System

Abstract

An object of the system according to the embodiment is to provide learning content customized in accordance with interests and learning styles of children.SOLUTION: A system according to an embodiment includes an interaction unit, a content generation unit, a gamification unit, an evaluation unit, and a mobile application providing unit. The dialogue unit dialogues with the children and grasps their interests and learning styles. The content generation unit generates learning content customized based on the information grasped by the dialogue unit. The gamification unit introduces gamification elements into the learning content. The evaluator provides self-evaluation and feedback on the learning content. The mobile App providing unit provides learning content through the mobile App.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] With conventional technology, it is difficult to provide customized learning content that matches children's interests and learning styles, and there is room for improvement in terms of increasing motivation to learn.

[0005] The system according to the embodiment aims to provide customized learning content that matches children's interests and learning styles. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, a content generation unit, a gamification unit, an evaluation unit, and a mobile app provision unit. The dialogue unit dialogues with children to understand their interests and learning styles. The content generation unit generates customized learning content based on the information obtained by the dialogue unit. The gamification unit introduces gamification elements into the learning content. The evaluation unit provides self-evaluation and feedback on the learning content. The mobile app provision unit provides the learning content through a mobile app. [Effects of the Invention]

[0007] The system according to the embodiment can provide customized learning content that matches the interests and learning styles of children. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A learning support system according to an embodiment of the present invention utilizes a generative AI to provide customized learning content tailored to children's interests and learning styles. This allows the learning support system to provide customized learning content tailored to children's interests and learning styles, thereby increasing their motivation to study.

[0029] A learning support system according to an embodiment includes a dialogue unit, a content generation unit, a gamification unit, an evaluation unit, and a mobile application provision unit. The dialogue unit dialogues with children to understand their interests and learning styles. For example, the dialogue unit allows a generation AI to ask questions such as "What is your favorite subject?" and "How do you like to study?" and creates a profile based on the children's answers. The generation AI performs analysis based on prompts containing questions about the children's interests and learning styles. The content generation unit generates customized learning content based on the information grasped by the dialogue unit. For example, the generation AI provides math problems in a game format to a child who likes math, and learning content using historical storytelling to a child who likes history. The generation AI generates learning content based on the children's profile information. The gamification unit incorporates gamification elements into the learning content. For example, it provides a system that allows children to earn points based on their learning progress or a system that allows them to level up by solving quiz-style problems. The generation AI generates content based on instructions for learning content that includes gamification elements. The evaluation unit provides self-evaluation and feedback on the learning content. For example, the generation AI asks, "How did you do on this problem?" and provides appropriate feedback based on the children's answer. The generation AI generates feedback based on prompts including self-evaluation questions. The mobile app provision unit provides the learning content through a mobile app. For example, it creates an environment where children can learn anytime, anywhere using a smartphone or tablet. The generation AI generates content based on instructions for the learning content through the mobile app. As a result, the learning support system according to the embodiment provides customized learning content tailored to children's interests and learning styles, thereby increasing their motivation to study. For example, even children who are not good at math can learn in a fun way through game-style problems, and children who like history can learn deeply through storytelling. Furthermore, through self-evaluation and feedback, children can understand their own learning status and use it in their next studies.Furthermore, by utilizing mobile apps, we can provide an environment where students can learn anytime, anywhere.

[0030] The dialogue unit analyzes children's past learning history and grade data, and can track changes in their interests and learning styles. For example, the dialogue unit collects learning history data, and the generation AI analyzes that data to identify changes in their interests. For example, it identifies subjects and topics that they have spent a lot of time on in the past. The dialogue unit also analyzes grade data, and the generation AI evaluates the children's learning styles. For example, it identifies what learning methods are effective based on test scores and assignment submission status. The dialogue unit also analyzes a combination of learning history and grade data to track changes in their interests and learning styles. For example, it identifies the reasons why interests changed at a particular time and understands the background behind it. This makes it possible to track changes in children's interests and learning styles.

[0031] The dialogue unit collects feedback from parents and teachers, and the generation AI can analyze it. For example, the dialogue unit collects feedback from parents and teachers, and the generation AI analyzes that information to identify children's interests. For example, it analyzes observation records and comments provided by parents and teachers. The dialogue unit also evaluates children's interests based on the feedback provided by parents and teachers. For example, it reflects children's favorite activities and subjects reported by parents and teachers in the profile. The dialogue unit also regularly collects feedback from parents and teachers, and the generation AI analyzes that information to track changes in interests. For example, it collects feedback every semester to identify changes in interests. This makes it possible to analyze feedback from parents and teachers.

[0032] The dialogue unit can collect information indirectly through games and activities. The dialogue unit builds a system that indirectly collects children's interests through games and activities, for example. For example, interests are identified by analyzing choices and actions in a game. The dialogue unit also evaluates children's interests through activities. For example, interests are identified based on the frequency of participation in and responses to a particular activity. The dialogue unit also collects data on games and activities, and the generation AI analyzes the data to identify interests. For example, interests are evaluated by analyzing behavioral patterns and choices in a game. This makes it possible to collect information indirectly through games and activities.

[0033] The content generation unit can analyze children's learning progress in real time and dynamically adjust the learning content as needed. For example, the content generation unit collects learning progress data in real time, and the generation AI analyzes the data to dynamically adjust the learning content. For example, if progress is lagging, supplementary content is provided. The content generation unit also has the generation AI adjust the difficulty of the learning content according to children's learning progress. For example, if progress is rapid, more difficult questions are provided. The content generation unit also has the generation AI dynamically change the order of learning content based on the learning progress data. For example, if understanding of a particular topic is insufficient, content related to that topic is provided preferentially. This allows the learning content to be dynamically adjusted according to learning progress.

[0034] The content generation unit can provide multimodal learning content that stimulates different senses, such as sight, hearing, and touch, according to children's learning styles. For example, the content generation unit provides visual learning content according to children's learning styles. For example, it provides visual teaching materials using diagrams and graphs. To accommodate auditory learning styles, the content generation unit uses a generative AI to provide learning content in the form of audio commentary or podcasts. For example, it provides historical storytelling in audio. To accommodate tactile learning styles, the content generation unit uses a generative AI to provide interactive learning content. For example, it provides an app that allows learning through touch operations using a tablet. This makes it possible to provide multimodal learning content that stimulates different senses.

