system

Abstract

The system according to this embodiment aims to efficiently provide education and training to AI. [Solution] The system according to this embodiment comprises a creation unit, a teaching unit, and a question receiving unit. The creation unit creates an ignorant AI. The teaching unit conducts online lessons for the ignorant AI created by the creation unit. The question receiving unit receives questions from the AI ​​during the lessons conducted by the teaching unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] [[ID=3�]]In the prior art, education and training for AI are not efficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently perform education and training for AI.

Means for Solving the Problems

[0006] The system according to the embodiment includes a creation unit, a teaching unit, and a question reception unit. The creation unit creates a naive AI. The teaching unit conducts an online class for the naive AI created by the creation unit. The question reception unit receives questions from the AI during the class conducted by the teaching unit.

Effects of the Invention

[0007] The system according to this embodiment can efficiently provide education and training to AI. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The educational support system according to an embodiment of the present invention is a system that creates an ignorant AI and conducts lessons for a mischievous student AI that occasionally pretends to know things. The educational support system conducts explanations online, including writing on a whiteboard and giving verbal explanations, just like a real classroom lesson, and occasionally accepts questions from the AI. This system aims to be helpful in educational settings. First, the educational support system creates an ignorant AI. This AI has no basic knowledge and occasionally pretends to know things. Next, the educational support system conducts lessons online. The teacher gives explanations using a whiteboard and giving verbal explanations, and proceeds with the lesson for the student AI. For example, in a mathematics lesson, the teacher explains while writing mathematical formulas on a whiteboard, and the student AI tries to understand them. The student AI also occasionally asks questions. For example, it may ask questions such as, "What does this formula mean?" or "Why do we use this solution method?" The teacher answers these questions and continues the lesson. This system can improve teachers' skills and the quality of lessons by simulating lessons in educational settings. Furthermore, the student AI's ability to pretend to know things makes it easier for teachers to assess students' understanding. Additionally, online lessons are possible, allowing the system to accommodate teachers and students in remote locations. This enables the educational support system to improve teachers' skills and the quality of lessons.

[0029] The educational support system according to this embodiment comprises a creation unit, a teaching unit, and a question receiving unit. The creation unit creates an ignorant AI. The creation unit uses, for example, an algorithm to generate an AI that lacks basic knowledge. The creation unit may also include an algorithm that pretends to know things. For example, the creation unit programs the AI ​​to pretend to know things under specific conditions. The teaching unit conducts online lessons for the ignorant AI created by the creation unit. The teaching unit conducts lessons using, for example, a video conferencing tool. The teaching unit can also provide explanations using a whiteboard or verbally. For example, the teaching unit explains while drawing mathematical formulas and diagrams using a digital whiteboard. The question receiving unit receives questions from the AI ​​during lessons conducted by the teaching unit. The question receiving unit receives questions from the AI ​​using, for example, a chat function. The question receiving unit can also receive questions from the AI ​​using voice input. For example, the question receiving unit allows the AI ​​to input questions by voice, converts them into text, and displays them to the teacher. As a result, the educational support system according to this embodiment will be helpful in educational settings by creating an ignorant AI, conducting online lessons, and accepting questions from the AI.

[0030] The creation unit creates ignorant AI. For example, it uses algorithms to generate AI that lacks basic knowledge. Specifically, it generates AI without trained weights or biases during the initialization of the neural network. This ignorant AI has no specific domain knowledge or general knowledge and starts completely from a blank slate. The creation unit can also include algorithms that pretend to know. For example, if the AI ​​is programmed to pretend to know under certain conditions, it will be designed to confidently return incorrect answers to questions. This gives educators an opportunity to point out the AI's errors and teach the correct knowledge. Furthermore, the creation unit can also have the ability to monitor the AI's learning process and record its progress. This allows educators to track the AI's growth and adjust the curriculum as needed. The creation unit comprehensively manages the entire process of building the foundation of the AI, including initial setup, algorithm selection, and preparation of training data. This allows the creation unit to efficiently and effectively create ignorant AI that forms the basis of educational support systems.

[0031] The teaching department conducts online lessons for an uninformed AI created by the development department. The teaching department uses video conferencing tools, for example. Specifically, educators teach the AI ​​in real time via video conferencing, and the AI ​​learns the content. The teaching department can also provide explanations using a whiteboard or verbally. For example, it can use a digital whiteboard to draw formulas and diagrams while explaining, aiding visual understanding. The teaching department can also use presentation software to display slides and supplement explanations with text and images. Furthermore, the teaching department can include features for conducting quizzes and tests to assess the AI's understanding. This allows educators to track the AI's learning progress and adjust the lesson content as needed. The teaching department can also incorporate interactive elements to ensure the AI ​​learns the lesson content effectively. For example, it can ask the AI ​​questions and evaluate its answers to check its understanding. The teaching department can also provide archived recorded lessons so the AI ​​can review the learned content on its own. This allows the teaching department to provide effective education to an uninformed AI and support its learning.

[0032] The question reception unit receives questions from the AI ​​during lessons conducted by the teaching unit. For example, the question reception unit can receive questions from the AI ​​using a chat function. Specifically, if the AI ​​has questions during a lesson, it inputs them through the chat window and sends them to the educator. The question reception unit can also receive questions from the AI ​​using voice input. For example, the AI ​​can input a question by voice, which is then converted into text and displayed to the teacher. This voice recognition technology enables the AI ​​to ask questions in natural language, providing a more interactive learning environment. Furthermore, the question reception unit can also record the AI's question history for later reference. This allows educators to understand the AI's comprehension and learning progress and provide appropriate feedback. The question reception unit also assists the AI ​​in providing more appropriate answers by considering the context and background information when the AI ​​asks a question. For example, it can accurately understand the intent of the question and derive an appropriate answer based on what the AI ​​has learned in the past and the current lesson content. The question reception unit can also include a feedback function that allows the AI ​​to evaluate its own answers to questions and confirm its level of understanding. This allows the question-receiving department to effectively support the AI's learning process and facilitate smooth communication with educators.

