system

Abstract

The system according to this embodiment aims to make explanations of assignments and homework easier to understand. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and an explanation unit. The reception unit receives input of a problem from the user. The analysis unit analyzes the problem received by the reception unit. The generation unit generates an answer based on the problem analyzed by the analysis unit. The explanation unit explains the answer generated by the generation 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] In the prior art, there is a problem that the explanations of problems and assignments may be difficult to understand and learners may not be able to fully understand them.

[0005] The system according to the embodiment aims to make the explanations of problems and assignments easier to understand.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and an explanation unit. The reception unit receives an input of a problem from a user. The analysis unit analyzes the problem received by the reception unit. The generation unit generates an answer based on the problem analyzed by the analysis unit. The explanation unit explains the answer generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can make explanations of assignments and homework easier to understand. [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) An AI teacher system according to an embodiment of the present invention is a system in which a user inputs a problem and a teacher avatar explains the solution. The AI ​​teacher system receives input from the user of a problem for which they want to know the solution, the generating AI analyzes the problem, and generates a solution. The generated solution is explained through the teacher avatar in the pattern of "problem → explanation → example solution → summary." This makes it easier for the user to understand the process of solving the problem. For example, if there is a math problem that asks to "solve this using the quadratic formula," the user inputs the problem, the generating AI analyzes the problem, and generates a solution. The teacher avatar first presents the problem, then explains the quadratic formula, shows a specific example solution, and finally provides a summary. This makes it easier for the user to understand the process of solving the problem. This mechanism allows the user to understand not only the answer but also the process, thus improving the learning effect. In addition, since the generating AI has learned the "problem → explanation → example solution → summary" pattern used by teachers in actual lessons, it provides explanations that are easy for the user to understand. As a result, the AI ​​teacher system can perform analysis, answer generation, and answer explanation for problems input by the user.

[0029] The AI ​​teacher system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an explanation unit. The reception unit receives input of a problem from the user. The reception unit provides, for example, an interface for the user to input a problem for which they want to know the answer. The interface includes, but is not limited to, a web form, a chatbot, or voice input. The analysis unit analyzes the problem received by the reception unit using a generation AI. The analysis unit analyzes the problem using, for example, techniques such as natural language processing, data mining, and machine learning algorithms. The generation unit generates an answer based on the problem analyzed by the analysis unit using a generation AI. The generation unit generates the answer using, for example, a text generation AI (e.g., LLM). The generation unit can also generate the answer in the form of a mathematical formula or a code snippet using a generation AI. The explanation unit explains the answer generated by the generation unit. The explanation unit explains the answer using, for example, a teacher avatar. The teacher avatar is implemented using, for example, techniques such as a 3D model, speech synthesis, or animation. The explanation unit explains the answer using a pattern such as "problem → explanation → example solution → summary". This allows the AI ​​teacher system according to the embodiment to perform analysis, answer generation, and answer explanation for a problem input by the user. Some or all of the above-described processes in the analysis unit and generation unit are performed using a generation AI. For example, the analysis unit inputs the user-inputted problem into the generation AI, which then analyzes the problem. The generation unit inputs the problem analyzed by the analysis unit into the generation AI, which then generates the answer. Some or all of the above-described processes in the explanation unit may be performed using AI or not. For example, the explanation unit inputs the generated answer into an AI model, which then explains the answer.

[0030] The reception desk receives questions from users. The reception desk provides an interface for users to input questions for which they want answers. This interface may include, but is not limited to, web forms, chatbots, and voice input. Specifically, in a web form, the user enters the question into a text box and sends it to the reception desk by pressing a submit button. In a chatbot, the user inputs the question in a natural conversational format, and the chatbot responds in real time. With voice input, the user inputs the question by voice through a microphone, and it is converted to text using speech recognition technology. These interfaces are designed with user convenience in mind, making them intuitive and easy to use. Furthermore, the reception desk also has the ability to temporarily save user input and allow for review and correction of the input before sending it to the analysis department. For example, if a user's input is incomplete, the reception desk will prompt the user for additional information to complete it. The reception desk can also save the user's input history and refer to previously entered questions and answers. This allows users to review past questions or search for similar questions. The reception department plays a crucial role in efficiently and accurately receiving user input and ensuring a smooth data transfer to the subsequent analysis department.

[0031] The analysis unit uses generative AI to analyze problems received by the reception unit. The analysis unit analyzes problems using technologies such as natural language processing, data mining, and machine learning algorithms. Specifically, it uses natural language processing to understand the context and intent of the user-entered problem and extract important keywords and phrases. It uses data mining to search past databases for similar problems and answers and collect reference information. It uses machine learning algorithms to learn problem patterns and characteristics, enabling appropriate analysis of new problems. The analysis unit combines these technologies to comprehensively analyze user-entered problems and provide foundational data for optimal solution generation. Furthermore, the analysis unit uses generative AI to determine the difficulty level and category of the problem and select an appropriate solution generation algorithm. For example, it uses a mathematical formula analysis algorithm for mathematics problems and a code analysis algorithm for programming problems. The analysis unit plays a crucial role in quickly and accurately analyzing user problems and ensuring a smooth data transfer to the generation unit.

[0032] The generation unit uses generative AI to generate solutions based on problems analyzed by the analysis unit. The generation unit can generate solutions using, for example, text generation AI (e.g., LLM). It can also generate solutions in the form of mathematical formulas or code snippets using the generative AI. Specifically, the text generation AI generates solutions in natural language based on data provided by the analysis unit. For example, for mathematical problems, it generates detailed solutions including mathematical formulas, and for programming problems, it generates appropriate code snippets. The generation unit leverages the powerful language model of the generative AI to provide the optimal solution to the user's problem. Furthermore, the generation unit has the ability to evaluate the quality of the generated solutions and make corrections or additions as needed. For example, if the generated solution is incomplete, the generation unit generates additional information to complete the solution. The generation unit can also continuously improve the accuracy and quality of solutions based on user feedback. This allows the generation unit to provide users with high-quality solutions and improve the overall reliability and usefulness of the system.

