System

Abstract

An object of the system according to the embodiment is to improve logical explanation and listening ability through a bidirectional conversation.SOLUTION: A system includes an educational program providing unit, a two way conversation unit, and a generation and AI unit. The educational program providing unit provides an educational program. The two way conversation unit performs a two way conversation based on the program provided by the education program providing unit. The generation and AI unit analyzes the content of the conversation carried out by the two way conversation unit, and generates questions and comments on critical thinking.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, there is room for improvement in AI-based educational programs in terms of improving logical explanations and listening skills through two-way conversations.

[0005] The system according to the embodiment aims to improve logical explanations and listening skills through two-way conversations. [Means for solving the problem]

[0006] The system according to the embodiment includes an educational program providing unit, a two-way conversation unit, and a generation AI unit. The educational program providing unit provides an educational program. The two-way conversation unit conducts two-way conversations based on the program provided by the educational program providing unit. The generation AI unit analyzes the content of the conversation conducted by the two-way conversation unit and generates questions and critical thinking comments. [Effects of the Invention]

[0007] The system according to the embodiment can improve logical explanations and listening skills through two-way conversations. [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) An educational program according to an embodiment of the present invention is a system that provides an educational program for developing skills in areas that are difficult for AI to master, and is introduced into student education. This system provides practical training in listening and speaking through two-way conversations between a person and an AI. This allows the educational program to improve students' listening and speaking skills, and enhance their ability to explain things logically and to listen well.

[0029] An educational program according to an embodiment includes an educational program providing unit, a two-way conversation unit, and a generation AI unit. The educational program providing unit provides an educational program. For example, it can provide online courses and workshops. The educational program providing unit can also provide teaching materials. The two-way conversation unit conducts two-way conversations based on the program provided by the educational program providing unit. For example, the conversations can be conducted using chat, voice calls, or video calls. The two-way conversation unit can also record the conversation content and play it back later. The generation AI unit analyzes the conversation content conducted by the two-way conversation unit and generates questions and critical thinking comments. For example, the generation AI unit analyzes the conversation content using natural language processing technology and generates appropriate questions and comments. The generation AI unit can also perform sentiment analysis and generate comments based on the emotions. Furthermore, the generation AI unit uses keyword extraction technology to extract important parts of the conversation content and generate questions and comments based on them. As a result, the educational program according to an embodiment provides an educational program that develops skills in areas difficult to achieve with AI. By introducing this into student education, practical training in listening and speaking can be provided, aiming to improve logical explanation skills and listening comprehension.

[0030] The educational program providing unit is able to monitor students' progress in real time and provide individually customized feedback. For example, the educational program providing unit builds a system that monitors students' progress in real time as they progress through a program and provides individually customized feedback. For example, appropriate assignments and supplementary materials are automatically provided according to their learning progress. The educational program providing unit also collects students' learning data in real time and provides individually customized feedback based on their progress. For example, if a particular skill is lacking, additional training is suggested to strengthen that skill. The educational program providing unit also develops a system that monitors students' progress in real time and provides individually customized feedback. For example, appropriate feedback and advice is automatically provided according to their learning progress. This makes it possible to improve learning effectiveness by providing appropriate feedback according to students' progress.

[0031] The educational program provision unit collects background information about students and can propose optimal learning paths based on that information. The educational program provision unit collects background information such as students' academic backgrounds and areas of interest, and builds a system that proposes optimal learning paths based on that information. For example, it provides a learning plan customized according to the students' interests. The educational program provision unit also collects background information about students participating in the program and proposes optimal learning paths based on that information. For example, it provides appropriate teaching materials and training according to the students' academic backgrounds and areas of interest. The educational program provision unit also collects background information about students and develops a system that proposes optimal learning paths based on that information. For example, it provides learning plans that are individually customized according to the students' interests and academic backgrounds. This makes it possible to improve learning effectiveness by providing optimal learning paths that suit each student's individual background.

[0032] The educational program provision department can integrate educational programs into an online platform, allowing students to participate from remote locations. For example, the educational program provision department builds a system that integrates educational programs into an online platform and allows students to participate from remote locations. For example, it allows students to take classes and training in real time via the Internet. The educational program provision department also uses an online platform to allow students to participate in educational programs from remote locations. For example, it uses a video conferencing system to allow students in remote locations to participate in classes in real time. The educational program provision department also integrates educational programs into an online platform and develops a system that allows students to participate from remote locations. For example, it provides online teaching materials and training so that students in remote locations can learn at their own pace. This allows students in remote locations to participate in educational programs, thereby expanding learning opportunities.

