System

Abstract

An object of the system according to the embodiment is to provide an optimal learning environment to each student and to follow the progress of learning.SOLUTION: A system includes a lesson providing unit, a customization unit, and a progress tracking unit. The lesson providing unit provides a lesson using the generated AI. The customization unit provides learning environments individually customized by the generation AI. The progress tracker tracks the student's learning progress and provides feedback.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 an optimal learning environment for each student, which can lead to differences in learning progress.

[0005] The system according to the embodiment aims to provide an optimal learning environment for each student and to follow their learning progress. [Means for solving the problem]

[0006] The system according to the embodiment includes a lesson providing unit, a customization unit, and a progress tracking unit. The lesson providing unit provides lessons using a generation AI. The customization unit provides a learning environment that is individually customized by the generation AI. The progress tracking unit tracks the student's learning progress and provides feedback. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal learning environment for each student and follow their learning progress. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A learning support system according to an embodiment of the present invention is a system in which an AI teacher teaches children on a screen, providing them with "learning." This system provides a completely customized learning environment for each student, providing appropriate instruction according to each student's learning progress, tracking their learning progress and realizing a learning environment in which no one is left behind. This allows the learning support system to provide an effective learning environment for children. For example, by having an AI teacher teach on a screen, students can receive a high-quality education even from the comfort of their own home. Furthermore, because the individually customized learning environment provides instruction tailored to each student's learning needs, it tracks their learning progress and realizes a learning environment in which no one is left behind.

[0029] A learning assistance system according to an embodiment includes a lesson providing unit, a customization unit, and a progress tracking unit. The lesson providing unit provides lessons using a generation AI. For example, the generation AI automatically generates lesson content using a text generation AI (e.g., GPT-3) and explains it to students in an easy-to-understand manner. The generation AI can also use a multimodal generation AI to visually explain the lesson content. For example, in a math class, the generation AI might start the lesson by saying, "Today, let's learn about fraction addition," and provide explanations using specific examples. The customization unit provides a learning environment individually customized by the generation AI. For example, the generation AI generates individually customized learning content based on each student's learning progress and level of understanding. For example, if a student is struggling with a particular unit, the generation AI can provide that student with additional practice problems and supplementary explanations. Furthermore, more advanced content is provided to faster-progressing students. The progress tracking unit tracks students' learning progress and provides feedback. For example, the generation AI analyzes the accuracy rate and answer time of the problems solved by the student, and based on the results, provides feedback such as, "You did well on this problem. Next time, try a more difficult problem." As a result, the learning support system according to the embodiment provides an individually customized learning environment and provides appropriate guidance according to the student's learning progress, realizing a learning environment where no one is left behind.

[0030] The lesson providing unit can generate multimodal teaching materials to accommodate different learning styles for the lesson content. For example, the lesson providing unit explains the lesson content using diagrams, graphs, and animations to accommodate visual learning styles. For example, in a math class, adding fractions is shown using animation. The lesson providing unit also explains the lesson content through audio to accommodate auditory learning styles. For example, important events are explained through audio in a history class. The lesson providing unit also provides interactive teaching materials to accommodate tactile learning styles. For example, a virtual experiment is performed in a science class so that students can actually operate the device. This makes it possible to improve students' learning effectiveness by accommodating different learning styles.

[0031] The lesson providing unit generates lesson content that corresponds to different cultures and languages, and can provide an international learning environment. For example, the lesson providing unit builds a system that automatically translates lesson content to accommodate different languages. For example, lessons are provided in multiple languages ​​such as English, French, and Chinese. The lesson providing unit also generates lesson content that takes cultural background into consideration to accommodate different cultures. For example, the history of each country is introduced in a history class. Furthermore, the lesson providing unit implements online classes in which students from different countries can participate to provide an international learning environment. For example, international discussions are held. In this way, an international learning environment can be provided by accommodating different cultures and languages.

