System

Abstract

An object of a system according to an embodiment is to provide individualized education adapted to a learner and to realize efficient learning support.SOLUTION: A system according to an embodiment includes a AI tutor, a question answering unit, a teaching material providing unit, a teaching material development supporting unit, and a visualization providing unit. The AI tutor is loaded with the generated AI. The question answering part performs question answering adapted to the learner. A teaching material providing part provides a teaching material adapted to the learner. A teaching material development support part supports teaching material development. The visualization providing unit provides the visualized teaching material.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide personalized education adapted to learners, and have not provided sufficient, efficient learning support.

[0005] The system according to the embodiment aims to provide personalized education adapted to the learner and realize efficient learning support. [Means for solving the problem]

[0006] The system according to the embodiment includes an AI tutor, a question answering unit, a teaching material providing unit, a teaching material development support unit, and a visualization providing unit. The AI ​​tutor is equipped with a generative AI. The question answering unit provides question answers adapted to the learner. The teaching material providing unit provides teaching materials adapted to the learner. The teaching material development support unit supports teaching material development. The visualization providing unit provides visualized teaching materials. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized education adapted to the learner and realize efficient learning support. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The educational system according to the embodiment of the present invention provides a new educational method for solving problems in STEM education by utilizing generative AI. This enables the educational system to provide question-answering, teaching materials, support for teaching material development, and visualization adapted to the learner.

[0029] An educational system according to an embodiment includes an AI tutor, a question-and-answer unit, a learning material providing unit, a learning material development support unit, and a visualization providing unit. The AI ​​tutor is equipped with a generative AI and provides question-and-answer and learning material adapted to the learner. The question-and-answer unit provides question-and-answers adapted to the learner. For example, the question-and-answer unit generates appropriate questions based on the learner's level of understanding and progress. The question-and-answer unit can also generate answers to the learner's questions using the generative AI. The learning material providing unit provides learning material adapted to the learner. For example, the learning material providing unit generates customized learning material based on the learner's interests and concerns. The learning material providing unit can also provide visualized learning material using the generative AI. The learning material development support unit supports learning material development. For example, the learning material development support unit automatically updates the content of the learning material using the generative AI to reflect the latest research results and trends. The learning material development support unit can also automatically translate the content of the learning material into different languages ​​using the generative AI. The visualization providing unit provides visualized learning material. For example, the visualization providing unit uses the generative AI to provide learning content in a visually easy-to-understand format. The visualization unit can also provide educational materials visualized using generative AI in virtual reality (VR) or augmented reality (AR), enabling the educational system to provide question-answering and educational materials tailored to the learner, support educational material development, and provide visualizations.

[0030] The question-and-answer unit can analyze a learner's past learning history and identify and suggest the most effective learning pattern. For example, the question-and-answer unit analyzes a learner's past learning data to identify which learning pattern was most effective. For example, if studying at a specific time of day improves grades, the unit encourages the learner to study at that time. The question-and-answer unit also uses generative AI to suggest optimal learning plans based on the learner's past test results and assignment submission status. For example, it can create a learning schedule that focuses on weak areas. The question-and-answer unit also analyzes a learner's learning history to identify successful learning methods and materials. For example, if video materials were effective, it can suggest materials in a similar format. This makes it possible to suggest optimal learning patterns based on the learner's past learning history.

[0031] The question-answering unit can monitor a learner's real-time reaction speed and answer time and adjust the difficulty level based on that. For example, the question-answering unit monitors the learner's reaction speed and answer time in real time when solving a problem, and the generation AI adjusts the difficulty level of the problem based on that data. For example, if the answer is fast, the difficulty level is increased, and if the answer is slow, the difficulty level is decreased. The question-answering unit also analyzes the learner's answer time and provides hints and additional explanations at the appropriate time. For example, if the learner cannot obtain the answer within a certain time, a hint is displayed. The question-answering unit also creates an individualized learning plan based on the learner's reaction speed data. For example, it proposes a learning plan that focuses on areas where the learner is slow to respond. This allows the difficulty level to be adjusted based on the learner's real-time reaction speed and answer time.

[0032] The teaching material providing unit generates example problems and problems based on the learner's interests and hobbies, thereby improving learning motivation. For example, the teaching material providing unit collects data on the learner's interests and hobbies, and the generation AI generates personalized example problems and problems based on that data. For example, a learner who is interested in sports is provided with sports-related math problems. The teaching material providing unit also generates teaching materials that reflect the learner's hobbies and interests, thereby improving learning motivation. For example, a learner who is interested in music is provided with music-related physics problems. The teaching material providing unit also creates a customized learning plan based on the learner's interests. For example, a learner who is interested in space is provided with space-related science problems. In this way, example problems and problems based on the learner's interests and hobbies are generated, thereby improving learning motivation.

