can be provided. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input question category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0036] When generating learning content, the generation unit can determine generation priorities based on the time when the questions were submitted. For example, the generation unit generates learning content by prioritizing questions submitted recently. The generation unit can also prioritize questions submitted before an important event. The generation unit can also prioritize questions that have remained unanswered for a long period of time. This allows for timely provision of learning content by determining generation priorities based on the time when the questions were submitted. Some or all of the above-described processing by the generation unit may be performed using, or without, AI. For example, the generation unit can input data on the time when the questions were submitted into the generation AI and have the generation AI determine the generation priorities.

[0037] When generating learning content, the generation unit can adjust the generation order based on the relevance of the questions. For example, the generation unit can adjust the generation order of the learning content based on the topic to which the question is related. The generation unit can also adjust the generation order of the learning content based on the learning unit to which the question is related. The generation unit can also adjust the generation order of the learning content based on the learning objective to which the question is related. By adjusting the generation order based on the relevance of the questions, more relevant learning content can be provided. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question relevance data into the generation AI and have the generation AI adjust the generation order.

[0038] The presentation unit can select the optimal display method when presenting learning content by referring to the user's past learning history. For example, the presentation unit can prioritize providing display methods that the user has previously preferred. The presentation unit can also suggest the optimal display method based on the user's past learning history. Furthermore, the presentation unit can analyze the user's past learning history and provide a display method that is easy to understand visually. In this way, the optimal display method can be provided by referring to the user's past learning history. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's past learning history data into a generating AI and have the generating AI select the optimal display method.

[0039] The presentation unit can customize the displayed content based on the user's current learning status when presenting learning content. For example, the presentation unit can prioritize displaying information related to the topic the user is currently studying. The presentation unit can also display information of an appropriate difficulty level according to the user's learning progress. Furthermore, the presentation unit can customize the displayed content based on the user's learning goals. This allows for more effective learning by customizing the displayed content based on the user's learning status. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's learning status data into a generating AI and have the generating AI perform the customization of the displayed content.

[0040] The presentation unit can select the optimal display method when presenting learning content, taking into account the user's geographical location information. For example, if the user is in a specific region, the presentation unit can prioritize displaying information related to that region. Furthermore, if the user is traveling, the presentation unit can prioritize displaying information related to their travel destination. Also, if the user is at home, the presentation unit can prioritize displaying information related to their home. This allows for the prioritization of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal display method.

[0041] The presentation unit can analyze the user's social media activity and adjust the displayed content when presenting learning content. For example, the presentation unit can display relevant information based on information the user has shared on social media. It can also display relevant information based on topics the user follows on social media. Furthermore, it can display relevant information based on groups the user participates in on social media. In this way, relevant information can be displayed by analyzing the user's social media activity. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's social media activity data into a generating AI and have the generating AI perform the adjustment of the displayed content.

[0042] When providing explanations, the explanation unit can select the optimal explanation method by referring to the user's past learning history. For example, the explanation unit can prioritize the explanation method that the user has used favorably in the past. The explanation unit can also suggest the optimal explanation method based on the user's past learning history. The explanation unit can also analyze the user's past learning history and provide an explanation method that is visually easy to understand. In this way, the optimal explanation method can be provided by referring to the user's past learning history. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input the user's past learning history data into the generation AI and have the generation AI select the optimal explanation method.

[0043] The commentary unit can customize the content of the commentary based on the user's current learning status when providing commentary. For example, the commentary unit prioritizes explaining information related to the topic the user is currently studying. The commentary unit can also provide commentary at an appropriate level of difficulty according to the user's learning progress. The commentary unit can also customize the content of the commentary based on the user's learning goals. This enables more effective learning by customizing the content of the commentary based on the user's learning status. Some or all of the above-described processing in the commentary unit may be performed using, for example, AI, or may be performed without using AI. For example, the commentary unit can input the user's learning status data into a generation AI and have the generation AI customize the content of the commentary.