[0032] The customization unit can analyze a student's learning history and performance data, and predict and customize future learning content. The customization unit, for example, analyzes a student's past learning history and builds a system that predicts future learning content. For example, it determines the next learning content based on past grades and learning patterns. The customization unit also provides an individually customized learning plan based on the student's performance data. For example, it strengthens areas of strength and reinforces areas of weakness. The customization unit also integrates learning history and performance data to develop a system that dynamically adjusts future learning content. For example, it changes the content according to the student's learning progress. In this way, it is possible to predict and customize future learning content based on the student's learning history and performance data, thereby providing an individually optimized learning environment.

[0033] The customization unit can enhance motivation to learn by associating learning content based on the student's interests and hobbies. The customization unit, for example, analyzes the student's interests and hobbies and builds a system that associates learning content based on the analysis. For example, a student who likes sports is provided with sports-related math problems. The customization unit also customizes learning content based on the student's hobbies. For example, a student who likes music is provided with music-related history lessons. The customization unit also enhances motivation to learn by incorporating topics that interest the student into the learning content. For example, a student who is interested in science is provided with lessons on the latest science and technology. In this way, learning content can be associated with the student's interests and hobbies, thereby enhancing motivation to learn.

[0034] The customization unit can optimize a student's learning environment depending on different locations, such as at home or at school. For example, the customization unit builds a system that provides an optimal learning environment for students when studying at home. For example, it provides learning content that is suited to the home's internet environment and devices. The customization unit also provides learning plans that are suited to classroom facilities and timetables in order to optimize the learning environment at school. For example, it conducts lessons using classroom projectors and tablets. The customization unit also develops a system that dynamically adjusts the learning environment in different locations. For example, it provides content that is suited to studying in a library, cafe, etc. This allows students to study effectively anywhere by optimizing their learning environment depending on different locations.

[0035] The customization unit can provide group learning and pair learning opportunities according to the student's learning progress, and promote a cooperative learning environment. For example, the customization unit builds a system that analyzes the student's learning progress and provides appropriate group learning and pair learning opportunities. For example, students at the same progress are divided into groups to engage in collaborative learning. The customization unit also provides group learning and pair learning activities to promote a cooperative learning environment. For example, the customization unit assigns assignments to work on projects together. The customization unit also develops a system that dynamically adjusts groups and pairs according to the student's learning progress. For example, to support a student who is lagging behind, the student is paired with a student who is ahead. In this way, a cooperative learning environment can be promoted by providing group learning and pair learning opportunities according to the student's learning progress.

[0036] The progress tracking unit can analyze a student's learning progress in real time and provide instant feedback. The progress tracking unit, for example, builds a system that analyzes a student's learning progress in real time and provides instant feedback. For example, it displays whether the problem is correct or incorrect and provides an explanation immediately after solving the problem. The progress tracking unit also tracks the learning progress in real time and provides feedback according to the progress. For example, if the progress is behind, it provides additional practice problems. The progress tracking unit also analyzes the student's learning data in real time and provides individually customized feedback. For example, it strengthens strong areas and reinforces weak areas. In this way, by analyzing a student's learning progress in real time and providing instant feedback, it is possible to improve learning effectiveness.

[0037] The progress tracking unit can track a student's learning data over the long term and analyze learning trends and patterns to optimize feedback. The progress tracking unit, for example, builds a system that tracks a student's learning data over the long term and analyzes learning trends and patterns. For example, it provides feedback based on past grades and learning history. The progress tracking unit also analyzes learning data over the long term and provides individually customized feedback. For example, if a student's grades tend to decline at a certain period, it can provide focused support at that time. The progress tracking unit also analyzes a student's learning patterns and develops a system that provides optimal feedback. For example, it adjusts the content and timing of the feedback according to the student's learning progress. In this way, it is possible to improve learning effectiveness by tracking a student's learning data over the long term and optimizing feedback by analyzing their learning trends and patterns.

