system

Abstract

To reduce burdens on a parent and a teacher by providing education matched with individual learning patterns and needs.SOLUTION: The system comprises a training data analyzer, a learning plan generator, a AI teacher component, a teaching material generator, a VR class component, a AI teacher assistant component, and a learning support tool component. The learning data analysis unit analyzes the learning data. The learning plan generation part generates a learning plan on the basis of the learning data. The AI supervising part performs individual instruction based on the learning plan. The teaching material generation unit generates a teaching material based on the learning plan. The VR classroom component provides a VR classroom based on the learning plan. The AI teacher assistant component assists the teacher based on the learning plan. The learning support tool unit supports learning at home and school based on the learning plan.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 made it difficult to provide education tailored to individual learning patterns and needs, placing a heavy burden on parents and teachers.

[0005] The system according to the embodiment aims to provide education tailored to individual learning patterns and needs, thereby reducing the burden on parents and teachers. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning data analysis unit, a learning plan generation unit, an AI teacher unit, a learning material generation unit, a VR classroom unit, an AI teacher assistant unit, and a learning support tool unit. The learning data analysis unit analyzes learning data. The learning plan generation unit generates a learning plan based on the learning data analyzed by the learning data analysis unit. The AI ​​teacher unit provides individual instruction based on the learning plan generated by the learning plan generation unit. The learning material generation unit generates learning materials based on the learning plan generated by the learning plan generation unit. The VR classroom unit provides a VR classroom based on the learning plan generated by the learning plan generation unit. The AI ​​teacher assistant unit assists the teacher based on the learning plan generated by the learning plan generation unit. The learning support tool unit supports learning at home and at school based on the learning plan generated by the learning plan generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide education tailored to individual learning patterns and needs, thereby reducing the burden on parents and teachers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The learning support system according to an embodiment of the present invention utilizes AI technology to provide comprehensive support for children's learning and the educational activities of parents and teachers. This system provides a next-generation learning experience tailored to individual learning patterns and needs, and creates next-generation education by combining AI teachers, teaching material development, VR classrooms, and AI teaching assistants. As a result, the learning support system reduces the burden on parents and teachers and creates an environment in which children can learn more effectively.

[0029] A learning support system according to an embodiment includes a learning data analysis unit, a learning plan generation unit, an AI teacher unit, a learning material generation unit, a VR classroom unit, an AI teacher assistant unit, and a learning support tool unit. The learning data analysis unit analyzes children's learning data. For example, the learning data analysis unit identifies areas that need reinforcement based on past learning history and test results. The learning data analysis unit can also analyze learning styles such as visual, auditory, and tactile. The learning plan generation unit generates a learning plan based on the learning data analyzed by the learning data analysis unit. For example, the learning plan generation unit uses a generation AI to provide a learning plan tailored to individual learning patterns and needs. The learning plan generation unit can also dynamically adjust the learning plan according to learning progress. The AI ​​teacher unit provides individualized instruction based on the learning plan generated by the learning plan generation unit. For example, the AI ​​teacher unit uses a generation AI to answer children's questions in real time and provide guidance on the next step based on their level of understanding. The learning material generation unit generates learning materials based on the learning plan generated by the learning plan generation unit. For example, the teaching material generation unit uses a generation AI to generate customized teaching materials in real time according to children's learning progress. The VR classroom unit provides a VR classroom based on the learning plan generated by the learning plan generation unit. For example, the VR classroom unit uses VR technology to provide an immersive learning experience. The AI ​​teacher assistant unit assists teachers based on the learning plan generated by the learning plan generation unit. For example, the AI ​​teacher assistant unit supports the progress of lessons and provides appropriate answers to children's questions. The learning support tool unit supports learning at home and at school based on the learning plan generated by the learning plan generation unit. For example, the learning support tool unit analyzes learning progress at home and provides appropriate advice to parents. As a result, the learning support system according to the embodiment can comprehensively support children's learning and reduce the burden on parents and teachers. For example, by providing learning plans tailored to individual learning patterns, children can learn more effectively.In addition, the introduction of AI teachers and AI teacher assistants will reduce the burden on teachers and enable more children to receive high-quality education. Furthermore, by supporting learning both at home and at school, it will be possible to create a consistent learning environment for children.

[0030] The learning data analysis unit can analyze children's learning styles and suggest optimal learning methods based on that analysis. The learning data analysis unit, for example, uses generative AI to analyze children's learning styles. For example, generative AI collects past learning data and test results and identifies which sense of learning is most effective: visual, auditory, or tactile. The learning data analysis unit can also suggest learning materials that make extensive use of diagrams and graphs for children who excel at visual learning. Furthermore, the learning data analysis unit can suggest audio learning materials or lecture-style materials for children who excel at auditory learning. This makes it possible to provide optimal learning methods that match each child's learning style.

[0031] The learning plan generation unit can analyze children's learning data in real time and dynamically adjust the learning plan according to their learning progress. The learning plan generation unit, for example, uses a generation AI to analyze children's learning data in real time. For example, the generation AI monitors learning progress in real time, and if a child is struggling with a particular unit, it provides additional learning materials to reinforce that unit. The learning plan generation unit can also dynamically adjust the learning plan according to the child's learning progress. For example, the learning plan generation unit can adjust the learning pace according to the child's progress and suggest breaks as needed. This makes it possible to dynamically adjust the learning plan according to the child's learning progress.

[0032] The learning plan generation unit can incorporate game elements into the learning plan, allowing children to learn while having fun. The learning plan generation unit incorporates game elements into the learning plan, for example, by using a generation AI. For example, the generation AI provides quiz-style questions or a point-based learning game. The learning plan generation unit can also introduce a level-up or reward system, allowing children to progress through their learning while having fun. Furthermore, the learning plan generation unit can provide interactive learning content, allowing children to progress through their learning on their own. This can provide an environment where children can learn while having fun.

[0033] The learning plan generation unit can share the learning plan with the entire family, allowing parents and siblings to participate in the learning together. The learning plan generation unit, for example, uses a generation AI to share the learning plan with the entire family. For example, the generation AI creates a collaborative learning plan that all family members can participate in. The learning plan generation unit can also share the learning plan so that parents and siblings can support their children's learning. Furthermore, the learning plan generation unit can also provide activities and tasks that allow the entire family to progress with learning. This can provide an environment in which the entire family can participate in learning.