[0044] When providing commentary, the commentary unit can select the optimal commentary method by taking into account the user's geographical location information. For example, if the user is in a specific area, the commentary unit can prioritize commentary on information related to that area. Furthermore, if the user is traveling, the commentary unit can prioritize commentary on information related to the travel destination. Furthermore, if the user is at home, the commentary unit can prioritize commentary on information related to the home. In this way, by taking the user's geographical location information into consideration, highly relevant information can be prioritized for commentary. Some or all of the above-described processing in the commentary unit may be performed using, for example, AI, or may be performed without using AI. For example, the commentary unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal commentary method.

[0045] When providing commentary, the commentary unit can adjust the content of the commentary by analyzing the user's social media activity. The commentary unit can, for example, provide commentary on related information based on information shared by the user on social media. The commentary unit can also provide commentary on related information based on topics the user follows on social media. The commentary unit can also provide commentary on related information based on groups the user participates in on social media. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the commentary unit may be performed using, for example, AI, or may be performed without using AI. For example, the commentary unit can input the user's social media activity data into a generation AI and have the generation AI adjust the content of the commentary.

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

[0047] The reception unit can analyze the user's learning style and suggest the most suitable question format. For example, if the user prefers visual learning, image-based questions can be suggested with priority. If the user prefers auditory learning, audio-based questions can be suggested with priority. Furthermore, if the user prefers text-based questions, text-based questions can be suggested with priority. This allows for more effective learning by suggesting question formats that suit the user's learning style.

[0048] The reception unit can analyze the user's past learning results and provide appropriate question feedback. For example, for questions that the user has answered correctly in the past, the reception unit can suggest more difficult questions. Also, for questions that the user has answered incorrectly in the past, the reception unit can suggest the same question again. Furthermore, the reception unit can preferentially suggest questions related to topics that the user has previously found difficult. This allows the reception unit to provide appropriate feedback based on the user's past learning results, thereby improving learning effectiveness.

[0049] The reception unit can adjust the method of receiving questions taking into account the user's current learning environment. For example, if the user is in a quiet environment, voice questions can be given priority. If the user is in a noisy environment, text questions can be given priority. Furthermore, if the user is on the move, simple questions can be given priority. By adjusting the method of receiving questions according to the user's learning environment, a more appropriate learning experience can be provided.

[0050] The reception unit can suggest region-specific learning content taking into account the user's geographical location information. For example, if the user is in a specific region, questions about the history and culture of that region can be given priority. Also, if the user is traveling, questions about tourist spots and famous places at the travel destination can be given priority. Furthermore, if the user is at home, learning content related to the home can be suggested. This allows for the provision of highly relevant learning content based on the user's geographical location information.

[0051] The reception desk can analyze users' social media activity and suggest relevant learning content. For example, it can suggest relevant questions based on information users have shared on social media. It can also suggest relevant learning content based on topics users follow on social media. Furthermore, it can suggest relevant questions based on groups users participate in on social media. This allows for the provision of highly relevant learning content based on users' social media activity.

[0052] The generation unit can customize the content generated based on the user's learning objectives. For example, if a user is studying for a specific exam, it can prioritize generating content related to that exam. It can also generate content related to a specific skill if the user is trying to acquire that skill. Furthermore, if a user is working on a specific project, it can generate content related to that project. This allows for more effective learning by customizing learning content based on the user's learning objectives.

[0053] The generation unit can adjust the format of the generated learning content based on the user's learning style. For example, if the user prefers visual learning, it can generate learning content that makes extensive use of images and videos. If the user prefers auditory learning, it can also generate learning content that includes audio commentary. Furthermore, if the user prefers text-based learning, it can generate learning content that includes detailed text explanations. By adjusting the format of the learning content according to the user's learning style, more effective learning becomes possible.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The reception unit receives a question from a user. Questions from users may be in text, audio, or image format. For example, the reception unit analyzes the text entered by the user to understand the content of the question. Audio questions are converted into text using speech recognition technology. Image questions are analyzed using image analysis technology. Step 2: The generation unit uses a generation AI to generate learning content based on the question received by the reception unit. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit uses a text generation AI to generate an answer to the question. The generation unit may also use a multimodal generation AI to generate visual content for the question. Step 3: The presentation unit presents the learning content generated by the generation unit using AR. The presentation unit displays the generated content to the user using, for example, an AR device. For example, the presentation unit displays the generated battlefield scene or 3D models of important people using AR glasses. The presentation unit can also display the generated content using a smartphone or tablet. Step 4: The commentary unit uses multimodal generation AI to work in conjunction with the presentation unit to automatically provide explanations and commentary on what the child is looking at. For example, the commentary unit uses generation AI to analyze information related to a diagram in a textbook and display supplementary explanations using AR. The commentary unit can also provide audio explanations and commentary on what the child is looking at.

