System

Abstract

An object of the system according to the embodiment is to reduce the working time of educators, to share teaching materials, to automate scoring, and to improve the efficiency of communication.SOLUTION: A system according to an embodiment includes a teaching material sharing unit, a scoring automation unit, and a communication integration unit. The teaching material sharing unit shares a teaching material. The scoring automatization unit automates scoring using the generated AI. The communication integration unit integrates communication between the educator and the student.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, educators have to work long hours, and there is room for improvement in the efficiency of sharing teaching materials, grading, and communication.

[0005] The system according to the embodiment aims to reduce the working hours of educators, share teaching materials, automate grading, and improve the efficiency of communication. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning material sharing unit, a grading automation unit, and a communication integration unit. The learning material sharing unit shares learning materials. The grading automation unit automates grading using a generative AI. The communication integration unit integrates communication between educators and students. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the working hours of educators, share teaching materials, automate grading, and improve communication efficiency. [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 platform according to an embodiment of the present invention is a system designed to reduce work hours in educational settings. This system reduces the burden on educators and supports efficient educational activities by sharing teaching materials, automating grading, and integrating communication tools. As a result, the platform can significantly reduce educators' work hours and improve the efficiency of educational activities.

[0029] The platform according to the embodiment includes a teaching material sharing unit, a grading automation unit, and a communication integration unit. The teaching material sharing unit shares teaching materials. For example, teachers can upload lesson materials and workbooks they have created and share them with other teachers. The teaching material sharing unit also provides a teaching material library and a search function that allows teachers to quickly find necessary teaching materials. For example, teachers can search for teaching materials on a specific topic and use them immediately. The grading automation unit automates grading using a generation AI. For example, the generation AI analyzes students' answers and accurately grades them. The generation AI receives input from image data and text data on answer sheets, and outputs grading results based on that input. For example, answers to multiple-choice and essay questions are scanned, and the generation AI analyzes and grades the content. The communication integration unit integrates communication between educators and students. For example, it includes chat and video call functions, allowing for real-time questions and consultations. The communication integration unit can send and receive messages and manage video conference schedules. As a result, the platform according to the embodiment can significantly reduce educators' working hours and improve the efficiency of educational activities. For example, the sharing of teaching materials will save time in creating them, and the automation of grading will reduce the time spent on grading. Furthermore, the integration of communication tools will streamline communication between educators and between educators and students.

[0030] The teaching material sharing unit can automatically classify the content of teaching materials using a generative AI and recommend related teaching materials. For example, when teaching materials are uploaded, the generative AI analyzes the content and automatically classifies them into categories. For example, when mathematics teaching materials are uploaded, the generative AI analyzes the content and classifies them into categories such as algebra, geometry, and calculus. The generative AI also analyzes the content of the teaching materials and adds a function to recommend related teaching materials. For example, when algebra teaching materials are uploaded, the generative AI recommends related geometry and calculus teaching materials. The generative AI also analyzes the content of the teaching materials and automatically searches the library for other teaching materials related to the teaching materials and recommends them. For example, when history teaching materials are uploaded, the generative AI recommends related geography and social studies teaching materials. This automatically classifies the content of teaching materials and recommends related teaching materials, thereby reducing the effort required for educators to select teaching materials.

[0031] The teaching material sharing unit can use the generative AI to evaluate the quality of teaching materials and suggest areas for improvement. For example, when teaching materials are uploaded to the teaching material sharing unit, the generative AI analyzes the content and evaluates the quality. For example, it evaluates whether the content of the teaching materials is accurate and whether the information is up to date. In addition, a function is added in which the generative AI evaluates the quality of teaching materials and suggests areas for improvement. For example, if the content of the teaching materials is outdated, it will suggest adding the latest information. In addition, when teaching materials are uploaded, the generative AI analyzes the content, evaluates the quality, and suggests specific areas for improvement. For example, it will make suggestions to improve the layout or design of the teaching materials. In this way, by evaluating the quality of teaching materials and suggesting areas for improvement, the quality of teaching material creation by educators can be improved.