[0033] The teaching material provision unit generates teaching materials that correspond to different learning styles, thereby meeting the diverse needs of learners. For example, the teaching material provision unit analyzes the learner's learning style, and based on that, the generation AI generates teaching materials that correspond to visual, auditory, and tactile senses. For example, visual learners are provided with teaching materials that make extensive use of diagrams and graphs. The teaching material provision unit also customizes teaching materials according to the learner's learning style to maximize the effectiveness of learning. For example, auditory learners are provided with teaching materials that include audio commentary. The teaching material provision unit also uses the generation AI to create individualized learning plans based on the learner's learning style data. For example, tactile learners are provided with experiment kits and hands-on teaching materials. In this way, teaching materials that correspond to different learning styles are generated, meeting the diverse needs of learners.

[0034] The teaching material development support unit can automatically adjust the difficulty of teaching materials and generate versions suitable for different grades and levels of comprehension. For example, the teaching material development support unit analyzes the content of the teaching materials and automatically adjusts the difficulty level according to the learner's grade and level of comprehension. For example, it generates questions of different difficulty levels even on the same theme. The teaching material development support unit also uses a generation AI to generate personalized teaching materials based on the learner's comprehension data. For example, it provides basic content to learners with low levels of comprehension and applied content to learners with high levels of comprehension. The teaching material development support unit also adjusts the difficulty level of teaching materials in real time and provides appropriate versions according to the learner's progress. For example, it adjusts the difficulty level based on the learner's problem-solving speed and correct answer rate. This makes it possible to automatically adjust the difficulty of teaching materials and generate versions suitable for different grades and levels of comprehension.

[0035] The Teaching Materials Development Support Department can automatically update the content of teaching materials to reflect the latest research results and trends. For example, the Teaching Materials Development Support Department will build a system that regularly updates the content of teaching materials to reflect the latest research results and trends. For example, new scientific discoveries and technological advances will be incorporated into the teaching materials. The Teaching Materials Development Support Department will also have its generative AI automatically collect the latest information and update the content of the teaching materials. For example, it will revise the teaching materials based on the latest news and academic papers. The Teaching Materials Development Support Department will also update the content of the teaching materials in real time to provide learners with the latest information. For example, it will add new topics and examples. This will automatically update the content of the teaching materials to reflect the latest research results and trends.

[0036] The teaching materials development support unit can automatically translate the content of teaching materials into different languages ​​to accommodate an international educational environment. For example, the teaching materials development support unit builds a system that automatically translates the content of teaching materials and provides them in different languages. For example, it supports multiple languages ​​such as English, Japanese, and French. The teaching materials development support unit also uses a generative AI's automatic translation function to make the content of teaching materials multilingual. For example, it provides teaching materials in accordance with the learner's native language. The teaching materials development support unit also translates the content of teaching materials into different languages ​​to accommodate an international educational environment. For example, it provides teaching materials for international students and multicultural classes. This allows the content of teaching materials to be automatically translated into different languages ​​to accommodate an international educational environment.

[0037] The teaching material development support unit can provide the content of teaching materials in different media formats to provide a learning experience that suits the learner's preferences. For example, the teaching material development support unit builds a system that provides the content of teaching materials in different media formats, such as text, audio, and video. For example, the same content is provided to learners in multiple formats. The teaching material development support unit also provides teaching materials by having a generative AI select the optimal media format based on the learner's preferences. For example, video teaching materials are provided for visual learners, and audio teaching materials are provided for auditory learners. The teaching material development support unit also provides the content of teaching materials in different media formats to provide a learning experience that suits the learner's learning style. For example, teaching materials that combine text and video are provided. This allows the content of teaching materials to be provided in different media formats to provide a learning experience that suits the learner's preferences.

[0038] The visualization providing unit can dynamically change the level of detail and complexity of the visualization according to the learner's level of comprehension. For example, the visualization providing unit monitors the learner's level of comprehension in real time, and the generation AI dynamically changes the level of detail and complexity of the visualization. For example, detailed diagrams and graphs are provided when the level of comprehension is high, and simple diagrams are provided when the level of comprehension is low. In addition, the visualization providing unit adjusts the complexity of the visualization based on the learner's progress data. For example, if the learner understands a particular concept, it provides a more complex visualization. In addition, the visualization providing unit customizes the style of the visualization according to the learner's level of comprehension. For example, a basic visualization is provided when the level of comprehension is low, and a detailed visualization is provided when the level of comprehension is high. This allows the level of detail and complexity of the visualization to be dynamically changed according to the learner's level of comprehension.