[0056] (Example 2) An embodiment of the present invention provides an educational support system that receives questions from users, generates learning content using a generative AI, and presents it using augmented reality (AR). This educational support system receives questions from users, generates learning content in response to those questions, and presents it to the user using AR. Furthermore, the educational support system utilizes multimodal generative AI to automatically provide explanations and commentary on what the child is looking at. For example, if a user inputs a question about a textbook into the interactive AI, the generative AI generates learning content in response to that question and presents it to the user using AR. For instance, if the user asks "Tell me about World War II" in a history textbook, the generative AI generates content including an overview of the war and important events, and displays battlefield scenes and 3D models of important figures using AR. In addition, the educational support system utilizes multimodal generative AI to automatically provide explanations and commentary on what the child is looking at. For example, if a child is looking at a diagram in a textbook, the generative AI analyzes information related to the diagram and displays supplementary explanations using AR. This makes it easier for children to understand visually and promotes active learning. This new textbook overcomes the shortcomings of conventional textbooks and enables more effective learning. For example, if a student asks "Explain Newton's laws of motion" in a physics textbook, the generative AI will generate an explanation of the laws of motion and display experimental scenes and force vectors using augmented reality (AR). This allows for a visual understanding of abstract concepts. In this way, the educational support system can provide learning content using generative AI and AR in response to user questions, and promote active learning by automatically providing explanations and interpretations of what the child is seeing.

[0057] The education support system according to the embodiment includes a reception unit, a generation unit, a presentation unit, and an explanation unit. The reception unit receives a question from a user. The question from the user may be in, for example, text format, audio format, or image format, but is not limited to these examples. The reception unit receives, for example, a text format question. The reception unit can also receive audio format questions. The reception unit can also receive image format questions. For example, the reception unit analyzes text entered by the user to understand the question. Audio format questions are converted into text using speech recognition technology. Image format questions are analyzed using image analysis technology. The generation unit uses a generation AI to generate learning content based on the question received by the reception unit. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The generation unit generates an answer to the question using, for example, the text generation AI. The generation unit can also generate visual content for the question using the multimodal generation AI. For example, in the generation unit, the text generation AI generates text including an overview of the war and important events in response to the question, "Tell me about World War II." The multimodal generation AI generates 3D models of battlefield scenes and important figures. The presentation unit presents the learning content generated by the generation unit using AR. The presentation unit displays the generated content to the user, for example, using an AR device. For example, the presentation unit displays the generated battlefield scenes and 3D models of important figures using AR glasses. The presentation unit can also display the generated content using a smartphone or tablet. For example, the presentation unit displays the battlefield scenes and 3D models of important figures on a smartphone screen. The commentary unit uses the multimodal generation AI to work in conjunction with the presentation unit to automatically provide explanations and commentary on what a child is looking at. For example, when a child is looking at a diagram in a textbook, the generation AI analyzes information related to the diagram and displays supplemental explanations using AR. For example, the commentary unit analyzes information related to a diagram in a textbook and displays supplemental explanations using AR. The commentary unit can also provide audio explanations and commentary on what the child is looking at.For example, the explanation section provides audio explanations of information related to the diagrams in the textbook. This allows the educational support system, according to this embodiment, to provide learning content using generative AI and AR in response to user questions, and to automatically provide explanations and commentary on what the child is seeing, thereby promoting active learning.

[0058] The reception desk can estimate the user's emotions and adjust the timing of question acceptance based on the estimated emotions. For example, if the user is excited, the reception desk can immediately accept the question and provide a quick answer. If the user is tired, the reception desk can also slightly delay accepting the question to give them time to relax. Furthermore, if the user is focused, the reception desk can continuously accept questions without interruption. This allows for question acceptance at a more appropriate time by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0059] The reception desk can analyze the user's past question history and select an appropriate question reception method. For example, the reception desk can prioritize topics that the user has frequently asked about in the past. It can also prioritize suggesting question formats (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can accept questions at specific time periods based on the user's past question history. In this way, by analyzing the user's past question history, the optimal question reception method can be provided. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past question history data into a generating AI and have the generating AI select the optimal question reception method.