[0033] The explanation unit explains the answers generated by the generation unit. The explanation unit explains the answers, for example, through a teacher avatar. The teacher avatar is implemented using technologies such as 3D models, speech synthesis, and animation. Specifically, the teacher avatar provides a step-by-step explanation of the answer to the question entered by the user. For example, it explains the answer in a pattern such as "question → explanation → example answer → summary," making it easy for the user to understand. The teacher avatar communicates with the user in a natural conversational format and can respond to the user's questions and doubts in real time. Furthermore, the explanation unit also has the function of generating graphs and charts to visually display the generated answers in an easy-to-understand manner. For example, for a math problem, it illustrates the explanation of the formula, and for a programming problem, it visually displays the result of the code execution. This allows the user to understand the content of the answer more intuitively. The explanation unit plays a crucial role in providing clear and effective explanations of the answers to maximize the user's learning effectiveness.

[0034] The explanation section can explain the solution using the pattern of "problem → explanation → example solution → summary." For example, the explanation section first presents the problem, then explains the knowledge and methods necessary to solve it. Next, it shows a specific example solution, and finally provides a summary. For example, if there is a math problem that asks to "solve the quadratic equation using the quadratic formula," the explanation section first presents the problem, then explains the quadratic formula. Next, it shows a specific example solution, and finally provides a summary. This ensures that the explanation of the solution follows a consistent pattern, making it easier for the user to understand. Some or all of the above processing in the explanation section may be performed using AI, or it may not. For example, the explanation section inputs the generated solution into an AI model, and the AI ​​model explains the solution.

[0035] The analysis unit can analyze problems using generative AI. For example, the analysis unit can analyze the content of a problem using natural language processing technology. For instance, the generative AI receives the problem statement as input, analyzes its content, and extracts the information necessary for the answer. The analysis unit can also analyze problem patterns using data mining technology. For example, the generative AI analyzes similar problems based on past problem data and extracts their patterns. Furthermore, the analysis unit can analyze the characteristics of a problem using machine learning algorithms. For example, the generative AI extracts the features of a problem and classifies the problem based on them. As a result, using generative AI improves the accuracy of problem analysis. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit inputs a problem entered by the user into the generative AI, and the generative AI analyzes the problem.

[0036] The generation unit can generate answers using a generation AI. For example, the generation unit can generate answers using a text generation AI (e.g., LLM). The generation AI receives the content of a problem as input and generates an answer based on that content. For example, the generation AI generates an answer using mathematical formulas for a mathematics problem. The generation unit can also use the generation AI to generate code snippets for programming problems. For example, the generation AI receives a programming problem statement as input and generates appropriate code based on its content. Furthermore, the generation unit can use the generation AI to generate text-format answers to general questions. For example, the generation AI receives a question statement as input and generates an appropriate answer based on its content. This improves the accuracy of answer generation by using a generation AI. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs a problem analyzed by the analysis unit into the generation AI, and the generation AI generates an answer.

[0037] The explanation unit can explain the answer through a teacher avatar. The teacher avatar can be implemented using technologies such as 3D models, speech synthesis, and animation. The teacher avatar provides the user with a visually easy-to-understand explanation of the answer. For example, the teacher avatar can explain the solution process step by step and highlight important points. The teacher avatar can also adjust the content of the explanation in response to the user's reactions. For example, if there is a part that the user finds difficult to understand, the teacher avatar will explain that part repeatedly. Furthermore, the teacher avatar can provide appropriate feedback according to the user's learning progress. For example, if the user enters the correct answer, the teacher avatar will display a message of praise. In this way, using a teacher avatar makes the explanation of the answer visually easy to understand. Some or all of the above processing in the explanation unit may be performed using AI or not. For example, the explanation unit inputs the generated answer into an AI model, and the AI ​​model explains the answer.

[0038] The reception desk can provide an interface for users to input questions for which they want to know the answer. For example, the reception desk can enable users to input questions using a web form. The web form includes a text box for entering the content of the question and a dropdown menu for selecting the type of question. The reception desk can also enable users to input questions using a chatbot. The chatbot collects the content of the question through interaction with the user and sends it to the reception desk in an appropriate format. Furthermore, the reception desk can enable users to input questions using voice input. Voice input involves the user dictating the question through a microphone, and the voice data is converted into text data. This makes it easier for users to input questions. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's voice data into a generating AI, which converts the voice data into text data.

[0039] The reception desk can analyze the user's past problem input history and suggest the optimal input method. For example, the reception desk can automatically display problem formats that the user has frequently entered in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest problem formats to be used during specific time periods based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's past input data into a generating AI, and the generating AI suggests the optimal input method.

[0040] The input system can filter questions based on the user's current learning status and areas of interest when questions are entered. For example, the input system can prioritize displaying questions related to the subjects or topics the user is currently studying. It can also suggest questions in areas of interest based on the user's past learning history. Furthermore, the input system can filter and display questions of appropriate difficulty according to the user's learning progress. This improves learning efficiency by providing questions tailored to the user's learning status and areas of interest. Some or all of the above processing in the input system may be performed using AI or not. For example, the input system inputs the user's learning data into a generating AI, which then filters and suggests appropriate questions.

[0041] The reception desk can prioritize inputting questions based on the user's geographical location when a question is entered. For example, if the user is in a specific region, the reception desk will prioritize displaying questions related to that region. Furthermore, if the user is at a school or learning facility, the reception desk can suggest questions related to what they are learning at that location. Additionally, if the user is traveling, the reception desk can prioritize displaying questions related to their travel destination. This improves the relevance of learning by providing questions based on geographical location information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk inputs the user's geographical location data into a generating AI, which then suggests highly relevant questions.

[0042] The reception desk can analyze the user's social media activity when a question is entered and input relevant questions. For example, the reception desk can suggest questions related to topics the user has shown interest in on social media. The reception desk can also collect and display relevant questions from educational accounts the user follows. Furthermore, the reception desk can suggest questions that the user might be interested in based on their social media activity history. This improves the relevance of learning by providing questions based on social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into a generating AI, which then suggests relevant questions.

[0043] The analysis unit can optimize its analysis algorithm by referring to past analysis data when analyzing a problem. For example, the analysis unit can refer to similar problems analyzed in the past and select the optimal analysis algorithm. The analysis unit can also adjust the parameters of the analysis algorithm based on past analysis data. Furthermore, the analysis unit can use past analysis results as feedback to improve the analysis algorithm. This improves the accuracy of the analysis by providing the optimal analysis algorithm based on past analysis data. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs past analysis data into the generative AI, and the generative AI optimizes the analysis algorithm.

[0044] The analysis unit can apply different analysis methods depending on the category of the problem. For example, it can apply mathematical formula analysis methods to mathematical problems to derive accurate answers. It can also apply text analysis methods to literary problems to generate appropriate answers. Furthermore, it can apply data analysis methods to scientific problems to provide answers based on experimental results. This improves the accuracy of the analysis by providing analysis methods appropriate to the category of the problem. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs the category of the problem into the generative AI, and the generative AI applies the appropriate analysis method.