[0033] The teaching staff can provide explanations using a whiteboard. For example, they can use a digital whiteboard to draw formulas and diagrams while explaining. They can also provide information visually using slide presentations. For example, they can display diagrams and graphs on slides and use them as a basis for explanations. Furthermore, they can provide explanations while writing on a whiteboard in real time. For example, they can use an online whiteboard to draw formulas and diagrams in real time while explaining. This allows them to provide lessons that are visually easy to understand by combining explanations using a whiteboard with other methods. Some or all of the above processes in the teaching staff may be performed using AI, or not. For example, the teaching staff can input formulas and diagrams drawn on a digital whiteboard into an AI, which can then analyze and supplement the explanation.

[0034] The teaching department can provide oral explanations. For example, the teaching department can provide oral explanations using a video conferencing tool. The teaching department can also provide audio-only explanations. For example, the teaching department can provide explanations using audio files. Furthermore, the teaching department can provide oral explanations in real time while simultaneously accepting questions. For example, the teaching department can provide oral explanations in real time using a video conferencing tool while accepting questions using the chat function. This allows for the provision of lessons that are easier to understand aurally through oral explanations. Some or all of the above processes in the teaching department may be performed using AI, for example, or not. For example, the teaching department can input audio data into an AI, which can then analyze and supplement the explanation.

[0035] The question reception unit can receive questions from AI. For example, the question reception unit can receive questions from AI using a chat function. The question reception unit can also receive questions from AI using voice input. For example, the question reception unit can have the AI ​​input a question by voice, convert it to text, and display it to the teacher. Furthermore, the question reception unit can receive questions from AI in real time. For example, the question reception unit can receive questions from AI in real time using a video conferencing tool. This enables interactive lessons by accepting questions from AI. Some or all of the above processing in the question reception unit may be performed using AI, or not using AI. For example, the question reception unit can input a question by voice into the AI, which can then analyze it and convert it to text.

[0036] The creation unit may include an algorithm for pretending to know something. For example, the creation unit may program an AI to pretend to know something under specific conditions. The creation unit may also adjust the frequency of pretending to know something. For example, the creation unit may set the AI ​​to pretend to know something with a certain probability. Furthermore, the creation unit may also randomly generate the content of the pretending to know something. For example, the creation unit may program the AI ​​to randomly generate content for pretending to know something and say it. This makes it easier for teachers to check students' understanding by having them pretend to know something. Some or all of the above processing in the creation unit may be performed using an AI, for example, or without an AI. For example, the creation unit may use an AI to generate content for pretending to know something, and the AI ​​may randomly generate content for pretending to know something.

[0037] The teaching department can conduct classes using online platforms. For example, the teaching department can conduct classes using video conferencing tools. The teaching department can also conduct classes using online whiteboards. For example, the teaching department can conduct classes while drawing formulas and diagrams on an online whiteboard. Furthermore, the teaching department can conduct classes in real time using online platforms. For example, the teaching department can conduct classes in real time using video conferencing tools and accept questions using a chat function. This allows the teaching department to accommodate teachers and students in remote locations by using online platforms. Some or all of the above processes in the teaching department may be performed using AI, or not. For example, the teaching department can input the content of classes on the online platform into an AI, which can then analyze and supplement the class.

[0038] The creation unit can add a function that allows the user to choose whether or not to give the ignorant AI specialized knowledge in a particular academic field when creating it. For example, if the user creates an ignorant AI specialized in mathematics, the creation unit can give it basic mathematical knowledge. The creation unit can also give the ignorant AI specialized in history, or basic historical knowledge. Furthermore, if the user creates an ignorant AI specialized in science, the creation unit can give it basic scientific knowledge. This allows for the provision of specialized lessons by giving the AI ​​specialized knowledge in a particular academic field. Some or all of the above processing in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input knowledge data on a particular academic field into a generating AI, which can then analyze it and give the ignorant AI knowledge.

[0039] The creation unit can set the optimal knowledge level when creating an ignorant AI by referring to the user's past learning history. For example, the creation unit can set the knowledge level of the ignorant AI based on what the user has learned in the past. The creation unit can also set the knowledge level of the ignorant AI by considering the depth of knowledge in a specific field from the user's past learning history. Furthermore, the creation unit can analyze the user's past learning history and create an ignorant AI that reflects the most effective learning method. In this way, an ignorant AI with the optimal knowledge level can be created by referring to the user's past learning history. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input the user's learning history data into a generating AI, and the generating AI can analyze it to set the knowledge level of the ignorant AI.

[0040] The creation unit can add a function to the creation of an ignorant AI that takes into account the user's geographical location and incorporates region-specific knowledge. For example, if the user lives in a specific region, the creation unit can give the ignorant AI knowledge about the history and culture of that region. Furthermore, if the user attends a school in a specific region, the creation unit can give the ignorant AI knowledge based on the educational curriculum of that region. Additionally, if the user speaks a specific regional language or dialect, the creation unit can give the ignorant AI knowledge about that language or dialect. This allows for the provision of region-specific lessons by incorporating region-specific knowledge. Some or all of the above-described processes in the creation unit may be performed using AI, or not. For example, the creation unit can input region-specific knowledge data into a generating AI, which can then analyze it and incorporate that knowledge into the ignorant AI.

[0041] The creation unit can add a function to analyze the user's social media activity and give the ignorant AI relevant knowledge when creating it. For example, the creation unit can give the ignorant AI relevant knowledge based on topics that the user frequently mentions on social media. The creation unit can also identify areas of interest from the user's social media activity and give the ignorant AI knowledge about those areas. Furthermore, the creation unit can analyze the interests of the user's followers and friends on social media and give the ignorant AI relevant knowledge. In this way, by analyzing social media activity, the ignorant AI can be given knowledge that matches the user's interests. Some or all of the above processing in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input social media activity data into a generating AI, which can then analyze it and give the ignorant AI relevant knowledge.

