System

Abstract

An object of a system according to an embodiment is to provide educational content that enables a child to actively learn.SOLUTION: A system according to an embodiment includes an interactive AI, a generation AI, and an AR presenting unit. The interactive AI receives the child's question. The generation AI generates educational content based on the questions received by the interactive AI. The AR presenting unit presents the educational content generated by the generation AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult for children to actively study with paper textbooks or tablet textbooks.

[0005] The system according to the embodiment aims to provide educational content that allows children to actively learn. [Means for solving the problem]

[0006] The system according to the embodiment includes an interactive AI, a generation AI, and an AR presentation unit. The interactive AI accepts questions from children. The generation AI generates educational content based on the questions accepted by the interactive AI. The AR presentation unit presents the educational content generated by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can provide educational content that allows children to actively learn. [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) The new-age textbook system according to an embodiment of the present invention is a system that uses generative AI and AR technology to provide children with active and diverse learning experiences, allowing them to deepen their learning and understanding based on their own interests and questions.

[0029] A new-age textbook system according to an embodiment includes an interactive AI, a generation AI, and an AR presentation unit. The interactive AI accepts questions from children. For example, if a child asks, "Tell me about the solar system," the interactive AI understands the question and sends a prompt to the generation AI. The generation AI generates educational content based on the questions accepted by the interactive AI. For example, if the generation AI receives a prompt such as, "Show me the arrangement of the planets in the solar system in 3DCG," it recreates the arrangement of the planets in the solar system in 3DCG and generates a video of the planets. In addition, in response to a prompt such as, "Tell me the sounds of dinosaurs," the generation AI generates audio that recreates the sounds of dinosaurs. The AR presentation unit presents the educational content generated by the generation AI. For example, the generated 3DCG image of the solar system is displayed overlaid on a real space via a tablet or smartphone. This allows children to learn with a sense of realism, as if they were actually there. The generated audio and video are also integrated into the real space using AR technology. This allows the new-age textbook system according to an embodiment to provide children with active and diverse learning experiences.

[0030] Conversational AI can analyze a child's learning history and automatically generate questions that correspond to each child's level of understanding. For example, conversational AI can analyze a child's past learning history and generate questions that correspond to their level of understanding. For example, it can automatically generate questions that complement areas of insufficient understanding based on content learned in the past. Conversational AI can also generate customized questions that correspond to each child's level of understanding based on a child's learning history. For example, if a child has a low level of understanding of a particular topic, it can generate basic questions related to that topic. Conversational AI can also analyze a child's learning history and generate questions in stages that correspond to their level of understanding. For example, it can start with basic questions and generate questions that gradually increase in difficulty. This can improve learning effectiveness by automatically generating questions that correspond to each child's level of understanding.

[0031] Conversational AI can evaluate a child's pronunciation and language skills and provide appropriate feedback. For example, conversational AI can analyze a child's pronunciation in real time and provide feedback to encourage accurate pronunciation. For example, it can point out pronunciation errors and provide audio guidance on the correct pronunciation. Conversational AI can also build systems that evaluate a child's language skills and provide appropriate feedback. For example, it can point out grammatical errors and provide feedback on the correct grammar. Conversational AI can also evaluate a child's pronunciation and language skills and suggest an individual learning plan. For example, it can provide customized content for pronunciation and grammar practice. This makes it possible to support language learning by evaluating pronunciation and language skills and providing appropriate feedback.

[0032] Conversational AI can converse with multiple children simultaneously and promote group discussions. Conversational AI can, for example, build a system that converses with multiple children simultaneously and promotes group discussions. For example, it can provide a forum for children to exchange opinions on a specific topic. Conversational AI can also be developed to converse with multiple children simultaneously and promote group discussions. For example, the conversational AI can pose questions as a discussion facilitator. Conversational AI can also build a system that converses with multiple children simultaneously and promotes the exchange of opinions. For example, children can listen to each other's opinions and deepen the discussion. This can support collaborative learning by conversing with multiple children simultaneously and promoting group discussions.