[0034] The AI ​​teacher module can provide individual feedback based on children's learning history to improve their understanding. For example, the AI ​​teacher module uses a generative AI to analyze children's learning history. For example, the generative AI points out specific areas for improvement based on past test results and learning progress. The AI ​​teacher module can also provide individual feedback to improve children's understanding. Furthermore, the AI ​​teacher module can suggest next steps and set learning goals to help children progress effectively in their studies. This makes it possible to provide individual feedback to improve children's understanding.

[0035] The AI ​​Teacher Unit can deepen children's understanding by having the generative AI present multiple answers to their questions and letting them choose the most appropriate answer. For example, the AI ​​Teacher Unit uses generative AI to present multiple answers to children's questions. For example, the generative AI can provide answers from different perspectives, helping children develop their ability to think for themselves. The AI ​​Teacher Unit can also deepen children's understanding by letting them choose the most appropriate answer. Furthermore, the AI ​​Teacher Unit can analyze the intention and background behind the question and provide an appropriate answer. This helps children develop their ability to think for themselves and deepen their understanding.

[0036] The AI ​​Teacher module can regularly report children's learning progress to parents and teachers, strengthening collaboration between home and school. For example, the AI ​​Teacher module uses a generative AI to regularly report children's learning progress to parents and teachers. For example, the generative AI can automatically generate weekly reports and send them by email. The AI ​​Teacher module can also monitor learning progress in real time and immediately report important progress and problems. Furthermore, the AI ​​Teacher module can provide an online dashboard that makes it easy for parents and teachers to check learning progress. This strengthens collaboration between home and school and provides consistent support for children's learning.

[0037] An AI teacher can provide cross-disciplinary instruction across different subjects and support comprehensive learning. For example, an AI teacher can use generative AI to provide cross-disciplinary instruction across different subjects. For example, generative AI can show the relationship between mathematics and science and provide a learning plan that integrates both subjects. An AI teacher can also show the relationship between history and geography and provide a comprehensive learning experience. Furthermore, an AI teacher can integrate knowledge from different subjects and allow children to understand the learning content from multiple perspectives. This can support comprehensive learning that integrates different subjects.

[0038] The teaching material generation unit can generate customized teaching materials in real time according to children's learning progress. The teaching material generation unit, for example, uses generation AI to analyze children's learning progress and generate customized teaching materials in real time. For example, if a child is struggling with a particular unit, the generation AI automatically generates teaching materials to reinforce that unit. The teaching material generation unit can also provide individual learning plans based on learning goals and generate the necessary teaching materials. Furthermore, the teaching material generation unit can monitor learning progress in real time and update teaching materials at the appropriate time. This makes it possible to provide customized teaching materials according to children's learning progress.

[0039] The teaching material generation unit can incorporate interactive elements into the teaching materials, allowing children to advance their learning on their own. The teaching material generation unit incorporates interactive elements into the teaching materials, for example, by using a generation AI. For example, the generation AI provides teaching materials that include quiz-style questions and simulations. The teaching material generation unit can also provide interactive content, allowing children to advance their learning on their own. Furthermore, the teaching material generation unit can also provide interactive learning games, allowing children to advance their learning while having fun. This can provide an environment in which children can advance their learning on their own.

[0040] The teaching material generation unit can automatically translate teaching materials into different languages ​​to provide an international learning environment. The teaching material generation unit, for example, uses a generation AI to automatically translate teaching materials into different languages. For example, the generation AI provides teaching materials in multiple languages, such as English, Japanese, and Spanish. The teaching material generation unit can also accurately translate the content of the teaching materials using a machine translation algorithm. Furthermore, the teaching material generation unit can utilize a translation memory and a language model to provide teaching materials that correspond to different languages. This makes it possible to provide an international learning environment.

[0041] The teaching material generation unit can provide teaching materials in both digital and analog formats, allowing children to select materials that suit their learning style. The teaching material generation unit can, for example, use generation AI to provide teaching materials in both digital and analog formats. For example, the generation AI prepares both digital teaching materials and printed teaching materials. The teaching material generation unit can also allow children to select digital teaching materials and analog teaching materials that suit their learning style. Furthermore, the teaching material generation unit can diversify the formats of digital teaching materials and the types of analog teaching materials, allowing children to select the learning method that suits them best. This makes it possible to provide teaching materials that suit children's learning styles.

[0042] The VR classroom unit uses a generation AI to analyze the learning content in the VR classroom in real time and adjust the content according to the children's level of understanding. The VR classroom unit, for example, uses a generation AI to analyze the learning content in the VR classroom in real time. For example, the generation AI monitors the children's level of understanding in real time and provides additional explanations or supplementary materials if their understanding is low. The VR classroom unit can also dynamically adjust the learning content to enable children to progress effectively in their studies. Furthermore, the VR classroom unit can provide real-time feedback to maximize children's learning effectiveness. This makes it possible to provide VR learning content that is tailored to the children's level of understanding.

[0043] The VR classroom unit can record the learning experience in the VR classroom so that it can be reviewed later. The VR classroom unit can, for example, use a generative AI to record the learning experience in the VR classroom. For example, the generative AI can record the learning content and make it accessible online. The VR classroom unit can also save the learning experience as log data so that it can be reviewed later. Furthermore, the VR classroom unit can record interactions so that children can review the learning content again. This allows the learning experience to be recorded so that children can review it later.

[0044] The VR Classroom Division can make the VR classroom available at home and incorporate it as part of home learning. The VR Classroom Division can, for example, use generative AI to make the VR classroom available at home. For example, the generative AI can make learning content accessible through a home VR device. The VR Classroom Division can also incorporate the VR classroom as part of home learning, allowing children to learn effectively at home. Furthermore, the VR Classroom Division can provide VR content for home learning, allowing children to enjoy an immersive learning experience at home. This allows the VR classroom to be used at home and enriches home learning.

[0045] The VR classroom unit can link learning content in the VR classroom with other subjects to provide a comprehensive learning experience. For example, the VR classroom unit uses generative AI to link learning content in the VR classroom with other subjects. For example, generative AI can review what was learned in history class in geography class. The VR classroom unit can also integrate knowledge from different subjects, allowing children to understand the learning content from multiple perspectives. Furthermore, the VR classroom unit can show connections between subjects and provide a comprehensive learning experience. This allows learning content in the VR classroom to be linked with other subjects and provide a comprehensive learning experience.