[0039] The visualization providing unit can add interactive elements to visualized teaching materials, allowing learners to manipulate them themselves to deepen their understanding. The visualization providing unit, for example, adds interactive elements to visualized teaching materials, allowing learners to manipulate them themselves to deepen their understanding. For example, it provides a function for rotating and scaling a molecular model. The visualization providing unit also allows learners to manipulate the visualized teaching materials to gain a deeper understanding of the learning content. For example, it provides a function for clicking on data points on a graph to display detailed information. The visualization providing unit also provides interactive visualized teaching materials, allowing learners to conduct experiments and simulations themselves. For example, it provides a function for recreating a physics experiment in a virtual environment. This allows interactive elements to be added to visualized teaching materials, allowing learners to manipulate them themselves to deepen their understanding.

[0040] The visualization unit can provide visualized teaching materials in virtual reality or augmented reality to provide a more immersive learning experience. For example, the visualization unit can provide visualized teaching materials in virtual reality (VR) to allow learners to have an immersive learning experience. For example, a chemistry experiment can be recreated in a VR environment. The visualization unit can also provide visualized teaching materials in augmented reality (AR) to allow learners to have a learning experience that combines the real world and virtual information. For example, a historical building can be recreated in AR to allow learners to experience it as if they were actually there. The visualization unit can also provide visualized teaching materials using VR or AR to allow learners to interactively explore learning content. For example, the structure of the human body can be displayed in AR, allowing learners to examine each part in detail. In this way, the visualization unit can provide visualized teaching materials in virtual reality or augmented reality to provide a more immersive learning experience.

[0041] The visualization unit can physically output visualized teaching materials using a 3D printer to support learning through touch. For example, the visualization unit physically outputs visualized teaching materials using a 3D printer, allowing learners to understand the learning content through touch. For example, the visualization unit creates molecular models or topographical models using a 3D printer. The visualization unit also uses a 3D printer to physically output visualized teaching materials so that learners can actually touch and learn. For example, it creates models of historical artifacts or buildings. The visualization unit also generates data for the generative AI to output visualized teaching materials using a 3D printer, allowing learners to deeply understand the learning content through touch. For example, it creates models of human organs. This allows the visualized teaching materials to be physically output using a 3D printer, supporting learning through touch.

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

[0043] The educational system can provide learning materials that suit the learner's learning style. For example, visual learners can be provided with learning materials that make extensive use of diagrams and graphs, while auditory learners can be provided with learning materials with audio commentary. Furthermore, tactile learners can be provided with experiment kits and hands-on learning materials. Furthermore, customized learning plans can be created according to the learner's learning style, maximizing the effectiveness of learning. This makes it possible to provide the most suitable learning materials according to the learner's learning style.

[0044] The educational system can generate examples and problems based on the learner's interests and hobbies, thereby improving their motivation to learn. For example, a learner who is interested in sports can be provided with sports-related math problems. A learner who is interested in music can be provided with music-related physics problems. A learner who is interested in space can be provided with space-related science problems. In this way, examples and problems can be generated based on the learner's interests and hobbies, improving their motivation to learn.

[0045] The educational system can generate learning materials that cater to different learning styles and meet the diverse needs of learners. For example, visual learners can be provided with learning materials that make extensive use of diagrams and graphs, while auditory learners can be provided with learning materials with audio commentary. Tactile learners can also be provided with experiment kits and hands-on learning materials. Furthermore, customized learning plans can be created based on each learner's learning style, maximizing the effectiveness of learning. This allows the system to generate learning materials that cater to different learning styles and meet the diverse needs of learners.

[0046] The education system can monitor a learner's real-time reaction speed and response time and adjust the difficulty level based on that. For example, if the response is fast, the difficulty level can be increased, and if the response is slow, the difficulty level can be decreased. Also, if the learner does not respond within a certain time frame, hints or additional explanations can be provided. Furthermore, based on the learner's reaction speed data, a personalized learning plan can be created, suggesting a learning plan that focuses on areas where the learner is slow to respond. This allows the difficulty level to be adjusted based on the learner's real-time reaction speed and response time.