[0042] The teaching department can add a function to provide course content specialized in specific academic fields. For example, if a user requests a course specializing in mathematics, the teaching department can provide mathematics course content. It can also provide history course content if a user requests a history course. Furthermore, it can provide science course content if a user requests a science course. This allows for the provision of specialized courses by offering course content tailored to specific academic fields. Some or all of the above processing in the teaching department may be performed using AI, for example, or without AI. For example, the teaching department can input course data related to a specific academic field into a generating AI, which can then analyze and provide course content.

[0043] The teaching department can provide optimal lesson content by referring to the user's past learning history. For example, the teaching department can provide optimal lesson content based on what the user has learned in the past. The teaching department can also adjust the lesson content by considering the user's level of understanding in a specific field based on the user's past learning history. Furthermore, the teaching department can analyze the user's past learning history and provide lesson content that reflects the most effective learning method. In this way, the teaching department can provide optimal lesson content by referring to the user's past learning history. Some or all of the above processing in the teaching department may be performed using AI, for example, or not using AI. For example, the teaching department can input the user's learning history data into a generating AI, which can then analyze it to provide optimal lesson content.

[0044] The teaching section can add a function to provide region-specific lesson content that takes into account the user's geographical location. For example, if the teaching section is located in a specific region, it can provide lesson content related to the history and culture of that region. Furthermore, if the teaching section is located in a specific region, it can provide lesson content based on the local educational curriculum. Additionally, if the teaching section is located in a specific region and speaks that region's language or dialect, it can provide lesson content related to that language or dialect. This allows for the provision of region-specific lesson content, thereby delivering lessons tailored to the region. Some or all of the above processing in the teaching section may be performed using AI, for example, or without AI. For instance, the teaching section could input region-specific lesson data into a generating AI, which could then analyze it to provide lesson content.

[0045] The teaching department can add a function to analyze users' social media activity and provide relevant lesson content. For example, the teaching department can provide relevant lesson content based on topics that users frequently mention on social media. It can also identify areas of interest from users' social media activity and provide lesson content related to those areas. Furthermore, the teaching department can analyze the interests of users' followers and friends on social media and provide relevant lesson content. This allows for the provision of lesson content tailored to users' interests by analyzing their social media activity. Some or all of the above processing in the teaching department may be performed using AI, for example, or without AI. For example, the teaching department can input social media activity data into a generating AI, which can then analyze it and provide relevant lesson content.

[0046] The question reception unit can be enhanced with a function to prioritize questions specific to particular academic fields. For example, the question reception unit could prioritize questions from users about mathematics. It could also prioritize questions from users about history. Furthermore, it could prioritize questions from users about science. This allows for more specialized question handling by prioritizing questions specific to particular academic fields. Some or all of the above processing in the question reception unit may be performed using AI, for example, or not. For example, the question reception unit could input question data related to a specific academic field into a generating AI, which could then analyze the data and prioritize the acceptance of those questions.

[0047] The question reception unit can provide the best answer by referring to the user's past question history. For example, the question reception unit can provide the best answer based on the content of questions the user has asked in the past. The question reception unit can also adjust the answer by considering the user's level of understanding in a particular field, based on the user's past question history. Furthermore, the question reception unit can analyze the user's past question history and provide an answer that reflects the most effective way of answering. In this way, the best answer can be provided by referring to the user's past question history. Some or all of the above processing in the question reception unit may be performed using AI, for example, or not using AI. For example, the question reception unit can input the user's question history data into a generating AI, which can then analyze it and provide the best answer.

[0048] The question reception unit can be enhanced with a function that prioritizes region-specific questions based on the user's geographical location. For example, if a user lives in a specific region, the question reception unit can prioritize questions related to that region. Furthermore, if a user attends a school in a specific region, the question reception unit can prioritize questions based on the local educational curriculum. Additionally, if a user speaks the language or dialect of a specific region, the question reception unit can prioritize questions related to that language or dialect. This allows for region-specific question handling by prioritizing region-specific questions. Some or all of the above processing in the question reception unit may be performed using AI, or not. For example, the question reception unit can input region-specific question data into a generating AI, which can then analyze the data and prioritize the acceptance of those questions.

[0049] The question reception unit can be enhanced with a function to analyze the user's social media activity and prioritize relevant questions. For example, the question reception unit can prioritize questions based on topics the user frequently mentions on social media. It can also identify areas of interest from the user's social media activity and prioritize questions related to those areas. Furthermore, the question reception unit can analyze the interests of the user's social media followers and friends and prioritize questions related to those interests. This allows for question responses tailored to the user's interests by analyzing their social media activity. Some or all of the above processing in the question reception unit may be performed using AI, for example, or not. For example, the question reception unit can input social media activity data into a generating AI, which can then analyze it and prioritize the reception of relevant questions.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The educational support system can also include a feedback function. This feedback function provides teachers with an evaluation of the lesson after it ends. For example, it analyzes the clarity of the teacher's explanations and the responses of the student AI during the lesson, and suggests areas for improvement to the teacher. The feedback function can also identify which parts of the lesson the students found difficult to understand, based on the content and frequency of the student AI's questions during the lesson. Furthermore, the feedback function can evaluate the pace of the lesson and the difficulty level of the content, and provide advice for the next lesson. This allows teachers to receive specific feedback to improve the quality of their lessons.

[0052] The educational support system can also include a customization feature. This customization feature allows users to tailor lesson content to their learning style and preferences. For example, if a user prefers visual learning, the customization feature can provide lessons that heavily utilize diagrams and graphs. If a user prefers auditory learning, it can provide lessons primarily based on audio explanations. Furthermore, the customization feature can adjust the pace of the lessons to match the user's learning speed. This ensures that users receive lessons best suited to their individual learning style.

[0053] The educational support system can also include a collaborative learning section. This section provides functions for multiple users to learn together. For example, it can provide online rooms for group discussions. It can also provide tools for users to work on projects collaboratively. Furthermore, it can provide functions for users to give each other feedback. This allows users to learn in cooperation with other learners.

[0054] The educational support system can also include a reflection section. This section provides functions for users to reflect on their own learning. For example, it can provide users with a summary of the learning content after the lesson ends. It can also provide tools for users to self-assess their level of understanding. Furthermore, it can support users in setting goals for their next learning session. This allows users to reflect on their learning and plan for their next learning session.