[0046] The AI ​​teacher assistant unit can analyze children's responses during class in real time and provide feedback to teachers. The AI ​​teacher assistant unit, for example, uses generative AI to analyze children's responses during class in real time. For example, generative AI can analyze children's level of understanding and concentration through facial expression recognition and voice analysis, and suggest appropriate teaching methods to teachers. The AI ​​teacher assistant unit can also collect behavioral data and analyze children's responses. Furthermore, the AI ​​teacher assistant unit can provide real-time feedback, allowing teachers to adjust the progress of the lesson. This allows the AI ​​teacher assistant unit to analyze children's responses during class in real time and provide feedback to teachers.

[0047] The AI ​​teacher assistant unit can support the progress of lessons, allowing teachers to focus on individual instruction. The AI ​​teacher assistant unit can support the progress of lessons, for example, by using generative AI. For example, generative AI can automate the management of lesson plans and the provision of teaching materials. The AI ​​teacher assistant unit can also answer questions and provide feedback, allowing teachers to focus on individual instruction. Furthermore, the AI ​​teacher assistant unit can monitor the progress of lessons in real time and make adjustments as necessary. This can support the progress of lessons, allowing teachers to focus on individual instruction.

[0048] The AI ​​teacher assistant unit can monitor the progress of home learning and provide feedback to parents. The AI ​​teacher assistant unit monitors the progress of home learning using, for example, a generation AI. For example, the generation AI automatically generates a learning progress report and sends it to parents by email. The AI ​​teacher assistant unit can also record study time and evaluate the learning content and provide feedback to parents. Furthermore, the AI ​​teacher assistant unit can monitor the progress of home learning in real time and immediately report important progress and problems. This makes it possible to monitor the progress of home learning and provide feedback to parents.

[0049] The AI ​​teacher assistant unit can provide cross-disciplinary support across different subjects and support comprehensive learning. The AI ​​teacher assistant unit, for example, uses generative AI to provide cross-disciplinary support across different subjects. For example, generative AI can show the relationship between mathematics and science and provide a learning plan that integrates both subjects. The AI ​​teacher assistant unit can also show the relationship between history and geography to provide a comprehensive learning experience. Furthermore, the AI ​​teacher assistant unit can integrate knowledge from different subjects to enable children to understand the learning content from multiple perspectives. This allows for cross-disciplinary support across different subjects and support comprehensive learning.

[0050] The learning support tool unit can integrate learning data from home and school and provide a consistent learning plan. The learning support tool unit integrates learning data from home and school using, for example, a generation AI. For example, the generation AI reflects learning progress at home in the school learning plan. The learning support tool unit can also centrally manage learning data from home and school using a learning data collection method and integration algorithm. Furthermore, the learning support tool unit can also securely integrate learning data while taking privacy protection into consideration. This makes it possible to integrate learning data from home and school and provide a consistent learning plan.

[0051] The learning support tool section strengthens collaboration between homes and schools by using learning support tools, and is able to share children's learning progress in real time. The learning support tool section strengthens collaboration between homes and schools by using, for example, generative AI. For example, generative AI provides an online dashboard so that parents and teachers can constantly check learning status. The learning support tool section can also share learning progress in real time and immediately report important progress and problems. Furthermore, the learning support tool section can use communication tools to provide an environment that makes it easy for parents and teachers to collaborate. This strengthens collaboration between homes and schools, and allows children's learning progress to be shared in real time.

[0052] The learning support tools department can make learning support tools available at both home and school, creating a consistent learning environment. For example, the learning support tools department can use generative AI to make learning support tools available at both home and school. For example, the generative AI can use the same learning platform at home and school. The learning support tools department can also prepare the necessary devices and internet connections to provide a consistent learning environment at home and school. Furthermore, the learning support tools department can standardize teaching materials and share learning plans to ensure that children progress consistently at home and school. This makes it possible to make learning support tools available at both home and school, creating a consistent learning environment.

[0053] The learning support tool section can use learning support tools to provide cross-disciplinary support across different subjects and support comprehensive learning. The learning support tool section, for example, uses generative AI to provide cross-disciplinary support across different subjects. For example, generative AI can show the relationship between mathematics and science and provide a learning plan that integrates both subjects. The learning support tool section can also show the relationship between history and geography and provide a comprehensive learning experience. Furthermore, the learning support tool section can integrate knowledge from different subjects and enable children to understand the learning content from multiple perspectives. This allows for cross-disciplinary support across different subjects and support comprehensive learning.

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

[0055] The learning support system can further include a learning motivation improvement unit. The learning motivation improvement unit, for example, uses a generation AI to analyze children's learning motivation and provide appropriate motivation improvement measures. For example, the generation AI may introduce a reward system based on learning progress, allowing children to earn points or badges each time they achieve a goal. The learning motivation improvement unit can also provide content that attracts children's attention and maintain their interest in learning. Furthermore, the learning motivation improvement unit can automatically generate encouraging messages from parents or teachers and send them to children. This can improve children's learning motivation and improve their learning effectiveness.

[0056] The learning support system can further include a learning environment optimization unit. The learning environment optimization unit, for example, uses a generative AI to analyze children's learning environment and propose the optimal learning environment. For example, the generative AI adjusts the lighting and acoustics during learning to provide an environment where children can easily concentrate. The learning environment optimization unit can also propose a layout for the learning space to enable children to study comfortably. Furthermore, the learning environment optimization unit can analyze data on posture and gaze during learning and propose appropriate learning postures. This allows children to study in the optimal learning environment.

[0057] The learning support system can further include a learning progress sharing unit. The learning progress sharing unit uses, for example, a generation AI to share children's learning progress with parents and teachers in real time. For example, the generation AI automatically generates learning progress reports and sends them to parents and teachers by email. The learning progress sharing unit can also provide an online dashboard so that parents and teachers can constantly check the learning situation. Furthermore, the learning progress sharing unit can immediately report important progress and problems so that parents and teachers can take appropriate action. This strengthens cooperation between home and school and enables consistent support for children's learning.

[0058] The learning support system can further include a learning style adaptation unit. The learning style adaptation unit uses, for example, generative AI to analyze children's learning styles and suggest optimal learning methods. For example, the generative AI may identify which of visual, auditory, or tactile is most effective and customize learning materials based on that. The learning style adaptation unit can also suggest learning tools and activities according to the learning style. Furthermore, the learning style adaptation unit can dynamically adjust the learning method according to changes in learning style. This makes it possible to provide the optimal learning method that matches each child's learning style.