[0033] The educational program provision department can collaborate with different educational institutions and companies to diversify the content of the programs. For example, the educational program provision department collaborates with different educational institutions and companies to build a system that diversifies the content of the educational programs. For example, it invites experts from different fields to give special lectures. The educational program provision department also collaborates with educational institutions and companies to diversify the content of the programs. For example, it provides practical training that incorporates work experience from companies. The educational program provision department also collaborates with different educational institutions and companies to develop a system that diversifies the content of the educational programs. For example, it holds workshops by experts from different fields. In this way, by collaborating with different educational institutions and companies, it is possible to diversify the content of the programs and improve learning effectiveness.

[0034] The two-way conversation unit can analyze students' speech and provide feedback on areas for improvement in pronunciation and intonation. For example, the two-way conversation unit will build a system in which a generation AI analyzes students' speech in real time and provides feedback on areas for improvement in pronunciation and intonation. For example, it will evaluate the accuracy of pronunciation and the naturalness of intonation and suggest specific areas for improvement. The two-way conversation unit will also analyze students' speech using a generation AI and provide feedback on areas for improvement in pronunciation and intonation. For example, it will point out pronunciation errors and show the correct pronunciation. The two-way conversation unit will also develop a system in which a generation AI analyzes students' speech and provides feedback on areas for improvement in pronunciation and intonation. For example, it will analyze intonation patterns and provide advice to encourage natural speaking. This will allow students to improve their speaking skills by providing feedback on areas for improvement in their pronunciation and intonation.

[0035] The two-way conversation unit allows the generative AI to provide related additional information and reference materials based on the content of a student's utterance. For example, the two-way conversation unit builds a system in which the generative AI provides related additional information and reference materials based on the content of a student's utterance. For example, it automatically presents articles and papers related to the content of the utterance. The two-way conversation unit also analyzes the content of a student's utterance and provides related additional information and reference materials. For example, it presents statistical data and research results related to the content of the utterance. The two-way conversation unit also develops a system in which the generative AI provides related additional information and reference materials based on the content of a student's utterance. For example, it presents videos and presentations related to the content of the utterance. This makes it possible to improve learning effectiveness by providing related additional information and reference materials based on the content of a student's utterance.

[0036] The two-way conversation unit uses a generative AI to support conversations in different languages, promoting multilingual learning. For example, the two-way conversation unit builds a system in which a generative AI supports conversations in different languages ​​to promote multilingual learning. For example, it supports conversations in multiple languages, such as English, Japanese, and French. The two-way conversation unit also uses a generative AI to support conversations in different languages ​​to promote multilingual learning. For example, it provides conversation practice in a language selected by the student. The two-way conversation unit also develops a system in which a generative AI supports conversations in different languages ​​to promote multilingual learning. For example, it supports pronunciation and grammar practice in different languages. This makes it possible to promote multilingual learning by supporting conversations in different languages.

[0037] The two-way conversation unit will use visual aids to deepen students' understanding when they interact with the generative AI. For example, the two-way conversation unit will build a system that uses visual aids (images and videos) to deepen students' understanding when they interact with the generative AI. For example, it will automatically display images and videos related to the content of the conversation. The two-way conversation unit will also use visual aids to deepen students' understanding during a dialogue with the generative AI. For example, it will display charts and illustrations related to the content of the conversation. The two-way conversation unit will also develop a system that uses visual aids to deepen students' understanding when they interact with the generative AI. For example, it will display animations and slides related to the content of the conversation. This will allow students to deepen their understanding by using visual aids.

[0038] The generative AI department can evaluate students' logical explanations and suggest specific areas for improvement. For example, the generative AI department will build a system in which the generative AI evaluates students' logical explanations in real time and suggests specific areas for improvement. For example, it will evaluate the consistency and persuasiveness of the logic and suggest specific areas for improvement. The generative AI department will also evaluate students' logical explanations and suggest specific areas for improvement. For example, it will point out logical leaps and inconsistencies and suggest ways to improve them. The generative AI department will also develop a system in which the generative AI evaluates students' logical explanations and suggests specific areas for improvement. For example, it will analyze the structure of logic and suggest more effective ways of explaining. This will enable the evaluation of students' logical explanations and suggest specific areas for improvement, thereby improving their logical explanation skills.

[0039] The generative AI department can improve debate skills by providing counterarguments and different perspectives based on the content of students' explanations. For example, the generative AI department builds a system that improves debate skills by using a generative AI to provide counterarguments and different perspectives based on the content of students' explanations. For example, it presents counterarguments to students' opinions and provides debate practice. The generative AI department also analyzes students' explanations and provides counterarguments and different perspectives. For example, it presents questions from different perspectives to students' opinions and provides debate skills. The generative AI department also develops a system that improves debate skills by using a generative AI to provide counterarguments and different perspectives based on the content of students' explanations. For example, it presents counterarguments to students' opinions and provides debate practice. This makes it possible to improve debate skills by providing counterarguments and different perspectives based on the content of students' explanations.