[0060] The reception unit can filter questions based on the user's current learning progress and areas of interest. For example, the reception unit can prioritize questions related to the topic the user is currently studying. It can also filter relevant questions based on the user's areas of interest. Furthermore, the reception unit can accept questions of appropriate difficulty according to the user's learning progress. This allows the reception unit to receive more relevant questions by filtering them based on the user's learning progress and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's learning progress data into a generating AI and have the generating AI perform the question filtering.

[0061] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user emotions. For example, when the user is excited, the reception unit can prioritize important questions. Furthermore, when the user is relaxed, the reception unit can prioritize general questions. Furthermore, when the user is tired, the reception unit can prioritize simple questions. Thus, by determining the priority of questions according to the user's emotions, important questions can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0062] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving questions related to the home. In this way, by taking the user's geographical location information into account, highly relevant questions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant questions.

[0063] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. The reception unit can receive related questions based on, for example, information shared by the user on social media. The reception unit can also receive related questions based on topics the user follows on social media. The reception unit can also receive related questions based on groups the user participates in on social media. In this way, related questions can be received by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to select related questions.

[0064] The generation unit can estimate the user's emotions and adjust the presentation of the generated learning content based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate learning content that progresses at a relaxed pace. If the user is in a hurry, the generation unit can also generate concise learning content that gets straight to the point. If the user is excited, the generation unit can also generate learning content with visually stimulating effects. By adjusting the presentation of the learning content according to the user's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0065] When generating the learning content, the generation unit can adjust the level of detail of the generated learning content based on the importance of the question. For example, the generation unit generates learning content that includes detailed explanations for important questions. The generation unit can also generate learning content that includes concise explanations for general questions. The generation unit can also generate short learning content that focuses on the main points for simple questions. In this way, by adjusting the level of detail of the learning content based on the importance of the question, an appropriate amount of information can be provided. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input question importance data into the generation AI and have the generation AI adjust the level of detail of the learning content.

[0066] When generating learning content, the generation unit can apply different generation algorithms depending on the question category. For example, the generation unit can apply an algorithm that provides chronological explanations to questions about history. The generation unit can also apply an algorithm that emphasizes experimental results to questions about science. The generation unit can also apply an algorithm that provides step-by-step explanations to questions about mathematics. In this way, by applying a generation algorithm depending on the question category, more appropriate learning content can be provided. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input question category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0067] The generation unit can estimate the user's emotions and adjust the length of the learning content it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise learning content. If the user is relaxed, the generation unit can also generate longer learning content with detailed explanations. If the user is excited, the generation unit can also generate learning content with visually stimulating effects. By adjusting the length of the learning content according to the user's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0068] When generating learning content, the generation unit can determine generation priorities based on the time when the questions were submitted. For example, the generation unit generates learning content by prioritizing questions submitted recently. The generation unit can also prioritize questions submitted before an important event. The generation unit can also prioritize questions that have remained unanswered for a long period of time. This allows for timely provision of learning content by determining generation priorities based on the time when the questions were submitted. Some or all of the above-described processing by the generation unit may be performed using, or without, AI. For example, the generation unit can input data on the time when the questions were submitted into the generation AI and have the generation AI determine the generation priorities.

[0069] When generating learning content, the generation unit can adjust the generation order based on the relevance of the questions. For example, the generation unit can adjust the generation order of the learning content based on the topic to which the question is related. The generation unit can also adjust the generation order of the learning content based on the learning unit to which the question is related. The generation unit can also adjust the generation order of the learning content based on the learning objective to which the question is related. By adjusting the generation order based on the relevance of the questions, more relevant learning content can be provided. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question relevance data into the generation AI and have the generation AI adjust the generation order.