[0038] The progress tracking unit can share students' learning progress with parents and teachers and work together to support their learning. The progress tracking unit, for example, builds a system that shares students' learning progress with parents and teachers in real time. For example, it allows students' learning progress and grades to be checked online. The progress tracking unit also allows parents and teachers to work together to support learning by sharing learning progress. For example, it can provide learning support at home or additional instruction at school. The progress tracking unit also shares students' learning data so that parents and teachers can provide appropriate feedback. For example, it can provide advice and support according to learning progress. In this way, students' learning progress can be shared with parents and teachers, and they can work together to support learning, thereby improving learning effectiveness.

[0039] The progress tracking unit can provide rewards and incentives according to the student's learning progress, thereby increasing motivation to learn. The progress tracking unit, for example, builds a system that provides rewards and incentives according to the student's learning progress. For example, points or badges are awarded when a specific goal is achieved. The progress tracking unit also provides rewards to increase motivation according to the student's learning progress. For example, benefits are provided when grades improve. The progress tracking unit also develops a system that provides individually customized incentives based on the student's learning data. For example, special tasks are provided to strengthen areas of strength. In this way, rewards and incentives according to the student's learning progress can be provided, thereby increasing motivation to learn.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The lesson provider can increase students' motivation to learn by gamifying the content of lessons. For example, in math classes, students accumulate points for each problem they solve, and when they reach a certain number of points, they receive a virtual reward. In history classes, the lesson proceeds in a quiz format, and students can move on to the next stage with each correct answer. Furthermore, in science classes, students can conduct virtual experiments and move on to the next step based on the results of the experiments. In this way, gamifying the content of lessons can increase students' motivation to learn.

[0042] The lesson provider can deepen students' understanding by relating the lesson content to real life. For example, in math classes, they show examples of everyday applications such as shopping and cooking. In science classes, they use real-life examples related to environmental issues and health. Furthermore, in history classes, they explain the relevance to modern society and show how past events affect the present. This allows students to deepen their understanding by relating the lesson content to real life.

[0043] The lesson delivery department can cater to students' learning styles by delivering lesson content in different media formats. For example, visual learners can receive explanations using diagrams, graphs, and animations. Auditory learners can receive lessons in audio or podcast format. And tactile learners can receive interactive learning materials and experiment kits. In this way, lesson content can be delivered in different media formats to cater to students' learning styles.

[0044] The teaching department can accommodate a diverse range of students by adapting the content of lessons to different cultures and backgrounds. For example, by covering the history and literature of different cultural spheres, students can broaden their international perspective. It also provides content that encourages empathy for students from different backgrounds. Furthermore, by holding discussions that incorporate diverse perspectives, students can develop an attitude of respect for different opinions. In this way, the teaching department can accommodate a diverse range of students by adapting the content of lessons to different cultures and backgrounds.

[0045] The customization department can predict and customize future learning content based on a student's learning history and performance data. For example, it can analyze past grades and learning patterns to determine the next learning content. It can also provide learning plans to strengthen strong areas and reinforce weak areas. Furthermore, a system will be developed that dynamically adjusts content according to the student's learning progress. This makes it possible to predict and customize future learning content based on the student's learning history and performance data, thereby providing an individually optimized learning environment.

[0046] The customization unit can optimize a student's learning environment for different locations. For example, when studying at home, learning content is provided that is appropriate for the internet environment and device. To optimize the learning environment at school, learning plans are provided that are appropriate for classroom facilities and timetables. Furthermore, content suitable for studying in libraries, cafes, etc. is provided. This allows students to study effectively anywhere by optimizing their learning environment for different locations.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The lesson provider uses a generative AI to provide lessons. For example, the generative AI uses a text generation AI (e.g., GPT-3) to automatically generate lesson content and explain it to students in an easy-to-understand manner. The generative AI can also use a multimodal generative AI to visually explain the lesson content. For example, in a math lesson, the generative AI might start the lesson by saying, "Today, let's learn about adding fractions," and then use specific examples to explain the lesson. Step 2: The customization section uses the generative AI to provide an individually customized learning environment. For example, the generative AI generates individually customized learning content based on each student's learning progress and level of understanding. For example, if a student is struggling with a particular unit, the generative AI will provide that student with additional practice problems and supplementary explanations. It will also provide more advanced content to students who are progressing quickly. Step 3: The progress tracking unit tracks the student's learning progress and provides feedback. For example, the generation AI analyzes the student's accuracy rate and the time it took to solve the problems, and based on the results, provides feedback such as, "You did well on this problem. Let's try a more difficult problem next time."