[0040] The generative AI department can visualize the content of students' explanations, making them easier to understand visually. For example, the generative AI department builds a system in which the generative AI visualizes the content of students' explanations, making them easier to understand visually. For example, it converts the content of explanations into diagrams and graphs and displays them visually. The generative AI department also visualizes the content of students' explanations, making them easier to understand visually. For example, it converts the content of explanations into mind maps and flowcharts. The generative AI department also develops a system in which the generative AI visualizes the content of students' explanations, making them easier to understand visually. For example, it converts the content of explanations into infographics and displays them visually. In this way, visualizing the content of students' explanations makes them easier to understand visually.

[0041] The Generative AI Department allows students to create presentation materials in collaboration with generative AI, thereby cultivating practical skills. For example, the Generative AI Department will build a system in which students create presentation materials in collaboration with generative AI, thereby cultivating practical skills. For example, the Generative AI will propose the structure and design of the materials, and students will complete the content. The Generative AI Department will also create presentation materials in collaboration with generative AI, thereby cultivating practical skills. For example, the Generative AI will provide a template for the materials, and students will fill in the content. The Generative AI Department will also develop a system in which students create presentation materials in collaboration with generative AI, thereby cultivating practical skills. For example, the Generative AI will propose the layout and design of the materials, and students will complete the content. This allows students to create presentation materials in collaboration with generative AI, thereby cultivating practical skills.

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

[0043] The educational program provider can provide customized learning plans tailored to each student's learning style. For example, visual learners can be provided with materials that make extensive use of visual aids, while auditory learners can be provided with primarily audio materials. The educational program provider can also analyze each student's learning style and suggest optimal learning methods based on that analysis. For example, students who prefer hands-on training can be provided with exercises using real-life scenarios. Furthermore, the educational program provider can provide feedback tailored to each student's learning style. For example, visual learners can be provided with feedback using graphs and charts, while auditory learners can be provided with audio feedback. This allows for customized learning plans tailored to each student's learning style, thereby enhancing learning effectiveness.

[0044] The educational program offering department can propose future learning plans based on students' learning histories. For example, it can analyze past learning data and suggest the next content and skills to learn. The educational program offering department can also predict learning progress based on students' learning histories and provide feedback at the appropriate time. For example, it can predict the time it will take to master a particular skill and suggest additional training at that time. Furthermore, the educational program offering department can evaluate the effectiveness of learning based on students' learning histories and suggest areas for improvement. For example, it can analyze past learning data, evaluate which learning method was most effective, and adjust the learning plan based on that. In this way, it is possible to improve learning effectiveness by proposing future learning plans based on students' learning histories.

[0045] The educational program provider can suggest environmental settings to improve students' concentration while studying. For example, it can suggest lighting and music that are suitable for studying. The educational program provider can also analyze environmental settings to improve students' concentration and suggest the optimal learning environment based on that analysis. For example, it can suggest noise-canceling headphones if a quiet environment is required. Furthermore, the educational program provider can suggest environmental settings to improve students' concentration and provide advice to maximize learning outcomes. For example, it can suggest taking appropriate breaks while studying. In this way, it is possible to improve learning outcomes by suggesting environmental settings to improve students' concentration.

[0046] The educational program provider can compare a student's learning outcomes with those of other students and provide a relative evaluation. For example, a student can check their own progress by comparing it with that of other students taking the same program. The educational program provider can also compare a student's learning outcomes with those of other students and provide feedback to increase a sense of competition. For example, special rewards can be provided to top students. Furthermore, the educational program provider can compare a student's learning outcomes with those of other students and provide advice to increase learning motivation. For example, the educational program provider can introduce the success stories of other students and use them to improve their learning methods. In this way, students can improve their learning effectiveness by comparing their learning outcomes with those of other students.

[0047] The educational program provision department can collect data on students' behavior while they study and analyze their learning patterns based on that data. For example, it can analyze study times and methods and propose optimal learning plans. The educational program provision department can also evaluate the effectiveness of learning based on student behavior data and suggest areas for improvement. For example, if a particular learning method is not effective, it can suggest an alternative method. Furthermore, the educational program provision department can collect student behavior data and analyze learning patterns based on that data to provide advice to maximize learning effectiveness. For example, it can identify times when students' concentration levels drop and suggest taking breaks during those times. In this way, analyzing learning patterns based on student behavior data can improve learning effectiveness.