[0047] The education system can analyze a learner's past learning history and identify and suggest the most effective learning pattern. For example, if studying at a specific time of day improves grades, it can encourage the learner to study at that time. It can also suggest optimal learning plans based on past test results and assignment submission status. It can also identify successful learning methods and materials and suggest materials in a similar format. This makes it possible to suggest optimal learning patterns based on a learner's past learning history.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The AI ​​tutor is equipped with generative AI, which responds to questions and provides learning materials tailored to the learner, enabling flexible responses to meet the learner's needs. Step 2: The question-answering unit performs question-answering tailored to the learner. For example, it generates appropriate questions based on the learner's level of understanding and progress, and uses a generation AI to generate answers to the learner's questions. Step 3: The teaching material provider provides learning materials adapted to the learner. For example, it generates customized learning materials based on the learner's interests and provides the learning materials visualized using generation AI. Step 4: The Teaching Materials Development Support Department supports the development of teaching materials. For example, it uses generative AI to automatically update the content of teaching materials to reflect the latest research findings and trends. It can also automatically translate teaching materials into different languages. Step 5: The visualization provider provides visualized learning materials. For example, the learning content can be presented in a visually easy-to-understand format using generative AI, and can also be presented in virtual reality (VR) or augmented reality (AR).

[0050] (Example 2) The educational system according to the embodiment of the present invention provides a new educational method for solving problems in STEM education by utilizing generative AI. This enables the educational system to provide question-answering, teaching materials, support for teaching material development, and visualization adapted to the learner.

[0051] An educational system according to an embodiment includes an AI tutor, a question-and-answer unit, a learning material providing unit, a learning material development support unit, and a visualization providing unit. The AI ​​tutor is equipped with a generative AI and provides question-and-answer and learning material adapted to the learner. The question-and-answer unit provides question-and-answers adapted to the learner. For example, the question-and-answer unit generates appropriate questions based on the learner's level of understanding and progress. The question-and-answer unit can also generate answers to the learner's questions using the generative AI. The learning material providing unit provides learning material adapted to the learner. For example, the learning material providing unit generates customized learning material based on the learner's interests and concerns. The learning material providing unit can also provide visualized learning material using the generative AI. The learning material development support unit supports learning material development. For example, the learning material development support unit automatically updates the content of the learning material using the generative AI to reflect the latest research results and trends. The learning material development support unit can also automatically translate the content of the learning material into different languages ​​using the generative AI. The visualization providing unit provides visualized learning material. For example, the visualization providing unit uses the generative AI to provide learning content in a visually easy-to-understand format. The visualization unit can also provide educational materials visualized using generative AI in virtual reality (VR) or augmented reality (AR), enabling the educational system to provide question-answering and educational materials tailored to the learner, support educational material development, and provide visualizations.

[0052] The question-answering unit uses the generative AI to estimate the learner's emotions and provide feedback and encouraging messages according to their emotions. For example, when a learner solves a problem, the generative AI analyzes the learner's facial expressions and voice in real time to estimate their emotions. For example, if the learner is confused, the generative AI provides encouraging messages or additional hints. The question-answering unit also provides personalized feedback based on the learner's emotional data. For example, if the learner is tired, the generative AI displays a message encouraging them to take a break. The question-answering unit also monitors the learner's emotional state and provides interactive activities to elicit positive emotions. For example, if a learner is feeling stressed, the generative AI suggests a simple game to help them relax. This makes it possible to provide feedback and encouraging messages according to the learner's emotions.

[0053] The question-and-answer unit can analyze a learner's past learning history and identify and suggest the most effective learning pattern. For example, the question-and-answer unit analyzes a learner's past learning data to identify which learning pattern was most effective. For example, if studying at a specific time of day improves grades, the unit encourages the learner to study at that time. The question-and-answer unit also uses generative AI to suggest optimal learning plans based on the learner's past test results and assignment submission status. For example, it can create a learning schedule that focuses on weak areas. The question-and-answer unit also analyzes a learner's learning history to identify successful learning methods and materials. For example, if video materials were effective, it can suggest materials in a similar format. This makes it possible to suggest optimal learning patterns based on the learner's past learning history.

[0054] The question-answering unit can monitor a learner's real-time reaction speed and answer time and adjust the difficulty level based on that. For example, the question-answering unit monitors the learner's reaction speed and answer time in real time when solving a problem, and the generation AI adjusts the difficulty level of the problem based on that data. For example, if the answer is fast, the difficulty level is increased, and if the answer is slow, the difficulty level is decreased. The question-answering unit also analyzes the learner's answer time and provides hints and additional explanations at the appropriate time. For example, if the learner cannot obtain the answer within a certain time, a hint is displayed. The question-answering unit also creates an individualized learning plan based on the learner's reaction speed data. For example, it proposes a learning plan that focuses on areas where the learner is slow to respond. This allows the difficulty level to be adjusted based on the learner's real-time reaction speed and answer time.