[0035] The content generation unit can introduce a collaboration function to enable children with different learning styles to learn together. For example, the content generation unit builds an online platform where children with different learning styles can learn together. For example, they can learn through group discussions or collaborative projects. The content generation unit also uses the collaboration function to introduce a system where children with different learning styles can teach each other. For example, it promotes pair learning where children teach each other their favorite subjects. The content generation unit also provides interactive tools for children with different learning styles to learn together. For example, it provides an online whiteboard where they can solve problems together. This allows children with different learning styles to learn together.

[0036] The content generation unit can translate learning content into different languages ​​to promote learning from an international perspective. For example, the content generation unit builds a system that automatically translates learning content into different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. The content generation unit also provides learning content translated into different languages ​​to promote learning from an international perspective. For example, it provides content to deepen intercultural understanding. The content generation unit also promotes discussions and projects from an international perspective based on the translated learning content. For example, it provides an online platform where children from different countries can learn together. This makes it possible to translate into different languages ​​and promote learning from an international perspective.

[0037] The gamification unit can analyze children's gameplay data and dynamically adjust the optimal difficulty level and reward system. For example, the gamification unit collects gameplay data in real time, and the generation AI analyzes the data to dynamically adjust the game difficulty. For example, the difficulty level of questions can be changed according to the player's skill level. The gamification unit also allows the generation AI to design an optimal reward system based on children's gameplay data. For example, bonus points can be provided when certain achievement conditions are met. The gamification unit also analyzes gameplay data, and the generation AI can dynamically adjust the reward system to maintain children's motivation. For example, special rewards can be provided when children answer correctly consecutively. This allows the optimal difficulty level and reward system to be dynamically adjusted.

[0038] The gamification unit can customize in-game choices and story development based on children's interests. In the gamification unit, for example, a generation AI customizes in-game choices and story development based on children's interest data. For example, a story may develop based on a favorite character or theme. The gamification unit also dynamically changes in-game choices to match children's interests. For example, it may provide choices related to topics of particular interest. The gamification unit also analyzes children's interest data and a generation AI customizes the game's story development. For example, it may provide a story based on a historical event that interests children. This makes it possible to customize in-game choices and story development based on children's interests.

[0039] The gamification unit can introduce different game formats for different learning subjects and provide the optimal gamification elements for each subject. For example, the gamification unit uses a generation AI to design the optimal game format for different learning subjects. For example, it provides a puzzle game for mathematics and a storytelling game for history. The gamification unit also introduces different gamification elements for each learning subject, and the generation AI dynamically adjusts the elements. For example, it provides an experiment simulation game for science. The gamification unit also uses a generation AI to provide the optimal game format for different learning subjects, increasing children's motivation to learn. For example, it provides a word quiz game for English. This makes it possible to provide the optimal gamification elements for different learning subjects.

[0040] The gamification section introduces a competitive element into the game, allowing children to compete with their friends and family, thereby increasing their motivation to learn. The gamification section, for example, introduces a competitive element into the game and creates a system in which children can compete with their friends and family. For example, it provides a score ranking or a battle mode. The gamification section also uses the competitive element to increase children's motivation to learn. For example, it provides a system in which children can earn points and receive rewards by competing with friends. The gamification section also dynamically adjusts the competitive element in the game, allowing children to compete with their friends and family, thereby increasing their motivation to learn. For example, it provides regularly held online tournaments. This introduces a competitive element and increases children's motivation to learn.

[0041] The evaluation unit can analyze children's self-assessment data and provide feedback that is optimal for each individual learning style. For example, the evaluation unit collects children's self-assessment data, and the generation AI analyzes that data to provide feedback that is optimal for each individual learning style. For example, specific areas for improvement are suggested based on the results of self-assessment. The evaluation unit also allows the generation AI to provide feedback tailored to each individual learning style based on the self-assessment data. For example, feedback using diagrams and graphs is provided for visual learning styles. The evaluation unit also analyzes children's self-assessment data, and the generation AI dynamically adjusts the feedback that is optimal for each individual learning style. For example, audio commentary is provided for auditory learning styles. This makes it possible to provide feedback that is optimal for each individual learning style.

[0042] The evaluation unit customizes the content of the feedback to match children's interests, promoting more effective learning. In the evaluation unit, for example, the generation AI customizes the content of the feedback based on children's interest data. For example, feedback is provided using favorite characters or themes. The evaluation unit also dynamically adjusts the content of the feedback to match children's interests. For example, feedback is provided using specific examples related to topics that interest them. The evaluation unit also analyzes children's interest data, and the generation AI customizes the content of the feedback. For example, feedback related to subjects that interest them is provided. This makes it possible to provide feedback that matches children's interests, promoting effective learning.

[0043] The evaluation department can share the self-assessment and feedback process with parents and teachers, enabling them to jointly understand the learning situation. For example, the evaluation department can build a system to share children's self-assessment data and feedback with parents and teachers. For example, it can share data through an online platform. The evaluation department can also enable parents and teachers to check children's self-assessment and feedback in real time. For example, it can provide a dedicated app or dashboard. The evaluation department can also share the self-assessment and feedback process with parents and teachers, developing a system to jointly understand the learning situation. For example, it can provide regular reports and notification functions. This allows it to jointly understand the learning situation with parents and teachers.

[0044] The evaluation unit can visually display the feedback so that children can intuitively understand it. The evaluation unit, for example, builds a system that visually displays the feedback. For example, the content of the feedback is visualized using graphs or charts. The evaluation unit also visually displays the feedback so that children can intuitively understand it. For example, the evaluation unit provides feedback using icons or illustrations. The evaluation unit also develops a system that visually displays the content of the feedback so that children can easily understand it. For example, the evaluation unit provides feedback using animations or interactive elements. In this way, the feedback is visually displayed so that children can intuitively understand it.