[0033] Conversational AI can respond to questions in different languages ​​and support multilingual learning. Conversational AI, for example, builds a system that responds to questions in different languages ​​and supports multilingual learning. For example, it can realize dialogue in multiple languages ​​such as English, French, and Chinese. Conversational AI can also develop multilingual conversational AI that allows children to ask questions in different languages. For example, it can be equipped with a language switching function so that the learning language can be selected. Conversational AI can also respond to questions in different languages ​​and support multilingual learning. For example, children can ask questions in the language they want to learn and receive answers in that language. This allows it to respond to questions in different languages ​​and support multilingual learning, thereby broadening the scope of language learning.

[0034] Generative AI can automatically suggest what to learn next based on a child's learning progress. For example, generative AI can analyze a child's learning progress and build a system that automatically suggests what to learn next. For example, it can suggest the next learning topic based on learning history. Generative AI can also analyze a child's learning progress in real time and suggest what to learn next. For example, it can prioritize suggesting topics with low levels of understanding. Generative AI can also automatically suggest what to learn next based on a child's learning progress. For example, it can provide a customized learning plan based on learning progress. This can improve learning efficiency by suggesting what to learn next based on learning progress.

[0035] Generative AI can generate content that suits different learning styles. Generative AI builds systems that generate content that suits different learning styles, such as visual, auditory, and tactile. For example, visual learners are provided with images and videos. Generative AI is also developed to generate content that suits different learning styles, providing children with the optimal learning experience. For example, audio content is provided to auditory learners. Generative AI also generates content that suits different learning styles, providing children with a diverse learning experience. For example, interactive 3DCG is provided to tactile learners. In this way, by generating content that suits different learning styles, it is possible to provide the optimal learning experience for each individual learner.

[0036] Generative AI can link with other educational platforms and incorporate external educational content. For example, generative AI can link with other educational platforms and build a system that incorporates external educational content. For example, it can acquire content from online educational platforms. Generative AI can also develop generative AI that links with other educational platforms and incorporates external educational content. For example, it can automatically acquire educational video and audio content. Generative AI can also link with other educational platforms and incorporate external educational content. For example, it can integrate external educational resources and provide them to children. This allows learning resources to be expanded by linking with other educational platforms and incorporating external educational content.

[0037] The generative AI can share the content it generates in real time with other children to promote collaborative learning. For example, the generative AI builds a system that shares the content it generates in real time with other children to promote collaborative learning. For example, the generated content is shared online. The generative AI also develops a generative AI that shares the content it generates in real time with other children to promote collaborative learning. For example, the generated content is stored on the cloud and made accessible. The generative AI also shares the content it generates in real time with other children to promote collaborative learning. For example, a platform is provided for sharing the generated content. This allows the content generated in real time to be shared and promotes collaborative learning, thereby improving learning effectiveness.

[0038] The AR presentation unit can use AR technology to make a virtual teacher or character appear in real space, thereby realizing an interactive lesson. For example, the AR presentation unit uses AR technology to make a virtual teacher or character appear in real space, thereby building a system that realizes an interactive lesson. For example, a virtual teacher poses questions to children. The AR presentation unit also develops AR technology that makes a virtual teacher or character appear in real space, thereby realizing an interactive lesson. For example, a virtual character conducts a lesson while interacting with children. The AR presentation unit also uses AR technology to make a virtual teacher or character appear in real space, thereby realizing an interactive lesson. For example, a virtual teacher explains the procedure of an experiment to children. In this way, by making a virtual teacher or character appear, an interactive lesson can be realized, thereby improving learning effectiveness.

[0039] The AR presentation unit can use AR technology to present interactive 3D models that children can actually manipulate by moving their hands. For example, the AR presentation unit uses AR technology to build a system that presents interactive 3D models that children can actually manipulate by moving their hands. For example, a 3D model of the human body can be manipulated to learn about its internal structure. The AR presentation unit also displays interactive 3D models in real space, allowing children to manipulate them by moving their hands. For example, a 3D model of the structure of a chemical molecule can be displayed and the molecular bonds can be manipulated. The AR presentation unit also uses AR technology to present interactive 3D models that children can actually manipulate by moving their hands. For example, a 3D model of a historical building can be manipulated to explore its interior. In this way, presenting interactive 3D models can deepen children's understanding.