[0045] The analysis unit can determine the priority of analysis based on the submission date of each problem. For example, the analysis unit will prioritize problems with approaching deadlines. It can also postpone the analysis of problems with longer submission deadlines. Furthermore, the analysis unit can automatically adjust the analysis schedule according to the submission dates. This improves the efficiency of the analysis by setting priorities based on submission dates. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit inputs problem submission date data into the generation AI, which then determines the analysis priority.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant academic literature during problem analysis. For example, the analysis unit can improve its analysis methods by referring to relevant academic literature. Furthermore, the analysis unit can supplement its analysis results based on knowledge gained from academic literature. In addition, the analysis unit can improve the accuracy of its analysis by utilizing academic literature. Thus, the accuracy of the analysis is improved by referring to academic literature. Some or all of the above processes in the analysis unit are performed using a generating AI. For example, the analysis unit inputs relevant academic literature data into the generating AI, which then improves the analysis methods.

[0047] The generation unit can optimize its generation algorithm by referring to past generation data when generating answers. For example, the generation unit can refer to similar answers generated in the past and select the optimal generation algorithm. The generation unit can also adjust the parameters of the generation algorithm based on past generation data. Furthermore, the generation unit can use past generation results as feedback to improve the generation algorithm. This improves the accuracy of generation by providing the optimal generation algorithm based on past generation data. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs past generation data into the generation AI, and the generation AI optimizes the generation algorithm.

[0048] The generation unit can apply different generation methods depending on the difficulty level of the problem when generating answers. For example, the generation unit can apply a simple generation method to easy problems to quickly generate answers. It can also apply a detailed generation method to difficult problems to generate accurate answers. Furthermore, it can apply a balanced generation method to problems of moderate difficulty to generate appropriate answers. This improves the accuracy of generation by providing generation methods according to the difficulty level of the problem. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs problem difficulty data into the generation AI, and the generation AI applies an appropriate generation method.

[0049] The generation unit can determine the generation priority based on the submission dates of the questions when generating answers. For example, the generation unit can prioritize generating questions with approaching deadlines. It can also postpone generating questions with longer submission deadlines. Furthermore, the generation unit can automatically adjust the generation schedule according to the submission dates. This improves generation efficiency by setting priorities based on submission dates. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs question submission date data into the generation AI, and the generation AI determines the generation priority.

[0050] The generation unit can improve the accuracy of its output by referring to relevant academic literature during the output generation process. For example, the generation unit can improve its generation method by referring to relevant academic literature. Furthermore, the generation unit can supplement the output results based on knowledge gained from academic literature. In addition, the generation unit can improve the accuracy of its output by utilizing academic literature. Thus, referencing academic literature improves the accuracy of the output. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs relevant academic literature data into the generation AI, which then improves its generation method.

[0051] The explanation unit can select the most appropriate explanation method by referring to the user's past learning history when explaining the answer. For example, the explanation unit will prioritize using explanation methods that the user found easy to understand in the past. The explanation unit can also use appropriate examples based on the user's past learning history. Furthermore, the explanation unit can provide more detailed explanations for topics that the user has struggled with in the past. This improves the understanding of the answer by providing the most appropriate explanation method based on past learning history. Some or all of the above processing in the explanation unit may be performed using AI or not. For example, the explanation unit inputs the user's past learning data into a generating AI, and the generating AI selects the most appropriate explanation method.

[0052] The explanation unit can apply different explanation methods depending on the category of the problem when explaining the solution. For example, the explanation unit can use mathematical formulas to explain mathematical problems, making them easier to understand visually. It can also provide detailed explanations using text for literary problems. Furthermore, it can use experimental results and data to explain scientific problems. By providing explanation methods appropriate to the category of the problem, the understanding of the solution is improved. Some or all of the above processing in the explanation unit may be performed using AI or not. For example, the explanation unit inputs the category data of the problem into a generating AI, and the generating AI applies an appropriate explanation method.

[0053] The explanation unit can select the most suitable explanation method based on the user's device information when explaining the answer. For example, if the user is using a smartphone, the explanation unit can provide a display method that is adapted to the screen size. Furthermore, if the user is using a tablet, the explanation unit can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the explanation unit can provide a concise and highly visible display method. This improves the understanding of the answer by providing an explanation method based on device information. Some or all of the above processing in the explanation unit may be performed using AI, or not. For example, the explanation unit inputs the user's device information into a generating AI, which then selects the most suitable explanation method.

[0054] The explanation unit can improve the accuracy of its explanations by referring to relevant academic literature when explaining answers. For example, the explanation unit can improve its explanation methods by referring to relevant academic literature. The explanation unit can also supplement the content of its explanations based on knowledge gained from academic literature. Furthermore, the explanation unit can improve the accuracy of its explanations by utilizing academic literature. Thus, the accuracy of the explanation is improved by referring to academic literature. Some or all of the above processes in the explanation unit may be performed using AI or not. For example, the explanation unit inputs relevant academic literature data into a generating AI, and the generating AI improves the explanation methods.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The analysis unit can optimize its analysis algorithm by referring to past analysis data when analyzing a problem. For example, it can select the optimal analysis algorithm by referring to similar problems analyzed in the past. The analysis unit can also adjust the parameters of the analysis algorithm based on past analysis data. Furthermore, the analysis unit can improve the analysis algorithm by utilizing past analysis results as feedback. This improves the accuracy of the analysis by providing the optimal analysis algorithm based on past analysis data. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs past analysis data into the generative AI, and the generative AI optimizes the analysis algorithm.

[0057] The generation unit can optimize its generation algorithm by referring to past generation data when generating answers. For example, it can refer to similar answers generated in the past to select the optimal generation algorithm. The generation unit can also adjust the parameters of the generation algorithm based on past generation data. Furthermore, the generation unit can use past generation results as feedback to improve the generation algorithm. This improves the accuracy of generation by providing the optimal generation algorithm based on past generation data. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs past generation data into the generation AI, and the generation AI optimizes the generation algorithm.