[0045] The mobile app providing unit can analyze mobile app usage data and dynamically adjust app functions based on children's learning patterns. For example, the mobile app providing unit collects mobile app usage data in real time, and the generation AI analyzes the data to dynamically adjust app functions. For example, functions are customized according to usage frequency and study time. The mobile app providing unit also dynamically adjusts the mobile app functions based on children's learning patterns. For example, a reminder function is provided for children who study during specific times. The mobile app providing unit also dynamically adjusts app functions based on mobile app usage data. For example, new content is provided according to learning progress. This makes it possible to dynamically adjust app functions based on learning patterns.

[0046] The mobile app providing unit can monitor children's learning progress in real time through the mobile app and adjust learning content as needed. The mobile app providing unit, for example, builds a system that monitors children's learning progress in real time through the mobile app. For example, it displays study time and correct answer rate in real time. The mobile app providing unit also has a generation AI that dynamically adjusts learning content based on the learning progress data. For example, if progress is falling behind, it provides supplementary content. The mobile app providing unit also analyzes learning progress data collected through the mobile app, and the generation AI adjusts learning content as needed. For example, if understanding of a particular topic is insufficient, it prioritizes providing content related to that topic. This makes it possible to monitor learning progress in real time and adjust learning content.

[0047] The mobile app provider can introduce a function that allows parents and teachers to collaborate and share children's learning status through the mobile app. The mobile app provider, for example, introduces a function that allows parents and teachers to check children's learning status in real time through the mobile app. For example, it provides a dashboard that displays learning progress and grades. The mobile app provider also builds a system that allows parents and teachers to share children's learning status and provide feedback through the mobile app. For example, it provides a comment function and a message function. The mobile app provider also introduces a function that allows parents and teachers to share children's learning status through the mobile app and jointly create learning plans. For example, it provides a function to set learning goals and schedules. This allows collaboration with parents and teachers and sharing learning status.

[0048] The mobile app providing unit can provide multiple learning modules corresponding to different learning subjects and skills through the mobile app. For example, the mobile app providing unit provides learning modules corresponding to different learning subjects through the mobile app. For example, dedicated modules are provided for each subject, such as mathematics, English, and science. The mobile app providing unit also provides learning modules corresponding to different skills, and the generation AI dynamically adjusts the modules. For example, it provides modules that improve problem-solving skills and critical thinking skills. The mobile app providing unit also provides multiple learning modules through the mobile app, allowing children to select modules that suit their interests and learning styles. For example, it provides game-style modules and storytelling-style modules. This makes it possible to provide multiple learning modules corresponding to different learning subjects and skills.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The dialogue unit collects feedback from parents and teachers to understand children's interests and learning styles, and the generation AI can analyze it. For example, it analyzes observation records and comments provided by parents and teachers and reflects children's favorite activities and subjects in their profiles. The dialogue unit also uses the generation AI to evaluate children's interests based on feedback from parents and teachers. For example, it reflects children's favorite activities and subjects reported by parents and teachers in their profiles. The dialogue unit also regularly collects feedback from parents and teachers, and the generation AI analyzes that information to track changes in interests. For example, it collects feedback every semester to identify changes in interests. This allows it to analyze feedback from parents and teachers.

[0051] The dialogue unit analyzes children's past learning history and grade data to track changes in their interests and learning styles. For example, learning history data is collected and the generation AI analyzes that data to identify changes in their interests. For example, it identifies subjects and topics that they have spent a lot of time on in the past. The dialogue unit also analyzes grade data, and the generation AI evaluates the children's learning styles. For example, it identifies what learning methods are effective based on test scores and assignment submission status. The dialogue unit also analyzes a combination of learning history and grade data to track changes in their interests and learning styles. For example, it identifies the reasons why interests changed at a particular time and understands the background behind it. This makes it possible to track changes in children's interests and learning styles.

[0052] The dialogue unit can collect information indirectly through games and activities. For example, a system can be built that indirectly collects children's interests through games and activities. For example, interests are identified by analyzing choices and actions in a game. The dialogue unit also evaluates children's interests through activities. For example, interests are identified based on the frequency of participation in and responses to a particular activity. The dialogue unit also collects data on games and activities, and the generation AI analyzes the data to identify interests. For example, interests are evaluated by analyzing behavioral patterns and choices in a game. This makes it possible to indirectly collect information through games and activities.

[0053] The content generation unit can analyze children's learning progress in real time and dynamically adjust the learning content as needed. For example, learning progress data is collected in real time, and the generation AI analyzes that data to dynamically adjust the learning content. For example, if progress is lagging, supplementary content is provided. The content generation unit also adjusts the difficulty of the learning content according to children's learning progress. For example, if progress is rapid, more difficult questions are provided. The content generation unit also dynamically changes the order of learning content based on the learning progress data. For example, if understanding of a particular topic is insufficient, content related to that topic is provided preferentially. This allows the learning content to be dynamically adjusted according to learning progress.

[0054] The content generation unit can provide multimodal learning content that stimulates different senses, such as sight, hearing, and touch, according to children's learning styles. For example, visual learning content can be provided according to children's learning styles. For example, visual teaching materials using diagrams and graphs can be provided. To accommodate auditory learning styles, the content generation unit's generative AI can provide learning content in the form of audio commentary or podcasts. For example, historical storytelling can be provided audio. To accommodate tactile learning styles, the content generation unit's generative AI can provide interactive learning content. For example, it can provide an app that allows learning through touch operations on a tablet. This makes it possible to provide multimodal learning content that stimulates different senses.

[0055] The content generation unit can introduce collaboration functions to enable children with different learning styles to learn together. For example, it can build an online platform where children with different learning styles can learn together, for example, through group discussions or collaborative projects. The content generation unit can also use the collaboration functions to introduce a system where children with different learning styles can teach each other. For example, it can promote paired learning where children teach each other their favorite subjects. The content generation unit can also provide interactive tools for children with different learning styles to learn together. For example, it can provide an online whiteboard where children can solve problems together. This allows children with different learning styles to learn together.