[0048] The educational program provision department can collect data on students' behavior while they study and analyze their learning patterns based on that data. For example, it can analyze study times and methods and propose optimal learning plans. The educational program provision department can also evaluate the effectiveness of learning based on student behavior data and suggest areas for improvement. For example, if a particular learning method is not effective, it can suggest an alternative method. Furthermore, the educational program provision department can collect student behavior data and analyze learning patterns based on that data to provide advice to maximize learning effectiveness. For example, it can identify times when students' concentration levels drop and suggest taking breaks during those times. In this way, analyzing learning patterns based on student behavior data can improve learning effectiveness.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The educational program provider provides educational programs. For example, they can provide online courses or workshops. They can also provide educational materials. Step 2: The two-way conversation unit conducts two-way conversations based on the programs provided by the educational program provider. For example, the conversation can be conducted using chat, voice calls, or video calls. The conversation can also be recorded and played back later. Step 3: The generation AI unit analyzes the content of the conversation conducted by the two-way conversation unit and generates questions and critical thinking comments. For example, it uses natural language processing technology to analyze the content of the conversation and generate appropriate questions and comments. It can also perform sentiment analysis and generate comments based on emotions. Furthermore, it uses keyword extraction technology to extract important parts of the conversation content and generate questions and comments based on them.

[0051] (Example 2) An educational program according to an embodiment of the present invention is a system that provides an educational program for developing skills in areas that are difficult for AI to master, and is introduced into student education. This system provides practical training in listening and speaking through two-way conversations between a person and an AI. This allows the educational program to improve students' listening and speaking skills, and enhance their ability to explain things logically and to listen well.

[0052] An educational program according to an embodiment includes an educational program providing unit, a two-way conversation unit, and a generation AI unit. The educational program providing unit provides an educational program. For example, it can provide online courses and workshops. The educational program providing unit can also provide teaching materials. The two-way conversation unit conducts two-way conversations based on the program provided by the educational program providing unit. For example, the conversations can be conducted using chat, voice calls, or video calls. The two-way conversation unit can also record the conversation content and play it back later. The generation AI unit analyzes the conversation content conducted by the two-way conversation unit and generates questions and critical thinking comments. For example, the generation AI unit analyzes the conversation content using natural language processing technology and generates appropriate questions and comments. The generation AI unit can also perform sentiment analysis and generate comments based on the emotions. Furthermore, the generation AI unit uses keyword extraction technology to extract important parts of the conversation content and generate questions and comments based on them. As a result, the educational program according to an embodiment provides an educational program that develops skills in areas difficult to achieve with AI. By introducing this into student education, practical training in listening and speaking can be provided, aiming to improve logical explanation skills and listening comprehension.

[0053] The educational program providing unit is able to monitor students' progress in real time and provide individually customized feedback. For example, the educational program providing unit builds a system that monitors students' progress in real time as they progress through a program and provides individually customized feedback. For example, appropriate assignments and supplementary materials are automatically provided according to their learning progress. The educational program providing unit also collects students' learning data in real time and provides individually customized feedback based on their progress. For example, if a particular skill is lacking, additional training is suggested to strengthen that skill. The educational program providing unit also develops a system that monitors students' progress in real time and provides individually customized feedback. For example, appropriate feedback and advice is automatically provided according to their learning progress. This makes it possible to improve learning effectiveness by providing appropriate feedback according to students' progress.

[0054] The educational program provision unit collects background information about students and can propose optimal learning paths based on that information. The educational program provision unit collects background information such as students' academic backgrounds and areas of interest, and builds a system that proposes optimal learning paths based on that information. For example, it provides a learning plan customized according to the students' interests. The educational program provision unit also collects background information about students participating in the program and proposes optimal learning paths based on that information. For example, it provides appropriate teaching materials and training according to the students' academic backgrounds and areas of interest. The educational program provision unit also collects background information about students and develops a system that proposes optimal learning paths based on that information. For example, it provides learning plans that are individually customized according to the students' interests and academic backgrounds. This makes it possible to improve learning effectiveness by providing optimal learning paths that suit each student's individual background.

[0055] The educational program providing unit can use the emotion estimation function to analyze students' emotions while studying and provide interactive content to maintain their motivation. The educational program providing unit, for example, uses the emotion estimation function to analyze students' emotions while studying in real time and build a system to provide interactive content to maintain their motivation. For example, game elements are introduced to elicit positive emotions while studying. The educational program providing unit also analyzes students' emotions while studying with the emotion estimation function and provides interactive content to maintain their motivation. For example, encouraging messages and success stories are presented while studying. The educational program providing unit also uses the emotion estimation function to develop a system to analyze students' emotions while studying and provide interactive content to maintain their motivation. For example, an interactive quiz is provided to elicit positive emotions while studying. This makes it possible to analyze students' emotions while studying and provide interactive content to maintain their motivation, thereby improving learning effectiveness.