[0058] The explanation unit can select the most appropriate explanation method by referring to the user's past learning history when explaining the answer. For example, it can prioritize using explanation methods that the user found easy to understand in the past. The explanation unit can also use appropriate examples based on the user's past learning history. Furthermore, the explanation unit can provide more detailed explanations for topics that the user has struggled with in the past. This improves the understanding of the answer by providing the most appropriate explanation method based on the user's past learning history. Some or all of the above processing in the explanation unit may be performed using AI or not. For example, the explanation unit inputs the user's past learning data into a generating AI, and the generating AI selects the most appropriate explanation method.

[0059] The reception desk can analyze the user's past problem input history and suggest the optimal input method. For example, it can automatically display problem formats that the user has frequently entered in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest problem formats to be used during specific time periods based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's past input data into a generating AI, and the generating AI suggests the optimal input method.

[0060] The analysis unit can improve the accuracy of its analysis by referring to relevant academic literature during problem analysis. For example, it can improve its analysis method by referring to relevant academic literature. Furthermore, the analysis unit can supplement its analysis results based on knowledge gained from academic literature. In addition, the analysis unit can improve the accuracy of its analysis by utilizing academic literature. Thus, the accuracy of the analysis is improved by referring to academic literature. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs relevant academic literature data into the generative AI, which then improves the analysis method.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives questions from users. The reception desk provides an interface for users to input questions for which they want to know the answer. This interface may include a web form, a chatbot, or voice input. Step 2: The analysis unit uses generative AI to analyze the problem received by the reception unit. The analysis unit analyzes the problem using technologies such as natural language processing, data mining, and machine learning algorithms. Step 3: The generation unit uses a generation AI to generate a solution based on the problem analyzed by the analysis unit. The generation unit generates the solution using a text generation AI (e.g., LLM). The generation unit can also generate the solution in the form of mathematical formulas or code snippets. Step 4: The explanation section explains the answer generated by the generation section. The explanation section explains the answer through a teacher avatar. The teacher avatar is implemented using technologies such as 3D models, speech synthesis, and animation. The explanation section explains the answer in the pattern of "problem → explanation → example answer → summary".

[0063] (Example of form 2) An AI teacher system according to an embodiment of the present invention is a system in which a user inputs a problem and a teacher avatar explains the solution. The AI ​​teacher system receives input from the user of a problem for which they want to know the solution, the generating AI analyzes the problem, and generates a solution. The generated solution is explained through the teacher avatar in the pattern of "problem → explanation → example solution → summary." This makes it easier for the user to understand the process of solving the problem. For example, if there is a math problem that asks to "solve this using the quadratic formula," the user inputs the problem, the generating AI analyzes the problem, and generates a solution. The teacher avatar first presents the problem, then explains the quadratic formula, shows a specific example solution, and finally provides a summary. This makes it easier for the user to understand the process of solving the problem. This mechanism allows the user to understand not only the answer but also the process, thus improving the learning effect. In addition, since the generating AI has learned the "problem → explanation → example solution → summary" pattern used by teachers in actual lessons, it provides explanations that are easy for the user to understand. As a result, the AI ​​teacher system can perform analysis, answer generation, and answer explanation for problems input by the user.

[0064] The AI ​​teacher system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an explanation unit. The reception unit receives input of a problem from the user. The reception unit provides, for example, an interface for the user to input a problem for which they want to know the answer. The interface includes, but is not limited to, a web form, a chatbot, or voice input. The analysis unit analyzes the problem received by the reception unit using a generation AI. The analysis unit analyzes the problem using, for example, techniques such as natural language processing, data mining, and machine learning algorithms. The generation unit generates an answer based on the problem analyzed by the analysis unit using a generation AI. The generation unit generates the answer using, for example, a text generation AI (e.g., LLM). The generation unit can also generate the answer in the form of a mathematical formula or a code snippet using a generation AI. The explanation unit explains the answer generated by the generation unit. The explanation unit explains the answer using, for example, a teacher avatar. The teacher avatar is implemented using, for example, techniques such as a 3D model, speech synthesis, or animation. The explanation unit explains the answer using a pattern such as "problem → explanation → example solution → summary". This allows the AI ​​teacher system according to the embodiment to perform analysis, answer generation, and answer explanation for a problem input by the user. Some or all of the above-described processes in the analysis unit and generation unit are performed using a generation AI. For example, the analysis unit inputs the user-inputted problem into the generation AI, which then analyzes the problem. The generation unit inputs the problem analyzed by the analysis unit into the generation AI, which then generates the answer. Some or all of the above-described processes in the explanation unit may be performed using AI or not. For example, the explanation unit inputs the generated answer into an AI model, which then explains the answer.

[0065] The reception desk receives questions from users. The reception desk provides an interface for users to input questions for which they want answers. This interface may include, but is not limited to, web forms, chatbots, and voice input. Specifically, in a web form, the user enters the question into a text box and sends it to the reception desk by pressing a submit button. In a chatbot, the user inputs the question in a natural conversational format, and the chatbot responds in real time. With voice input, the user inputs the question by voice through a microphone, and it is converted to text using speech recognition technology. These interfaces are designed with user convenience in mind, making them intuitive and easy to use. Furthermore, the reception desk also has the ability to temporarily save user input and allow for review and correction of the input before sending it to the analysis department. For example, if a user's input is incomplete, the reception desk will prompt the user for additional information to complete it. The reception desk can also save the user's input history and refer to previously entered questions and answers. This allows users to review past questions or search for similar questions. The reception department plays a crucial role in efficiently and accurately receiving user input and ensuring a smooth data transfer to the subsequent analysis department.

[0066] The analysis unit uses generative AI to analyze problems received by the reception unit. The analysis unit analyzes problems using technologies such as natural language processing, data mining, and machine learning algorithms. Specifically, it uses natural language processing to understand the context and intent of the user-entered problem and extract important keywords and phrases. It uses data mining to search past databases for similar problems and answers and collect reference information. It uses machine learning algorithms to learn problem patterns and characteristics, enabling appropriate analysis of new problems. The analysis unit combines these technologies to comprehensively analyze user-entered problems and provide foundational data for optimal solution generation. Furthermore, the analysis unit uses generative AI to determine the difficulty level and category of the problem and select an appropriate solution generation algorithm. For example, it uses a mathematical formula analysis algorithm for mathematics problems and a code analysis algorithm for programming problems. The analysis unit plays a crucial role in quickly and accurately analyzing user problems and ensuring a smooth data transfer to the generation unit.