[0056] The content generation unit can translate learning content into different languages ​​to promote learning from an international perspective. For example, a system can be built that automatically translates learning content into different languages. For example, it can support multiple languages ​​such as English, French, and Chinese. The content generation unit can also provide learning content translated into different languages ​​to promote learning from an international perspective. For example, it can provide content to deepen intercultural understanding. The content generation unit can also promote discussions and projects from an international perspective based on the translated learning content. For example, it can provide an online platform where children from different countries can learn together. This can translate into different languages ​​to promote learning from an international perspective.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The dialogue unit interacts with children to understand their interests and learning styles. For example, the generative AI asks questions such as "What is your favorite subject?" and "How do you like to study?" and creates a profile based on the children's answers. Step 2: The content generation unit generates customized learning content based on the information gathered by the dialogue unit. For example, the generation AI may provide math problems in the form of a game to a child who likes math, or provide learning content using historical storytelling to a child who likes history. Step 3: The gamification department introduces gamification elements into the learning content, such as a system that allows students to earn points based on their learning progress, or a system that allows students to level up by solving quiz-style questions. Step 4: The evaluation unit provides self-evaluation and feedback on the learning content. For example, the generation AI asks, "How did you do on this question?" and provides appropriate feedback based on the child's answer. Step 5: The mobile app provider provides learning content through mobile apps, for example, using smartphones or tablets, creating an environment where learning can be done anytime, anywhere.

[0059] (Example 2) A learning support system according to an embodiment of the present invention utilizes a generative AI to provide customized learning content tailored to children's interests and learning styles. This allows the learning support system to provide customized learning content tailored to children's interests and learning styles, thereby increasing their motivation to study.

[0060] A learning support system according to an embodiment includes a dialogue unit, a content generation unit, a gamification unit, an evaluation unit, and a mobile application provision unit. The dialogue unit dialogues with children to understand their interests and learning styles. For example, the dialogue unit allows a generation AI to ask questions such as "What is your favorite subject?" and "How do you like to study?" and creates a profile based on the children's answers. The generation AI performs analysis based on prompts containing questions about the children's interests and learning styles. The content generation unit generates customized learning content based on the information grasped by the dialogue unit. For example, the generation AI provides math problems in a game format to a child who likes math, and learning content using historical storytelling to a child who likes history. The generation AI generates learning content based on the children's profile information. The gamification unit incorporates gamification elements into the learning content. For example, it provides a system that allows children to earn points based on their learning progress or a system that allows them to level up by solving quiz-style problems. The generation AI generates content based on instructions for learning content that includes gamification elements. The evaluation unit provides self-evaluation and feedback on the learning content. For example, the generation AI asks, "How did you do on this problem?" and provides appropriate feedback based on the children's answer. The generation AI generates feedback based on prompts including self-evaluation questions. The mobile app provision unit provides the learning content through a mobile app. For example, it creates an environment where children can learn anytime, anywhere using a smartphone or tablet. The generation AI generates content based on instructions for the learning content through the mobile app. As a result, the learning support system according to the embodiment provides customized learning content tailored to children's interests and learning styles, thereby increasing their motivation to study. For example, even children who are not good at math can learn in a fun way through game-style problems, and children who like history can learn deeply through storytelling. Furthermore, through self-evaluation and feedback, children can understand their own learning status and use it in their next studies.Furthermore, by utilizing mobile apps, we can provide an environment where students can learn anytime, anywhere.

[0061] The dialogue unit analyzes children's facial expressions and tone of voice to estimate their emotional state and more accurately grasp their interests. For example, the dialogue unit uses a generative AI to capture children's facial expressions with a camera and analyze their emotional state using facial recognition technology. For example, it detects smiling or surprised expressions and estimates their interests based on those emotions. The dialogue unit also collects children's vocal tones with a microphone and estimates their emotional state using voice analysis technology. For example, it analyzes the pitch and intensity of their voices to evaluate their level of excitement or calmness. The dialogue unit also analyzes a combination of facial expressions and tone of voice to more accurately estimate their emotional state. For example, it evaluates the combination of a smiling face and a high-pitched voice as a positive emotion and identifies their interests. This allows for a more accurate understanding of children's interests.

[0062] The dialogue unit analyzes children's past learning history and grade data, and can track changes in their interests and learning styles. For example, the dialogue unit collects learning history data, and the generation AI analyzes that data to identify changes in their interests. For example, it identifies subjects and topics that they have spent a lot of time on in the past. The dialogue unit also analyzes grade data, and the generation AI evaluates the children's learning styles. For example, it identifies what learning methods are effective based on test scores and assignment submission status. The dialogue unit also analyzes a combination of learning history and grade data to track changes in their interests and learning styles. For example, it identifies the reasons why interests changed at a particular time and understands the background behind it. This makes it possible to track changes in children's interests and learning styles.

[0063] The dialogue unit uses an emotion estimation function to analyze how children feel in response to specific questions and can create a profile based on those emotions. For example, the dialogue unit uses the generation AI to ask children questions and then analyzes their facial expressions and tone of voice when they answer to estimate their emotions. For example, it analyzes their reactions to the question, "What is your favorite subject?" The dialogue unit also uses the emotion estimation function to reflect positive emotions expressed by children in their profiles. For example, it evaluates smiling faces and excited voices as positive emotions. The dialogue unit also analyzes emotional responses to questions and creates a profile based on the results. For example, if a child expresses positive emotions toward a specific subject, it records that subject in the profile as an area of ​​high interest. This allows profiles to be created based on children's emotions.

[0064] The dialogue unit collects feedback from parents and teachers, and the generation AI can analyze it. For example, the dialogue unit collects feedback from parents and teachers, and the generation AI analyzes that information to identify children's interests. For example, it analyzes observation records and comments provided by parents and teachers. The dialogue unit also evaluates children's interests based on the feedback provided by parents and teachers. For example, it reflects children's favorite activities and subjects reported by parents and teachers in the profile. The dialogue unit also regularly collects feedback from parents and teachers, and the generation AI analyzes that information to track changes in interests. For example, it collects feedback every semester to identify changes in interests. This makes it possible to analyze feedback from parents and teachers.