[0049] (Example 2) A learning support system according to an embodiment of the present invention is a system in which an AI teacher teaches children on a screen, providing them with "learning." This system provides a completely customized learning environment for each student, providing appropriate instruction according to each student's learning progress, tracking their learning progress and realizing a learning environment in which no one is left behind. This allows the learning support system to provide an effective learning environment for children. For example, by having an AI teacher teach on a screen, students can receive a high-quality education even from the comfort of their own home. Furthermore, because the individually customized learning environment provides instruction tailored to each student's learning needs, it tracks their learning progress and realizes a learning environment in which no one is left behind.

[0050] A learning assistance system according to an embodiment includes a lesson providing unit, a customization unit, and a progress tracking unit. The lesson providing unit provides lessons using a generation AI. For example, the generation AI automatically generates lesson content using a text generation AI (e.g., GPT-3) and explains it to students in an easy-to-understand manner. The generation AI can also use a multimodal generation AI to visually explain the lesson content. For example, in a math class, the generation AI might start the lesson by saying, "Today, let's learn about fraction addition," and provide explanations using specific examples. The customization unit provides a learning environment individually customized by the generation AI. For example, the generation AI generates individually customized learning content based on each student's learning progress and level of understanding. For example, if a student is struggling with a particular unit, the generation AI can provide that student with additional practice problems and supplementary explanations. Furthermore, more advanced content is provided to faster-progressing students. The progress tracking unit tracks students' learning progress and provides feedback. For example, the generation AI analyzes the accuracy rate and answer time of the problems solved by the student, and based on the results, provides feedback such as, "You did well on this problem. Next time, try a more difficult problem." As a result, the learning support system according to the embodiment provides an individually customized learning environment and provides appropriate guidance according to the student's learning progress, realizing a learning environment where no one is left behind.

[0051] The lesson provider uses generative AI to perform sentiment analysis on lesson content and dynamically change the lesson content based on students' interests. For example, the lesson provider analyzes students' facial expressions and tone of voice during class to assess their level of interest in real time. For example, it provides more detailed explanations and additional examples for topics that interest students. The lesson provider also builds a system that dynamically changes the lesson content based on students' reactions. For example, if a student looks bored, it can speed up the lesson or add interactive elements. The lesson provider also uses sentiment analysis to identify topics that are likely to interest students and generate lesson content related to those topics. For example, if a student is interested in science, it can incorporate science-related examples and experiments. This makes it possible to dynamically change lesson content based on students' interests and improve learning effectiveness.

[0052] The lesson providing unit can analyze students' facial expressions and tone of voice during class, evaluate their level of understanding and interest in real time, and adjust the progress of the class. For example, the lesson providing unit builds a system that analyzes students' facial expressions and tone of voice and evaluates their level of understanding in real time. For example, if a student looks confused, the lesson proceeds slowly and additional explanations are provided. The lesson providing unit also dynamically adjusts the progress of the class based on the student's response. For example, if it is determined that the student understands, it moves on to the next topic. The lesson providing unit also analyzes the student's tone of voice and evaluates their level of interest. For example, if it is determined that interest is low, it changes the lesson content and switches to a more interesting topic. In this way, the progress of the class can be adjusted based on the student's level of understanding and interest, thereby improving the effectiveness of learning.

[0053] The lesson providing unit can use the emotion estimation function to detect stress or anxiety felt by students during class and provide interactive exercises to help them relax. The lesson providing unit, for example, builds a system to detect stress or anxiety by analyzing students' facial expressions and tone of voice. For example, if a student is feeling tense, it provides them with simple exercises to help them relax. Furthermore, if the lesson providing unit uses the emotion estimation function to determine that a student is feeling stressed, it plays music or videos that have a relaxing effect. Furthermore, if a student is feeling anxious, it provides them with interactive exercises to help them relax. For example, it provides instruction on deep breathing or simple stretching. This reduces students' stress and anxiety and allows them to proceed with their studies in a relaxed state.