[0055] The teaching material providing unit generates example problems and problems based on the learner's interests and hobbies, thereby improving learning motivation. For example, the teaching material providing unit collects data on the learner's interests and hobbies, and the generation AI generates personalized example problems and problems based on that data. For example, a learner who is interested in sports is provided with sports-related math problems. The teaching material providing unit also generates teaching materials that reflect the learner's hobbies and interests, thereby improving learning motivation. For example, a learner who is interested in music is provided with music-related physics problems. The teaching material providing unit also creates a customized learning plan based on the learner's interests. For example, a learner who is interested in space is provided with space-related science problems. In this way, example problems and problems based on the learner's interests and hobbies are generated, thereby improving learning motivation.

[0056] The teaching material provision unit generates teaching materials that correspond to different learning styles, thereby meeting the diverse needs of learners. For example, the teaching material provision unit analyzes the learner's learning style, and based on that, the generation AI generates teaching materials that correspond to visual, auditory, and tactile senses. For example, visual learners are provided with teaching materials that make extensive use of diagrams and graphs. The teaching material provision unit also customizes teaching materials according to the learner's learning style to maximize the effectiveness of learning. For example, auditory learners are provided with teaching materials that include audio commentary. The teaching material provision unit also uses the generation AI to create individualized learning plans based on the learner's learning style data. For example, tactile learners are provided with experiment kits and hands-on teaching materials. In this way, teaching materials that correspond to different learning styles are generated, meeting the diverse needs of learners.

[0057] The teaching material providing unit can use the emotion estimation function to provide interactive games or activities to help the learner relax when they are feeling stressed. For example, the teaching material providing unit monitors the learner's emotional state in real time and provides interactive games to help the learner relax when they are feeling stressed. For example, it suggests simple puzzle games or relaxation activities. Furthermore, the teaching material providing unit allows the generation AI to customize activities to reduce stress based on the learner's emotional data. For example, it provides guided deep breathing or meditation. Furthermore, the teaching material providing unit allows the generation AI to provide music or images to help the learner relax when they are feeling stressed. For example, it displays images of natural sounds or scenery. In this way, it is possible to provide interactive games or activities to help the learner relax when they are feeling stressed.

[0058] The teaching materials development support unit performs sentiment analysis on the content of the teaching materials and can suggest expressions and designs that elicit positive emotions. For example, the teaching materials development support unit performs sentiment analysis on the text and images of the teaching materials and suggests expressions and designs that elicit positive emotions. For example, it uses bright colors and positive language. The teaching materials development support unit also analyzes the content of the teaching materials and suggests storytelling and characters that will evoke positive emotions in learners. For example, it may incorporate stories that depict successful experiences. The teaching materials development support unit also performs sentiment analysis on the design of the teaching materials and suggests layouts and fonts that will elicit positive emotions. For example, it may use fonts that are easy to read and a visually appealing layout. In this way, it is possible to perform sentiment analysis on the content of the teaching materials and suggest expressions and designs that elicit positive emotions.

[0059] The teaching material development support unit can automatically adjust the difficulty of teaching materials and generate versions suitable for different grades and levels of comprehension. For example, the teaching material development support unit analyzes the content of the teaching materials and automatically adjusts the difficulty level according to the learner's grade and level of comprehension. For example, it generates questions of different difficulty levels even on the same theme. The teaching material development support unit also uses a generation AI to generate personalized teaching materials based on the learner's comprehension data. For example, it provides basic content to learners with low levels of comprehension and applied content to learners with high levels of comprehension. The teaching material development support unit also adjusts the difficulty level of teaching materials in real time and provides appropriate versions according to the learner's progress. For example, it adjusts the difficulty level based on the learner's problem-solving speed and correct answer rate. This makes it possible to automatically adjust the difficulty of teaching materials and generate versions suitable for different grades and levels of comprehension.