[0032] The teaching material sharing unit can be adapted to different educational levels, making it usable in a wide range of educational settings. For example, the teaching material sharing unit adapts the platform to each educational level: elementary school, middle school, high school, and university. For example, elementary school teaching materials are easy to understand, middle school teaching materials are intermediate, high school teaching materials are advanced, and university teaching materials are specialized. In addition, teaching materials corresponding to each educational level are registered on the platform, making it easy for users to search for them. For example, it makes it easy for elementary school teachers to search for elementary school teaching materials. In addition, teaching materials corresponding to different educational levels are registered on the platform, making it easy for users to find teaching materials that suit their educational level. For example, it makes it easy for university professors to search for university teaching materials. In this way, by adapting to different educational levels, it can be made usable in a wide range of educational settings.

[0033] The teaching material sharing unit can be made to support different languages, making it usable in international educational settings. The teaching material sharing unit, for example, makes the platform usable in different languages, making it usable in international educational settings. For example, it supports languages ​​such as English, Japanese, French, and Chinese. Furthermore, teaching materials corresponding to each language are registered on the platform, making it easy for users to search for teaching materials that suit their language. For example, it makes it easy for English teachers to search for English teaching materials. Furthermore, teaching materials corresponding to different languages ​​are registered on the platform, making it easy for users to find teaching materials that suit their language. For example, it makes it easy for Chinese professors to search for Chinese teaching materials. In this way, by supporting different languages, it can be made usable in international educational settings.

[0034] The automated scoring unit uses the generation AI to understand the context of the answer and can provide more accurate scoring. The automated scoring unit, for example, adds a function whereby the generation AI understands the context of the answer and provides more accurate scoring. For example, it analyzes answers to essay questions and provides accurate scoring based on the context. It also adds a function whereby the generation AI understands the context of the answer and evaluates the content of the answer. For example, it analyzes answers to essay questions and provides evaluation based on the context. It also adds a function whereby the generation AI understands the context of the answer and evaluates the content of the answer. For example, it analyzes answers to math questions and evaluates the process of the answer. This allows the context of the answer to be understood and provides more accurate scoring, thereby improving the accuracy of scoring.

[0035] The automated scoring unit can use a generative AI to evaluate the style and expression of answers and provide feedback. For example, the automated scoring unit introduces a function where the generative AI evaluates the style and expression of answers and provides feedback. For example, it analyzes essay answers and provides feedback on grammar and expression. It also adds a function where the generative AI evaluates the style and expression of answers and suggests specific areas for improvement. For example, it provides feedback on the structure and logic of the answer. It also introduces a function where the generative AI evaluates the style and expression of answers and provides feedback. For example, it analyzes presentation answers and provides feedback on the speaking style and slide design. In this way, by evaluating the style and expression of answers and providing feedback, it is possible to improve students' expressiveness and logical ability.

[0036] The automated scoring unit can be adapted to different subjects, making it usable for a wide range of educational content. The automated scoring unit, for example, can adapt the generative AI to different subjects, making it usable for a wide range of educational content. For example, it can adapt to subjects such as mathematics, science, and history. In addition, a generative AI can be developed for each subject, making it easy for users to use. For example, a generative AI that answers mathematics problems, a generative AI that evaluates science lab reports, etc. In addition, a generative AI can be developed for different subjects, making it easy for users to use a generative AI that suits their subject. For example, a generative AI that evaluates history essays, a generative AI that evaluates science lab reports, etc. This makes it possible to adapt to different subjects, making it usable for a wide range of educational content.

[0037] The automated scoring unit can accommodate different evaluation criteria, enabling flexible scoring. The automated scoring unit can, for example, make the generation AI compatible with different evaluation criteria, enabling flexible scoring. For example, it can accommodate both qualitative and quantitative evaluation. In addition, a generation AI compatible with each evaluation criterion can be developed, making it easy for users to use. For example, a generation AI that performs qualitative evaluation, a generation AI that performs quantitative evaluation, etc. In addition, a generation AI compatible with different evaluation criteria can be developed, making it easy for users to use a generation AI that matches their own evaluation criteria. For example, a generation AI that performs qualitative evaluation, a generation AI that performs quantitative evaluation, etc. This makes it possible to accommodate different evaluation criteria, enabling flexible scoring.