[0054] The lesson providing unit can generate multimodal teaching materials to accommodate different learning styles for the lesson content. For example, the lesson providing unit explains the lesson content using diagrams, graphs, and animations to accommodate visual learning styles. For example, in a math class, adding fractions is shown using animation. The lesson providing unit also explains the lesson content through audio to accommodate auditory learning styles. For example, important events are explained through audio in a history class. The lesson providing unit also provides interactive teaching materials to accommodate tactile learning styles. For example, a virtual experiment is performed in a science class so that students can actually operate the device. This makes it possible to improve students' learning effectiveness by accommodating different learning styles.

[0055] The lesson providing unit generates lesson content that corresponds to different cultures and languages, and can provide an international learning environment. For example, the lesson providing unit builds a system that automatically translates lesson content to accommodate different languages. For example, lessons are provided in multiple languages ​​such as English, French, and Chinese. The lesson providing unit also generates lesson content that takes cultural background into consideration to accommodate different cultures. For example, the history of each country is introduced in a history class. Furthermore, the lesson providing unit implements online classes in which students from different countries can participate to provide an international learning environment. For example, international discussions are held. In this way, an international learning environment can be provided by accommodating different cultures and languages.

[0056] The lesson providing unit can use the emotion estimation function to provide messages and activities to improve motivation according to the student's emotions. The lesson providing unit, for example, analyzes the student's emotions and provides messages to improve motivation. For example, if the student is feeling down, it displays an encouraging message. The lesson providing unit also uses the emotion estimation function to provide activities according to the student's emotions. For example, if the student is tired, it suggests a short break to refresh the student. The lesson providing unit also provides interactive games and quizzes to improve motivation based on the student's emotion data. For example, it conducts quizzes related to the learning content. In this way, by providing messages and activities to improve motivation according to the student's emotions, it is possible to increase the student's motivation to learn.

[0057] The customization unit can analyze a student's learning history and performance data, and predict and customize future learning content. The customization unit, for example, analyzes a student's past learning history and builds a system that predicts future learning content. For example, it determines the next learning content based on past grades and learning patterns. The customization unit also provides an individually customized learning plan based on the student's performance data. For example, it strengthens areas of strength and reinforces areas of weakness. The customization unit also integrates learning history and performance data to develop a system that dynamically adjusts future learning content. For example, it changes the content according to the student's learning progress. In this way, it is possible to predict and customize future learning content based on the student's learning history and performance data, thereby providing an individually optimized learning environment.

[0058] The customization unit can enhance motivation to learn by associating learning content based on the student's interests and hobbies. The customization unit, for example, analyzes the student's interests and hobbies and builds a system that associates learning content based on the analysis. For example, a student who likes sports is provided with sports-related math problems. The customization unit also customizes learning content based on the student's hobbies. For example, a student who likes music is provided with music-related history lessons. The customization unit also enhances motivation to learn by incorporating topics that interest the student into the learning content. For example, a student who is interested in science is provided with lessons on the latest science and technology. In this way, learning content can be associated with the student's interests and hobbies, thereby enhancing motivation to learn.

[0059] The customization unit can use the emotion estimation function to analyze students' emotional responses to learning and provide learning content that elicits positive emotions. The customization unit, for example, builds a system that analyzes students' emotions and provides learning content that elicits positive emotions. For example, it provides interactive learning materials that allow students to enjoy learning. The customization unit also uses the emotion estimation function to analyze students' emotional responses to learning and provides feedback that elicits positive emotions. For example, it displays messages of praise or encouragement. The customization unit also provides learning activities that elicit positive emotions based on the students' emotional data. For example, it conducts game-style learning or group work. In this way, by analyzing students' emotional responses and providing learning content that elicits positive emotions, it is possible to increase students' motivation to learn.