[0055] The educational support system can also include an interactive section. This interactive section provides features to enable users to actively participate in lessons. For example, it could offer a chat function that allows users to post questions in real time. It could also provide features that allow users to participate in quizzes and surveys during lessons. Furthermore, it could offer a discussion function that allows users to exchange opinions with other users during lessons. This enables users to actively participate in lessons.

[0056] The educational support system can also include a personalization component. This component personalizes lesson content based on the user's learning history and interests. For example, it adjusts the content of the next lesson based on what the user has learned previously. It can also incorporate relevant topics into the lesson according to the user's interests. Furthermore, it can adjust the pace of the lesson to match the user's learning pace. This allows users to receive lessons that are best suited to their own learning style.

[0057] The educational support system can also include an engagement section. This section provides functions to enhance user engagement in learning. For example, it can provide incentives for users to actively participate in lessons. It can also provide tools for users to provide feedback on lesson content. Furthermore, it can provide functions for users to collaborate with other users to solve problems during lessons. This allows users to engage more actively in the lessons.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The creation unit creates an ignorant AI. The creation unit uses an algorithm that generates an AI that lacks basic knowledge. It is also possible to program the AI ​​to pretend to know things under specific conditions. Step 2: The teaching team conducts online lessons for the uninformed AI created by the development team. The teaching team can use video conferencing tools to conduct lessons and explain concepts while drawing formulas and diagrams on a digital whiteboard. Step 3: The Question Reception Department receives questions from the AI ​​during lessons conducted by the Teaching Department. The Question Reception Department receives questions from the AI ​​using chat functions or voice input, converts them into text, and displays them to the teacher.

[0060] (Example of form 2) The educational support system according to an embodiment of the present invention is a system that creates an ignorant AI and conducts lessons for a mischievous student AI that occasionally pretends to know things. The educational support system conducts explanations online, including writing on a whiteboard and giving verbal explanations, just like a real classroom lesson, and occasionally accepts questions from the AI. This system aims to be helpful in educational settings. First, the educational support system creates an ignorant AI. This AI has no basic knowledge and occasionally pretends to know things. Next, the educational support system conducts lessons online. The teacher gives explanations using a whiteboard and giving verbal explanations, and proceeds with the lesson for the student AI. For example, in a mathematics lesson, the teacher explains while writing mathematical formulas on a whiteboard, and the student AI tries to understand them. The student AI also occasionally asks questions. For example, it may ask questions such as, "What does this formula mean?" or "Why do we use this solution method?" The teacher answers these questions and continues the lesson. This system can improve teachers' skills and the quality of lessons by simulating lessons in educational settings. Furthermore, the student AI's ability to pretend to know things makes it easier for teachers to assess students' understanding. Additionally, online lessons are possible, allowing the system to accommodate teachers and students in remote locations. This enables the educational support system to improve teachers' skills and the quality of lessons.

[0061] The educational support system according to this embodiment comprises a creation unit, a teaching unit, and a question receiving unit. The creation unit creates an ignorant AI. The creation unit uses, for example, an algorithm to generate an AI that lacks basic knowledge. The creation unit may also include an algorithm that pretends to know things. For example, the creation unit programs the AI ​​to pretend to know things under specific conditions. The teaching unit conducts online lessons for the ignorant AI created by the creation unit. The teaching unit conducts lessons using, for example, a video conferencing tool. The teaching unit can also provide explanations using a whiteboard or verbally. For example, the teaching unit explains while drawing mathematical formulas and diagrams using a digital whiteboard. The question receiving unit receives questions from the AI ​​during lessons conducted by the teaching unit. The question receiving unit receives questions from the AI ​​using, for example, a chat function. The question receiving unit can also receive questions from the AI ​​using voice input. For example, the question receiving unit allows the AI ​​to input questions by voice, converts them into text, and displays them to the teacher. As a result, the educational support system according to this embodiment will be helpful in educational settings by creating an ignorant AI, conducting online lessons, and accepting questions from the AI.

[0062] The creation unit creates ignorant AI. For example, it uses algorithms to generate AI that lacks basic knowledge. Specifically, it generates AI without trained weights or biases during the initialization of the neural network. This ignorant AI has no specific domain knowledge or general knowledge and starts completely from a blank slate. The creation unit can also include algorithms that pretend to know. For example, if the AI ​​is programmed to pretend to know under certain conditions, it will be designed to confidently return incorrect answers to questions. This gives educators an opportunity to point out the AI's errors and teach the correct knowledge. Furthermore, the creation unit can also have the ability to monitor the AI's learning process and record its progress. This allows educators to track the AI's growth and adjust the curriculum as needed. The creation unit comprehensively manages the entire process of building the foundation of the AI, including initial setup, algorithm selection, and preparation of training data. This allows the creation unit to efficiently and effectively create ignorant AI that forms the basis of educational support systems.

[0063] The teaching department conducts online lessons for an uninformed AI created by the development department. The teaching department uses video conferencing tools, for example. Specifically, educators teach the AI ​​in real time via video conferencing, and the AI ​​learns the content. The teaching department can also provide explanations using a whiteboard or verbally. For example, it can use a digital whiteboard to draw formulas and diagrams while explaining, aiding visual understanding. The teaching department can also use presentation software to display slides and supplement explanations with text and images. Furthermore, the teaching department can include features for conducting quizzes and tests to assess the AI's understanding. This allows educators to track the AI's learning progress and adjust the lesson content as needed. The teaching department can also incorporate interactive elements to ensure the AI ​​learns the lesson content effectively. For example, it can ask the AI ​​questions and evaluate its answers to check its understanding. The teaching department can also provide archived recorded lessons so the AI ​​can review the learned content on its own. This allows the teaching department to provide effective education to an uninformed AI and support its learning.