[0065] The dialogue unit can collect information indirectly through games and activities. The dialogue unit builds a system that indirectly collects children's interests through games and activities, for example. For example, interests are identified by analyzing choices and actions in a game. The dialogue unit also evaluates children's interests through activities. For example, interests are identified based on the frequency of participation in and responses to a particular activity. The dialogue unit also collects data on games and activities, and the generation AI analyzes the data to identify interests. For example, interests are evaluated by analyzing behavioral patterns and choices in a game. This makes it possible to collect information indirectly through games and activities.

[0066] The dialogue unit uses an emotion estimation function to monitor in real time what emotions children are feeling while they are learning and reflect that information in their profiles. For example, the dialogue unit monitors children's facial expressions and tone of voice in real time while they are learning and analyzes their emotional state using the emotion estimation function. For example, it collects emotional data using a camera or microphone. The dialogue unit also uses the emotion estimation function to evaluate children's emotional state while they are learning in real time and reflects that information in their profiles. For example, if they show positive emotions, it records that information in their profiles. The dialogue unit also collects emotional data while they are learning, and the generation AI analyzes that data to identify their interests. For example, if they show positive emotions toward specific learning content, it reflects that content in their profile as a high area of ​​interest. This allows emotions during learning to be monitored in real time and reflected in their profiles.

[0067] The content generation unit can analyze children's learning progress in real time and dynamically adjust the learning content as needed. For example, the content generation unit collects learning progress data in real time, and the generation AI analyzes the data to dynamically adjust the learning content. For example, if progress is lagging, supplementary content is provided. The content generation unit also has the generation AI adjust the difficulty of the learning content according to children's learning progress. For example, if progress is rapid, more difficult questions are provided. The content generation unit also has the generation AI dynamically change the order of learning content based on the learning progress data. For example, if understanding of a particular topic is insufficient, content related to that topic is provided preferentially. This allows the learning content to be dynamically adjusted according to learning progress.

[0068] The content generation unit can provide multimodal learning content that stimulates different senses, such as sight, hearing, and touch, according to children's learning styles. For example, the content generation unit provides visual learning content according to children's learning styles. For example, it provides visual teaching materials using diagrams and graphs. To accommodate auditory learning styles, the content generation unit uses a generative AI to provide learning content in the form of audio commentary or podcasts. For example, it provides historical storytelling in audio. To accommodate tactile learning styles, the content generation unit uses a generative AI to provide interactive learning content. For example, it provides an app that allows learning through touch operations using a tablet. This makes it possible to provide multimodal learning content that stimulates different senses.

[0069] The content generation unit can use the emotion estimation function to analyze how children feel about the learning content and adjust the content based on that emotion. For example, the content generation unit monitors the emotional state of children while they are learning in real time and analyzes their emotions using the emotion estimation function. For example, if they show positive emotions, it continues to provide that content. The content generation unit also uses the emotion estimation function to adjust the learning content if children show negative emotions. For example, it lowers the difficulty level or provides content in a different format. The content generation unit also collects emotional data about the learning content, and the generation AI analyzes that data to dynamically adjust the content. For example, it prioritizes providing content that shows positive emotions. This makes it possible to adjust the content based on their emotions about the learning content.

[0070] The content generation unit can introduce a collaboration function to enable children with different learning styles to learn together. For example, the content generation unit builds an online platform where children with different learning styles can learn together. For example, they can learn through group discussions or collaborative projects. The content generation unit also uses the collaboration function to introduce a system where children with different learning styles can teach each other. For example, it promotes pair learning where children teach each other their favorite subjects. The content generation unit also provides interactive tools for children with different learning styles to learn together. For example, it provides an online whiteboard where they can solve problems together. This allows children with different learning styles to learn together.

[0071] The content generation unit can translate learning content into different languages ​​to promote learning from an international perspective. For example, the content generation unit builds a system that automatically translates learning content into different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. The content generation unit also provides learning content translated into different languages ​​to promote learning from an international perspective. For example, it provides content to deepen intercultural understanding. The content generation unit also promotes discussions and projects from an international perspective based on the translated learning content. For example, it provides an online platform where children from different countries can learn together. This makes it possible to translate into different languages ​​and promote learning from an international perspective.

[0072] The content generation unit can use the emotion estimation function to identify the type of learning content that evokes the most positive emotions in children and provide that type preferentially. For example, the content generation unit monitors the emotional state of children while they are learning in real time and uses the emotion estimation function to identify the type of content that evokes the most positive emotions. For example, it evaluates video-type and game-type content. The content generation unit can also use the emotion estimation function to identify the type of learning content that evokes the most positive emotions in children and provide that type preferentially. For example, it can provide content in an interactive quiz format. The content generation unit can also collect emotion data about the learning content, and the generation AI can analyze the data to identify the type of content that evokes positive emotions. For example, it can provide storytelling-type content preferentially. This allows the type of learning content that evokes positive emotions to be provided preferentially.

[0073] The gamification unit can analyze children's gameplay data and dynamically adjust the optimal difficulty level and reward system. For example, the gamification unit collects gameplay data in real time, and the generation AI analyzes the data to dynamically adjust the game difficulty. For example, the difficulty level of questions can be changed according to the player's skill level. The gamification unit also allows the generation AI to design an optimal reward system based on children's gameplay data. For example, bonus points can be provided when certain achievement conditions are met. The gamification unit also analyzes gameplay data, and the generation AI can dynamically adjust the reward system to maintain children's motivation. For example, special rewards can be provided when children answer correctly consecutively. This allows the optimal difficulty level and reward system to be dynamically adjusted.

[0074] The gamification unit can customize in-game choices and story development based on children's interests. In the gamification unit, for example, a generation AI customizes in-game choices and story development based on children's interest data. For example, a story may develop based on a favorite character or theme. The gamification unit also dynamically changes in-game choices to match children's interests. For example, it may provide choices related to topics of particular interest. The gamification unit also analyzes children's interest data and a generation AI customizes the game's story development. For example, it may provide a story based on a historical event that interests children. This makes it possible to customize in-game choices and story development based on children's interests.