[0056] The educational program provision department can integrate educational programs into an online platform, allowing students to participate from remote locations. For example, the educational program provision department builds a system that integrates educational programs into an online platform and allows students to participate from remote locations. For example, it allows students to take classes and training in real time via the Internet. The educational program provision department also uses an online platform to allow students to participate in educational programs from remote locations. For example, it uses a video conferencing system to allow students in remote locations to participate in classes in real time. The educational program provision department also integrates educational programs into an online platform and develops a system that allows students to participate from remote locations. For example, it provides online teaching materials and training so that students in remote locations can learn at their own pace. This allows students in remote locations to participate in educational programs, thereby expanding learning opportunities.

[0057] The educational program provision department can collaborate with different educational institutions and companies to diversify the content of the programs. For example, the educational program provision department collaborates with different educational institutions and companies to build a system that diversifies the content of the educational programs. For example, it invites experts from different fields to give special lectures. The educational program provision department also collaborates with educational institutions and companies to diversify the content of the programs. For example, it provides practical training that incorporates work experience from companies. The educational program provision department also collaborates with different educational institutions and companies to develop a system that diversifies the content of the educational programs. For example, it holds workshops by experts from different fields. In this way, by collaborating with different educational institutions and companies, it is possible to diversify the content of the programs and improve learning effectiveness.

[0058] The educational program providing unit can use the emotion estimation function to introduce gamification elements to increase students' motivation to learn. The educational program providing unit, for example, uses the emotion estimation function to build a system that introduces gamification elements to increase students' motivation to learn. For example, it allows students to earn points or badges according to their learning progress. The educational program providing unit also uses the emotion estimation function to introduce gamification elements to increase students' motivation to learn. For example, it introduces game elements to elicit positive emotions while learning. The educational program providing unit also uses the emotion estimation function to develop a system that introduces gamification elements to increase students' motivation to learn. For example, it provides level-ups and rewards according to their learning progress. In this way, introducing gamification elements to increase students' motivation to learn can improve learning effectiveness.

[0059] The two-way conversation unit can analyze students' speech and provide feedback on areas for improvement in pronunciation and intonation. For example, the two-way conversation unit will build a system in which a generation AI analyzes students' speech in real time and provides feedback on areas for improvement in pronunciation and intonation. For example, it will evaluate the accuracy of pronunciation and the naturalness of intonation and suggest specific areas for improvement. The two-way conversation unit will also analyze students' speech using a generation AI and provide feedback on areas for improvement in pronunciation and intonation. For example, it will point out pronunciation errors and show the correct pronunciation. The two-way conversation unit will also develop a system in which a generation AI analyzes students' speech and provides feedback on areas for improvement in pronunciation and intonation. For example, it will analyze intonation patterns and provide advice to encourage natural speaking. This will allow students to improve their speaking skills by providing feedback on areas for improvement in their pronunciation and intonation.

[0060] The two-way conversation unit allows the generative AI to provide related additional information and reference materials based on the content of a student's utterance. For example, the two-way conversation unit builds a system in which the generative AI provides related additional information and reference materials based on the content of a student's utterance. For example, it automatically presents articles and papers related to the content of the utterance. The two-way conversation unit also analyzes the content of a student's utterance and provides related additional information and reference materials. For example, it presents statistical data and research results related to the content of the utterance. The two-way conversation unit also develops a system in which the generative AI provides related additional information and reference materials based on the content of a student's utterance. For example, it presents videos and presentations related to the content of the utterance. This makes it possible to improve learning effectiveness by providing related additional information and reference materials based on the content of a student's utterance.

[0061] The two-way conversation unit uses a generative AI to support conversations in different languages, promoting multilingual learning. For example, the two-way conversation unit builds a system in which a generative AI supports conversations in different languages ​​to promote multilingual learning. For example, it supports conversations in multiple languages, such as English, Japanese, and French. The two-way conversation unit also uses a generative AI to support conversations in different languages ​​to promote multilingual learning. For example, it provides conversation practice in a language selected by the student. The two-way conversation unit also develops a system in which a generative AI supports conversations in different languages ​​to promote multilingual learning. For example, it supports pronunciation and grammar practice in different languages. This makes it possible to promote multilingual learning by supporting conversations in different languages.