[0060] The Teaching Materials Development Support Department can automatically update the content of teaching materials to reflect the latest research results and trends. For example, the Teaching Materials Development Support Department will build a system that regularly updates the content of teaching materials to reflect the latest research results and trends. For example, new scientific discoveries and technological advances will be incorporated into the teaching materials. The Teaching Materials Development Support Department will also have its generative AI automatically collect the latest information and update the content of the teaching materials. For example, it will revise the teaching materials based on the latest news and academic papers. The Teaching Materials Development Support Department will also update the content of the teaching materials in real time to provide learners with the latest information. For example, it will add new topics and examples. This will automatically update the content of the teaching materials to reflect the latest research results and trends.

[0061] The teaching materials development support unit can automatically translate the content of teaching materials into different languages ​​to accommodate an international educational environment. For example, the teaching materials development support unit builds a system that automatically translates the content of teaching materials and provides them in different languages. For example, it supports multiple languages ​​such as English, Japanese, and French. The teaching materials development support unit also uses a generative AI's automatic translation function to make the content of teaching materials multilingual. For example, it provides teaching materials in accordance with the learner's native language. The teaching materials development support unit also translates the content of teaching materials into different languages ​​to accommodate an international educational environment. For example, it provides teaching materials for international students and multicultural classes. This allows the content of teaching materials to be automatically translated into different languages ​​to accommodate an international educational environment.

[0062] The teaching material development support unit can provide the content of teaching materials in different media formats to provide a learning experience that suits the learner's preferences. For example, the teaching material development support unit builds a system that provides the content of teaching materials in different media formats, such as text, audio, and video. For example, the same content is provided to learners in multiple formats. The teaching material development support unit also provides teaching materials by having a generative AI select the optimal media format based on the learner's preferences. For example, video teaching materials are provided for visual learners, and audio teaching materials are provided for auditory learners. The teaching material development support unit also provides the content of teaching materials in different media formats to provide a learning experience that suits the learner's learning style. For example, teaching materials that combine text and video are provided. This allows the content of teaching materials to be provided in different media formats to provide a learning experience that suits the learner's preferences.

[0063] The teaching material development support unit can use the emotion estimation function to evaluate the emotional impact that the content of the teaching material has on the learner and select the most appropriate teaching material. For example, the teaching material development support unit monitors the learner's emotional reactions to the content of the teaching material in real time and selects the most appropriate teaching material based on that data. For example, it prioritizes providing teaching materials with a high number of positive emotional reactions. The teaching material development support unit also uses the emotion estimation function to build a system that evaluates the emotional impact that the content of the teaching material has on the learner. For example, it evaluates the effectiveness of the teaching material based on the emotion score. The teaching material development support unit also analyzes the learner's emotion data and selects the most appropriate teaching material. For example, it provides teaching materials with content and format that are likely to interest the learner. In this way, the emotion estimation function is used to evaluate the emotional impact that the content of the teaching material has on the learner and select the most appropriate teaching material.

[0064] The visualization provider can estimate the learner's emotions using the generation AI and adjust the visualization style and color according to the emotion. For example, the visualization provider monitors the learner's emotional state in real time, and the generation AI adjusts the visualization style and color according to the emotion. For example, if the learner is relaxed, calm colors are used. The visualization provider also customizes the visualization style based on the learner's emotional data. For example, if the learner is excited, vivid colors and dynamic animations are used. The visualization provider also analyzes the learner's emotional state and suggests the optimal visualization style. For example, if the learner is concentrating, a simple and intuitive design is used. This makes it possible to adjust the visualization style and color according to the learner's emotions.

[0065] The visualization providing unit can dynamically change the level of detail and complexity of the visualization according to the learner's level of comprehension. For example, the visualization providing unit monitors the learner's level of comprehension in real time, and the generation AI dynamically changes the level of detail and complexity of the visualization. For example, detailed diagrams and graphs are provided when the level of comprehension is high, and simple diagrams are provided when the level of comprehension is low. In addition, the visualization providing unit adjusts the complexity of the visualization based on the learner's progress data. For example, if the learner understands a particular concept, it provides a more complex visualization. In addition, the visualization providing unit customizes the style of the visualization according to the learner's level of comprehension. For example, a basic visualization is provided when the level of comprehension is low, and a detailed visualization is provided when the level of comprehension is high. This allows the level of detail and complexity of the visualization to be dynamically changed according to the learner's level of comprehension.