[0038] The communication integration unit can use the generation AI to analyze the content of the conversation and automatically generate appropriate answers and advice. For example, the communication integration unit adds a function where the generation AI analyzes the content of the conversation and automatically generates appropriate answers and advice. For example, the generation AI automatically generates an answer to a question from a student. The generation AI also adds a function where it analyzes the content of the conversation and provides specific advice. For example, the generation AI provides specific advice in response to a student's consultation. The generation AI also adds a function where it analyzes the content of the conversation and automatically generates appropriate answers and advice. For example, in a conversation between teachers, the generation AI provides appropriate advice. This makes it possible to analyze the content of the conversation and automatically generate appropriate answers and advice, thereby facilitating communication between educators and students.

[0039] The communication integration unit can make the platform compatible with different devices, making it usable in a wide range of environments. The communication integration unit, for example, makes the platform compatible with different devices such as smartphones, tablets, and PCs, making it usable in a wide range of environments. For example, a chat function on a smartphone, a video call function on a tablet, etc. The unit also develops interfaces compatible with each device, making it easy for users to use. For example, an app for smartphones, an app for tablets, and a web interface for PCs. The unit also develops platforms compatible with different devices, making it easy for users to use an interface that suits their device. For example, an app for smartphones, an app for tablets, and a web interface for PCs. This makes it possible to support different devices and make it usable in a wide range of environments.

[0040] The communication integration unit can accommodate different communication formats, enabling flexible communication. The communication integration unit, for example, makes the platform compatible with different communication formats such as text, voice, and video, enabling flexible communication. For example, text chat, voice calls, and video calls. The unit also develops functions that support each communication format, making them easy for users to use. For example, a text chat function, a voice call function, and a video call function. The unit also develops a platform that supports different communication formats, making it easy for users to use a format that suits their needs. For example, a text chat function, a voice call function, and a video call function. This allows for flexible communication by supporting different communication formats.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The platform can further include a learning progress management unit. The learning progress management unit tracks students' learning progress in real time and provides feedback to teachers. For example, it can record which learning materials students have studied and for how long, thereby visualizing their learning progress. The learning progress management unit can also analyze students' learning data and evaluate the effectiveness of their learning. For example, it can compare test results after studying specific learning materials to measure learning effectiveness. Furthermore, the learning progress management unit can recommend the next learning material that students should study based on their learning pace. This makes it easier for teachers to understand students' learning status and provide individualized instruction.

[0043] The platform may further include a learning motivation improvement unit. The learning motivation improvement unit provides a function for increasing students' motivation to learn. For example, learning goals may be set and badges or points may be awarded according to the degree of achievement. The learning motivation improvement unit may also provide a ranking function for promoting competition among students. For example, a learning progress ranking within a class may be displayed and rewards may be given to students who rank highly. Furthermore, the learning motivation improvement unit may also include a function for reporting students' learning results to parents and teachers. This makes it easier for students to maintain their motivation to learn.

[0044] The platform can further include a learning style adaptation unit. The learning style adaptation unit recommends the most suitable learning materials and methods according to the student's learning style. For example, for a student who prefers visual learning, it can recommend materials that make heavy use of videos and illustrations. For a student who prefers auditory learning, it can recommend audio materials and podcasts. Furthermore, the learning style adaptation unit can analyze the student's learning style and suggest areas for improvement in the learning method. For example, if a student is biased towards visual learning, it can suggest that they incorporate auditory learning methods. This allows students to study efficiently using a learning method that suits them best.

[0045] The platform may further include a learning environment optimization unit. The learning environment optimization unit provides a function for optimizing a student's learning environment. For example, it may include a function for adjusting the lighting and volume of the learning environment. The learning environment optimization unit may also provide environmental sounds and music to improve a student's concentration. For example, it may play sounds of nature or relaxing music. Furthermore, the learning environment optimization unit may monitor a student's learning environment and suggest improvements. For example, if the learning environment is noisy, it may suggest studying in a quieter place. This allows a student to study in an optimal environment.

[0046] The platform can further include a learning resource management unit. The learning resource management unit centrally manages learning resources used by students. For example, it manages resources such as textbooks, reference books, and online learning materials, and makes them easily accessible to students. The learning resource management unit can also track resource usage and recommend popular resources. For example, it can recommend learning materials that are used by many students. Furthermore, the learning resource management unit can provide updated information on resources. For example, when a new textbook or reference book is added, it notifies students of this information. This allows students to quickly access the resources they need.