[0062] The two-way conversation unit will use visual aids to deepen students' understanding when they interact with the generative AI. For example, the two-way conversation unit will build a system that uses visual aids (images and videos) to deepen students' understanding when they interact with the generative AI. For example, it will automatically display images and videos related to the content of the conversation. The two-way conversation unit will also use visual aids to deepen students' understanding during a dialogue with the generative AI. For example, it will display charts and illustrations related to the content of the conversation. The two-way conversation unit will also develop a system that uses visual aids to deepen students' understanding when they interact with the generative AI. For example, it will display animations and slides related to the content of the conversation. This will allow students to deepen their understanding by using visual aids.

[0063] The two-way conversation unit uses the emotion estimation function to generate a dialogue scenario according to the student's emotions, thereby enabling more effective learning. The two-way conversation unit, for example, uses the emotion estimation function to generate a dialogue scenario according to the student's emotions, thereby building a system that enables more effective learning. For example, if a student is nervous, it provides a dialogue scenario to help the student relax. The two-way conversation unit also analyzes the student's emotions using the emotion estimation function to generate a dialogue scenario according to the student's emotions. For example, if a student is excited, it provides a dialogue scenario that makes use of that excitement. The two-way conversation unit also uses the emotion estimation function to generate a dialogue scenario according to the student's emotions, thereby developing a system that enables more effective learning. For example, if a student is depressed, it provides an encouraging dialogue scenario. In this way, by generating a dialogue scenario according to the student's emotions, it is possible to achieve more effective learning.

[0064] The generative AI department can evaluate students' logical explanations and suggest specific areas for improvement. For example, the generative AI department will build a system in which the generative AI evaluates students' logical explanations in real time and suggests specific areas for improvement. For example, it will evaluate the consistency and persuasiveness of the logic and suggest specific areas for improvement. The generative AI department will also evaluate students' logical explanations and suggest specific areas for improvement. For example, it will point out logical leaps and inconsistencies and suggest ways to improve them. The generative AI department will also develop a system in which the generative AI evaluates students' logical explanations and suggests specific areas for improvement. For example, it will analyze the structure of logic and suggest more effective ways of explaining. This will enable the evaluation of students' logical explanations and suggest specific areas for improvement, thereby improving their logical explanation skills.

[0065] The generative AI department can improve debate skills by providing counterarguments and different perspectives based on the content of students' explanations. For example, the generative AI department builds a system that improves debate skills by using a generative AI to provide counterarguments and different perspectives based on the content of students' explanations. For example, it presents counterarguments to students' opinions and provides debate practice. The generative AI department also analyzes students' explanations and provides counterarguments and different perspectives. For example, it presents questions from different perspectives to students' opinions and provides debate skills. The generative AI department also develops a system that improves debate skills by using a generative AI to provide counterarguments and different perspectives based on the content of students' explanations. For example, it presents counterarguments to students' opinions and provides debate practice. This makes it possible to improve debate skills by providing counterarguments and different perspectives based on the content of students' explanations.

[0066] The generative AI unit can use the emotion estimation function to analyze the emotions of students during their explanations and provide feedback that encourages them to give calm and logical explanations. For example, the generative AI unit uses the emotion estimation function to build a system that analyzes the emotions of students during their explanations in real time and provides feedback that encourages them to give calm and logical explanations. For example, if they appear emotional, it provides advice on how to stay calm. The generative AI unit also uses the emotion estimation function to analyze the emotions of students during their explanations and provides feedback that encourages them to give calm and logical explanations. For example, if they appear emotional, it provides advice on how to stay calm. The generative AI unit also uses the emotion estimation function to develop a system that analyzes the emotions of students during their explanations and provides feedback that encourages them to give calm and logical explanations. For example, if they appear emotional, it provides advice on how to stay calm. This makes it possible to analyze the emotions of students during their explanations and provide feedback that encourages them to give calm and logical explanations, thereby improving their ability to give logical explanations.

[0067] The generative AI department can visualize the content of students' explanations, making them easier to understand visually. For example, the generative AI department builds a system in which the generative AI visualizes the content of students' explanations, making them easier to understand visually. For example, it converts the content of explanations into diagrams and graphs and displays them visually. The generative AI department also visualizes the content of students' explanations, making them easier to understand visually. For example, it converts the content of explanations into mind maps and flowcharts. The generative AI department also develops a system in which the generative AI visualizes the content of students' explanations, making them easier to understand visually. For example, it converts the content of explanations into infographics and displays them visually. In this way, visualizing the content of students' explanations makes them easier to understand visually.