[0059] The learning support system can further include a learning outcome assessment unit. The learning outcome assessment unit uses, for example, generative AI to evaluate children's learning outcomes from multiple angles. For example, the generative AI evaluates not only test results but also deliverables such as projects and presentations. The learning outcome assessment unit can also monitor children's growth and progress over the long term and provide appropriate feedback. Furthermore, the learning outcome assessment unit can suggest next learning steps based on the evaluation results, allowing children to progress effectively in their studies. This makes it possible to evaluate children's learning outcomes from multiple angles and provide appropriate feedback.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The learning data analysis unit analyzes the children's learning data. For example, it identifies areas that need reinforcement based on past learning history and test results. It can also analyze learning styles such as visual, auditory, and tactile. Step 2: The learning plan generation unit generates a learning plan based on the learning data analyzed by the learning data analysis unit. For example, the generation AI can be used to provide a learning plan tailored to individual learning patterns and needs, and the learning plan can be dynamically adjusted according to the learning progress. Step 3: The AI ​​teacher provides individualized instruction based on the learning plan generated by the learning plan generator. For example, the AI ​​can use the generator to answer students' questions in real time and provide guidance on the next steps based on their level of understanding. Step 4: The teaching material generation unit generates teaching materials based on the learning plan generated by the learning plan generation unit. For example, using a generation AI, customized teaching materials are generated in real time according to the children's learning progress. Step 5: The VR classroom unit provides a VR classroom based on the lesson plan generated by the lesson plan generation unit, for example, by using VR technology to provide an immersive learning experience. Step 6: The AI ​​teacher assistant assists the teacher based on the lesson plan generated by the lesson plan generator, for example, by supporting the progress of the lesson and providing appropriate answers to students' questions. Step 7: The learning support tool unit supports learning at home and at school based on the learning plan generated by the learning plan generation unit. For example, it analyzes learning progress at home and provides appropriate advice to parents.

[0062] (Example 2) The learning support system according to an embodiment of the present invention utilizes AI technology to provide comprehensive support for children's learning and the educational activities of parents and teachers. This system provides a next-generation learning experience tailored to individual learning patterns and needs, and creates next-generation education by combining AI teachers, teaching material development, VR classrooms, and AI teaching assistants. As a result, the learning support system reduces the burden on parents and teachers and creates an environment in which children can learn more effectively.

[0063] A learning support system according to an embodiment includes a learning data analysis unit, a learning plan generation unit, an AI teacher unit, a learning material generation unit, a VR classroom unit, an AI teacher assistant unit, and a learning support tool unit. The learning data analysis unit analyzes children's learning data. For example, the learning data analysis unit identifies areas that need reinforcement based on past learning history and test results. The learning data analysis unit can also analyze learning styles such as visual, auditory, and tactile. The learning plan generation unit generates a learning plan based on the learning data analyzed by the learning data analysis unit. For example, the learning plan generation unit uses a generation AI to provide a learning plan tailored to individual learning patterns and needs. The learning plan generation unit can also dynamically adjust the learning plan according to learning progress. The AI ​​teacher unit provides individualized instruction based on the learning plan generated by the learning plan generation unit. For example, the AI ​​teacher unit uses a generation AI to answer children's questions in real time and provide guidance on the next step based on their level of understanding. The learning material generation unit generates learning materials based on the learning plan generated by the learning plan generation unit. For example, the teaching material generation unit uses a generation AI to generate customized teaching materials in real time according to children's learning progress. The VR classroom unit provides a VR classroom based on the learning plan generated by the learning plan generation unit. For example, the VR classroom unit uses VR technology to provide an immersive learning experience. The AI ​​teacher assistant unit assists teachers based on the learning plan generated by the learning plan generation unit. For example, the AI ​​teacher assistant unit supports the progress of lessons and provides appropriate answers to children's questions. The learning support tool unit supports learning at home and at school based on the learning plan generated by the learning plan generation unit. For example, the learning support tool unit analyzes learning progress at home and provides appropriate advice to parents. As a result, the learning support system according to the embodiment can comprehensively support children's learning and reduce the burden on parents and teachers. For example, by providing learning plans tailored to individual learning patterns, children can learn more effectively.In addition, the introduction of AI teachers and AI teacher assistants will reduce the burden on teachers and enable more children to receive high-quality education. Furthermore, by supporting learning both at home and at school, it will be possible to create a consistent learning environment for children.

[0064] The learning data analysis unit can analyze children's learning styles and suggest optimal learning methods based on that analysis. The learning data analysis unit, for example, uses generative AI to analyze children's learning styles. For example, generative AI collects past learning data and test results and identifies which sense of learning is most effective: visual, auditory, or tactile. The learning data analysis unit can also suggest learning materials that make extensive use of diagrams and graphs for children who excel at visual learning. Furthermore, the learning data analysis unit can suggest audio learning materials or lecture-style materials for children who excel at auditory learning. This makes it possible to provide optimal learning methods that match each child's learning style.

[0065] The learning plan generation unit can analyze children's learning data in real time and dynamically adjust the learning plan according to their learning progress. The learning plan generation unit, for example, uses a generation AI to analyze children's learning data in real time. For example, the generation AI monitors learning progress in real time, and if a child is struggling with a particular unit, it provides additional learning materials to reinforce that unit. The learning plan generation unit can also dynamically adjust the learning plan according to the child's learning progress. For example, the learning plan generation unit can adjust the learning pace according to the child's progress and suggest breaks as needed. This makes it possible to dynamically adjust the learning plan according to the child's learning progress.

[0066] The lesson plan generation unit uses an emotion estimation function to monitor children's emotions while they are learning and can suggest taking a break if they feel stressed or tired. The lesson plan generation unit monitors children's emotions while they are learning using, for example, a generation AI. For example, the generation AI analyzes facial expressions and voice tones to encourage breaks if stress levels are high. The lesson plan generation unit can also collect biometric data (heart rate and electrodermal activity) using sensors and suggest taking a break if they feel stressed or tired. Furthermore, the lesson plan generation unit can use an emotion estimation algorithm to analyze children's emotions and suggest taking a break at appropriate times. This can reduce children's stress and fatigue and improve their learning efficiency.

[0067] The learning plan generation unit can incorporate game elements into the learning plan, allowing children to learn while having fun. The learning plan generation unit incorporates game elements into the learning plan, for example, by using a generation AI. For example, the generation AI provides quiz-style questions or a point-based learning game. The learning plan generation unit can also introduce a level-up or reward system, allowing children to progress through their learning while having fun. Furthermore, the learning plan generation unit can provide interactive learning content, allowing children to progress through their learning on their own. This can provide an environment where children can learn while having fun.