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

[0048] Step 1: The Teaching Materials Sharing Unit allows teachers to share teaching materials. For example, teachers can upload lesson materials and workbooks they have created and share them with other teachers. The Teaching Materials Sharing Unit also provides a teaching materials library and a search function that allows teachers to quickly find the teaching materials they need. For example, teachers can search for teaching materials on a specific topic and make them immediately available for use. Step 2: The automated scoring unit automates scoring using the generation AI. For example, the generation AI analyzes students' answers and accurately scores them. The input to the generation AI is image data and text data from the answer sheet, and the generation AI outputs the scoring results based on this. For example, the answers to multiple-choice and written questions are scanned, and the generation AI analyzes the content and scores them. Step 3: The communication integration unit integrates communication between educators and students. For example, it has chat and video call functions, allowing for real-time questions and consultations. The communication integration unit can send and receive messages and manage video conference schedules.

[0049] (Example 2) The platform according to an embodiment of the present invention is a system designed to reduce work hours in educational settings. This system reduces the burden on educators and supports efficient educational activities by sharing teaching materials, automating grading, and integrating communication tools. As a result, the platform can significantly reduce educators' work hours and improve the efficiency of educational activities.

[0050] The platform according to the embodiment includes a teaching material sharing unit, a grading automation unit, and a communication integration unit. The teaching material sharing unit shares teaching materials. For example, teachers can upload lesson materials and workbooks they have created and share them with other teachers. The teaching material sharing unit also provides a teaching material library and a search function that allows teachers to quickly find necessary teaching materials. For example, teachers can search for teaching materials on a specific topic and use them immediately. The grading automation unit automates grading using a generation AI. For example, the generation AI analyzes students' answers and accurately grades them. The generation AI receives input from image data and text data on answer sheets, and outputs grading results based on that input. For example, answers to multiple-choice and essay questions are scanned, and the generation AI analyzes and grades the content. The communication integration unit integrates communication between educators and students. For example, it includes chat and video call functions, allowing for real-time questions and consultations. The communication integration unit can send and receive messages and manage video conference schedules. As a result, the platform according to the embodiment can significantly reduce educators' working hours and improve the efficiency of educational activities. For example, the sharing of teaching materials will save time in creating them, and the automation of grading will reduce the time spent on grading. Furthermore, the integration of communication tools will streamline communication between educators and between educators and students.

[0051] The teaching material sharing unit can automatically classify the content of teaching materials using a generative AI and recommend related teaching materials. For example, when teaching materials are uploaded, the generative AI analyzes the content and automatically classifies them into categories. For example, when mathematics teaching materials are uploaded, the generative AI analyzes the content and classifies them into categories such as algebra, geometry, and calculus. The generative AI also analyzes the content of the teaching materials and adds a function to recommend related teaching materials. For example, when algebra teaching materials are uploaded, the generative AI recommends related geometry and calculus teaching materials. The generative AI also analyzes the content of the teaching materials and automatically searches the library for other teaching materials related to the teaching materials and recommends them. For example, when history teaching materials are uploaded, the generative AI recommends related geography and social studies teaching materials. This automatically classifies the content of teaching materials and recommends related teaching materials, thereby reducing the effort required for educators to select teaching materials.

[0052] The teaching material sharing unit can use the generative AI to evaluate the quality of teaching materials and suggest areas for improvement. For example, when teaching materials are uploaded to the teaching material sharing unit, the generative AI analyzes the content and evaluates the quality. For example, it evaluates whether the content of the teaching materials is accurate and whether the information is up to date. In addition, a function is added in which the generative AI evaluates the quality of teaching materials and suggests areas for improvement. For example, if the content of the teaching materials is outdated, it will suggest adding the latest information. In addition, when teaching materials are uploaded, the generative AI analyzes the content, evaluates the quality, and suggests specific areas for improvement. For example, it will make suggestions to improve the layout or design of the teaching materials. In this way, by evaluating the quality of teaching materials and suggesting areas for improvement, the quality of teaching material creation by educators can be improved.

[0053] The teaching material sharing unit can recommend optimal teaching materials based on the user's emotions using the emotion estimation function. For example, when searching for teaching materials, the teaching material sharing unit uses the emotion estimation function to analyze the user's emotions and recommend optimal teaching materials based on the emotions. For example, if the user is feeling stressed, the teaching material sharing unit recommends relaxing teaching materials. Furthermore, the emotion estimation function is used to build a system that recommends teaching materials based on the user's emotions. For example, if the user is excited, teaching materials that improve concentration are recommended. Furthermore, when recommending teaching materials, the emotion estimation function is used to analyze the user's emotions and recommend optimal teaching materials based on the emotions. For example, if the user is tired, easy teaching materials are recommended. In this way, by recommending optimal teaching materials based on the user's emotions, it is possible to improve learning effectiveness.