[0060] The customization unit can optimize a student's learning environment depending on different locations, such as at home or at school. For example, the customization unit builds a system that provides an optimal learning environment for students when studying at home. For example, it provides learning content that is suited to the home's internet environment and devices. The customization unit also provides learning plans that are suited to classroom facilities and timetables in order to optimize the learning environment at school. For example, it conducts lessons using classroom projectors and tablets. The customization unit also develops a system that dynamically adjusts the learning environment in different locations. For example, it provides content that is suited to studying in a library, cafe, etc. This allows students to study effectively anywhere by optimizing their learning environment depending on different locations.

[0061] The customization unit can provide group learning and pair learning opportunities according to the student's learning progress, and promote a cooperative learning environment. For example, the customization unit builds a system that analyzes the student's learning progress and provides appropriate group learning and pair learning opportunities. For example, students at the same progress are divided into groups to engage in collaborative learning. The customization unit also provides group learning and pair learning activities to promote a cooperative learning environment. For example, the customization unit assigns assignments to work on projects together. The customization unit also develops a system that dynamically adjusts groups and pairs according to the student's learning progress. For example, to support a student who is lagging behind, the student is paired with a student who is ahead. In this way, a cooperative learning environment can be promoted by providing group learning and pair learning opportunities according to the student's learning progress.

[0062] The customization unit can use the emotion estimation function to dynamically adjust the learning environment based on the student's emotional response to learning. The customization unit, for example, analyzes the student's emotions and builds a system that dynamically adjusts the learning environment. For example, if a student is feeling stressed, it provides a relaxing environment. The customization unit also uses the emotion estimation function to adjust the learning environment based on the student's emotional response. For example, it provides a quiet environment so that the student can concentrate. The customization unit also develops a system that dynamically changes the learning environment based on the student's emotional data. For example, if a student is tired, it suggests a short break. In this way, the learning environment can be dynamically adjusted based on the student's emotional response to provide an optimal learning environment.

[0063] The progress tracking unit can analyze a student's learning progress in real time and provide instant feedback. The progress tracking unit, for example, builds a system that analyzes a student's learning progress in real time and provides instant feedback. For example, it displays whether the problem is correct or incorrect and provides an explanation immediately after solving the problem. The progress tracking unit also tracks the learning progress in real time and provides feedback according to the progress. For example, if the progress is behind, it provides additional practice problems. The progress tracking unit also analyzes the student's learning data in real time and provides individually customized feedback. For example, it strengthens strong areas and reinforces weak areas. In this way, by analyzing a student's learning progress in real time and providing instant feedback, it is possible to improve learning effectiveness.

[0064] The progress tracking unit can track a student's learning data over the long term and analyze learning trends and patterns to optimize feedback. The progress tracking unit, for example, builds a system that tracks a student's learning data over the long term and analyzes learning trends and patterns. For example, it provides feedback based on past grades and learning history. The progress tracking unit also analyzes learning data over the long term and provides individually customized feedback. For example, if a student's grades tend to decline at a certain period, it can provide focused support at that time. The progress tracking unit also analyzes a student's learning patterns and develops a system that provides optimal feedback. For example, it adjusts the content and timing of the feedback according to the student's learning progress. In this way, it is possible to improve learning effectiveness by tracking a student's learning data over the long term and optimizing feedback by analyzing their learning trends and patterns.

[0065] The progress tracking unit can use the emotion estimation function to analyze a student's emotional response to learning and reinforce positive feedback. The progress tracking unit, for example, analyzes a student's emotions and builds a system to reinforce positive feedback. For example, if a student is happy, it provides words of praise or encouraging messages. The progress tracking unit also uses the emotion estimation function to adjust feedback based on the student's emotional response. For example, if a student is depressed, it displays an encouraging message. The progress tracking unit also develops a system to reinforce positive feedback based on the student's emotional data. For example, if the emotion score is high, it provides additional praise or rewards. In this way, by analyzing a student's emotional response and reinforcing positive feedback, it is possible to increase motivation to learn.