[0070] The presentation unit can estimate the user's emotions and adjust the display method of the presented learning content based on the estimated user emotions. For example, if the user is nervous, the presentation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the presentation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the presentation unit can provide a display method that focuses on the main points. This allows for more effective learning by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit can be performed using, for example, an AI, or without an AI. For example, the presentation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0071] The presentation unit can select the optimal display method when presenting learning content by referring to the user's past learning history. For example, the presentation unit can prioritize providing display methods that the user has previously preferred. The presentation unit can also suggest the optimal display method based on the user's past learning history. Furthermore, the presentation unit can analyze the user's past learning history and provide a display method that is easy to understand visually. In this way, the optimal display method can be provided by referring to the user's past learning history. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's past learning history data into a generating AI and have the generating AI select the optimal display method.

[0072] The presentation unit can customize the displayed content based on the user's current learning status when presenting learning content. For example, the presentation unit can prioritize displaying information related to the topic the user is currently studying. The presentation unit can also display information of an appropriate difficulty level according to the user's learning progress. Furthermore, the presentation unit can customize the displayed content based on the user's learning goals. This allows for more effective learning by customizing the displayed content based on the user's learning status. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's learning status data into a generating AI and have the generating AI perform the customization of the displayed content.

[0073] The presentation unit can estimate the user's emotions and determine the priority of the learning content to be presented based on the estimated user emotions. For example, if the user is excited, the presentation unit will prioritize displaying important information. It can also prioritize displaying general information if the user is relaxed. Furthermore, it can prioritize displaying simple information if the user is tired. This allows for the prioritization of learning content according to the user's emotions, thereby ensuring that important information is displayed first. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using AI, or not. For example, the presentation unit can input user facial expression data into a generative AI and have the generative AI perform the user emotion estimation.

[0074] The presentation unit can select the optimal display method when presenting learning content, taking into account the user's geographical location information. For example, if the user is in a specific region, the presentation unit can prioritize displaying information related to that region. Furthermore, if the user is traveling, the presentation unit can prioritize displaying information related to their travel destination. Also, if the user is at home, the presentation unit can prioritize displaying information related to their home. This allows for the prioritization of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal display method.

[0075] The presentation unit can analyze the user's social media activity and adjust the displayed content when presenting learning content. For example, the presentation unit can display relevant information based on information the user has shared on social media. It can also display relevant information based on topics the user follows on social media. Furthermore, it can display relevant information based on groups the user participates in on social media. In this way, relevant information can be displayed by analyzing the user's social media activity. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's social media activity data into a generating AI and have the generating AI perform the adjustment of the displayed content.

[0076] The commentary unit can estimate the user's emotions and adjust its commentary method based on the estimated emotions. For example, if the user is nervous, the commentary unit will provide commentary in a calm voice. If the user is relaxed, the commentary unit can provide commentary in a cheerful voice. If the user is in a hurry, the commentary unit can provide quick and concise commentary. By adjusting the commentary method according to the user's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the commentary unit may be performed using AI, or not using AI. For example, the commentary unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0077] When providing explanations, the explanation unit can select the optimal explanation method by referring to the user's past learning history. For example, the explanation unit can prioritize the explanation method that the user has used favorably in the past. The explanation unit can also suggest the optimal explanation method based on the user's past learning history. The explanation unit can also analyze the user's past learning history and provide an explanation method that is visually easy to understand. In this way, the optimal explanation method can be provided by referring to the user's past learning history. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input the user's past learning history data into the generation AI and have the generation AI select the optimal explanation method.

[0078] The commentary unit can customize the content of the commentary based on the user's current learning status when providing commentary. For example, the commentary unit prioritizes explaining information related to the topic the user is currently studying. The commentary unit can also provide commentary at an appropriate level of difficulty according to the user's learning progress. The commentary unit can also customize the content of the commentary based on the user's learning goals. This enables more effective learning by customizing the content of the commentary based on the user's learning status. Some or all of the above-described processing in the commentary unit may be performed using, for example, AI, or may be performed without using AI. For example, the commentary unit can input the user's learning status data into a generation AI and have the generation AI customize the content of the commentary.