[0064] The question reception unit receives questions from the AI ​​during lessons conducted by the teaching unit. For example, the question reception unit can receive questions from the AI ​​using a chat function. Specifically, if the AI ​​has questions during a lesson, it inputs them through the chat window and sends them to the educator. The question reception unit can also receive questions from the AI ​​using voice input. For example, the AI ​​can input a question by voice, which is then converted into text and displayed to the teacher. This voice recognition technology enables the AI ​​to ask questions in natural language, providing a more interactive learning environment. Furthermore, the question reception unit can also record the AI's question history for later reference. This allows educators to understand the AI's comprehension and learning progress and provide appropriate feedback. The question reception unit also assists the AI ​​in providing more appropriate answers by considering the context and background information when the AI ​​asks a question. For example, it can accurately understand the intent of the question and derive an appropriate answer based on what the AI ​​has learned in the past and the current lesson content. The question reception unit can also include a feedback function that allows the AI ​​to evaluate its own answers to questions and confirm its level of understanding. This allows the question-receiving department to effectively support the AI's learning process and facilitate smooth communication with educators.

[0065] The teaching staff can provide explanations using a whiteboard. For example, they can use a digital whiteboard to draw formulas and diagrams while explaining. They can also provide information visually using slide presentations. For example, they can display diagrams and graphs on slides and use them as a basis for explanations. Furthermore, they can provide explanations while writing on a whiteboard in real time. For example, they can use an online whiteboard to draw formulas and diagrams in real time while explaining. This allows them to provide lessons that are visually easy to understand by combining explanations using a whiteboard with other methods. Some or all of the above processes in the teaching staff may be performed using AI, or not. For example, the teaching staff can input formulas and diagrams drawn on a digital whiteboard into an AI, which can then analyze and supplement the explanation.

[0066] The teaching department can provide oral explanations. For example, the teaching department can provide oral explanations using a video conferencing tool. The teaching department can also provide audio-only explanations. For example, the teaching department can provide explanations using audio files. Furthermore, the teaching department can provide oral explanations in real time while simultaneously accepting questions. For example, the teaching department can provide oral explanations in real time using a video conferencing tool while accepting questions using the chat function. This allows for the provision of lessons that are easier to understand aurally through oral explanations. Some or all of the above processes in the teaching department may be performed using AI, for example, or not. For example, the teaching department can input audio data into an AI, which can then analyze and supplement the explanation.

[0067] The question reception unit can receive questions from AI. For example, the question reception unit can receive questions from AI using a chat function. The question reception unit can also receive questions from AI using voice input. For example, the question reception unit can have the AI ​​input a question by voice, convert it to text, and display it to the teacher. Furthermore, the question reception unit can receive questions from AI in real time. For example, the question reception unit can receive questions from AI in real time using a video conferencing tool. This enables interactive lessons by accepting questions from AI. Some or all of the above processing in the question reception unit may be performed using AI, or not using AI. For example, the question reception unit can input a question by voice into the AI, which can then analyze it and convert it to text.

[0068] The creation unit may include an algorithm for pretending to know something. For example, the creation unit may program an AI to pretend to know something under specific conditions. The creation unit may also adjust the frequency of pretending to know something. For example, the creation unit may set the AI ​​to pretend to know something with a certain probability. Furthermore, the creation unit may also randomly generate the content of the pretending to know something. For example, the creation unit may program the AI ​​to randomly generate content for pretending to know something and say it. This makes it easier for teachers to check students' understanding by having them pretend to know something. Some or all of the above processing in the creation unit may be performed using an AI, for example, or without an AI. For example, the creation unit may use an AI to generate content for pretending to know something, and the AI ​​may randomly generate content for pretending to know something.

[0069] The teaching department can conduct classes using online platforms. For example, the teaching department can conduct classes using video conferencing tools. The teaching department can also conduct classes using online whiteboards. For example, the teaching department can conduct classes while drawing formulas and diagrams on an online whiteboard. Furthermore, the teaching department can conduct classes in real time using online platforms. For example, the teaching department can conduct classes in real time using video conferencing tools and accept questions using a chat function. This allows the teaching department to accommodate teachers and students in remote locations by using online platforms. Some or all of the above processes in the teaching department may be performed using AI, or not. For example, the teaching department can input the content of classes on the online platform into an AI, which can then analyze and supplement the class.

[0070] The creation unit can estimate the user's emotions and adjust the knowledge level of the ignorant AI based on the estimated emotions. For example, if the user is stressed, the creation unit can set the knowledge level of the ignorant AI low and ask simple questions. Conversely, if the user is relaxed, the creation unit can set the knowledge level of the ignorant AI high and ask difficult questions. Furthermore, if the user is excited, the creation unit can set the knowledge level of the ignorant AI to a medium level and ask questions of appropriate difficulty. In this way, by adjusting the knowledge level of the ignorant AI according to the user's emotions, more appropriate lessons can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input user emotion data into the generative AI, which can analyze it and adjust its knowledge level.

[0071] The creation unit can add a function that allows the user to choose whether or not to give the ignorant AI specialized knowledge in a particular academic field when creating it. For example, if the user creates an ignorant AI specialized in mathematics, the creation unit can give it basic mathematical knowledge. The creation unit can also give the ignorant AI specialized in history, or basic historical knowledge. Furthermore, if the user creates an ignorant AI specialized in science, the creation unit can give it basic scientific knowledge. This allows for the provision of specialized lessons by giving the AI ​​specialized knowledge in a particular academic field. Some or all of the above processing in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input knowledge data on a particular academic field into a generating AI, which can then analyze it and give the ignorant AI knowledge.

[0072] The creation unit can set the optimal knowledge level when creating an ignorant AI by referring to the user's past learning history. For example, the creation unit can set the knowledge level of the ignorant AI based on what the user has learned in the past. The creation unit can also set the knowledge level of the ignorant AI by considering the depth of knowledge in a specific field from the user's past learning history. Furthermore, the creation unit can analyze the user's past learning history and create an ignorant AI that reflects the most effective learning method. In this way, an ignorant AI with the optimal knowledge level can be created by referring to the user's past learning history. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input the user's learning history data into a generating AI, and the generating AI can analyze it to set the knowledge level of the ignorant AI.