[0066] The progress tracking unit can share students' learning progress with parents and teachers and work together to support their learning. The progress tracking unit, for example, builds a system that shares students' learning progress with parents and teachers in real time. For example, it allows students' learning progress and grades to be checked online. The progress tracking unit also allows parents and teachers to work together to support learning by sharing learning progress. For example, it can provide learning support at home or additional instruction at school. The progress tracking unit also shares students' learning data so that parents and teachers can provide appropriate feedback. For example, it can provide advice and support according to learning progress. In this way, students' learning progress can be shared with parents and teachers, and they can work together to support learning, thereby improving learning effectiveness.

[0067] The progress tracking unit can provide rewards and incentives according to the student's learning progress, thereby increasing motivation to learn. The progress tracking unit, for example, builds a system that provides rewards and incentives according to the student's learning progress. For example, points or badges are awarded when a specific goal is achieved. The progress tracking unit also provides rewards to increase motivation according to the student's learning progress. For example, benefits are provided when grades improve. The progress tracking unit also develops a system that provides individually customized incentives based on the student's learning data. For example, special tasks are provided to strengthen areas of strength. In this way, rewards and incentives according to the student's learning progress can be provided, thereby increasing motivation to learn.

[0068] The progress tracking unit can use the emotion estimation function to adjust the content and timing of feedback based on the student's emotional response to learning. The progress tracking unit, for example, builds a system that analyzes the student's emotions and adjusts the content and timing of feedback. For example, if the student is tired, the feedback is delayed. The progress tracking unit also uses the emotion estimation function to adjust feedback based on the student's emotional response. For example, if the student is concentrating, feedback is provided immediately. The progress tracking unit also develops a system that dynamically changes the content and timing of feedback based on the student's emotional data. For example, if the student is feeling stressed, an encouraging message is displayed. In this way, the learning effect can be improved by adjusting the content and timing of feedback based on the student's emotional response.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The lesson provider can increase students' motivation to learn by gamifying the content of lessons. For example, in math classes, students accumulate points for each problem they solve, and when they reach a certain number of points, they receive a virtual reward. In history classes, the lesson proceeds in a quiz format, and students can move on to the next stage with each correct answer. Furthermore, in science classes, students can conduct virtual experiments and move on to the next step based on the results of the experiments. In this way, gamifying the content of lessons can increase students' motivation to learn.

[0071] The lesson provider can use the emotion estimation function to provide refreshment time according to the student's emotions. For example, if it determines that the student is tired, it will suggest a short break and provide simple exercises to refresh. If the student is feeling stressed, it will play music or videos that have a relaxing effect. Furthermore, if the student is lacking concentration, it will provide an interactive game to improve concentration. In this way, by providing refreshment time according to the student's emotions, it is possible to improve learning effectiveness.

[0072] The lesson provider can deepen students' understanding by relating the lesson content to real life. For example, in math classes, they show examples of everyday applications such as shopping and cooking. In science classes, they use real-life examples related to environmental issues and health. Furthermore, in history classes, they explain the relevance to modern society and show how past events affect the present. This allows students to deepen their understanding by relating the lesson content to real life.

[0073] The lesson provider can use the emotion estimation function to provide personalized feedback based on the student's emotions. For example, if the student is happy, it can display words of praise or encouraging messages. If the student is depressed, it can provide encouraging messages or advice. Furthermore, if the student is excited, it can suggest activities that will help the student utilize that excitement in their studies. In this way, providing personalized feedback based on the student's emotions can increase their motivation to learn.

[0074] The lesson delivery department can cater to students' learning styles by delivering lesson content in different media formats. For example, visual learners can receive explanations using diagrams, graphs, and animations. Auditory learners can receive lessons in audio or podcast format. And tactile learners can receive interactive learning materials and experiment kits. In this way, lesson content can be delivered in different media formats to cater to students' learning styles.