[0068] The learning plan generation unit can share the learning plan with the entire family, allowing parents and siblings to participate in the learning together. The learning plan generation unit, for example, uses a generation AI to share the learning plan with the entire family. For example, the generation AI creates a collaborative learning plan that all family members can participate in. The learning plan generation unit can also share the learning plan so that parents and siblings can support their children's learning. Furthermore, the learning plan generation unit can also provide activities and tasks that allow the entire family to progress with learning. This can provide an environment in which the entire family can participate in learning.

[0069] The learning plan generation unit can use the emotion estimation function to identify the learning content that children are most interested in and build a learning plan around that content. The learning plan generation unit can, for example, use a generation AI to identify the learning content that children are most interested in. For example, the generation AI can analyze children's interests using the emotion estimation function and prioritize providing learning materials related to themes that interest them. The learning plan generation unit can also identify children's interests through questionnaire surveys and behavioral data analysis. Furthermore, the learning plan generation unit can build a learning plan around the learning content that children are interested in, allowing children to actively engage in their studies. This allows children to progress with their studies centered on the learning content that they are most interested in.

[0070] The AI ​​teacher module can provide individual feedback based on children's learning history to improve their understanding. For example, the AI ​​teacher module uses a generative AI to analyze children's learning history. For example, the generative AI points out specific areas for improvement based on past test results and learning progress. The AI ​​teacher module can also provide individual feedback to improve children's understanding. Furthermore, the AI ​​teacher module can suggest next steps and set learning goals to help children progress effectively in their studies. This makes it possible to provide individual feedback to improve children's understanding.

[0071] The AI ​​Teacher Unit can deepen children's understanding by having the generative AI present multiple answers to their questions and letting them choose the most appropriate answer. For example, the AI ​​Teacher Unit uses generative AI to present multiple answers to children's questions. For example, the generative AI can provide answers from different perspectives, helping children develop their ability to think for themselves. The AI ​​Teacher Unit can also deepen children's understanding by letting them choose the most appropriate answer. Furthermore, the AI ​​Teacher Unit can analyze the intention and background behind the question and provide an appropriate answer. This helps children develop their ability to think for themselves and deepen their understanding.

[0072] The AI ​​teacher module can use emotion estimation to understand the emotions behind children's questions and respond appropriately. For example, the AI ​​teacher module can use generation AI to understand the emotions behind children's questions. For example, the generation AI can offer words of encouragement if the question comes from anxiety or stress through voice tone analysis and facial expression recognition. The AI ​​teacher module can also use emotion estimation algorithms to analyze children's emotions and respond appropriately. Furthermore, the AI ​​teacher module can provide feedback based on children's emotions through analysis of the content of their questions. This allows for appropriate responses based on children's emotions.

[0073] The AI ​​Teacher module can regularly report children's learning progress to parents and teachers, strengthening collaboration between home and school. For example, the AI ​​Teacher module uses a generative AI to regularly report children's learning progress to parents and teachers. For example, the generative AI can automatically generate weekly reports and send them by email. The AI ​​Teacher module can also monitor learning progress in real time and immediately report important progress and problems. Furthermore, the AI ​​Teacher module can provide an online dashboard that makes it easy for parents and teachers to check learning progress. This strengthens collaboration between home and school and provides consistent support for children's learning.

[0074] An AI teacher can provide cross-disciplinary instruction across different subjects and support comprehensive learning. For example, an AI teacher can use generative AI to provide cross-disciplinary instruction across different subjects. For example, generative AI can show the relationship between mathematics and science and provide a learning plan that integrates both subjects. An AI teacher can also show the relationship between history and geography and provide a comprehensive learning experience. Furthermore, an AI teacher can integrate knowledge from different subjects and allow children to understand the learning content from multiple perspectives. This can support comprehensive learning that integrates different subjects.

[0075] The AI ​​teacher can use emotion estimation to provide a learning environment where children can be most relaxed. The AI ​​teacher can also use generative AI to provide a learning environment where children can be most relaxed. For example, generative AI can analyze emotional data during learning and suggest relaxing music or background sounds. The AI ​​teacher can also adjust lighting and interactive elements to provide a learning environment where children can relax and concentrate. Furthermore, the AI ​​teacher can use emotion estimation algorithms to monitor children's stress levels and suggest appropriate relaxation methods. This can provide a learning environment where children can be relaxed.

[0076] The teaching material generation unit can generate customized teaching materials in real time according to children's learning progress. The teaching material generation unit, for example, uses generation AI to analyze children's learning progress and generate customized teaching materials in real time. For example, if a child is struggling with a particular unit, the generation AI automatically generates teaching materials to reinforce that unit. The teaching material generation unit can also provide individual learning plans based on learning goals and generate the necessary teaching materials. Furthermore, the teaching material generation unit can monitor learning progress in real time and update teaching materials at the appropriate time. This makes it possible to provide customized teaching materials according to children's learning progress.

[0077] The teaching material generation unit can incorporate interactive elements into the teaching materials, allowing children to advance their learning on their own. The teaching material generation unit incorporates interactive elements into the teaching materials, for example, by using a generation AI. For example, the generation AI provides teaching materials that include quiz-style questions and simulations. The teaching material generation unit can also provide interactive content, allowing children to advance their learning on their own. Furthermore, the teaching material generation unit can also provide interactive learning games, allowing children to advance their learning while having fun. This can provide an environment in which children can advance their learning on their own.

[0078] The teaching material generation unit can use the emotion estimation function to reflect themes that children are most interested in in the teaching materials. The teaching material generation unit, for example, uses generation AI to identify themes that children are most interested in. For example, the generation AI uses the emotion estimation function to analyze children's interests and preferentially provide teaching materials related to the areas of interest. The teaching material generation unit can also identify children's interests through questionnaire surveys and behavioral data analysis. Furthermore, the teaching material generation unit can reflect themes of interest in the teaching materials, allowing children to actively engage in their studies. This allows learning to be centered on the themes that children are most interested in.

[0079] The teaching material generation unit can automatically translate teaching materials into different languages ​​to provide an international learning environment. The teaching material generation unit, for example, uses a generation AI to automatically translate teaching materials into different languages. For example, the generation AI provides teaching materials in multiple languages, such as English, Japanese, and Spanish. The teaching material generation unit can also accurately translate the content of the teaching materials using a machine translation algorithm. Furthermore, the teaching material generation unit can utilize a translation memory and a language model to provide teaching materials that correspond to different languages. This makes it possible to provide an international learning environment.