[0073] The creation unit can estimate the user's emotions and adjust the frequency of the ignorant AI's pretentious behavior based on the estimated user emotions. For example, if the user is stressed, the creation unit can set the frequency of the ignorant AI's pretentious behavior to a low level. Conversely, if the user is relaxed, the creation unit can set the frequency of the ignorant AI's pretentious behavior to a high level. Furthermore, if the user is excited, the creation unit can set the frequency of the ignorant AI's pretentious behavior to a medium level. This allows for the provision of more appropriate lessons by adjusting the frequency of pretentious behavior according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input user emotion data into the generative AI, which can then analyze it and adjust the frequency of pretentious behavior.

[0074] The creation unit can add a function to the creation of an ignorant AI that takes into account the user's geographical location and incorporates region-specific knowledge. For example, if the user lives in a specific region, the creation unit can give the ignorant AI knowledge about the history and culture of that region. Furthermore, if the user attends a school in a specific region, the creation unit can give the ignorant AI knowledge based on the educational curriculum of that region. Additionally, if the user speaks a specific regional language or dialect, the creation unit can give the ignorant AI knowledge about that language or dialect. This allows for the provision of region-specific lessons by incorporating region-specific knowledge. Some or all of the above-described processes in the creation unit may be performed using AI, or not. For example, the creation unit can input region-specific knowledge data into a generating AI, which can then analyze it and incorporate that knowledge into the ignorant AI.

[0075] The creation unit can add a function to analyze the user's social media activity and give the ignorant AI relevant knowledge when creating it. For example, the creation unit can give the ignorant AI relevant knowledge based on topics that the user frequently mentions on social media. The creation unit can also identify areas of interest from the user's social media activity and give the ignorant AI knowledge about those areas. Furthermore, the creation unit can analyze the interests of the user's followers and friends on social media and give the ignorant AI relevant knowledge. In this way, by analyzing social media activity, the ignorant AI can be given knowledge that matches the user's interests. Some or all of the above processing in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input social media activity data into a generating AI, which can then analyze it and give the ignorant AI relevant knowledge.

[0076] The lesson unit can estimate the user's emotions and adjust the pace of the lesson based on the estimated emotions. For example, if the user is stressed, the lesson unit can slow down the pace to make it easier to understand. If the user is relaxed, the lesson unit can proceed at a normal pace. Furthermore, if the user is excited, the lesson unit can speed up the pace to make it more engaging. By adjusting the pace of the lesson according to the user's emotions, a more appropriate lesson can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the lesson unit may be performed using AI, or not using AI. For example, the lesson unit can input user emotion data into a generative AI, which can then analyze it to adjust the pace of the lesson.

[0077] The teaching department can add a function to provide course content specialized in specific academic fields. For example, if a user requests a course specializing in mathematics, the teaching department can provide mathematics course content. It can also provide history course content if a user requests a history course. Furthermore, it can provide science course content if a user requests a science course. This allows for the provision of specialized courses by offering course content tailored to specific academic fields. Some or all of the above processing in the teaching department may be performed using AI, for example, or without AI. For example, the teaching department can input course data related to a specific academic field into a generating AI, which can then analyze and provide course content.

[0078] The teaching department can provide optimal lesson content by referring to the user's past learning history. For example, the teaching department can provide optimal lesson content based on what the user has learned in the past. The teaching department can also adjust the lesson content by considering the user's level of understanding in a specific field based on the user's past learning history. Furthermore, the teaching department can analyze the user's past learning history and provide lesson content that reflects the most effective learning method. In this way, the teaching department can provide optimal lesson content by referring to the user's past learning history. Some or all of the above processing in the teaching department may be performed using AI, for example, or not using AI. For example, the teaching department can input the user's learning history data into a generating AI, which can then analyze it to provide optimal lesson content.

[0079] The lesson unit can estimate the user's emotions and adjust the lesson content based on those emotions. For example, if the user is stressed, the lesson unit can simplify the lesson content to make it easier to understand. If the user is relaxed, the lesson unit can provide standard lesson content. Furthermore, if the user is excited, the lesson unit can make the lesson content more difficult and challenging. This allows for more appropriate lessons to be provided by adjusting the lesson content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the lesson unit may be performed using AI, or not. For example, the lesson unit can input user emotion data into a generative AI, which can then analyze it and adjust the lesson content.

[0080] The teaching section can add a function to provide region-specific lesson content that takes into account the user's geographical location. For example, if the teaching section is located in a specific region, it can provide lesson content related to the history and culture of that region. Furthermore, if the teaching section is located in a specific region, it can provide lesson content based on the local educational curriculum. Additionally, if the teaching section is located in a specific region and speaks that region's language or dialect, it can provide lesson content related to that language or dialect. This allows for the provision of region-specific lesson content, thereby delivering lessons tailored to the region. Some or all of the above processing in the teaching section may be performed using AI, for example, or without AI. For instance, the teaching section could input region-specific lesson data into a generating AI, which could then analyze it to provide lesson content.

[0081] The teaching department can add a function to analyze users' social media activity and provide relevant lesson content. For example, the teaching department can provide relevant lesson content based on topics that users frequently mention on social media. It can also identify areas of interest from users' social media activity and provide lesson content related to those areas. Furthermore, the teaching department can analyze the interests of users' followers and friends on social media and provide relevant lesson content. This allows for the provision of lesson content tailored to users' interests by analyzing their social media activity. Some or all of the above processing in the teaching department may be performed using AI, for example, or without AI. For example, the teaching department can input social media activity data into a generating AI, which can then analyze it and provide relevant lesson content.

[0082] The question reception unit can estimate the user's emotions and adjust the question reception method based on the estimated emotions. For example, if the user is stressed, the question reception unit can provide a simple question format and minimize the input steps. If the user is relaxed, the question reception unit can also provide a detailed question format and suggest a customizable question method. Furthermore, if the user is in a hurry, the question reception unit can prioritize voice input and receive questions quickly. This allows for more appropriate question reception by adjusting the question reception method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question reception unit may be performed using AI or not using AI. For example, the question reception unit can input user emotion data into a generative AI, which can analyze it and adjust the question reception method.