[0066] The visualization providing unit can add interactive elements to visualized teaching materials, allowing learners to manipulate them themselves to deepen their understanding. The visualization providing unit, for example, adds interactive elements to visualized teaching materials, allowing learners to manipulate them themselves to deepen their understanding. For example, it provides a function for rotating and scaling a molecular model. The visualization providing unit also allows learners to manipulate the visualized teaching materials to gain a deeper understanding of the learning content. For example, it provides a function for clicking on data points on a graph to display detailed information. The visualization providing unit also provides interactive visualized teaching materials, allowing learners to conduct experiments and simulations themselves. For example, it provides a function for recreating a physics experiment in a virtual environment. This allows interactive elements to be added to visualized teaching materials, allowing learners to manipulate them themselves to deepen their understanding.

[0067] The visualization unit can provide visualized teaching materials in virtual reality or augmented reality to provide a more immersive learning experience. For example, the visualization unit can provide visualized teaching materials in virtual reality (VR) to allow learners to have an immersive learning experience. For example, a chemistry experiment can be recreated in a VR environment. The visualization unit can also provide visualized teaching materials in augmented reality (AR) to allow learners to have a learning experience that combines the real world and virtual information. For example, a historical building can be recreated in AR to allow learners to experience it as if they were actually there. The visualization unit can also provide visualized teaching materials using VR or AR to allow learners to interactively explore learning content. For example, the structure of the human body can be displayed in AR, allowing learners to examine each part in detail. In this way, the visualization unit can provide visualized teaching materials in virtual reality or augmented reality to provide a more immersive learning experience.

[0068] The visualization unit can physically output visualized teaching materials using a 3D printer to support learning through touch. For example, the visualization unit physically outputs visualized teaching materials using a 3D printer, allowing learners to understand the learning content through touch. For example, the visualization unit creates molecular models or topographical models using a 3D printer. The visualization unit also uses a 3D printer to physically output visualized teaching materials so that learners can actually touch and learn. For example, it creates models of historical artifacts or buildings. The visualization unit also generates data for the generative AI to output visualized teaching materials using a 3D printer, allowing learners to deeply understand the learning content through touch. For example, it creates models of human organs. This allows the visualized teaching materials to be physically output using a 3D printer, supporting learning through touch.

[0069] The visualization providing unit can use the emotion estimation function to evaluate the emotional impact that visualized teaching materials have on learners and select the optimal visualization method. The visualization providing unit, for example, monitors learners' emotional reactions to visualized teaching materials in real time and selects the optimal visualization method based on that data. For example, it prioritizes the use of visualization methods that have a high number of positive emotional reactions. The visualization providing unit also uses the emotion estimation function to build a system that evaluates the emotional impact that visualized teaching materials have on learners. For example, it evaluates the effectiveness of visualization methods based on emotion scores. The visualization providing unit also analyzes learners' emotion data and selects the optimal visualization method. For example, it uses visualization styles and colors that are likely to interest learners. In this way, the emotion estimation function is used to evaluate the emotional impact that visualized teaching materials have on learners and select the optimal visualization method.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The education system can estimate a learner's emotions and adjust the learning environment based on the estimated emotions. For example, if a learner is feeling stressed, it can provide relaxing music and videos. If a learner is concentrating, it can minimize notifications and alerts to maintain focus. Furthermore, if a learner is excited, it can provide interactive activities to motivate them to learn. This allows the system to provide an optimal learning environment according to the learner's emotions.

[0072] The education system can estimate a learner's emotions and customize the learning content based on the estimated emotions. For example, if a learner is confused, it can provide more detailed explanations or additional hints. If a learner is confident, it can provide challenging problems to encourage further growth. Furthermore, if a learner is tired, it can suggest messages encouraging them to take a break or activities to relax. This allows it to provide optimal learning content according to the learner's emotions.

[0073] The educational system can estimate the learner's emotions and provide feedback based on the estimated emotions. For example, if the learner succeeds, it can provide positive feedback and messages of praise. If the learner fails, it can provide encouraging messages and advice on how to proceed to the next step. Furthermore, if the learner has neutral emotions, it can provide information about their learning progress and goals to maintain their motivation. This allows it to provide optimal feedback according to the learner's emotions.

[0074] The education system can estimate the learner's emotions and adjust the learning progress based on the estimated emotions. For example, if the learner is feeling stressed, the learning pace can be slowed down or a break can be inserted. If the learner is focused, the learning pace can be maintained and more content can be covered. Furthermore, if the learner is excited, interactive activities and challenging problems can be provided to motivate the learner. This makes it possible to provide optimal learning progress according to the learner's emotions.

[0075] The educational system can estimate a learner's emotions and improve their learning motivation based on the estimated emotions. For example, if a learner is excited, it can provide interactive games and activities to motivate them to learn. If a learner is tired, it can provide music and videos to help them relax. Furthermore, if a learner is confused, it can provide additional hints and detailed explanations to deepen their understanding. This makes it possible to provide optimal learning motivation according to the learner's emotions.