[0080] The teaching material generation unit can provide teaching materials in both digital and analog formats, allowing children to select materials that suit their learning style. The teaching material generation unit can, for example, use generation AI to provide teaching materials in both digital and analog formats. For example, the generation AI prepares both digital teaching materials and printed teaching materials. The teaching material generation unit can also allow children to select digital teaching materials and analog teaching materials that suit their learning style. Furthermore, the teaching material generation unit can diversify the formats of digital teaching materials and the types of analog teaching materials, allowing children to select the learning method that suits them best. This makes it possible to provide teaching materials that suit children's learning styles.

[0081] The teaching material generation unit uses the emotion estimation function to identify teaching materials that children will enjoy learning from most, and can focus their learning on those materials. The teaching material generation unit, for example, uses a generation AI to identify teaching materials that children will enjoy learning from most. For example, the generation AI uses the emotion estimation function to analyze children's enjoyment and prioritizes providing teaching materials related to themes that children will enjoy learning from. The teaching material generation unit can also identify children's enjoyment through the analysis of questionnaire surveys and behavioral data. Furthermore, the teaching material generation unit can create a learning plan centered on teaching materials that children will enjoy learning from, allowing children to actively engage in learning. This makes it possible to provide teaching materials that children will enjoy learning from.

[0082] The VR classroom unit uses a generation AI to analyze the learning content in the VR classroom in real time and adjust the content according to the children's level of understanding. The VR classroom unit, for example, uses a generation AI to analyze the learning content in the VR classroom in real time. For example, the generation AI monitors the children's level of understanding in real time and provides additional explanations or supplementary materials if their understanding is low. The VR classroom unit can also dynamically adjust the learning content to enable children to progress effectively in their studies. Furthermore, the VR classroom unit can provide real-time feedback to maximize children's learning effectiveness. This makes it possible to provide VR learning content that is tailored to the children's level of understanding.

[0083] The VR classroom unit can record the learning experience in the VR classroom so that it can be reviewed later. The VR classroom unit can, for example, use a generative AI to record the learning experience in the VR classroom. For example, the generative AI can record the learning content and make it accessible online. The VR classroom unit can also save the learning experience as log data so that it can be reviewed later. Furthermore, the VR classroom unit can record interactions so that children can review the learning content again. This allows the learning experience to be recorded so that children can review it later.

[0084] The VR classroom unit can use the emotion estimation function to provide VR content that children are most interested in. The VR classroom unit, for example, uses the generation AI to identify VR content that children are most interested in. For example, the generation AI uses the emotion estimation function to analyze children's interests and prioritize providing VR experiences related to themes that interest them. The VR classroom unit can also identify children's interests through surveys and behavioral data analysis. Furthermore, the VR classroom unit can provide VR content that children are interested in, allowing them to actively engage in their studies. This allows the VR content that children are most interested in to be provided.

[0085] The VR Classroom Division can make the VR classroom available at home and incorporate it as part of home learning. The VR Classroom Division can, for example, use generative AI to make the VR classroom available at home. For example, the generative AI can make learning content accessible through a home VR device. The VR Classroom Division can also incorporate the VR classroom as part of home learning, allowing children to learn effectively at home. Furthermore, the VR Classroom Division can provide VR content for home learning, allowing children to enjoy an immersive learning experience at home. This allows the VR classroom to be used at home and enriches home learning.

[0086] The VR classroom unit can link learning content in the VR classroom with other subjects to provide a comprehensive learning experience. For example, the VR classroom unit uses generative AI to link learning content in the VR classroom with other subjects. For example, generative AI can review what was learned in history class in geography class. The VR classroom unit can also integrate knowledge from different subjects, allowing children to understand the learning content from multiple perspectives. Furthermore, the VR classroom unit can show connections between subjects and provide a comprehensive learning experience. This allows learning content in the VR classroom to be linked with other subjects and provide a comprehensive learning experience.

[0087] The VR classroom unit can use emotion estimation functions to provide a VR environment where children can learn in the most relaxed manner. For example, the VR classroom unit can use generative AI to provide a VR environment where children can learn in the most relaxed manner. For example, the generative AI can analyze emotional data during learning and suggest relaxing VR content and background sounds. The VR classroom unit can also adjust lighting and interactive elements to provide a VR environment where children can relax and concentrate on their studies. Furthermore, the VR classroom unit can use emotion estimation algorithms to monitor children's stress levels and suggest appropriate ways to relax. This allows for a VR environment where children can learn in a relaxed manner.

[0088] The AI ​​teacher assistant unit can analyze children's responses during class in real time and provide feedback to teachers. The AI ​​teacher assistant unit, for example, uses generative AI to analyze children's responses during class in real time. For example, generative AI can analyze children's level of understanding and concentration through facial expression recognition and voice analysis, and suggest appropriate teaching methods to teachers. The AI ​​teacher assistant unit can also collect behavioral data and analyze children's responses. Furthermore, the AI ​​teacher assistant unit can provide real-time feedback, allowing teachers to adjust the progress of the lesson. This allows the AI ​​teacher assistant unit to analyze children's responses during class in real time and provide feedback to teachers.

[0089] The AI ​​teacher assistant unit can support the progress of lessons, allowing teachers to focus on individual instruction. The AI ​​teacher assistant unit can support the progress of lessons, for example, by using generative AI. For example, generative AI can automate the management of lesson plans and the provision of teaching materials. The AI ​​teacher assistant unit can also answer questions and provide feedback, allowing teachers to focus on individual instruction. Furthermore, the AI ​​teacher assistant unit can monitor the progress of lessons in real time and make adjustments as necessary. This can support the progress of lessons, allowing teachers to focus on individual instruction.

[0090] The AI ​​teacher assistant unit can use an emotion estimation function to provide support that is tailored to children's emotions. For example, the AI ​​teacher assistant unit can use a generation AI to provide support that is tailored to children's emotions. For example, if a child feels stressed while studying, the generation AI can suggest an activity to help them relax. The AI ​​teacher assistant unit can also use an emotion estimation algorithm to analyze children's emotions and provide appropriate feedback. Furthermore, the AI ​​teacher assistant unit can provide support to improve motivation, helping children to actively engage in their studies. This makes it possible to provide support that is tailored to children's emotions.