[0068] The Generative AI Department allows students to create presentation materials in collaboration with generative AI, thereby cultivating practical skills. For example, the Generative AI Department will build a system in which students create presentation materials in collaboration with generative AI, thereby cultivating practical skills. For example, the Generative AI will propose the structure and design of the materials, and students will complete the content. The Generative AI Department will also create presentation materials in collaboration with generative AI, thereby cultivating practical skills. For example, the Generative AI will provide a template for the materials, and students will fill in the content. The Generative AI Department will also develop a system in which students create presentation materials in collaboration with generative AI, thereby cultivating practical skills. For example, the Generative AI will propose the layout and design of the materials, and students will complete the content. This allows students to create presentation materials in collaboration with generative AI, thereby cultivating practical skills.

[0069] The generative AI department can use the emotion estimation function to analyze the audience's emotional reactions to the student's explanation and identify areas for improvement in the presentation. For example, the generative AI department uses the emotion estimation function to analyze the audience's emotional reactions to the student's explanation in real time and build a system to identify areas for improvement in the presentation. For example, it may suggest areas for improvement based on the audience's emotion score. The generative AI department can also use the emotion estimation function to analyze the audience's emotional reactions to the student's explanation and identify areas for improvement in the presentation. For example, it may suggest areas for improvement based on the audience's emotion score. The generative AI department can also use the emotion estimation function to develop a system to analyze the audience's emotional reactions to the student's explanation and identify areas for improvement in the presentation. For example, it may suggest areas for improvement based on the audience's emotion score. This makes it possible to identify areas for improvement in the presentation by analyzing the audience's emotional reactions to the student's explanation.

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

[0071] The educational program provider can provide customized learning plans tailored to each student's learning style. For example, visual learners can be provided with materials that make extensive use of visual aids, while auditory learners can be provided with primarily audio materials. The educational program provider can also analyze each student's learning style and suggest optimal learning methods based on that analysis. For example, students who prefer hands-on training can be provided with exercises using real-life scenarios. Furthermore, the educational program provider can provide feedback tailored to each student's learning style. For example, visual learners can be provided with feedback using graphs and charts, while auditory learners can be provided with audio feedback. This allows for customized learning plans tailored to each student's learning style, thereby enhancing learning effectiveness.

[0072] The educational program offering department can propose future learning plans based on students' learning histories. For example, it can analyze past learning data and suggest the next content and skills to learn. The educational program offering department can also predict learning progress based on students' learning histories and provide feedback at the appropriate time. For example, it can predict the time it will take to master a particular skill and suggest additional training at that time. Furthermore, the educational program offering department can evaluate the effectiveness of learning based on students' learning histories and suggest areas for improvement. For example, it can analyze past learning data, evaluate which learning method was most effective, and adjust the learning plan based on that. In this way, it is possible to improve learning effectiveness by proposing future learning plans based on students' learning histories.

[0073] The educational program providing unit can use the emotion estimation function to monitor students' stress levels and suggest breaks at appropriate times. For example, if stress increases during study, the unit can suggest breaks to relax. The educational program providing unit can also analyze students' stress levels with the emotion estimation function and suggest activities to reduce stress. For example, it can suggest stretching or deep breathing exercises. The educational program providing unit can also use the emotion estimation function to monitor students' stress levels and provide feedback to reduce stress. For example, if stress increases, the unit can provide encouraging messages or relaxing music. In this way, by monitoring students' stress levels and suggesting breaks at appropriate times, it is possible to improve learning effectiveness.

[0074] The educational program provider can suggest environmental settings to improve students' concentration while studying. For example, it can suggest lighting and music that are suitable for studying. The educational program provider can also analyze environmental settings to improve students' concentration and suggest the optimal learning environment based on that analysis. For example, it can suggest noise-canceling headphones if a quiet environment is required. Furthermore, the educational program provider can suggest environmental settings to improve students' concentration and provide advice to maximize learning outcomes. For example, it can suggest taking appropriate breaks while studying. In this way, it is possible to improve learning outcomes by suggesting environmental settings to improve students' concentration.

[0075] The educational program providing unit can use the emotion estimation function to analyze students' emotions during study and adjust the study content. For example, if negative emotions increase during study, it can provide tasks with a lower level of difficulty. The educational program providing unit can also maintain students' motivation to study by analyzing students' emotions with the emotion estimation function and adjusting the study content. For example, if positive emotions increase, it can provide challenging tasks. Furthermore, the educational program providing unit can also use the emotion estimation function to analyze students' emotions during study and adjust the study content to maximize learning effectiveness. For example, if emotions are stable, it can provide a regular study plan. In this way, by analyzing students' emotions during study and adjusting the study content, it is possible to improve learning effectiveness.

[0076] The educational program provider can compare a student's learning outcomes with those of other students and provide a relative evaluation. For example, a student can check their own progress by comparing it with that of other students taking the same program. The educational program provider can also compare a student's learning outcomes with those of other students and provide feedback to increase a sense of competition. For example, special rewards can be provided to top students. Furthermore, the educational program provider can compare a student's learning outcomes with those of other students and provide advice to increase learning motivation. For example, the educational program provider can introduce the success stories of other students and use them to improve their learning methods. In this way, students can improve their learning effectiveness by comparing their learning outcomes with those of other students.