[0076] The educational system can provide learning materials that suit the learner's learning style. For example, visual learners can be provided with learning materials that make extensive use of diagrams and graphs, while auditory learners can be provided with learning materials with audio commentary. Furthermore, tactile learners can be provided with experiment kits and hands-on learning materials. Furthermore, customized learning plans can be created according to the learner's learning style, maximizing the effectiveness of learning. This makes it possible to provide the most suitable learning materials according to the learner's learning style.

[0077] The educational system can generate examples and problems based on the learner's interests and hobbies, thereby improving their motivation to learn. For example, a learner who is interested in sports can be provided with sports-related math problems. A learner who is interested in music can be provided with music-related physics problems. A learner who is interested in space can be provided with space-related science problems. In this way, examples and problems can be generated based on the learner's interests and hobbies, improving their motivation to learn.

[0078] The educational system can generate learning materials that cater to different learning styles and meet the diverse needs of learners. For example, visual learners can be provided with learning materials that make extensive use of diagrams and graphs, while auditory learners can be provided with learning materials with audio commentary. Tactile learners can also be provided with experiment kits and hands-on learning materials. Furthermore, customized learning plans can be created based on each learner's learning style, maximizing the effectiveness of learning. This allows the system to generate learning materials that cater to different learning styles and meet the diverse needs of learners.

[0079] The education system can monitor a learner's real-time reaction speed and response time and adjust the difficulty level based on that. For example, if the response is fast, the difficulty level can be increased, and if the response is slow, the difficulty level can be decreased. Also, if the learner does not respond within a certain time frame, hints or additional explanations can be provided. Furthermore, based on the learner's reaction speed data, a personalized learning plan can be created, suggesting a learning plan that focuses on areas where the learner is slow to respond. This allows the difficulty level to be adjusted based on the learner's real-time reaction speed and response time.

[0080] The education system can analyze a learner's past learning history and identify and suggest the most effective learning pattern. For example, if studying at a specific time of day improves grades, it can encourage the learner to study at that time. It can also suggest optimal learning plans based on past test results and assignment submission status. It can also identify successful learning methods and materials and suggest materials in a similar format. This makes it possible to suggest optimal learning patterns based on a learner's past learning history.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The AI ​​tutor is equipped with generative AI, which responds to questions and provides learning materials tailored to the learner, enabling flexible responses to meet the learner's needs. Step 2: The question-answering unit performs question-answering tailored to the learner. For example, it generates appropriate questions based on the learner's level of understanding and progress, and uses a generation AI to generate answers to the learner's questions. Step 3: The teaching material provider provides learning materials adapted to the learner. For example, it generates customized learning materials based on the learner's interests and provides the learning materials visualized using generation AI. Step 4: The Teaching Materials Development Support Department supports the development of teaching materials. For example, it uses generative AI to automatically update the content of teaching materials to reflect the latest research findings and trends. It can also automatically translate teaching materials into different languages. Step 5: The visualization provider provides visualized learning materials. For example, the learning content can be presented in a visually easy-to-understand format using generative AI, and can also be presented in virtual reality (VR) or augmented reality (AR).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0119] The 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.

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

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. An AI tutor equipped with generative AI, a question answering unit that performs question answering adapted to the learner; a teaching material providing unit that provides teaching materials adapted to the learner; The Teaching Materials Development Support Department, which supports the development of teaching materials, a visualization providing unit that provides visualized educational materials; A system characterized by:

2. The question answering unit The generative AI estimates the learner's emotions and provides feedback and encouraging messages according to the emotions.

2. The system of claim 1.

3. The question answering unit Analyzing the learner's past learning history and identifying and suggesting the most effective learning pattern 2. The system of claim 1.

4. The question answering unit Monitor the learner's real-time reaction speed and response time and adjust the difficulty accordingly.

2. The system of claim 1.

5. The teaching material providing unit Generate examples and questions based on the learner's interests and hobbies to improve their motivation to learn 2. The system of claim 1.

6. The teaching material providing unit Generate learning materials that cater to different learning styles and meet the diverse needs of learners 2. The system of claim 1.

7. The teaching material providing unit Offering interactive games and activities to help learners relax when they are feeling stressed 2. The system of claim 1.

8. The teaching material development support department Conduct sentiment analysis on the content of the educational materials and propose expressions and designs that elicit positive emotions 2. The system of claim 1.

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