[0079] The commentary unit can estimate the user's emotions and determine the priority of the commentary based on the estimated user's emotions. For example, if the user is excited, the commentary unit can prioritize explaining important information. Also, if the user is relaxed, the commentary unit can prioritize explaining general information. Also, if the user is tired, the commentary unit can prioritize explaining simple information. By determining the priority of the commentary according to the user's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the commentary unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the commentary unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0080] When providing commentary, the commentary unit can select the optimal commentary method by taking into account the user's geographical location information. For example, if the user is in a specific area, the commentary unit can prioritize commentary on information related to that area. Furthermore, if the user is traveling, the commentary unit can prioritize commentary on information related to the travel destination. Furthermore, if the user is at home, the commentary unit can prioritize commentary on information related to the home. In this way, by taking the user's geographical location information into consideration, highly relevant information can be prioritized for commentary. Some or all of the above-described processing in the commentary unit may be performed using, for example, AI, or may be performed without using AI. For example, the commentary unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal commentary method.

[0081] When providing commentary, the commentary unit can adjust the content of the commentary by analyzing the user's social media activity. The commentary unit can, for example, provide commentary on related information based on information shared by the user on social media. The commentary unit can also provide commentary on related information based on topics the user follows on social media. The commentary unit can also provide commentary on related information based on groups the user participates in on social media. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the commentary unit may be performed using, for example, AI, or may be performed without using AI. For example, the commentary unit can input the user's social media activity data into a generation AI and have the generation AI adjust the content of the commentary. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, presentation unit, and commentary unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives questions from the user in text, voice, or image format. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates learning content using a generation AI. The presentation unit is realized, for example, by the output device 40 of the smart device 14 and displays the generated content using AR. The commentary unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically provides explanations and commentary on what the child is viewing. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, presentation unit, and commentary unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives voice-based questions from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates learning content using a generation AI. The presentation unit is realized, for example, by the speaker 240 of the smart glasses 214 and displays the generated content using AR. The commentary unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically provides explanations and commentary on what the child is looking at. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, presentation unit, and commentary unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives voice-based questions from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates learning content using a generation AI. The presentation unit is realized, for example, by the display 343 of the headset-type terminal 314 and displays the generated content using AR. The commentary unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically provides explanations and commentary on what the child is looking at. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, presentation unit, and commentary unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice-based questions from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates learning content using a generation AI. The presentation unit is realized, for example, by the speaker 240 of the robot 414 and displays the generated content in AR. The commentary unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically provides explanations and commentary on what the child is looking at.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The reception unit can analyze the user's learning style and suggest the most suitable question format. For example, if the user prefers visual learning, image-based questions can be suggested with priority. If the user prefers auditory learning, audio-based questions can be suggested with priority. Furthermore, if the user prefers text-based questions, text-based questions can be suggested with priority. This allows for more effective learning by suggesting question formats that suit the user's learning style.

[0084] The reception unit can estimate the user's emotions and adjust the difficulty of questions based on the estimated user's emotions. For example, if the user is relaxed, questions with a high level of difficulty can be given priority. Also, if the user is tired, questions with a low level of difficulty can be given priority. Furthermore, if the user is excited, questions with a moderate level of difficulty can be given priority. In this way, by adjusting the difficulty of questions according to the user's emotions, a more appropriate learning experience can be provided.

[0085] The reception unit can analyze the user's past learning results and provide appropriate question feedback. For example, for questions that the user has answered correctly in the past, the reception unit can suggest more difficult questions. Also, for questions that the user has answered incorrectly in the past, the reception unit can suggest the same question again. Furthermore, the reception unit can preferentially suggest questions related to topics that the user has previously found difficult. This allows the reception unit to provide appropriate feedback based on the user's past learning results, thereby improving learning effectiveness.

[0086] The reception unit can adjust the method of receiving questions taking into account the user's current learning environment. For example, if the user is in a quiet environment, voice questions can be given priority. If the user is in a noisy environment, text questions can be given priority. Furthermore, if the user is on the move, simple questions can be given priority. By adjusting the method of receiving questions according to the user's learning environment, a more appropriate learning experience can be provided.

[0087] The reception unit can estimate the user's emotions and suggest question categories based on the estimated user emotions. For example, if the user is relaxed, questions related to interesting topics can be suggested. If the user is tired, questions related to easy topics can be suggested. Furthermore, if the user is excited, questions related to challenging topics can be suggested. This allows for more effective learning by suggesting question categories according to the user's emotions.