[0054] The teaching material sharing unit can be adapted to different educational levels, making it usable in a wide range of educational settings. For example, the teaching material sharing unit adapts the platform to each educational level: elementary school, middle school, high school, and university. For example, elementary school teaching materials are easy to understand, middle school teaching materials are intermediate, high school teaching materials are advanced, and university teaching materials are specialized. In addition, teaching materials corresponding to each educational level are registered on the platform, making it easy for users to search for them. For example, it makes it easy for elementary school teachers to search for elementary school teaching materials. In addition, teaching materials corresponding to different educational levels are registered on the platform, making it easy for users to find teaching materials that suit their educational level. For example, it makes it easy for university professors to search for university teaching materials. In this way, by adapting to different educational levels, it can be made usable in a wide range of educational settings.

[0055] The teaching material sharing unit can be made to support different languages, making it usable in international educational settings. The teaching material sharing unit, for example, makes the platform usable in different languages, making it usable in international educational settings. For example, it supports languages ​​such as English, Japanese, French, and Chinese. Furthermore, teaching materials corresponding to each language are registered on the platform, making it easy for users to search for teaching materials that suit their language. For example, it makes it easy for English teachers to search for English teaching materials. Furthermore, teaching materials corresponding to different languages ​​are registered on the platform, making it easy for users to find teaching materials that suit their language. For example, it makes it easy for Chinese professors to search for Chinese teaching materials. In this way, by supporting different languages, it can be made usable in international educational settings.

[0056] The teaching material sharing unit can use the emotion estimation function to recommend the teaching material that the user is most interested in in real time. For example, when searching for teaching materials, the teaching material sharing unit uses the emotion estimation function to analyze the user's emotions and recommend the teaching material that the user is most interested in in real time based on the emotions. For example, the teaching material sharing unit recommends teaching materials related to a topic that the user is interested in. Furthermore, a system is constructed using the emotion estimation function to recommend the teaching material that the user is most interested in in real time based on the user's emotions. For example, if the user is excited, teaching materials that pique the user's interest are recommended. Furthermore, when recommending teaching materials, the emotion estimation function is used to analyze the user's emotions and recommend the teaching material that the user is most interested in in real time based on the emotions. For example, if the user is curious, teaching materials that stimulate the user's curiosity are recommended. In this way, the teaching material that the user is most interested in can be recommended in real time, thereby increasing the user's motivation to learn.

[0057] The automated scoring unit uses the generation AI to understand the context of the answer and can provide more accurate scoring. The automated scoring unit, for example, adds a function whereby the generation AI understands the context of the answer and provides more accurate scoring. For example, it analyzes answers to essay questions and provides accurate scoring based on the context. It also adds a function whereby the generation AI understands the context of the answer and evaluates the content of the answer. For example, it analyzes answers to essay questions and provides evaluation based on the context. It also adds a function whereby the generation AI understands the context of the answer and evaluates the content of the answer. For example, it analyzes answers to math questions and evaluates the process of the answer. This allows the context of the answer to be understood and provides more accurate scoring, thereby improving the accuracy of scoring.

[0058] The automated scoring unit can use a generative AI to evaluate the style and expression of answers and provide feedback. For example, the automated scoring unit introduces a function where the generative AI evaluates the style and expression of answers and provides feedback. For example, it analyzes essay answers and provides feedback on grammar and expression. It also adds a function where the generative AI evaluates the style and expression of answers and suggests specific areas for improvement. For example, it provides feedback on the structure and logic of the answer. It also introduces a function where the generative AI evaluates the style and expression of answers and provides feedback. For example, it analyzes presentation answers and provides feedback on the speaking style and slide design. In this way, by evaluating the style and expression of answers and providing feedback, it is possible to improve students' expressiveness and logical ability.