[0067] The generation unit uses generative AI to generate solutions based on problems analyzed by the analysis unit. The generation unit can generate solutions using, for example, text generation AI (e.g., LLM). It can also generate solutions in the form of mathematical formulas or code snippets using the generative AI. Specifically, the text generation AI generates solutions in natural language based on data provided by the analysis unit. For example, for mathematical problems, it generates detailed solutions including mathematical formulas, and for programming problems, it generates appropriate code snippets. The generation unit leverages the powerful language model of the generative AI to provide the optimal solution to the user's problem. Furthermore, the generation unit has the ability to evaluate the quality of the generated solutions and make corrections or additions as needed. For example, if the generated solution is incomplete, the generation unit generates additional information to complete the solution. The generation unit can also continuously improve the accuracy and quality of solutions based on user feedback. This allows the generation unit to provide users with high-quality solutions and improve the overall reliability and usefulness of the system.

[0068] The explanation unit explains the answers generated by the generation unit. The explanation unit explains the answers, for example, through a teacher avatar. The teacher avatar is implemented using technologies such as 3D models, speech synthesis, and animation. Specifically, the teacher avatar provides a step-by-step explanation of the answer to the question entered by the user. For example, it explains the answer in a pattern such as "question → explanation → example answer → summary," making it easy for the user to understand. The teacher avatar communicates with the user in a natural conversational format and can respond to the user's questions and doubts in real time. Furthermore, the explanation unit also has the function of generating graphs and charts to visually display the generated answers in an easy-to-understand manner. For example, for a math problem, it illustrates the explanation of the formula, and for a programming problem, it visually displays the result of the code execution. This allows the user to understand the content of the answer more intuitively. The explanation unit plays a crucial role in providing clear and effective explanations of the answers to maximize the user's learning effectiveness.

[0069] The explanation section can explain the solution using the pattern of "problem → explanation → example solution → summary." For example, the explanation section first presents the problem, then explains the knowledge and methods necessary to solve it. Next, it shows a specific example solution, and finally provides a summary. For example, if there is a math problem that asks to "solve the quadratic equation using the quadratic formula," the explanation section first presents the problem, then explains the quadratic formula. Next, it shows a specific example solution, and finally provides a summary. This ensures that the explanation of the solution follows a consistent pattern, making it easier for the user to understand. Some or all of the above processing in the explanation section may be performed using AI, or it may not. For example, the explanation section inputs the generated solution into an AI model, and the AI ​​model explains the solution.

[0070] The analysis unit can analyze problems using generative AI. For example, the analysis unit can analyze the content of a problem using natural language processing technology. For instance, the generative AI receives the problem statement as input, analyzes its content, and extracts the information necessary for the answer. The analysis unit can also analyze problem patterns using data mining technology. For example, the generative AI analyzes similar problems based on past problem data and extracts their patterns. Furthermore, the analysis unit can analyze the characteristics of a problem using machine learning algorithms. For example, the generative AI extracts the features of a problem and classifies the problem based on them. As a result, using generative AI improves the accuracy of problem analysis. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit inputs a problem entered by the user into the generative AI, and the generative AI analyzes the problem.

[0071] The generation unit can generate answers using a generation AI. For example, the generation unit can generate answers using a text generation AI (e.g., LLM). The generation AI receives the content of a problem as input and generates an answer based on that content. For example, the generation AI generates an answer using mathematical formulas for a mathematics problem. The generation unit can also use the generation AI to generate code snippets for programming problems. For example, the generation AI receives a programming problem statement as input and generates appropriate code based on its content. Furthermore, the generation unit can use the generation AI to generate text-format answers to general questions. For example, the generation AI receives a question statement as input and generates an appropriate answer based on its content. This improves the accuracy of answer generation by using a generation AI. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs a problem analyzed by the analysis unit into the generation AI, and the generation AI generates an answer.

[0072] The explanation unit can explain the answer through a teacher avatar. The teacher avatar can be implemented using technologies such as 3D models, speech synthesis, and animation. The teacher avatar provides the user with a visually easy-to-understand explanation of the answer. For example, the teacher avatar can explain the solution process step by step and highlight important points. The teacher avatar can also adjust the content of the explanation in response to the user's reactions. For example, if there is a part that the user finds difficult to understand, the teacher avatar will explain that part repeatedly. Furthermore, the teacher avatar can provide appropriate feedback according to the user's learning progress. For example, if the user enters the correct answer, the teacher avatar will display a message of praise. In this way, using a teacher avatar makes the explanation of the answer visually easy to understand. Some or all of the above processing in the explanation unit may be performed using AI or not. For example, the explanation unit inputs the generated answer into an AI model, and the AI ​​model explains the answer.

[0073] The reception desk can provide an interface for users to input questions for which they want to know the answer. For example, the reception desk can enable users to input questions using a web form. The web form includes a text box for entering the content of the question and a dropdown menu for selecting the type of question. The reception desk can also enable users to input questions using a chatbot. The chatbot collects the content of the question through interaction with the user and sends it to the reception desk in an appropriate format. Furthermore, the reception desk can enable users to input questions using voice input. Voice input involves the user dictating the question through a microphone, and the voice data is converted into text data. This makes it easier for users to input questions. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's voice data into a generating AI, which converts the voice data into text data.

[0074] The reception desk can estimate the user's emotions and customize the input interface for the question based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick question entry. This improves the ease of input by providing an interface that responds 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 reception desk may be performed using AI or not. For example, the reception desk inputs the user's facial expression data into the generative AI, and the generative AI estimates the user's emotions.

[0075] The reception desk can analyze the user's past problem input history and suggest the optimal input method. For example, the reception desk can automatically display problem formats that the user has frequently entered in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest problem formats to be used during specific time periods based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's past input data into a generating AI, and the generating AI suggests the optimal input method.

[0076] The input system can filter questions based on the user's current learning status and areas of interest when questions are entered. For example, the input system can prioritize displaying questions related to the subjects or topics the user is currently studying. It can also suggest questions in areas of interest based on the user's past learning history. Furthermore, the input system can filter and display questions of appropriate difficulty according to the user's learning progress. This improves learning efficiency by providing questions tailored to the user's learning status and areas of interest. Some or all of the above processing in the input system may be performed using AI or not. For example, the input system inputs the user's learning data into a generating AI, which then filters and suggests appropriate questions.

[0077] The reception desk can estimate the user's emotions and determine the priority of the input questions based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize displaying easy questions to increase their confidence in answering. Conversely, if the user is relaxed, the reception desk can prioritize displaying more difficult questions to encourage a challenge. Furthermore, if the user is in a hurry, the reception desk can prioritize displaying questions that can be answered quickly. This improves learning efficiency by setting priorities 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 reception desk may be performed using AI or not. For example, the reception desk inputs the user's facial expression data into the generative AI, and the generative AI estimates the user's emotions.