[0040] The AR presentation unit uses AR technology to enable children in different locations to learn collaboratively in the same virtual space. For example, the AR presentation unit uses AR technology to build a system where children in different locations can learn collaboratively in the same virtual space. For example, children learn together in a virtual classroom. The AR presentation unit also develops AR technology that enables children in different locations to learn collaboratively in the same virtual space. For example, children conduct experiments together in a virtual laboratory. The AR presentation unit also uses AR technology to enable children in different locations to learn collaboratively in the same virtual space. For example, children do research together in a virtual library. This allows children in different locations to learn collaboratively in the same virtual space, thereby promoting collaborative learning.

[0041] The AR presentation unit can use AR technology to virtually recreate a real classroom environment, enabling participation from remote locations. The AR presentation unit, for example, uses AR technology to build a system that virtually recreates a real classroom environment, enabling participation from remote locations. For example, children in remote locations participate in a virtual classroom. The AR presentation unit also develops AR technology that virtually recreates a real classroom environment, enabling participation from remote locations. For example, children in remote locations take classes in a virtual classroom. The AR presentation unit also uses AR technology to virtually recreate a real classroom environment, enabling participation from remote locations. For example, children in remote locations participate in discussions in a virtual classroom. This virtually recreates a real classroom environment, enabling participation from remote locations, thereby expanding learning opportunities.

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

[0043] Conversational AI can also provide a dashboard that visualizes a child's learning progress based on their learning history. For example, it can display past learning content and comprehension levels in graphs and charts, allowing children to see their learning progress at a glance. Conversational AI can also suggest the next learning content and goals based on their learning progress. For example, if a child's understanding of a particular topic is low, it can suggest supplementary materials or practice problems related to that topic. Furthermore, conversational AI can provide appropriate feedback to children based on their learning progress. For example, it can encourage their motivation to learn by praising them when they achieve their goals.

[0044] Conversational AI can also provide customized learning plans based on a child's learning style. For example, it can provide image- and video-heavy content to visual learners, while suggesting a learning plan centered on audio content to auditory learners. Conversational AI can also analyze a child's learning style and suggest the optimal learning method. For example, it can recommend learning using interactive 3D models for tactile learners. Conversational AI can also provide feedback based on learning style. For example, providing visual feedback to visual learners and audio feedback to auditory learners can improve learning effectiveness.

[0045] Conversational AI can also provide a dashboard that visualizes a child's learning progress based on their learning history. For example, it can display past learning content and comprehension levels in graphs and charts, allowing children to see their learning progress at a glance. Conversational AI can also suggest the next learning content and goals based on their learning progress. For example, if a child's understanding of a particular topic is low, it can suggest supplementary materials or practice problems related to that topic. Furthermore, conversational AI can provide appropriate feedback to children based on their learning progress. For example, it can encourage their motivation to learn by praising them when they achieve their goals.

[0046] Conversational AI can interact with multiple children simultaneously and promote group discussions. For example, it provides a forum for children to exchange opinions on a specific topic. Conversational AI can also be developed to interact with multiple children simultaneously and promote group discussions. For example, the conversational AI can pose questions as a discussion facilitator. Conversational AI can also be built to interact with multiple children simultaneously and promote the exchange of opinions. For example, children can listen to each other's opinions and deepen the discussion. This can support collaborative learning by interacting with multiple children simultaneously and promoting group discussions.

[0047] Conversational AI can respond to questions in different languages ​​and support multilingual learning. For example, it can realize dialogue in multiple languages ​​such as English, French, and Chinese. Conversational AI can also be developed to support multilingual conversations, allowing children to ask questions in different languages. For example, it can be equipped with a language switching function, allowing children to select the language they want to learn. Conversational AI can also respond to questions in different languages ​​and support multilingual learning. For example, children can ask questions in the language they want to learn and receive answers in that language. This allows it to respond to questions in different languages ​​and support multilingual learning, broadening the scope of language learning.

[0048] Generative AI can automatically suggest what to learn next based on a child's learning progress. For example, it can suggest the next learning topic based on learning history. Generative AI can also analyze a child's learning progress in real time and suggest what to learn next. For example, it can prioritize topics with low levels of understanding. Generative AI can also automatically suggest what to learn next based on a child's learning progress. For example, it can provide a customized learning plan based on learning progress. This can improve learning efficiency by suggesting what to learn next based on learning progress.