[0059] The automated grading unit can customize feedback based on a student's emotions using the emotion estimation function. The automated grading unit, for example, uses the emotion estimation function to customize feedback based on a student's emotions. For example, if a student is feeling stressed, an encouraging message is provided. A system is also constructed that uses the emotion estimation function to customize feedback based on a student's emotions. For example, if a student is excited, positive feedback is provided. The emotion estimation function is also used to customize feedback based on a student's emotions. For example, if a student is tired, simple improvements are suggested. In this way, customizing feedback based on a student's emotions can increase the student's motivation to learn.

[0060] The automated scoring unit can be adapted to different subjects, making it usable for a wide range of educational content. The automated scoring unit, for example, can adapt the generative AI to different subjects, making it usable for a wide range of educational content. For example, it can adapt to subjects such as mathematics, science, and history. In addition, a generative AI can be developed for each subject, making it easy for users to use. For example, a generative AI that answers mathematics problems, a generative AI that evaluates science lab reports, etc. In addition, a generative AI can be developed for different subjects, making it easy for users to use a generative AI that suits their subject. For example, a generative AI that evaluates history essays, a generative AI that evaluates science lab reports, etc. This makes it possible to adapt to different subjects, making it usable for a wide range of educational content.

[0061] The automated scoring unit can accommodate different evaluation criteria, enabling flexible scoring. The automated scoring unit can, for example, make the generation AI compatible with different evaluation criteria, enabling flexible scoring. For example, it can accommodate both qualitative and quantitative evaluation. In addition, a generation AI compatible with each evaluation criterion can be developed, making it easy for users to use. For example, a generation AI that performs qualitative evaluation, a generation AI that performs quantitative evaluation, etc. In addition, a generation AI compatible with different evaluation criteria can be developed, making it easy for users to use a generation AI that matches their own evaluation criteria. For example, a generation AI that performs qualitative evaluation, a generation AI that performs quantitative evaluation, etc. This makes it possible to accommodate different evaluation criteria, enabling flexible scoring.

[0062] The automated grading unit can use the emotion estimation function to adjust the grading results based on the student's emotions. The automated grading unit, for example, uses the emotion estimation function to adjust the grading results based on the student's emotions. For example, if the student is feeling stressed, the grading results are slightly relaxed. A system is also constructed that uses the emotion estimation function to adjust the grading results based on the student's emotions. For example, if the student is excited, the grading results are slightly stricter. The emotion estimation function is also used to adjust the grading results based on the student's emotions. For example, if the student is tired, the grading results are slightly relaxed. In this way, adjusting the grading results based on the student's emotions can increase the student's motivation to learn.

[0063] The communication integration unit can use the generation AI to analyze the content of the conversation and automatically generate appropriate answers and advice. For example, the communication integration unit adds a function where the generation AI analyzes the content of the conversation and automatically generates appropriate answers and advice. For example, the generation AI automatically generates an answer to a question from a student. The generation AI also adds a function where it analyzes the content of the conversation and provides specific advice. For example, the generation AI provides specific advice in response to a student's consultation. The generation AI also adds a function where it analyzes the content of the conversation and automatically generates appropriate answers and advice. For example, in a conversation between teachers, the generation AI provides appropriate advice. This makes it possible to analyze the content of the conversation and automatically generate appropriate answers and advice, thereby facilitating communication between educators and students.

[0064] The communication integration unit can use generative AI to analyze the tone and emotions of a conversation and suggest appropriate communication methods. For example, the communication integration unit will introduce a function where the generative AI analyzes the tone and emotions of a conversation and suggests appropriate communication methods. For example, if a student is feeling stressed, the generative AI will suggest responding in a gentle tone. In addition, a function will be added where the generative AI analyzes the tone and emotions of a conversation and suggests specific communication methods. For example, if a student is excited, the generative AI will suggest a calm response. In addition, a function will be introduced where the generative AI analyzes the tone and emotions of a conversation and suggests appropriate communication methods. For example, in a conversation between teachers, the generative AI will suggest constructive feedback. This will allow for smoother communication between educators and students by analyzing the tone and emotions of a conversation and suggesting appropriate communication methods.

[0065] The communication integration unit can use the emotion estimation function to suggest an optimal communication method based on the user's emotions. The communication integration unit, for example, uses the emotion estimation function to suggest an optimal communication method based on the user's emotions. For example, if the user is sad, it suggests an encouraging message. Also, a system is constructed that uses the emotion estimation function to suggest an optimal communication method based on the user's emotions. For example, if the user is angry, it suggests a calm response. Also, the emotion estimation function is used to suggest an optimal communication method based on the user's emotions. For example, if the user is happy, it suggests a message of empathy. In this way, by suggesting an optimal communication method based on the user's emotions, the quality of communication can be improved.