[0078] The reception desk can prioritize inputting questions based on the user's geographical location when a question is entered. For example, if the user is in a specific region, the reception desk will prioritize displaying questions related to that region. Furthermore, if the user is at a school or learning facility, the reception desk can suggest questions related to what they are learning at that location. Additionally, if the user is traveling, the reception desk can prioritize displaying questions related to their travel destination. This improves the relevance of learning by providing questions based on geographical location information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk inputs the user's geographical location data into a generating AI, which then suggests highly relevant questions.

[0079] The reception desk can analyze the user's social media activity when a question is entered and input relevant questions. For example, the reception desk can suggest questions related to topics the user has shown interest in on social media. The reception desk can also collect and display relevant questions from educational accounts the user follows. Furthermore, the reception desk can suggest questions that the user might be interested in based on their social media activity history. This improves the relevance of learning by providing questions based on social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into a generating AI, which then suggests relevant questions.

[0080] The analysis unit can estimate the user's emotions and adjust the problem analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can analyze the problem using a simple analysis method. If the user is relaxed, the analysis unit can also analyze the problem using a more detailed analysis method. Furthermore, if the user is in a hurry, the analysis unit can analyze the problem using a method that allows for rapid analysis. This improves the efficiency of the analysis by providing an analysis method that is appropriate 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit are performed using the generative AI. For example, the analysis unit inputs the user's facial expression data into the generative AI, and the generative AI estimates the user's emotions.

[0081] The analysis unit can optimize its analysis algorithm by referring to past analysis data when analyzing a problem. For example, the analysis unit can refer to similar problems analyzed in the past and select the optimal analysis algorithm. The analysis unit can also adjust the parameters of the analysis algorithm based on past analysis data. Furthermore, the analysis unit can use past analysis results as feedback to improve the analysis algorithm. This improves the accuracy of the analysis by providing the optimal analysis algorithm based on past analysis data. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs past analysis data into the generative AI, and the generative AI optimizes the analysis algorithm.

[0082] The analysis unit can apply different analysis methods depending on the category of the problem. For example, it can apply mathematical formula analysis methods to mathematical problems to derive accurate answers. It can also apply text analysis methods to literary problems to generate appropriate answers. Furthermore, it can apply data analysis methods to scientific problems to provide answers based on experimental results. This improves the accuracy of the analysis by providing analysis methods appropriate to the category of the problem. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs the category of the problem into the generative AI, and the generative AI applies the appropriate analysis method.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. This improves the understanding of the analysis results by providing a display method that matches 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 analysis unit is performed using generative AI. For example, the analysis unit inputs the user's facial expression data into the generative AI, and the generative AI estimates the user's emotions.

[0084] The analysis unit can determine the priority of analysis based on the submission date of each problem. For example, the analysis unit will prioritize problems with approaching deadlines. It can also postpone the analysis of problems with longer submission deadlines. Furthermore, the analysis unit can automatically adjust the analysis schedule according to the submission dates. This improves the efficiency of the analysis by setting priorities based on submission dates. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit inputs problem submission date data into the generation AI, which then determines the analysis priority.

[0085] The analysis unit can improve the accuracy of its analysis by referring to relevant academic literature during problem analysis. For example, the analysis unit can improve its analysis methods by referring to relevant academic literature. Furthermore, the analysis unit can supplement its analysis results based on knowledge gained from academic literature. In addition, the analysis unit can improve the accuracy of its analysis by utilizing academic literature. Thus, the accuracy of the analysis is improved by referring to academic literature. Some or all of the above processes in the analysis unit are performed using a generating AI. For example, the analysis unit inputs relevant academic literature data into the generating AI, which then improves the analysis methods.

[0086] The generation unit can estimate the user's emotions and adjust the method of generating answers based on the estimated emotions. For example, if the user is stressed, the generation unit can generate simple and easy-to-understand answers. It can also generate detailed answers if the user is relaxed. Furthermore, if the user is in a hurry, the generation unit can generate answers quickly. This improves the efficiency of answer generation by providing a generation method that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions.

[0087] The generation unit can optimize its generation algorithm by referring to past generation data when generating answers. For example, the generation unit can refer to similar answers generated in the past and select the optimal generation algorithm. The generation unit can also adjust the parameters of the generation algorithm based on past generation data. Furthermore, the generation unit can use past generation results as feedback to improve the generation algorithm. This improves the accuracy of generation by providing the optimal generation algorithm based on past generation data. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs past generation data into the generation AI, and the generation AI optimizes the generation algorithm.

[0088] The generation unit can apply different generation methods depending on the difficulty level of the problem when generating answers. For example, the generation unit can apply a simple generation method to easy problems to quickly generate answers. It can also apply a detailed generation method to difficult problems to generate accurate answers. Furthermore, it can apply a balanced generation method to problems of moderate difficulty to generate appropriate answers. This improves the accuracy of generation by providing generation methods according to the difficulty level of the problem. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs problem difficulty data into the generation AI, and the generation AI applies an appropriate generation method.

[0089] The generation unit can estimate the user's emotions and adjust the display method of the generated answers based on the estimated user emotions. For example, if the user is nervous, the generation unit can provide a simple and highly visible display method. If the user is relaxed, the generation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can provide a concise display method. This improves the understanding of the answers by providing a display method that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit is performed using the generation AI. For example, the generation unit inputs the user's facial expression data into the generation AI, and the generation AI estimates the user's emotions.

[0090] The generation unit can determine the generation priority based on the submission dates of the questions when generating answers. For example, the generation unit can prioritize generating questions with approaching deadlines. It can also postpone generating questions with longer submission deadlines. Furthermore, the generation unit can automatically adjust the generation schedule according to the submission dates. This improves generation efficiency by setting priorities based on submission dates. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs question submission date data into the generation AI, and the generation AI determines the generation priority.

[0091] The generation unit can improve the accuracy of its output by referring to relevant academic literature during the output generation process. For example, the generation unit can improve its generation method by referring to relevant academic literature. Furthermore, the generation unit can supplement the output results based on knowledge gained from academic literature. In addition, the generation unit can improve the accuracy of its output by utilizing academic literature. Thus, referencing academic literature improves the accuracy of the output. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs relevant academic literature data into the generation AI, which then improves its generation method.