[0049] Generative AI can generate content that caters to different learning styles. For example, we will build a system that generates content that caters to different learning styles, such as visual, auditory, and tactile. For example, visual learners will be provided with images and videos. Generative AI will also be developed to generate content that caters to different learning styles, providing children with the optimal learning experience. For example, audio content will be provided to auditory learners. Generative AI will also generate content that caters to different learning styles, providing children with a diverse learning experience. For example, interactive 3DCG will be provided to tactile learners. In this way, by generating content that caters to different learning styles, it is possible to provide the optimal learning experience for each individual learner.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The conversational AI accepts a question from the child. For example, if the child asks, "Tell me about the solar system," the conversational AI understands the question and sends a prompt to the generation AI. Step 2: The generation AI generates educational content based on the questions received by the conversational AI. For example, if the generation AI receives a prompt such as "Show me the arrangement of the planets in the solar system in 3DCG," it will recreate the arrangement of the planets in the solar system in 3DCG and generate a video of it. Similarly, if the generation AI receives a prompt such as "Tell me the sound of a dinosaur," it will generate audio that recreates the sound of a dinosaur. Step 3: The AR presentation unit presents the educational content generated by the AI. For example, a 3D CG image of the solar system is displayed overlaid on the real world via a tablet or smartphone. This allows children to learn with a sense of realism, as if they were actually there. The generated audio and video are also integrated into the real world using AR technology.

[0052] (Example 2) The new-age textbook system according to an embodiment of the present invention is a system that uses generative AI and AR technology to provide children with active and diverse learning experiences, allowing them to deepen their learning and understanding based on their own interests and questions.

[0053] A new-age textbook system according to an embodiment includes an interactive AI, a generation AI, and an AR presentation unit. The interactive AI accepts questions from children. For example, if a child asks, "Tell me about the solar system," the interactive AI understands the question and sends a prompt to the generation AI. The generation AI generates educational content based on the questions accepted by the interactive AI. For example, if the generation AI receives a prompt such as, "Show me the arrangement of the planets in the solar system in 3DCG," it recreates the arrangement of the planets in the solar system in 3DCG and generates a video of the planets. In addition, in response to a prompt such as, "Tell me the sounds of dinosaurs," the generation AI generates audio that recreates the sounds of dinosaurs. The AR presentation unit presents the educational content generated by the generation AI. For example, the generated 3DCG image of the solar system is displayed overlaid on a real space via a tablet or smartphone. This allows children to learn with a sense of realism, as if they were actually there. The generated audio and video are also integrated into the real space using AR technology. This allows the new-age textbook system according to an embodiment to provide children with active and diverse learning experiences.

[0054] Conversational AI can analyze a child's learning history and automatically generate questions that correspond to each child's level of understanding. For example, conversational AI can analyze a child's past learning history and generate questions that correspond to their level of understanding. For example, it can automatically generate questions that complement areas of insufficient understanding based on content learned in the past. Conversational AI can also generate customized questions that correspond to each child's level of understanding based on a child's learning history. For example, if a child has a low level of understanding of a particular topic, it can generate basic questions related to that topic. Conversational AI can also analyze a child's learning history and generate questions in stages that correspond to their level of understanding. For example, it can start with basic questions and generate questions that gradually increase in difficulty. This can improve learning effectiveness by automatically generating questions that correspond to each child's level of understanding.

[0055] Conversational AI can evaluate a child's pronunciation and language skills and provide appropriate feedback. For example, conversational AI can analyze a child's pronunciation in real time and provide feedback to encourage accurate pronunciation. For example, it can point out pronunciation errors and provide audio guidance on the correct pronunciation. Conversational AI can also build systems that evaluate a child's language skills and provide appropriate feedback. For example, it can point out grammatical errors and provide feedback on the correct grammar. Conversational AI can also evaluate a child's pronunciation and language skills and suggest an individual learning plan. For example, it can provide customized content for pronunciation and grammar practice. This makes it possible to support language learning by evaluating pronunciation and language skills and providing appropriate feedback.

[0056] Conversational AI can use its emotion estimation function to analyze a child's emotional state in real time and engage in dialogue to increase their motivation to learn. For example, conversational AI can use its emotion estimation function to analyze a child's emotional state in real time from their facial expressions and voice, and engage in dialogue to increase their motivation to learn. For example, if a child is tired, it can offer words of encouragement. Conversational AI can also build a system that analyzes a child's emotional state in real time and engages in dialogue to increase their motivation to learn. For example, it generates questions and comments to elicit positive emotions. Conversational AI can also use its emotion estimation function to engage in dialogue based on a child's emotional state, increasing their motivation to learn. For example, it generates questions related to topics that interest the child. This allows for real-time analysis of a child's emotional state and dialogue to increase their motivation to learn, thereby improving learning effectiveness.