[0066] The communication integration unit can make the platform compatible with different devices, making it usable in a wide range of environments. The communication integration unit, for example, makes the platform compatible with different devices such as smartphones, tablets, and PCs, making it usable in a wide range of environments. For example, a chat function on a smartphone, a video call function on a tablet, etc. The unit also develops interfaces compatible with each device, making it easy for users to use. For example, an app for smartphones, an app for tablets, and a web interface for PCs. The unit also develops platforms compatible with different devices, making it easy for users to use an interface that suits their device. For example, an app for smartphones, an app for tablets, and a web interface for PCs. This makes it possible to support different devices and make it usable in a wide range of environments.

[0067] The communication integration unit can accommodate different communication formats, enabling flexible communication. The communication integration unit, for example, makes the platform compatible with different communication formats such as text, voice, and video, enabling flexible communication. For example, text chat, voice calls, and video calls. The unit also develops functions that support each communication format, making them easy for users to use. For example, a text chat function, a voice call function, and a video call function. The unit also develops a platform that supports different communication formats, making it easy for users to use a format that suits their needs. For example, a text chat function, a voice call function, and a video call function. This allows for flexible communication by supporting different communication formats.

[0068] The communication integration unit can use the emotion estimation function to adjust the timing and content of communication based on the user's emotions. The communication integration unit, for example, uses the emotion estimation function to adjust the timing and content of communication based on the user's emotions. For example, if the user is feeling stressed, an encouraging message is sent at an appropriate time. Furthermore, a system is constructed that uses the emotion estimation function to adjust the timing and content of communication based on the user's emotions. For example, if the user is excited, a calm response is suggested. Furthermore, the emotion estimation function is used to adjust the timing and content of communication based on the user's emotions. For example, if the user is tired, a simple message is sent. In this way, the quality of communication can be improved by adjusting the timing and content of communication based on the user's emotions.

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

[0070] The platform can further include a learning progress management unit. The learning progress management unit tracks students' learning progress in real time and provides feedback to teachers. For example, it can record which learning materials students have studied and for how long, thereby visualizing their learning progress. The learning progress management unit can also analyze students' learning data and evaluate the effectiveness of their learning. For example, it can compare test results after studying specific learning materials to measure learning effectiveness. Furthermore, the learning progress management unit can recommend the next learning material that students should study based on their learning pace. This makes it easier for teachers to understand students' learning status and provide individualized instruction.

[0071] The platform may further include a learning motivation improvement unit. The learning motivation improvement unit provides a function for increasing students' motivation to learn. For example, learning goals may be set and badges or points may be awarded according to the degree of achievement. The learning motivation improvement unit may also provide a ranking function for promoting competition among students. For example, a learning progress ranking within a class may be displayed and rewards may be given to students who rank highly. Furthermore, the learning motivation improvement unit may also include a function for reporting students' learning results to parents and teachers. This makes it easier for students to maintain their motivation to learn.

[0072] The platform can further include a learning style adaptation unit. The learning style adaptation unit recommends the most suitable learning materials and methods according to the student's learning style. For example, for a student who prefers visual learning, it can recommend materials that make heavy use of videos and illustrations. For a student who prefers auditory learning, it can recommend audio materials and podcasts. Furthermore, the learning style adaptation unit can analyze the student's learning style and suggest areas for improvement in the learning method. For example, if a student is biased towards visual learning, it can suggest that they incorporate auditory learning methods. This allows students to study efficiently using a learning method that suits them best.

[0073] The platform may further include a learning environment optimization unit. The learning environment optimization unit provides a function for optimizing a student's learning environment. For example, it may include a function for adjusting the lighting and volume of the learning environment. The learning environment optimization unit may also provide environmental sounds and music to improve a student's concentration. For example, it may play sounds of nature or relaxing music. Furthermore, the learning environment optimization unit may monitor a student's learning environment and suggest improvements. For example, if the learning environment is noisy, it may suggest studying in a quieter place. This allows a student to study in an optimal environment.