[0075] The gamification unit uses an emotion estimation function to analyze the emotions children are feeling in the game and adjust game elements based on those emotions. For example, the gamification unit monitors children's facial expressions and tone of voice in real time while playing the game and analyzes their emotional state using the emotion estimation function. For example, if a child shows positive emotions, the gamification unit strengthens those game elements. The gamification unit also uses the emotion estimation function to adjust game elements if the child shows negative emotions, for example, by lowering the difficulty or increasing the rewards. The gamification unit also collects in-game emotional data, and the generation AI analyzes the data to dynamically adjust game elements. For example, it prioritizes providing game elements that indicate positive emotions. This makes it possible to adjust game elements based on emotions in the game.

[0076] The gamification unit can introduce different game formats for different learning subjects and provide the optimal gamification elements for each subject. For example, the gamification unit uses a generation AI to design the optimal game format for different learning subjects. For example, it provides a puzzle game for mathematics and a storytelling game for history. The gamification unit also introduces different gamification elements for each learning subject, and the generation AI dynamically adjusts the elements. For example, it provides an experiment simulation game for science. The gamification unit also uses a generation AI to provide the optimal game format for different learning subjects, increasing children's motivation to learn. For example, it provides a word quiz game for English. This makes it possible to provide the optimal gamification elements for different learning subjects.

[0077] The gamification section introduces a competitive element into the game, allowing children to compete with their friends and family, thereby increasing their motivation to learn. The gamification section, for example, introduces a competitive element into the game and creates a system in which children can compete with their friends and family. For example, it provides a score ranking or a battle mode. The gamification section also uses the competitive element to increase children's motivation to learn. For example, it provides a system in which children can earn points and receive rewards by competing with friends. The gamification section also dynamically adjusts the competitive element in the game, allowing children to compete with their friends and family, thereby increasing their motivation to learn. For example, it provides regularly held online tournaments. This introduces a competitive element and increases children's motivation to learn.

[0078] The gamification unit uses an emotion estimation function to identify the game elements that children enjoy most and apply those elements to other learning content. For example, the gamification unit monitors children's emotional states in real time while they play a game and uses the emotion estimation function to identify the game elements they enjoy most. For example, it analyzes smiles and excited voices. The gamification unit also uses the emotion estimation function to identify the game elements that children enjoy most and apply those elements to other learning content. For example, it introduces game formats that show positive emotions to other subjects. The gamification unit also collects emotional data in the game, and the generation AI analyzes the data to identify the game elements that children enjoy most. For example, it applies specific game mechanics to other learning content. This allows the enjoyed game elements to be applied to other learning content.

[0079] The evaluation unit can analyze children's self-assessment data and provide feedback that is optimal for each individual learning style. For example, the evaluation unit collects children's self-assessment data, and the generation AI analyzes that data to provide feedback that is optimal for each individual learning style. For example, specific areas for improvement are suggested based on the results of self-assessment. The evaluation unit also allows the generation AI to provide feedback tailored to each individual learning style based on the self-assessment data. For example, feedback using diagrams and graphs is provided for visual learning styles. The evaluation unit also analyzes children's self-assessment data, and the generation AI dynamically adjusts the feedback that is optimal for each individual learning style. For example, audio commentary is provided for auditory learning styles. This makes it possible to provide feedback that is optimal for each individual learning style.

[0080] The evaluation unit customizes the content of the feedback to match children's interests, promoting more effective learning. In the evaluation unit, for example, the generation AI customizes the content of the feedback based on children's interest data. For example, feedback is provided using favorite characters or themes. The evaluation unit also dynamically adjusts the content of the feedback to match children's interests. For example, feedback is provided using specific examples related to topics that interest them. The evaluation unit also analyzes children's interest data, and the generation AI customizes the content of the feedback. For example, feedback related to subjects that interest them is provided. This makes it possible to provide feedback that matches children's interests, promoting effective learning.

[0081] The evaluation unit uses an emotion estimation function to analyze how children feel about the feedback and can adjust the feedback based on that emotion. For example, the evaluation unit monitors the facial expressions and tone of voice of children receiving feedback in real time and uses the emotion estimation function to analyze their emotional state. For example, if they show positive emotions, the evaluation unit strengthens the feedback. The evaluation unit also uses the emotion estimation function to adjust the content of the feedback if children show negative emotions. For example, it adds words of encouragement or specific suggestions for improvement. The evaluation unit also collects emotional data about the feedback, and the generation AI analyzes that data to dynamically adjust the feedback. For example, it preferentially provides feedback formats that show positive emotions. This makes it possible to adjust the feedback based on the emotions they feel about the feedback.

[0082] The evaluation department can share the self-assessment and feedback process with parents and teachers, enabling them to jointly understand the learning situation. For example, the evaluation department can build a system to share children's self-assessment data and feedback with parents and teachers. For example, it can share data through an online platform. The evaluation department can also enable parents and teachers to check children's self-assessment and feedback in real time. For example, it can provide a dedicated app or dashboard. The evaluation department can also share the self-assessment and feedback process with parents and teachers, developing a system to jointly understand the learning situation. For example, it can provide regular reports and notification functions. This allows it to jointly understand the learning situation with parents and teachers.

[0083] The evaluation unit can visually display the feedback so that children can intuitively understand it. The evaluation unit, for example, builds a system that visually displays the feedback. For example, the content of the feedback is visualized using graphs or charts. The evaluation unit also visually displays the feedback so that children can intuitively understand it. For example, the evaluation unit provides feedback using icons or illustrations. The evaluation unit also develops a system that visually displays the content of the feedback so that children can easily understand it. For example, the evaluation unit provides feedback using animations or interactive elements. In this way, the feedback is visually displayed so that children can intuitively understand it.