[0057] Conversational AI can converse with multiple children simultaneously and promote group discussions. Conversational AI can, for example, build a system that converses with multiple children simultaneously and promotes group discussions. For example, it can provide a forum for children to exchange opinions on a specific topic. Conversational AI can also be developed to converse with multiple children simultaneously and promote group discussions. For example, the conversational AI can pose questions as a discussion facilitator. Conversational AI can also build a system that converses with multiple children simultaneously and promotes the exchange of opinions. For example, children can listen to each other's opinions and deepen the discussion. This can support collaborative learning by conversing with multiple children simultaneously and promoting group discussions.

[0058] Conversational AI can respond to questions in different languages ​​and support multilingual learning. Conversational AI, for example, builds a system that responds to questions in different languages ​​and supports multilingual learning. For example, it can realize dialogue in multiple languages ​​such as English, French, and Chinese. Conversational AI can also develop multilingual conversational AI that allows children to ask questions in different languages. For example, it can be equipped with a language switching function so that the learning language can be selected. Conversational AI can also respond to questions in different languages ​​and support multilingual learning. For example, children can ask questions in the language they want to learn and receive answers in that language. This allows it to respond to questions in different languages ​​and support multilingual learning, thereby broadening the scope of language learning.

[0059] Conversational AI can use its emotion estimation function to generate music and videos that correspond to a child's emotions, optimizing the learning environment. For example, conversational AI can use its emotion estimation function to build a system that generates music and videos that correspond to a child's emotional state, optimizing the learning environment. For example, calm music can be played when the child wants to relax. Conversational AI can also analyze a child's emotional state in real time and generate appropriate music and videos. For example, videos and music can be provided to increase concentration. Conversational AI can also use its emotion estimation function to generate music and videos that correspond to a child's emotions, optimizing the learning environment. For example, videos that elicit positive emotions can be displayed. In this way, by generating music and videos that correspond to emotions and optimizing the learning environment, learning effectiveness can be improved.

[0060] Generative AI can automatically suggest what to learn next based on a child's learning progress. For example, generative AI can analyze a child's learning progress and build a system that automatically suggests what to learn next. For example, it can suggest the next learning topic based on learning history. Generative AI can also analyze a child's learning progress in real time and suggest what to learn next. For example, it can prioritize suggesting topics with low levels of understanding. Generative AI can also automatically suggest what to learn next based on a child's learning progress. For example, it can provide a customized learning plan based on learning progress. This can improve learning efficiency by suggesting what to learn next based on learning progress.

[0061] Generative AI can generate content that suits different learning styles. Generative AI builds systems that generate content that suits different learning styles, such as visual, auditory, and tactile. For example, visual learners are provided with images and videos. Generative AI is also developed to generate content that suits different learning styles, providing children with the optimal learning experience. For example, audio content is provided to auditory learners. Generative AI also generates content that suits different learning styles, providing children with a diverse learning experience. For example, interactive 3DCG is provided to tactile learners. In this way, by generating content that suits different learning styles, it is possible to provide the optimal learning experience for each individual learner.

[0062] The generation AI can use the emotion estimation function to select themes that interest children and generate content based on those themes. For example, the generation AI can use the emotion estimation function to select themes that interest children and build a system that generates content based on those themes. For example, it can provide content related to topics that interest children. The generation AI can also analyze children's emotional states in real time to select themes that interest them. For example, it can prioritize themes that evoke strong positive emotions. The generation AI can also use the emotion estimation function to select themes that interest children and generate content based on those themes. For example, it can provide videos and 3DCG related to themes that interest children. This can increase children's motivation to learn by generating content based on themes that interest them.

[0063] Generative AI can link with other educational platforms and incorporate external educational content. For example, generative AI can link with other educational platforms and build a system that incorporates external educational content. For example, it can acquire content from online educational platforms. Generative AI can also develop generative AI that links with other educational platforms and incorporates external educational content. For example, it can automatically acquire educational video and audio content. Generative AI can also link with other educational platforms and incorporate external educational content. For example, it can integrate external educational resources and provide them to children. This allows learning resources to be expanded by linking with other educational platforms and incorporating external educational content.