[0083] The question reception unit can be enhanced with a function to prioritize questions specific to particular academic fields. For example, the question reception unit could prioritize questions from users about mathematics. It could also prioritize questions from users about history. Furthermore, it could prioritize questions from users about science. This allows for more specialized question handling by prioritizing questions specific to particular academic fields. Some or all of the above processing in the question reception unit may be performed using AI, for example, or not. For example, the question reception unit could input question data related to a specific academic field into a generating AI, which could then analyze the data and prioritize the acceptance of those questions.

[0084] The question reception unit can provide the best answer by referring to the user's past question history. For example, the question reception unit can provide the best answer based on the content of questions the user has asked in the past. The question reception unit can also adjust the answer by considering the user's level of understanding in a particular field, based on the user's past question history. Furthermore, the question reception unit can analyze the user's past question history and provide an answer that reflects the most effective way of answering. In this way, the best answer can be provided by referring to the user's past question history. Some or all of the above processing in the question reception unit may be performed using AI, for example, or not using AI. For example, the question reception unit can input the user's question history data into a generating AI, which can then analyze it and provide the best answer.

[0085] The question reception unit can estimate the user's emotions and prioritize questions based on those emotions. For example, if the user is stressed, the question reception unit will prioritize simple questions. Conversely, if the user is relaxed, the question reception unit can prioritize detailed questions. Furthermore, if the user is in a hurry, the question reception unit can prioritize questions that require a quick answer. This allows for more appropriate question handling by prioritizing questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question reception unit may be performed using AI or not. For example, the question reception unit can input user emotion data into a generative AI, which can then analyze it to determine the priority of questions.

[0086] The question reception unit can be enhanced with a function that prioritizes region-specific questions based on the user's geographical location. For example, if a user lives in a specific region, the question reception unit can prioritize questions related to that region. Furthermore, if a user attends a school in a specific region, the question reception unit can prioritize questions based on the local educational curriculum. Additionally, if a user speaks the language or dialect of a specific region, the question reception unit can prioritize questions related to that language or dialect. This allows for region-specific question handling by prioritizing region-specific questions. Some or all of the above processing in the question reception unit may be performed using AI, or not. For example, the question reception unit can input region-specific question data into a generating AI, which can then analyze the data and prioritize the acceptance of those questions.

[0087] The question reception unit can be enhanced with a function to analyze the user's social media activity and prioritize relevant questions. For example, the question reception unit can prioritize questions based on topics the user frequently mentions on social media. It can also identify areas of interest from the user's social media activity and prioritize questions related to those areas. Furthermore, the question reception unit can analyze the interests of the user's social media followers and friends and prioritize questions related to those interests. This allows for question responses tailored to the user's interests by analyzing their social media activity. Some or all of the above processing in the question reception unit may be performed using AI, for example, or not. For example, the question reception unit can input social media activity data into a generating AI, which can then analyze it and prioritize the reception of relevant questions.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The educational support system can also include a feedback function. This feedback function provides teachers with an evaluation of the lesson after it ends. For example, it analyzes the clarity of the teacher's explanations and the responses of the student AI during the lesson, and suggests areas for improvement to the teacher. The feedback function can also identify which parts of the lesson the students found difficult to understand, based on the content and frequency of the student AI's questions during the lesson. Furthermore, the feedback function can evaluate the pace of the lesson and the difficulty level of the content, and provide advice for the next lesson. This allows teachers to receive specific feedback to improve the quality of their lessons.

[0090] The educational support system can also include a customization feature. This customization feature allows users to tailor lesson content to their learning style and preferences. For example, if a user prefers visual learning, the customization feature can provide lessons that heavily utilize diagrams and graphs. If a user prefers auditory learning, it can provide lessons primarily based on audio explanations. Furthermore, the customization feature can adjust the pace of the lessons to match the user's learning speed. This ensures that users receive lessons best suited to their individual learning style.

[0091] Educational support systems can also include a motivation component. This component provides features to enhance users' motivation to learn. For example, it can reward users when they achieve specific goals. It can also visualize users' learning progress, fostering a sense of accomplishment. Furthermore, it can send encouraging messages to users. This makes it easier for users to maintain their motivation to learn.

[0092] The educational support system can also include a collaborative learning section. This section provides functions for multiple users to learn together. For example, it can provide online rooms for group discussions. It can also provide tools for users to work on projects collaboratively. Furthermore, it can provide functions for users to give each other feedback. This allows users to learn in cooperation with other learners.

[0093] The educational support system can also include a reflection section. This section provides functions for users to reflect on their own learning. For example, it can provide users with a summary of the learning content after the lesson ends. It can also provide tools for users to self-assess their level of understanding. Furthermore, it can support users in setting goals for their next learning session. This allows users to reflect on their learning and plan for their next learning session.

[0094] The educational support system can also be equipped with an emotion analysis unit. This unit analyzes the user's emotions in real time and reflects this in the lesson's progression. For example, if the emotion analysis unit is stressed during the lesson, it can slow down the pace. Conversely, if the user is relaxed, it can proceed at a normal pace. Furthermore, if the user is agitated, the emotion analysis unit can make the lesson more difficult. This allows for the provision of lessons tailored to the user's emotions.

[0095] The educational support system can also include an interactive section. This interactive section provides features to enable users to actively participate in lessons. For example, it could offer a chat function that allows users to post questions in real time. It could also provide features that allow users to participate in quizzes and surveys during lessons. Furthermore, it could offer a discussion function that allows users to exchange opinions with other users during lessons. This enables users to actively participate in lessons.

[0096] The educational support system can also include a personalization component. This component personalizes lesson content based on the user's learning history and interests. For example, it adjusts the content of the next lesson based on what the user has learned previously. It can also incorporate relevant topics into the lesson according to the user's interests. Furthermore, it can adjust the pace of the lesson to match the user's learning pace. This allows users to receive lessons that are best suited to their own learning style.