[0084] The evaluation unit can use the emotion estimation function to identify the feedback format that evokes the most positive emotions in children and provide that format preferentially. For example, the evaluation unit monitors the emotional state of children receiving feedback in real time and uses the emotion estimation function to identify the feedback format that indicates the most positive emotions. For example, it analyzes smiling faces and excited voices. The evaluation unit also uses the emotion estimation function to identify the feedback format that evokes the most positive emotions in children and provides that format preferentially. For example, it provides feedback that includes encouraging words and specific suggestions for improvement. The evaluation unit also collects emotional data regarding the feedback, and the generation AI analyzes the data to identify the feedback format that indicates the most positive emotions. For example, it provides visual feedback formats preferentially. This makes it possible to provide feedback formats that evoke positive emotions preferentially.

[0085] The mobile app providing unit can analyze mobile app usage data and dynamically adjust app functions based on children's learning patterns. For example, the mobile app providing unit collects mobile app usage data in real time, and the generation AI analyzes the data to dynamically adjust app functions. For example, functions are customized according to usage frequency and study time. The mobile app providing unit also dynamically adjusts the mobile app functions based on children's learning patterns. For example, a reminder function is provided for children who study during specific times. The mobile app providing unit also dynamically adjusts app functions based on mobile app usage data. For example, new content is provided according to learning progress. This makes it possible to dynamically adjust app functions based on learning patterns.

[0086] The mobile app providing unit can monitor children's learning progress in real time through the mobile app and adjust learning content as needed. The mobile app providing unit, for example, builds a system that monitors children's learning progress in real time through the mobile app. For example, it displays study time and correct answer rate in real time. The mobile app providing unit also has a generation AI that dynamically adjusts learning content based on the learning progress data. For example, if progress is falling behind, it provides supplementary content. The mobile app providing unit also analyzes learning progress data collected through the mobile app, and the generation AI adjusts learning content as needed. For example, if understanding of a particular topic is insufficient, it prioritizes providing content related to that topic. This makes it possible to monitor learning progress in real time and adjust learning content.

[0087] The mobile app provider can introduce a function that allows parents and teachers to collaborate and share children's learning status through the mobile app. The mobile app provider, for example, introduces a function that allows parents and teachers to check children's learning status in real time through the mobile app. For example, it provides a dashboard that displays learning progress and grades. The mobile app provider also builds a system that allows parents and teachers to share children's learning status and provide feedback through the mobile app. For example, it provides a comment function and a message function. The mobile app provider also introduces a function that allows parents and teachers to share children's learning status through the mobile app and jointly create learning plans. For example, it provides a function to set learning goals and schedules. This allows collaboration with parents and teachers and sharing learning status.

[0088] The mobile app providing unit can provide multiple learning modules corresponding to different learning subjects and skills through the mobile app. For example, the mobile app providing unit provides learning modules corresponding to different learning subjects through the mobile app. For example, dedicated modules are provided for each subject, such as mathematics, English, and science. The mobile app providing unit also provides learning modules corresponding to different skills, and the generation AI dynamically adjusts the modules. For example, it provides modules that improve problem-solving skills and critical thinking skills. The mobile app providing unit also provides multiple learning modules through the mobile app, allowing children to select modules that suit their interests and learning styles. For example, it provides game-style modules and storytelling-style modules. This makes it possible to provide multiple learning modules corresponding to different learning subjects and skills.

[0089] The mobile app providing unit can use an emotion estimation function to identify app features and designs that evoke the most positive emotions in children and prioritize the introduction of those elements. For example, the mobile app providing unit monitors the emotional state of children in real time while using the mobile app and uses the emotion estimation function to identify app features and designs that evoke the most positive emotions. For example, it analyzes smiling faces and excited voices. The mobile app providing unit also uses the emotion estimation function to identify app features and designs that evoke the most positive emotions in children and prioritize the introduction of those elements. For example, it provides interactive game elements and colorful designs. The mobile app providing unit also collects emotional data while the mobile app is being used, and the generation AI analyzes the data to identify features and designs that evoke the most positive emotions. For example, it dynamically adjusts the user interface design. This allows the app features and designs that evoke positive emotions to be introduced preferentially.

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

[0091] The dialogue unit collects feedback from parents and teachers to understand children's interests and learning styles, and the generation AI can analyze it. For example, it analyzes observation records and comments provided by parents and teachers and reflects children's favorite activities and subjects in their profiles. The dialogue unit also uses the generation AI to evaluate children's interests based on feedback from parents and teachers. For example, it reflects children's favorite activities and subjects reported by parents and teachers in their profiles. The dialogue unit also regularly collects feedback from parents and teachers, and the generation AI analyzes that information to track changes in interests. For example, it collects feedback every semester to identify changes in interests. This allows it to analyze feedback from parents and teachers.

[0092] The dialogue unit analyzes children's facial expressions and tone of voice to estimate their emotional state and more accurately grasp their interests. For example, the generative AI captures children's facial expressions with a camera and analyzes their emotional state using facial recognition technology. For example, it detects smiling or surprised expressions and estimates their interests based on those emotions. The dialogue unit also collects children's vocal tones with a microphone and estimates their emotional state using voice analysis technology. For example, it analyzes the pitch and intensity of their voices to evaluate their level of excitement or calmness. The dialogue unit also analyzes a combination of facial expressions and tone of voice to more accurately estimate their emotional state. For example, it evaluates the combination of a smiling face and a high-pitched voice as a positive emotion and identifies their interests. This allows for a more accurate understanding of children's interests.

[0093] The dialogue unit analyzes children's past learning history and grade data to track changes in their interests and learning styles. For example, learning history data is collected and the generation AI analyzes that data to identify changes in their interests. For example, it identifies subjects and topics that they have spent a lot of time on in the past. The dialogue unit also analyzes grade data, and the generation AI evaluates the children's learning styles. For example, it identifies what learning methods are effective based on test scores and assignment submission status. The dialogue unit also analyzes a combination of learning history and grade data to track changes in their interests and learning styles. For example, it identifies the reasons why interests changed at a particular time and understands the background behind it. This makes it possible to track changes in children's interests and learning styles.