[0064] The generative AI can share the content it generates in real time with other children to promote collaborative learning. For example, the generative AI builds a system that shares the content it generates in real time with other children to promote collaborative learning. For example, the generated content is shared online. The generative AI also develops a generative AI that shares the content it generates in real time with other children to promote collaborative learning. For example, the generated content is stored on the cloud and made accessible. The generative AI also shares the content it generates in real time with other children to promote collaborative learning. For example, a platform is provided for sharing the generated content. This allows the content generated in real time to be shared and promotes collaborative learning, thereby improving learning effectiveness.

[0065] The generation AI can use the emotion estimation function to adjust the difficulty level according to the child's emotions and provide an appropriate challenge. For example, the generation AI uses the emotion estimation function to build a system that adjusts the difficulty level according to the child's emotional state. For example, if the child is feeling stressed, the difficulty level is lowered. The generation AI also analyzes the child's emotional state in real time and provides content of an appropriate level of difficulty. For example, if the child has strong positive emotions, the difficulty level is increased. The generation AI also uses the emotion estimation function to adjust the difficulty level according to the child's emotions and provide an appropriate challenge. For example, the generation AI customizes the learning content based on the child's emotional state. This allows the difficulty level to be adjusted according to the child's emotions and provides an appropriate challenge, thereby maintaining motivation to learn.

[0066] The AR presentation unit can use AR technology to make a virtual teacher or character appear in real space, thereby realizing an interactive lesson. For example, the AR presentation unit uses AR technology to make a virtual teacher or character appear in real space, thereby building a system that realizes an interactive lesson. For example, a virtual teacher poses questions to children. The AR presentation unit also develops AR technology that makes a virtual teacher or character appear in real space, thereby realizing an interactive lesson. For example, a virtual character conducts a lesson while interacting with children. The AR presentation unit also uses AR technology to make a virtual teacher or character appear in real space, thereby realizing an interactive lesson. For example, a virtual teacher explains the procedure of an experiment to children. In this way, by making a virtual teacher or character appear, an interactive lesson can be realized, thereby improving learning effectiveness.

[0067] The AR presentation unit can use AR technology to present interactive 3D models that children can actually manipulate by moving their hands. For example, the AR presentation unit uses AR technology to build a system that presents interactive 3D models that children can actually manipulate by moving their hands. For example, a 3D model of the human body can be manipulated to learn about its internal structure. The AR presentation unit also displays interactive 3D models in real space, allowing children to manipulate them by moving their hands. For example, a 3D model of the structure of a chemical molecule can be displayed and the molecular bonds can be manipulated. The AR presentation unit also uses AR technology to present interactive 3D models that children can actually manipulate by moving their hands. For example, a 3D model of a historical building can be manipulated to explore its interior. In this way, presenting interactive 3D models can deepen children's understanding.

[0068] The AR presentation unit can use the emotion estimation function to add AR effects according to the child's emotions, thereby enriching the learning experience. For example, the AR presentation unit uses the emotion estimation function to build a system that adds AR effects according to the child's emotional state. For example, if the child is excited, the effect is emphasized. The AR presentation unit also analyzes the child's emotional state in real time and adds appropriate AR effects. For example, if the child is relaxed, a calm effect is displayed. The AR presentation unit also uses the emotion estimation function to add AR effects according to the child's emotions, thereby enriching the learning experience. For example, if the child is having fun, a positive effect is added. This makes it possible to enrich the learning experience by adding AR effects according to emotions.

[0069] The AR presentation unit uses AR technology to enable children in different locations to learn collaboratively in the same virtual space. For example, the AR presentation unit uses AR technology to build a system where children in different locations can learn collaboratively in the same virtual space. For example, children learn together in a virtual classroom. The AR presentation unit also develops AR technology that enables children in different locations to learn collaboratively in the same virtual space. For example, children conduct experiments together in a virtual laboratory. The AR presentation unit also uses AR technology to enable children in different locations to learn collaboratively in the same virtual space. For example, children do research together in a virtual library. This allows children in different locations to learn collaboratively in the same virtual space, thereby promoting collaborative learning.