[0092] The explanation unit can estimate the user's emotions and adjust the explanation method of the answer based on the estimated user emotions. For example, if the user is stressed, the explanation unit will provide a simple and intuitive explanation. If the user is relaxed, the explanation unit can also provide a detailed explanation. Furthermore, if the user is in a hurry, the explanation unit can provide a quick and concise explanation. This improves the understanding of the answer by providing an explanation method that is appropriate 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 explanation unit may be performed using AI or not. For example, the explanation unit inputs the user's facial expression data into the generative AI, and the generative AI estimates the user's emotions.

[0093] The explanation unit can select the most appropriate explanation method by referring to the user's past learning history when explaining the answer. For example, the explanation unit will prioritize using explanation methods that the user found easy to understand in the past. The explanation unit can also use appropriate examples based on the user's past learning history. Furthermore, the explanation unit can provide more detailed explanations for topics that the user has struggled with in the past. This improves the understanding of the answer by providing the most appropriate explanation method based on past learning history. Some or all of the above processing in the explanation unit may be performed using AI or not. For example, the explanation unit inputs the user's past learning data into a generating AI, and the generating AI selects the most appropriate explanation method.

[0094] The explanation unit can apply different explanation methods depending on the category of the problem when explaining the solution. For example, the explanation unit can use mathematical formulas to explain mathematical problems, making them easier to understand visually. It can also provide detailed explanations using text for literary problems. Furthermore, it can use experimental results and data to explain scientific problems. By providing explanation methods appropriate to the category of the problem, the understanding of the solution is improved. Some or all of the above processing in the explanation unit may be performed using AI or not. For example, the explanation unit inputs the category data of the problem into a generating AI, and the generating AI applies an appropriate explanation method.

[0095] The explanation unit can estimate the user's emotions and adjust the order of explanation in the answer based on the estimated emotions. For example, if the user is nervous, the explanation unit can start with the easy parts and gradually increase the difficulty. If the user is relaxed, the explanation unit can also start with the more difficult parts. Furthermore, if the user is in a hurry, the explanation unit can explain the main points first and postpone the details. This improves the understanding of the answer by providing an explanation order that is appropriate 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 explanation unit may be performed using AI or not. For example, the explanation unit inputs the user's facial expression data into the generative AI, and the generative AI estimates the user's emotions.

[0096] The explanation unit can select the most suitable explanation method based on the user's device information when explaining the answer. For example, if the user is using a smartphone, the explanation unit can provide a display method that is adapted to the screen size. Furthermore, if the user is using a tablet, the explanation unit can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the explanation unit can provide a concise and highly visible display method. This improves the understanding of the answer by providing an explanation method based on device information. Some or all of the above processing in the explanation unit may be performed using AI, or not. For example, the explanation unit inputs the user's device information into a generating AI, which then selects the most suitable explanation method.

[0097] The explanation unit can improve the accuracy of its explanations by referring to relevant academic literature when explaining answers. For example, the explanation unit can improve its explanation methods by referring to relevant academic literature. The explanation unit can also supplement the content of its explanations based on knowledge gained from academic literature. Furthermore, the explanation unit can improve the accuracy of its explanations by utilizing academic literature. Thus, the accuracy of the explanation is improved by referring to academic literature. Some or all of the above processes in the explanation unit may be performed using AI or not. For example, the explanation unit inputs relevant academic literature data into a generating AI, and the generating AI improves the explanation methods.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform a concise and to-the-point analysis. If the user is relaxed, the analysis unit can perform a detailed and comprehensive analysis. Furthermore, if the user is in a hurry, the analysis unit can perform a rapid analysis and provide results in a short time. This improves the efficiency and accuracy of the analysis by providing an analysis method that is tailored to the user's emotions. Emotion estimation is achieved using, for example, 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-described processes in the analysis unit are performed using generative AI. For example, the analysis unit inputs the user's facial expression data into the generative AI, which then estimates the user's emotions.

[0100] The generation unit can estimate the user's emotions and adjust the method of generating answers based on the estimated emotions. For example, if the user is stressed, the generation unit can generate simple and easy-to-understand answers. If the user is relaxed, the generation unit can generate detailed answers. Furthermore, if the user is in a hurry, the generation unit can generate answers quickly. This improves the efficiency of answer generation by providing a generation method that responds to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generation AI. The generation 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 generation unit is performed using the generation AI. For example, the generation unit inputs the user's facial expression data into the generation AI, and the generation AI estimates the user's emotions.

[0101] The explanation unit can estimate the user's emotions and adjust the explanation of the answer based on the estimated emotions. For example, if the user is stressed, the explanation unit can provide a simple and intuitive explanation. If the user is relaxed, the explanation unit can provide a detailed explanation. Furthermore, if the user is in a hurry, the explanation unit can provide a quick and concise explanation. This improves the understanding of the answer by providing an explanation method that is appropriate to the user's emotions. Emotion estimation is achieved using, for example, 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 explanation unit may be performed using AI or not. For example, the explanation unit inputs user facial expression data into the generative AI, and the generative AI estimates the user's emotions.

[0102] The reception desk can estimate the user's emotions and customize the input interface for the question based on the estimated emotions. For example, if the user is stressed, it can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick question entry. This improves the ease of input by providing an interface that responds to the user's emotions. Emotion estimation is achieved using, for example, 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 reception desk may be performed using AI or not. For example, the reception desk inputs the user's facial expression data into the generative AI, which then estimates the user's emotions.

[0103] The explanation unit can estimate the user's emotions and adjust the order of explanation in the answer based on the estimated emotions. For example, if the user is nervous, the explanation can start with the easy parts and gradually increase in difficulty. If the user is relaxed, the explanation can start with the more difficult parts. Furthermore, if the user is in a hurry, the main points can be explained first, and details can be postponed. This improves the understanding of the answer by providing an explanation order that is appropriate to the user's emotions. Emotion estimation can be achieved using, for example, 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 explanation unit may be performed using AI or not. For example, the explanation unit inputs the user's facial expression data into the generative AI, and the generative AI estimates the user's emotions.

[0104] The analysis unit can optimize its analysis algorithm by referring to past analysis data when analyzing a problem. For example, it can select the optimal analysis algorithm by referring to similar problems analyzed in the past. The analysis unit can also adjust the parameters of the analysis algorithm based on past analysis data. Furthermore, the analysis unit can improve the analysis algorithm by utilizing past analysis results as feedback. This improves the accuracy of the analysis by providing the optimal analysis algorithm based on past analysis data. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs past analysis data into the generative AI, and the generative AI optimizes the analysis algorithm.