[0094] The dialogue unit uses the emotion estimation function to analyze how children feel in response to specific questions and can create a profile based on those emotions. For example, the generation AI asks children questions and then analyzes their facial expressions and tone of voice when they answer to estimate their emotions. For example, it analyzes their reactions to the question, "What is your favorite subject?" The dialogue unit also uses the emotion estimation function to reflect positive emotions expressed by children in their profiles. For example, it evaluates smiling faces and excited voices as positive emotions. The dialogue unit also analyzes emotional responses to questions and creates a profile based on the results. For example, if a child expresses positive emotions toward a specific subject, it records that subject in the profile as an area of ​​high interest. This allows profiles to be created based on children's emotions.

[0095] The dialogue unit can collect information indirectly through games and activities. For example, a system can be built that indirectly collects children's interests through games and activities. For example, interests are identified by analyzing choices and actions in a game. The dialogue unit also evaluates children's interests through activities. For example, interests are identified based on the frequency of participation in and responses to a particular activity. The dialogue unit also collects data on games and activities, and the generation AI analyzes the data to identify interests. For example, interests are evaluated by analyzing behavioral patterns and choices in a game. This makes it possible to indirectly collect information through games and activities.

[0096] The content generation unit can analyze children's learning progress in real time and dynamically adjust the learning content as needed. For example, learning progress data is collected in real time, and the generation AI analyzes that data to dynamically adjust the learning content. For example, if progress is lagging, supplementary content is provided. The content generation unit also adjusts the difficulty of the learning content according to children's learning progress. For example, if progress is rapid, more difficult questions are provided. The content generation unit also dynamically changes the order of learning content based on the learning progress data. For example, if understanding of a particular topic is insufficient, content related to that topic is provided preferentially. This allows the learning content to be dynamically adjusted according to learning progress.

[0097] The content generation unit can provide multimodal learning content that stimulates different senses, such as sight, hearing, and touch, according to children's learning styles. For example, visual learning content can be provided according to children's learning styles. For example, visual teaching materials using diagrams and graphs can be provided. To accommodate auditory learning styles, the content generation unit's generative AI can provide learning content in the form of audio commentary or podcasts. For example, historical storytelling can be provided audio. To accommodate tactile learning styles, the content generation unit's generative AI can provide interactive learning content. For example, it can provide an app that allows learning through touch operations on a tablet. This makes it possible to provide multimodal learning content that stimulates different senses.

[0098] The content generation unit can use the emotion estimation function to analyze how children feel about the learning content and adjust the content based on that emotion. For example, it can monitor the emotional state of children while they are learning in real time and use the emotion estimation function to analyze their emotions. For example, if they show positive emotions, it can continue to provide that content. The content generation unit can also use the emotion estimation function to adjust the learning content if children show negative emotions. For example, it can lower the difficulty level or provide content in a different format. The content generation unit also collects emotional data about the learning content, and the generation AI analyzes that data to dynamically adjust the content. For example, it can prioritize providing content that shows positive emotions. This makes it possible to adjust the content based on their emotions about the learning content.

[0099] The content generation unit can introduce collaboration functions to enable children with different learning styles to learn together. For example, it can build an online platform where children with different learning styles can learn together, for example, through group discussions or collaborative projects. The content generation unit can also use the collaboration functions to introduce a system where children with different learning styles can teach each other. For example, it can promote paired learning where children teach each other their favorite subjects. The content generation unit can also provide interactive tools for children with different learning styles to learn together. For example, it can provide an online whiteboard where children can solve problems together. This allows children with different learning styles to learn together.

[0100] The content generation unit can translate learning content into different languages ​​to promote learning from an international perspective. For example, a system can be built that automatically translates learning content into different languages. For example, it can support multiple languages ​​such as English, French, and Chinese. The content generation unit can also provide learning content translated into different languages ​​to promote learning from an international perspective. For example, it can provide content to deepen intercultural understanding. The content generation unit can also promote discussions and projects from an international perspective based on the translated learning content. For example, it can provide an online platform where children from different countries can learn together. This can translate into different languages ​​to promote learning from an international perspective.

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

[0102] Step 1: The dialogue unit interacts with children to understand their interests and learning styles. For example, the generative AI asks questions such as "What is your favorite subject?" and "How do you like to study?" and creates a profile based on the children's answers. Step 2: The content generation unit generates customized learning content based on the information gathered by the dialogue unit. For example, the generation AI may provide math problems in the form of a game to a child who likes math, or provide learning content using historical storytelling to a child who likes history. Step 3: The gamification department introduces gamification elements into the learning content, such as a system that allows students to earn points based on their learning progress, or a system that allows students to level up by solving quiz-style questions. Step 4: The evaluation unit provides self-evaluation and feedback on the learning content. For example, the generation AI asks, "How did you do on this question?" and provides appropriate feedback based on the child's answer. Step 5: The mobile app provider provides learning content through mobile apps, for example, using smartphones or tablets, creating an environment where learning can be done anytime, anywhere.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

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

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Equipped with generative AI, The generated AI is The dialogue department engages in dialogue with children to understand their interests and learning styles. a content generation unit that generates customized learning content based on the information acquired by the dialogue unit; a gamification unit that introduces gamification elements into the learning content; an evaluation unit that provides self-evaluation and feedback for the learning content; a mobile application providing unit that provides the learning content through a mobile application; A system characterized by:

2. The dialogue unit Analyzing the children's facial expressions and tone of voice to estimate their emotional state and more accurately grasp their interests 2. The system of claim 1.

3. The dialogue unit Analyze the children's past learning history and grade data to track changes in their interests and learning styles.

2. The system of claim 1.

4. The dialogue unit Analyze how the child feels about specific questions and create a profile based on those feelings 2. The system of claim 1.

5. The dialogue unit Feedback from parents and teachers is collected and analyzed by the generative AI.

2. The system of claim 1.

6. The dialogue unit Collecting information indirectly through games and activities 2. The system of claim 1.

7. The dialogue unit Monitor how the children feel while they are learning in real time and reflect that information in their profile.

2. The system of claim 1.

Images