[0077] The educational program providing unit can use the emotion estimation function to analyze students' emotions during study and predict their learning progress. For example, if positive emotions increase, it predicts that their learning progress will accelerate. The educational program providing unit can also provide feedback at the appropriate time by analyzing students' emotions with the emotion estimation function and predicting their learning progress. For example, if negative emotions increase, it predicts that their learning progress will slow down and provides support early. Furthermore, the educational program providing unit can also use the emotion estimation function to analyze students' emotions during study and predict their learning progress, thereby providing advice to maximize their learning effectiveness. For example, if their emotions are stable, it provides a regular study plan. In this way, analyzing students' emotions during study and predicting their learning progress can improve their learning effectiveness.

[0078] The educational program provision department can collect data on students' behavior while they study and analyze their learning patterns based on that data. For example, it can analyze study times and methods and propose optimal learning plans. The educational program provision department can also evaluate the effectiveness of learning based on student behavior data and suggest areas for improvement. For example, if a particular learning method is not effective, it can suggest an alternative method. Furthermore, the educational program provision department can collect student behavior data and analyze learning patterns based on that data to provide advice to maximize learning effectiveness. For example, it can identify times when students' concentration levels drop and suggest taking breaks during those times. In this way, analyzing learning patterns based on student behavior data can improve learning effectiveness.

[0079] The educational program providing unit can use the emotion estimation function to analyze students' emotions during study and predict their learning progress. For example, if positive emotions increase, it predicts that their learning progress will accelerate. The educational program providing unit can also provide feedback at the appropriate time by analyzing students' emotions with the emotion estimation function and predicting their learning progress. For example, if negative emotions increase, it predicts that their learning progress will slow down and provides support early. Furthermore, the educational program providing unit can also use the emotion estimation function to analyze students' emotions during study and predict their learning progress, thereby providing advice to maximize their learning effectiveness. For example, if their emotions are stable, it provides a regular study plan. In this way, analyzing students' emotions during study and predicting their learning progress can improve their learning effectiveness.

[0080] The educational program provision department can collect data on students' behavior while they study and analyze their learning patterns based on that data. For example, it can analyze study times and methods and propose optimal learning plans. The educational program provision department can also evaluate the effectiveness of learning based on student behavior data and suggest areas for improvement. For example, if a particular learning method is not effective, it can suggest an alternative method. Furthermore, the educational program provision department can collect student behavior data and analyze learning patterns based on that data to provide advice to maximize learning effectiveness. For example, it can identify times when students' concentration levels drop and suggest taking breaks during those times. In this way, analyzing learning patterns based on student behavior data can improve learning effectiveness.

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

[0082] Step 1: The educational program provider provides educational programs. For example, they can provide online courses or workshops. They can also provide educational materials. Step 2: The two-way conversation unit conducts two-way conversations based on the programs provided by the educational program provider. For example, the conversation can be conducted using chat, voice calls, or video calls. The conversation can also be recorded and played back later. Step 3: The generation AI unit analyzes the content of the conversation conducted by the two-way conversation unit and generates questions and critical thinking comments. For example, it uses natural language processing technology to analyze the content of the conversation and generate appropriate questions and comments. It can also perform sentiment analysis and generate comments based on emotions. Furthermore, it uses keyword extraction technology to extract important parts of the conversation content and generate questions and comments based on them.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an educational program provision department that provides educational programs; a two-way conversation unit that conducts two-way conversation based on the program provided by the educational program providing unit; and a generation AI unit that analyzes the content of the conversation carried out by the two-way conversation unit and generates questions and critical thinking comments. A system characterized by:

2. The educational program providing unit Monitor student progress in real time and provide personalized feedback 2. The system of claim 1.

3. The educational program providing unit Collect background information about students and use it to suggest optimal learning paths 2. The system of claim 1.

4. The educational program providing unit Analyze students' emotions while learning and provide interactive content to keep them motivated 2. The system of claim 1.

5. The educational program providing unit The educational program will be integrated into an online platform, allowing remote participation.

2. The system of claim 1.

6. The educational program providing unit Diversify the content of the program by collaborating with different educational institutions and companies.

2. The system of claim 1.

7. The educational program providing unit Incorporating gamification elements to motivate students to learn 2. The system of claim 1.

8. The two-way conversation unit Analyze students' speech and provide feedback on areas for improvement in pronunciation and intonation 2. The system of claim 1.

Images