[0097] The educational support system can also include an emotional feedback unit. This unit estimates the user's emotions and provides feedback based on those estimates. For example, if the user is feeling stressed during class, the emotional feedback unit might send an encouraging message. If the user is relaxed, the unit might continue the lesson as usual. Furthermore, if the user is excited, the emotional feedback unit might offer a challenging task. This allows for the provision of feedback tailored to the user's emotions.

[0098] The educational support system can also include an engagement section. This section provides functions to enhance user engagement in learning. For example, it can provide incentives for users to actively participate in lessons. It can also provide tools for users to provide feedback on lesson content. Furthermore, it can provide functions for users to collaborate with other users to solve problems during lessons. This allows users to engage more actively in the lessons.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The creation unit creates an ignorant AI. The creation unit uses an algorithm that generates an AI that lacks basic knowledge. It is also possible to program the AI ​​to pretend to know things under specific conditions. Step 2: The teaching team conducts online lessons for the uninformed AI created by the development team. The teaching team can use video conferencing tools to conduct lessons and explain concepts while drawing formulas and diagrams on a digital whiteboard. Step 3: The Question Reception Department receives questions from the AI ​​during lessons conducted by the Teaching Department. The Question Reception Department receives questions from the AI ​​using chat functions or voice input, converts them into text, and displays them to the teacher.

[0101] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0104] Each of the multiple elements described above, including the creation unit, the teaching unit, and the question receiving unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses an algorithm to generate an ignorant AI. The teaching unit is implemented by the control unit 46A of the smart device 14 and conducts online lessons using a video conferencing tool. The question receiving unit is implemented by the control unit 46A of the smart device 14 and accepts questions from the AI ​​using a chat function or voice input. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the creation unit, the teaching unit, and the question receiving unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses an algorithm to generate an ignorant AI. The teaching unit is implemented by, for example, the control unit 46A of the smart glasses 214 and conducts online lessons using a video conferencing tool. The question receiving unit is implemented by, for example, the control unit 46A of the smart glasses 214 and receives questions from the AI ​​using a chat function or voice input. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

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

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

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

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

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

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

[0132] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0134] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0136] Each of the multiple elements described above, including the creation unit, the teaching unit, and the question receiving unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses an algorithm to generate an ignorant AI. The teaching unit is implemented by, for example, the control unit 46A of the headset terminal 314 and conducts online classes using a video conferencing tool. The question receiving unit is implemented by, for example, the control unit 46A of the headset terminal 314 and receives questions from the AI ​​using a chat function or voice input. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 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.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the creation unit, the teaching unit, and the question receiving unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses an algorithm to generate an ignorant AI. The teaching unit is implemented by, for example, the control unit 46A of the robot 414 and conducts online lessons using a video conferencing tool. The question receiving unit is implemented by, for example, the control unit 46A of the robot 414 and receives questions from the AI ​​using a chat function or voice input. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0154] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) The creation department that creates ignorant AI, The teaching unit conducts online lessons for the ignorant AI created by the aforementioned creation unit, The system includes a question reception unit that receives questions from AI during lessons conducted by the aforementioned teaching unit. A system characterized by the following features. (Note 2) The aforementioned teaching department, The explanation will be given using the whiteboard. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned teaching department, Give an oral explanation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned question reception department, Accepting questions from AI The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned creation unit, Includes an algorithm that pretends to know everything. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned teaching department, Conducting classes using online platforms The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned creation unit, It estimates the user's emotions and adjusts the knowledge level of the ignorant AI based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned creation unit, Add a feature that allows you to choose whether or not to give an ignorant AI specialized knowledge in a particular academic field. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned creation unit, When creating an ignorant AI, the user's past learning history is referenced to set the optimal knowledge level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned creation unit, It estimates the user's emotions and adjusts the frequency of the ignorant AI's know-it-all behavior based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned creation unit, When creating an ignorant AI, add a feature that takes the user's geographical location into account and gives it region-specific knowledge. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned creation unit, When creating an ignorant AI, add a feature that analyzes the user's social media activity and gives it relevant knowledge. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned teaching department, It estimates the user's emotions and adjusts the pace of the lesson based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned teaching department, Add a feature that provides course content specialized in specific academic fields. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned teaching department, The system provides optimal lesson content by referencing the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned teaching department, The system estimates the user's emotions and adjusts the lesson content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned teaching department, Add a feature that provides region-specific lesson content, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned teaching department, Add a feature that analyzes users' social media activity and provides relevant lesson content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned question reception department, The system estimates the user's emotions and adjusts how questions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned question reception department, We will add a feature that prioritizes questions specific to particular academic fields. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned question reception department, Provides the best answer by referring to the user's past question history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned question reception department, The system estimates the user's emotions and prioritizes questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned question reception department, We will add a feature that prioritizes region-specific questions by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned question reception department, We will add a feature that analyzes users' social media activity and prioritizes responses to relevant questions. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The creation department creates an ignorant AI, The teaching department conducts online lessons for the ignorant AI created by the aforementioned creation department, The system includes a question reception unit that receives questions from AI during lessons conducted by the aforementioned teaching unit. A system characterized by the following features.

2. The aforementioned teaching department, The explanation will be given using the whiteboard. The system according to feature 1.

3. The aforementioned teaching department, Give an oral explanation. The system according to feature 1.

4. The aforementioned question reception department, Accepting questions from AI The system according to feature 1.

5. The aforementioned creation unit, Includes an algorithm that pretends to know everything. The system according to feature 1.

6. The aforementioned teaching department, Conducting classes using online platforms The system according to feature 1.

7. The aforementioned creation unit, It estimates the user's emotions and adjusts the knowledge level of the ignorant AI based on the estimated user emotions. The system according to feature 1.

8. The aforementioned creation unit, Add a feature that allows you to choose whether or not to give an ignorant AI specialized knowledge in a particular academic field. The system according to feature 1.

9. The aforementioned creation unit, When creating an ignorant AI, the user's past learning history is referenced to set the optimal knowledge level. The system according to feature 1.

10. The aforementioned creation unit, It estimates the user's emotions and adjusts the frequency of the ignorant AI's know-it-all behavior based on the estimated user emotions. The system according to feature 1.

Images