[0091] The AI ​​teacher assistant unit can monitor the progress of home learning and provide feedback to parents. The AI ​​teacher assistant unit monitors the progress of home learning using, for example, a generation AI. For example, the generation AI automatically generates a learning progress report and sends it to parents by email. The AI ​​teacher assistant unit can also record study time and evaluate the learning content and provide feedback to parents. Furthermore, the AI ​​teacher assistant unit can monitor the progress of home learning in real time and immediately report important progress and problems. This makes it possible to monitor the progress of home learning and provide feedback to parents.

[0092] The AI ​​teacher assistant unit can provide cross-disciplinary support across different subjects and support comprehensive learning. The AI ​​teacher assistant unit, for example, uses generative AI to provide cross-disciplinary support across different subjects. For example, generative AI can show the relationship between mathematics and science and provide a learning plan that integrates both subjects. The AI ​​teacher assistant unit can also show the relationship between history and geography to provide a comprehensive learning experience. Furthermore, the AI ​​teacher assistant unit can integrate knowledge from different subjects to enable children to understand the learning content from multiple perspectives. This allows for cross-disciplinary support across different subjects and support comprehensive learning.

[0093] The AI ​​teacher assistant unit can use emotion estimation functions to provide a learning environment where children can be most relaxed. The AI ​​teacher assistant unit can also use generative AI to provide a learning environment where children can be most relaxed. For example, generative AI can analyze emotional data during learning and suggest relaxing music or background sounds. The AI ​​teacher assistant unit can also adjust lighting and interactive elements to provide a learning environment where children can relax and concentrate. Furthermore, the AI ​​teacher assistant unit can use emotion estimation algorithms to monitor children's stress levels and suggest appropriate relaxation methods. This can provide a learning environment where children can be relaxed.

[0094] The learning support tool unit can integrate learning data from home and school and provide a consistent learning plan. The learning support tool unit integrates learning data from home and school using, for example, a generation AI. For example, the generation AI reflects learning progress at home in the school learning plan. The learning support tool unit can also centrally manage learning data from home and school using a learning data collection method and integration algorithm. Furthermore, the learning support tool unit can also securely integrate learning data while taking privacy protection into consideration. This makes it possible to integrate learning data from home and school and provide a consistent learning plan.

[0095] The learning support tool section strengthens collaboration between homes and schools by using learning support tools, and is able to share children's learning progress in real time. The learning support tool section strengthens collaboration between homes and schools by using, for example, generative AI. For example, generative AI provides an online dashboard so that parents and teachers can constantly check learning status. The learning support tool section can also share learning progress in real time and immediately report important progress and problems. Furthermore, the learning support tool section can use communication tools to provide an environment that makes it easy for parents and teachers to collaborate. This strengthens collaboration between homes and schools, and allows children's learning progress to be shared in real time.

[0096] The learning support tool unit can use an emotion estimation function to monitor children's emotions while they are learning and provide appropriate support. The learning support tool unit, for example, uses a generation AI to monitor children's emotions while they are learning. For example, the generation AI analyzes children's stress levels and motivation through facial expression recognition and voice analysis. The learning support tool unit can also use an emotion estimation algorithm to monitor children's emotions in real time and provide appropriate feedback. Furthermore, the learning support tool unit can provide suggestions for reducing stress and support for improving motivation. This makes it possible to monitor children's emotions while they are learning and provide appropriate support.

[0097] The learning support tools department can make learning support tools available at both home and school, creating a consistent learning environment. For example, the learning support tools department can use generative AI to make learning support tools available at both home and school. For example, the generative AI can use the same learning platform at home and school. The learning support tools department can also prepare the necessary devices and internet connections to provide a consistent learning environment at home and school. Furthermore, the learning support tools department can standardize teaching materials and share learning plans to ensure that children progress consistently at home and school. This makes it possible to make learning support tools available at both home and school, creating a consistent learning environment.

[0098] The learning support tool section can use learning support tools to provide cross-disciplinary support across different subjects and support comprehensive learning. The learning support tool section, for example, uses generative AI to provide cross-disciplinary support across different subjects. For example, generative AI can show the relationship between mathematics and science and provide a learning plan that integrates both subjects. The learning support tool section can also show the relationship between history and geography and provide a comprehensive learning experience. Furthermore, the learning support tool section can integrate knowledge from different subjects and enable children to understand the learning content from multiple perspectives. This allows for cross-disciplinary support across different subjects and support comprehensive learning.

[0099] The learning support tool unit can use an emotion estimation function to provide an environment where children can learn in the most relaxed manner. The learning support tool unit can use, for example, a generative AI to provide an environment where children can learn in the most relaxed manner. For example, the generative AI can analyze emotional data during learning and suggest relaxing music or background sounds. The learning support tool unit can also adjust lighting and interactive elements to provide an environment where children can relax and concentrate on their studies. Furthermore, the learning support tool unit can use an emotion estimation algorithm to monitor children's stress levels and suggest appropriate ways to relax. This can provide an environment where children can learn in a relaxed manner.

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

[0101] The learning support system can further include a learning motivation improvement unit. The learning motivation improvement unit, for example, uses a generation AI to analyze children's learning motivation and provide appropriate motivation improvement measures. For example, the generation AI may introduce a reward system based on learning progress, allowing children to earn points or badges each time they achieve a goal. The learning motivation improvement unit can also provide content that attracts children's attention and maintain their interest in learning. Furthermore, the learning motivation improvement unit can automatically generate encouraging messages from parents or teachers and send them to children. This can improve children's learning motivation and improve their learning effectiveness.

[0102] The learning support system can further include a learning environment optimization unit. The learning environment optimization unit, for example, uses a generative AI to analyze children's learning environment and propose the optimal learning environment. For example, the generative AI adjusts the lighting and acoustics during learning to provide an environment where children can easily concentrate. The learning environment optimization unit can also propose a layout for the learning space to enable children to study comfortably. Furthermore, the learning environment optimization unit can analyze data on posture and gaze during learning and propose appropriate learning postures. This allows children to study in the optimal learning environment.

[0103] The learning support system can further include a learning progress sharing unit. The learning progress sharing unit uses, for example, a generation AI to share children's learning progress with parents and teachers in real time. For example, the generation AI automatically generates learning progress reports and sends them to parents and teachers by email. The learning progress sharing unit can also provide an online dashboard so that parents and teachers can constantly check the learning situation. Furthermore, the learning progress sharing unit can immediately report important progress and problems so that parents and teachers can take appropriate action. This strengthens cooperation between home and school and enables consistent support for children's learning.