[0070] The AR presentation unit can use AR technology to virtually recreate a real classroom environment, enabling participation from remote locations. The AR presentation unit, for example, uses AR technology to build a system that virtually recreates a real classroom environment, enabling participation from remote locations. For example, children in remote locations participate in a virtual classroom. The AR presentation unit also develops AR technology that virtually recreates a real classroom environment, enabling participation from remote locations. For example, children in remote locations take classes in a virtual classroom. The AR presentation unit also uses AR technology to virtually recreate a real classroom environment, enabling participation from remote locations. For example, children in remote locations participate in discussions in a virtual classroom. This virtually recreates a real classroom environment, enabling participation from remote locations, thereby expanding learning opportunities.

[0071] The AR presentation unit can use the emotion estimation function to customize AR content according to the child's emotions and provide a personalized learning experience. The AR presentation unit, for example, uses the emotion estimation function to build a system that customizes AR content according to the child's emotional state. For example, it provides AR content related to topics that the child is interested in. The AR presentation unit also analyzes the child's emotional state in real time and customizes appropriate AR content. For example, if the child is relaxed, it provides calm AR content. The AR presentation unit also uses the emotion estimation function to customize AR content according to the child's emotions and provide a personalized learning experience. For example, if the child is having fun, it provides positive AR content. In this way, by customizing AR content according to emotions, it is possible to provide a personalized learning experience.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] Conversational AI can also provide a dashboard that visualizes a child's learning progress based on their learning history. For example, it can display past learning content and comprehension levels in graphs and charts, allowing children to see their learning progress at a glance. Conversational AI can also suggest the next learning content and goals based on their learning progress. For example, if a child's understanding of a particular topic is low, it can suggest supplementary materials or practice problems related to that topic. Furthermore, conversational AI can provide appropriate feedback to children based on their learning progress. For example, it can encourage their motivation to learn by praising them when they achieve their goals.

[0074] Conversational AI can also provide customized learning plans based on a child's learning style. For example, it can provide image- and video-heavy content to visual learners, while suggesting a learning plan centered on audio content to auditory learners. Conversational AI can also analyze a child's learning style and suggest the optimal learning method. For example, it can recommend learning using interactive 3D models for tactile learners. Conversational AI can also provide feedback based on learning style. For example, providing visual feedback to visual learners and audio feedback to auditory learners can improve learning effectiveness.

[0075] Conversational AI can also provide a dashboard that visualizes a child's learning progress based on their learning history. For example, it can display past learning content and comprehension levels in graphs and charts, allowing children to see their learning progress at a glance. Conversational AI can also suggest the next learning content and goals based on their learning progress. For example, if a child's understanding of a particular topic is low, it can suggest supplementary materials or practice problems related to that topic. Furthermore, conversational AI can provide appropriate feedback to children based on their learning progress. For example, it can encourage their motivation to learn by praising them when they achieve their goals.

[0076] Conversational AI can also use its emotion estimation function to adjust the difficulty of learning content according to a child's emotional state. For example, if a child is feeling stressed, the difficulty level can be lowered, and if a child is relaxed, the difficulty level can be increased. Conversational AI can also use its emotion estimation function to suggest learning methods according to a child's emotional state. For example, if a child is tired, it can provide learning content that can be completed in a short amount of time, and if a child has high concentration, it can provide learning content that is longer. Furthermore, conversational AI can use its emotion estimation function to provide feedback according to a child's emotional state. For example, if a child is feeling down, it can offer encouraging words to bring out positive emotions and increase their motivation to learn.

[0077] Conversational AI can interact with multiple children simultaneously and promote group discussions. For example, it provides a forum for children to exchange opinions on a specific topic. Conversational AI can also be developed to interact with multiple children simultaneously and promote group discussions. For example, the conversational AI can pose questions as a discussion facilitator. Conversational AI can also be built to interact with multiple children simultaneously and promote the exchange of opinions. For example, children can listen to each other's opinions and deepen the discussion. This can support collaborative learning by interacting with multiple children simultaneously and promoting group discussions.