[0088] The reception unit can suggest region-specific learning content taking into account the user's geographical location information. For example, if the user is in a specific region, questions about the history and culture of that region can be given priority. Also, if the user is traveling, questions about tourist spots and famous places at the travel destination can be given priority. Furthermore, if the user is at home, learning content related to the home can be suggested. This allows for the provision of highly relevant learning content based on the user's geographical location information.

[0089] The reception unit can analyze the user's social media activity and suggest relevant learning content. For example, it can suggest related questions based on information the user has shared on social media. It can also suggest related learning content based on topics the user follows on social media. It can also suggest related questions based on groups the user has joined on social media. This makes it possible to provide highly relevant learning content based on the user's social media activity.

[0090] The generation unit can estimate the user's emotions and adjust the tone of the generated learning content based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate learning content in a calm tone. If the user is excited, the generation unit can generate learning content in a lively tone. Furthermore, if the user is tired, the generation unit can generate learning content in a calm tone. This allows for more effective learning by adjusting the tone of the learning content according to the user's emotions.

[0091] When generating learning content, the generator can customize the content to be generated based on the user's learning goals. For example, if the user is studying for a specific exam, the generator can prioritize generating content related to that exam. Also, if the user is trying to master a specific skill, the generator can generate content related to that skill. Furthermore, if the user is working on a specific project, the generator can generate content related to that project. This allows for more effective learning by customizing learning content based on the user's learning goals.

[0092] When generating study content, the generator can adjust the format of the content based on the user's learning style. For example, if the user prefers visual learning, the generator can generate study content that makes extensive use of images and videos. If the user prefers auditory learning, the generator can generate study content that includes audio commentary. Furthermore, if the user prefers text-based learning, the generator can generate study content that includes detailed text commentary. This allows for more effective learning by adjusting the format of the study content according to the user's learning style.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The reception unit receives a question from a user. Questions from users may be in text, audio, or image format. For example, the reception unit analyzes the text entered by the user to understand the content of the question. Audio questions are converted into text using speech recognition technology. Image questions are analyzed using image analysis technology. Step 2: The generation unit uses a generation AI to generate learning content based on the question received by the reception unit. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit uses a text generation AI to generate an answer to the question. The generation unit may also use a multimodal generation AI to generate visual content for the question. Step 3: The presentation unit presents the learning content generated by the generation unit using AR. The presentation unit displays the generated content to the user using, for example, an AR device. For example, the presentation unit displays the generated battlefield scene or 3D models of important people using AR glasses. The presentation unit can also display the generated content using a smartphone or tablet. Step 4: The commentary unit uses multimodal generation AI to work in conjunction with the presentation unit to automatically provide explanations and commentary on what the child is looking at. For example, the commentary unit uses generation AI to analyze information related to a diagram in a textbook and display supplementary explanations using AR. The commentary unit can also provide audio explanations and commentary on what the child is looking at.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0097] 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.

[0098] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] 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.

[0111] 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.

[0112] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0126] 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.

[0127] 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.

[0128] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

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

[0134] The 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.

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

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0166] [Explanation of symbols]

[0167] 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 reception unit that receives questions from users; a generation unit that generates study content based on the questions received by the reception unit; a presentation unit that presents the learning content generated by the generation unit using AR; and a commentary unit that cooperates with the presentation unit to automatically provide explanations and commentary on what the child is viewing. A system characterized by:

2. The reception unit Estimates the user's emotions and adjusts the timing of accepting questions based on the estimated user emotions. The system of claim 1 .

3. The reception unit Analyze the user's past question history and select the appropriate method for accepting questions The system of claim 1 .

4. The reception unit When questions are submitted, they are filtered based on the user's current learning progress and areas of interest. The system of claim 1 .

5. The reception unit Estimate the user's emotions and prioritize the questions to be accepted based on the estimated user emotions. The system of claim 1 .

6. The reception unit When accepting questions, prioritize relevant questions based on the user's geographic location. The system of claim 1 .

7. The reception unit When a question is received, the social media activity of the user is analyzed and related questions are received. The system of claim 1 .

8. The generation unit Estimates the user's emotions and adjusts the presentation of generated learning content based on the estimated user emotions. The system of claim 1 .

Images