[0104] The learning support system can further include a learning style adaptation unit. The learning style adaptation unit uses, for example, generative AI to analyze children's learning styles and suggest optimal learning methods. For example, the generative AI may identify which of visual, auditory, or tactile is most effective and customize learning materials based on that. The learning style adaptation unit can also suggest learning tools and activities according to the learning style. Furthermore, the learning style adaptation unit can dynamically adjust the learning method according to changes in learning style. This makes it possible to provide the optimal learning method that matches each child's learning style.

[0105] The learning support system can further include a learning outcome assessment unit. The learning outcome assessment unit uses, for example, generative AI to evaluate children's learning outcomes from multiple angles. For example, the generative AI evaluates not only test results but also deliverables such as projects and presentations. The learning outcome assessment unit can also monitor children's growth and progress over the long term and provide appropriate feedback. Furthermore, the learning outcome assessment unit can suggest next learning steps based on the evaluation results, allowing children to progress effectively in their studies. This makes it possible to evaluate children's learning outcomes from multiple angles and provide appropriate feedback.

[0106] The learning support system can also use emotion estimation functions to monitor children's emotions while they learn and provide appropriate support. For example, the generative AI analyzes children's stress levels and motivation through facial expression recognition and voice analysis. The learning support system can also use emotion estimation algorithms to monitor children's emotions in real time and provide appropriate feedback. Furthermore, the learning support system can provide suggestions for reducing stress and support for improving motivation. This allows the system to monitor children's emotions while they learn and provide appropriate support.

[0107] The learning support system can further use its emotion estimation function to identify the learning content that children are most interested in and build a learning plan around that content. For example, the generative AI can use its emotion estimation function to analyze children's interests and prioritize providing learning materials related to themes that interest them. The learning support system can also identify children's interests through questionnaire surveys and behavioral data analysis. Furthermore, the learning support system can build a learning plan around the learning content that interests them, allowing children to actively engage in their studies. This allows children to focus their learning on the learning content that interests them most.

[0108] The learning support system can also use emotion estimation functions to provide a learning environment where children can be most relaxed. For example, the generative AI can analyze emotional data during learning and suggest relaxing music or background sounds. The learning support system can also adjust lighting and interactive elements to provide a learning environment where children can relax and concentrate. Furthermore, the learning support system can use emotion estimation algorithms to monitor children's stress levels and suggest appropriate relaxation methods. This can provide a learning environment where children can be relaxed.

[0109] The learning support system can also use emotion estimation functions to monitor children's emotions while they are learning and suggest taking a break if they feel stressed or fatigued. For example, the generative AI can analyze facial expressions and voice tones to encourage breaks if stress levels are high. The learning support system can also collect biometric data (heart rate and electrodermal activity) using sensors to suggest taking a break if they feel stressed or fatigued. Furthermore, the learning support system can use emotion estimation algorithms to analyze children's emotions and suggest taking a break at appropriate times. This can reduce children's stress and fatigue and improve their learning efficiency.

[0110] The learning support system can also use emotion estimation functions to understand the emotions behind children's questions and provide appropriate responses. For example, the generative AI can offer words of encouragement if the question comes from anxiety or stress through voice tone analysis and facial expression recognition. The learning support system can also use emotion estimation algorithms to analyze children's emotions and provide appropriate responses. Furthermore, the learning support system can provide feedback based on children's emotions through analysis of the content of their questions. This allows for appropriate responses based on children's emotions.

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

[0112] Step 1: The learning data analysis unit analyzes the children's learning data. For example, it identifies areas that need reinforcement based on past learning history and test results. It can also analyze learning styles such as visual, auditory, and tactile. Step 2: The learning plan generation unit generates a learning plan based on the learning data analyzed by the learning data analysis unit. For example, the generation AI can be used to provide a learning plan tailored to individual learning patterns and needs, and the learning plan can be dynamically adjusted according to the learning progress. Step 3: The AI ​​teacher provides individualized instruction based on the learning plan generated by the learning plan generator. For example, the AI ​​can use the generator to answer students' questions in real time and provide guidance on the next steps based on their level of understanding. Step 4: The teaching material generation unit generates teaching materials based on the learning plan generated by the learning plan generation unit. For example, using a generation AI, customized teaching materials are generated in real time according to the children's learning progress. Step 5: The VR classroom unit provides a VR classroom based on the lesson plan generated by the lesson plan generation unit, for example, by using VR technology to provide an immersive learning experience. Step 6: The AI ​​teacher assistant assists the teacher based on the lesson plan generated by the lesson plan generator, for example, by supporting the progress of the lesson and providing appropriate answers to students' questions. Step 7: The learning support tool unit supports learning at home and at school based on the learning plan generated by the learning plan generation unit. For example, it analyzes learning progress at home and provides appropriate advice to parents.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0157] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, 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.

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

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

Claims

1. a learning data analysis unit that analyzes the learning data; a learning plan generation unit that generates a learning plan based on the learning data analyzed by the learning data analysis unit; an AI teaching unit that provides individual instruction based on the learning plan generated by the learning plan generation unit; a learning material generation unit that generates learning materials based on the learning plan generated by the learning plan generation unit; a VR classroom unit that provides a VR classroom based on the learning plan generated by the learning plan generation unit; an AI teacher assistant unit that assists a teacher based on the learning plan generated by the learning plan generation unit; a learning support tool unit that supports learning at home and at school based on the learning plan generated by the learning plan generation unit. A system characterized by:

2. The learning data analysis unit Analyze children's learning styles and suggest optimal learning methods based on that.

2. The system of claim 1.

3. The learning plan generation unit Incorporate game elements into your lesson plans to help children have fun while they learn 2. The system of claim 1.

4. The AI ​​teaching unit is Providing individual feedback based on children's learning history to improve comprehension 2. The system of claim 1.

5. The teaching material generation unit Generate customized learning materials in real time according to children's learning progress 2. The system of claim 1.

6. The VR classroom section is AI generates learning content in VR classrooms, analyzes it in real time, and adjusts the content according to the children's level of understanding.

2. The system of claim 1.

7. The AI ​​teacher assistant unit is Analyzing students' reactions in class in real time and providing feedback to teachers 2. The system of claim 1.

8. The learning support tool unit Integrate learning data from home and school to provide a consistent learning plan 2. The system of claim 1.

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