[0074] The platform can further include a learning resource management unit. The learning resource management unit centrally manages learning resources used by students. For example, it manages resources such as textbooks, reference books, and online learning materials, and makes them easily accessible to students. The learning resource management unit can also track resource usage and recommend popular resources. For example, it can recommend learning materials that are used by many students. Furthermore, the learning resource management unit can provide updated information on resources. For example, when a new textbook or reference book is added, it notifies students of this information. This allows students to quickly access the resources they need.

[0075] The platform can also use emotion estimation to monitor students' stress levels and suggest appropriate measures. For example, if a student is feeling high stress, it can suggest a break to relax. The emotion estimation function can also be used to suggest study plans based on the student's stress level. For example, if stress is high, it can suggest light study content, and if stress is low, it can suggest study content that requires concentration. The emotion estimation function can also be used to monitor students' stress levels and suggest counseling if necessary. This allows students to manage their stress while continuing their studies.

[0076] The platform can also use emotion estimation to analyze students' motivation and provide feedback to increase their motivation to learn. For example, if a student feels unmotivated, it can provide an encouraging message. The emotion estimation function can also be used to set learning goals based on the student's motivation. For example, if motivation is high, it can set challenging goals, and if motivation is low, it can set easier goals. The emotion estimation function can also be used to analyze students' motivation and suggest specific actions to increase their motivation to learn. This allows students to maintain their motivation while continuing their studies.

[0077] The platform can further use an emotion estimation function to customize learning content based on a student's emotions. For example, if a student is tired, it can provide easy learning content. The emotion estimation function can also be used to adjust the learning pace based on a student's emotions. For example, if a student is feeling stressed, it can suggest slowing down the learning pace. The emotion estimation function can also be used to customize learning methods based on a student's emotions. For example, if a student is excited, it can suggest learning methods to increase concentration. This allows students to study efficiently using a learning method that suits their emotions.

[0078] The platform can also use emotion estimation to adjust study timing based on a student's emotions. For example, if a student is tired, it can suggest a break. The emotion estimation function can also be used to set study priorities based on a student's emotions. For example, if a student is feeling stressed, it can prioritize learning content that helps them relax. The emotion estimation function can also be used to adjust a study schedule based on a student's emotions. For example, if a student is excited, it can schedule learning content that requires concentration. This allows students to study efficiently at times that suit their emotions.

[0079] The platform can also use emotion estimation to customize learning feedback based on a student's emotions. For example, if a student is stressed, it can provide an encouraging message. The emotion estimation function can also be used to adjust the content of feedback based on a student's emotions. For example, if a student is excited, it can provide positive feedback. The emotion estimation function can also be used to adjust the timing of feedback based on a student's emotions. For example, if a student is tired, it can suggest postponing the feedback. This allows students to receive feedback that matches their emotions, increasing their motivation to learn.

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

[0081] Step 1: The Teaching Materials Sharing Unit allows teachers to share teaching materials. For example, teachers can upload lesson materials and workbooks they have created and share them with other teachers. The Teaching Materials Sharing Unit also provides a teaching materials library and a search function that allows teachers to quickly find the teaching materials they need. For example, teachers can search for teaching materials on a specific topic and make them immediately available for use. Step 2: The automated scoring unit automates scoring using the generation AI. For example, the generation AI analyzes students' answers and accurately scores them. The input to the generation AI is image data and text data from the answer sheet, and the generation AI outputs the scoring results based on this. For example, the answers to multiple-choice and written questions are scanned, and the generation AI analyzes the content and scores them. Step 3: The communication integration unit integrates communication between educators and students. For example, it has chat and video call functions, allowing for real-time questions and consultations. The communication integration unit can send and receive messages and manage video conference schedules.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0110] 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.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0116] 7, 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.

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

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0126] 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.

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

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0141] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. A teaching materials sharing section where teaching materials are shared; A scoring automation unit that uses generative AI to automate scoring, a communication integration unit that integrates communication between educators and students; A system characterized by:

2. The teaching material sharing unit includes: The generative AI is used to automatically classify the content of the educational material and recommend related educational material.

2. The system of claim 1.

3. The teaching material sharing unit includes: The generative AI is used to evaluate the quality of the teaching materials and suggest improvements.

2. The system of claim 1.

4. The teaching material sharing unit includes: Recommending optimal learning materials based on user sentiment 2. The system of claim 1.

5. The teaching material sharing unit includes: Adaptable to different educational levels and usable in a wide range of educational settings 2. The system of claim 1.

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