[0078] Conversational AI can respond to questions in different languages ​​and support multilingual learning. For example, it can realize dialogue in multiple languages ​​such as English, French, and Chinese. Conversational AI can also be developed to support multilingual conversations, allowing children to ask questions in different languages. For example, it can be equipped with a language switching function, allowing children to select the language they want to learn. Conversational AI can also respond to questions in different languages ​​and support multilingual learning. For example, children can ask questions in the language they want to learn and receive answers in that language. This allows it to respond to questions in different languages ​​and support multilingual learning, broadening the scope of language learning.

[0079] Conversational AI can use its emotion estimation function to generate music and videos that correspond to a child's emotional state, optimizing the learning environment. For example, it can play calm music when the child wants to relax. Conversational AI can also analyze a child's emotional state in real time and generate appropriate music and videos. For example, it can provide videos and music to increase concentration. Conversational AI can also use its emotion estimation function to generate music and videos that correspond to a child's emotions, optimizing the learning environment. For example, it can display videos that elicit positive emotions. In this way, by generating music and videos that correspond to emotions and optimizing the learning environment, it is possible to improve learning effectiveness.

[0080] Generative AI can automatically suggest what to learn next based on a child's learning progress. For example, it can suggest the next learning topic based on learning history. Generative AI can also analyze a child's learning progress in real time and suggest what to learn next. For example, it can prioritize topics with low levels of understanding. Generative AI can also automatically suggest what to learn next based on a child's learning progress. For example, it can provide a customized learning plan based on learning progress. This can improve learning efficiency by suggesting what to learn next based on learning progress.

[0081] Generative AI can generate content that caters to different learning styles. For example, we will build a system that generates content that caters to different learning styles, such as visual, auditory, and tactile. For example, visual learners will be provided with images and videos. Generative AI will also be developed to generate content that caters to different learning styles, providing children with the optimal learning experience. For example, audio content will be provided to auditory learners. Generative AI will also generate content that caters to different learning styles, providing children with a diverse learning experience. For example, interactive 3DCG will be provided to tactile learners. In this way, by generating content that caters to different learning styles, it is possible to provide the optimal learning experience for each individual learner.

[0082] The generation AI can use its emotion estimation function to select themes that will interest children and generate content based on those themes. For example, it can provide content related to topics that interest children. The generation AI can also analyze children's emotional states in real time to select themes that will interest them. For example, it can prioritize themes that evoke strong positive emotions. The generation AI can also use its emotion estimation function to select themes that will interest children and generate content based on those themes. For example, it can provide videos and 3DCG related to themes that interest children. This can increase children's motivation to learn by generating content based on themes that interest them.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The conversational AI accepts a question from the child. For example, if the child asks, "Tell me about the solar system," the conversational AI understands the question and sends a prompt to the generation AI. Step 2: The generation AI generates educational content based on the questions received by the conversational AI. For example, if the generation AI receives a prompt such as "Show me the arrangement of the planets in the solar system in 3DCG," it will recreate the arrangement of the planets in the solar system in 3DCG and generate a video of it. Similarly, if the generation AI receives a prompt such as "Tell me the sound of a dinosaur," it will generate audio that recreates the sound of a dinosaur. Step 3: The AR presentation unit presents the educational content generated by the AI. For example, a 3D CG image of the solar system is displayed overlaid on the real world via a tablet or smartphone. This allows children to learn with a sense of realism, as if they were actually there. The generated audio and video are also integrated into the real world using AR technology.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the robot 414, 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 robot 414 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.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 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. Conversational AI and Generative AI and an AR presentation unit, The conversational AI is Accepting children's questions, The generated AI is generating educational content based on the questions received by the conversational AI; The AR presentation unit Presenting the educational content generated by the generation AI A system characterized by:

2. The conversational AI is Analyzes children's learning history and automatically generates questions based on their individual level of understanding 2. The system of claim 1.

3. The conversational AI is Evaluate children's pronunciation and language skills and provide appropriate feedback 2. The system of claim 1.

4. The conversational AI is Analyzes children's emotional state in real time and engages in dialogue to motivate them to learn 2. The system of claim 1.

5. The conversational AI is Interact with multiple children at once and facilitate group discussions 2. The system of claim 1.

6. The conversational AI is Responding to questions in different languages ​​and supporting multilingual learning 2. The system of claim 1.

7. The conversational AI is Optimizing the learning environment by generating music and images that respond to children's emotions 2. The system of claim 1.

8. The generated AI is Automatically suggest what to learn next based on your child's learning progress 2. The system of claim 1.

Images