[0075] The lesson provider can use the emotion estimation function to adjust the learning pace based on the student's emotions. For example, if the student is feeling impatient or anxious, the lesson progress can be slowed down and additional explanations can be provided. If the student is excited, the lesson progress can be sped up and more advanced content can be provided. Furthermore, if the student is relaxed, the lesson can be continued at a normal pace. In this way, adjusting the learning pace based on the student's emotions can improve learning effectiveness.

[0076] The teaching department can accommodate a diverse range of students by adapting the content of lessons to different cultures and backgrounds. For example, by covering the history and literature of different cultural spheres, students can broaden their international perspective. It also provides content that encourages empathy for students from different backgrounds. Furthermore, by holding discussions that incorporate diverse perspectives, students can develop an attitude of respect for different opinions. In this way, the teaching department can accommodate a diverse range of students by adapting the content of lessons to different cultures and backgrounds.

[0077] The customization department can predict and customize future learning content based on a student's learning history and performance data. For example, it can analyze past grades and learning patterns to determine the next learning content. It can also provide learning plans to strengthen strong areas and reinforce weak areas. Furthermore, a system will be developed that dynamically adjusts content according to the student's learning progress. This makes it possible to predict and customize future learning content based on the student's learning history and performance data, thereby providing an individually optimized learning environment.

[0078] The customization unit can use the emotion estimation function to provide learning content based on the student's emotions. For example, it can provide interactive learning materials that allow students to enjoy learning. It can also analyze emotional responses and provide feedback to elicit positive emotions. It can also provide learning activities to elicit positive emotions based on the emotion data. This makes it possible to analyze students' emotional responses and provide learning content to elicit positive emotions, thereby increasing their motivation to learn.

[0079] The customization unit can optimize a student's learning environment for different locations. For example, when studying at home, learning content is provided that is appropriate for the internet environment and device. To optimize the learning environment at school, learning plans are provided that are appropriate for classroom facilities and timetables. Furthermore, content suitable for studying in libraries, cafes, etc. is provided. This allows students to study effectively anywhere by optimizing their learning environment for different locations.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The lesson provider uses a generative AI to provide lessons. For example, the generative AI uses a text generation AI (e.g., GPT-3) to automatically generate lesson content and explain it to students in an easy-to-understand manner. The generative AI can also use a multimodal generative AI to visually explain the lesson content. For example, in a math lesson, the generative AI might start the lesson by saying, "Today, let's learn about adding fractions," and then use specific examples to explain the lesson. Step 2: The customization section uses the generative AI to provide an individually customized learning environment. For example, the generative AI generates individually customized learning content based on each student's learning progress and level of understanding. For example, if a student is struggling with a particular unit, the generative AI will provide that student with additional practice problems and supplementary explanations. It will also provide more advanced content to students who are progressing quickly. Step 3: The progress tracking unit tracks the student's learning progress and provides feedback. For example, the generation AI analyzes the student's accuracy rate and the time it took to solve the problems, and based on the results, provides feedback such as, "You did well on this problem. Let's try a more difficult problem next time."

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The 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.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] 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.

[0123] 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.

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

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

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0128] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0130] The data processing system 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A lesson provision department that provides lessons using generative AI; a customization unit that provides an individually customized learning environment by the generating AI; a progress tracking unit that tracks the student's learning progress and provides feedback. A system characterized by:

2. The lesson provider: Using generative AI to perform sentiment analysis on lesson content, the content of the lesson is dynamically changed based on the student's interests.

2. The system of claim 1.

3. The lesson provider: Generate multimodal learning materials to adapt lesson content to different learning styles 2. The system of claim 1.

4. The customization unit Analyze the student's learning history and performance data to predict and customize future learning content.

2. The system of claim 1.

5. The progress tracking unit Analyze the student's learning progress in real time and provide immediate feedback 2. The system of claim 1.

6. The lesson provider: Provide interactive exercises to detect and relax stress and anxiety experienced during the lesson 2. The system of claim 1.

7. The customization unit Analyze the student's emotional response to learning and provide learning content that elicits positive emotions.

2. The system of claim 1.

8. The progress tracking unit Analyze the student's emotional response to learning and reinforce positive feedback 2. The system of claim 1.

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