[0105] The generation unit can optimize its generation algorithm by referring to past generation data when generating answers. For example, it can refer to similar answers generated in the past to select the optimal generation algorithm. The generation unit can also adjust the parameters of the generation algorithm based on past generation data. Furthermore, the generation unit can use past generation results as feedback to improve the generation algorithm. This improves the accuracy of generation by providing the optimal generation algorithm based on past generation data. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs past generation data into the generation AI, and the generation AI optimizes the generation algorithm.

[0106] The explanation unit can select the most appropriate explanation method by referring to the user's past learning history when explaining the answer. For example, it can prioritize using explanation methods that the user found easy to understand in the past. The explanation unit can also use appropriate examples based on the user's past learning history. Furthermore, the explanation unit can provide more detailed explanations for topics that the user has struggled with in the past. This improves the understanding of the answer by providing the most appropriate explanation method based on the user's past learning history. Some or all of the above processing in the explanation unit may be performed using AI or not. For example, the explanation unit inputs the user's past learning data into a generating AI, and the generating AI selects the most appropriate explanation method.

[0107] The reception desk can analyze the user's past problem input history and suggest the optimal input method. For example, it can automatically display problem formats that the user has frequently entered in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest problem formats to be used during specific time periods based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's past input data into a generating AI, and the generating AI suggests the optimal input method.

[0108] The analysis unit can improve the accuracy of its analysis by referring to relevant academic literature during problem analysis. For example, it can improve its analysis method by referring to relevant academic literature. Furthermore, the analysis unit can supplement its analysis results based on knowledge gained from academic literature. In addition, the analysis unit can improve the accuracy of its analysis by utilizing academic literature. Thus, the accuracy of the analysis is improved by referring to academic literature. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs relevant academic literature data into the generative AI, which then improves the analysis method.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk receives questions from users. The reception desk provides an interface for users to input questions for which they want to know the answer. This interface may include a web form, a chatbot, or voice input. Step 2: The analysis unit uses generative AI to analyze the problem received by the reception unit. The analysis unit analyzes the problem using technologies such as natural language processing, data mining, and machine learning algorithms. Step 3: The generation unit uses a generation AI to generate a solution based on the problem analyzed by the analysis unit. The generation unit generates the solution using a text generation AI (e.g., LLM). The generation unit can also generate the solution in the form of mathematical formulas or code snippets. Step 4: The explanation section explains the answer generated by the generation section. The explanation section explains the answer through a teacher avatar. The teacher avatar is implemented using technologies such as 3D models, speech synthesis, and animation. The explanation section explains the answer in the pattern of "problem → explanation → example answer → summary".

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and explanation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for the user to input a problem for which they want to know the answer. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the problem using a generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an answer based on the analyzed problem. The explanation unit is implemented by the output device 40 of the smart device 14 and explains the answer through a teacher avatar. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and explanation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for the user to input a question for which they want to know the answer. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the question using a generation AI. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates an answer based on the analyzed question. The explanation unit is implemented, for example, by the speaker 240 of the smart glasses 214 and explains the answer through a teacher avatar. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and explanation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and provides an interface for the user to input a question for which they want to know the answer. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the question using a generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an answer based on the analyzed question. The explanation unit is implemented by the display 343 of the headset terminal 314 and explains the answer through a teacher avatar. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and explanation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and provides an interface for the user to input a problem for which they want to know the answer. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the problem using a generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an answer based on the analyzed problem. The explanation unit is implemented by the speaker 240 of the robot 414 and explains the answer through a teacher avatar. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A reception desk that receives problem input from users, An analysis unit that analyzes the problem received by the reception unit, A generation unit that generates an answer based on the problem analyzed by the analysis unit, The system includes an explanation unit that explains the answer generated by the generation unit. A system characterized by the following features. (Note 2) The above explanatory section is, The answer is explained using the pattern of "Problem → Explanation → Sample Solution → Summary". The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The generated AI analyzes the problem. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The AI ​​generates the answer. The system described in Appendix 1, characterized by the features described herein. (Note 5) The above explanatory section is, The teacher explains the answer through their avatar. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Provides an interface for users to input questions for which they want to know the answer. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and customizes the input interface for the problem based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past problem entry history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When a question is entered, filtering is performed based on the user's current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the input questions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter questions, the system prioritizes the input of questions that are more relevant to their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When a question is submitted, the system analyzes the user's social media activity and enters relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the user's emotions and adjust the problem analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing a problem, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing a problem, different analysis methods are applied depending on the problem category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing a problem, the priority of the analysis is determined based on when the problem was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing a problem, we refer to relevant academic literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system estimates the user's emotions and adjusts the response generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating solutions, the generation algorithm is optimized by referring to past generated data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating solutions, different generation methods are applied depending on the difficulty level of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts how the generated answers are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating solutions, the generation priority is determined based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating answers, we refer to relevant academic literature to improve the accuracy of the generation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The above explanatory section is, The system estimates the user's emotions and adjusts the explanation of the answer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The above explanatory section is, When explaining the answer, the system selects the most appropriate explanation method by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The above explanatory section is, When explaining the solution, apply different explanatory methods depending on the category of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 28) The above explanatory section is, The system estimates the user's emotions and adjusts the order of explanations in the answers based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The above explanatory section is, When explaining the answer, the most suitable explanation method is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The above explanatory section is, When explaining the answer, refer to relevant academic literature to improve the accuracy of the explanation. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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. A reception desk that receives problem input from users, An analysis unit that analyzes the problem received by the reception unit, A generation unit that generates an answer based on the problem analyzed by the analysis unit, The system includes an explanation unit that explains the answer generated by the generation unit. A system characterized by the following features.

2. The above explanatory section is, The solution will be explained in the following order: problem, explanation, example solution, and summary. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the problem using generative AI. The system according to feature 1.

4. The generating unit is The AI ​​generates the answer. The system according to feature 1.

5. The above explanatory section is, The teacher explains the answer through their avatar. The system according to feature 1.

6. The aforementioned reception unit is Provides an interface for users to input questions for which they want to know the answer. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and customizes the input interface for the problem based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past problem entry history and suggests the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When a question is entered, filtering is performed based on the user's current learning status and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the input questions based on the estimated user emotions. The system according to feature 1.

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