system

Abstract

The system according to this embodiment aims to resolve questions during reading in real time and provide appropriate supplementary information according to the user's level of understanding. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a judgment unit, and a display unit. The reception unit receives questions from the user. The generation unit generates answers to the questions received by the reception unit. The judgment unit determines the user's level of understanding based on the answers generated by the generation unit. The display unit displays supplementary images based on the level of understanding determined by the judgment 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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to solve doubts during reading in real time and it is difficult to provide appropriate supplementary information according to the user's understanding level.

[0005] The system according to the embodiment aims to solve doubts during reading in real time and provide appropriate supplementary information according to the user's understanding level.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a judgment unit, and a display unit. The reception unit receives questions from the user. The generation unit generates answers to the questions received by the reception unit. The judgment unit determines the user's level of understanding based on the answers generated by the generation unit. The display unit displays supplementary images based on the level of understanding determined by the judgment unit. [Effects of the Invention]

[0007] The system according to this embodiment can resolve questions during reading in real time and provide appropriate supplementary information according to the user's level of understanding. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The voice-interactive AI assistant system according to an embodiment of the present invention is a system that resolves questions arising during reading in real time. This system is activated when a user has a question while reading and says "Book" to an offline smartphone. The user can ask questions and discuss in a natural conversation. This AI assistant pre-scans the contents of the book and references, and the AI ​​learns from them to provide context-based answers. It also has a function to remember the user's reading history and past questions and adjust the level of conversation according to the individual user's level of understanding. Furthermore, it is possible to display supplementary images in addition to voice when explaining complex concepts, enabling multimodal support. For example, when a user has a question while reading, they say "Book" to an offline smartphone. In this case, the AI ​​assistant can be activated even if the smartphone is offline. For example, if the user asks, "What does this word mean?", the AI ​​assistant will explain the meaning of the word. Next, the AI ​​assistant pre-scans the contents of the book and references, and the AI ​​learns from them. As a result, the AI ​​assistant can provide context-based answers. For example, if a particular term is defined based on prior research, it can understand that definition and then answer. Furthermore, the AI ​​assistant remembers the user's reading history and past questions, adjusting the conversation level to suit each user's level of understanding. For example, it can explain things in simple terms for beginners and provide more detailed explanations for experts. It can also provide relevant information based on questions the user has asked in the past. In addition to voice, the AI ​​assistant can also display supplementary images when explaining complex concepts, enabling multimodal functionality. For example, when explaining specialized topics or models, it can display diagrams and graphs to aid user understanding. This system allows for real-time resolution of questions during reading, improving the user's learning experience. For example, it can provide an efficient learning method for curious working professionals and highly motivated undergraduate and graduate students. Moreover, the AI ​​assistant can appropriately explain terms and concepts that are difficult to understand in context, deepening comprehension.Furthermore, because it can be used offline, it can be utilized even in locations without an internet connection. This allows the voice-activated AI assistant system to resolve questions in real time while reading, improving the user's learning experience.

[0029] The voice-interactive AI assistant system according to this embodiment comprises a reception unit, a generation unit, a judgment unit, and a display unit. The reception unit receives questions from the user. For example, if the user asks, "What does this word mean?", the reception unit can receive the question. The generation unit generates an answer to the question received by the reception unit. For example, the generation unit generates an answer to the question using AI. The generation unit can generate an appropriate answer to the question using, for example, natural language processing technology. The judgment unit determines the user's level of understanding based on the answer generated by the generation unit. For example, the judgment unit can evaluate the user's reaction to the answer and their level of understanding. The judgment unit can determine the user's level of understanding based, for example, the correct answer rate of a quiz or the accuracy of the answer. The display unit displays supplementary images based on the level of understanding determined by the judgment unit. For example, the display unit can display supplementary images when explaining complex concepts. The display unit can display supplementary images such as diagrams, charts, or photographs. As a result, the voice-interactive AI assistant system according to this embodiment can resolve questions while reading by providing real-time answers to user questions and displaying supplementary information according to the user's level of understanding.

[0030] The reception desk receives questions from users. For example, if a user asks, "What does this word mean?", the reception desk can receive that question. Specifically, the reception desk uses speech recognition technology to convert the user's utterance into text data. The speech recognition technology uses an algorithm that analyzes the user's utterance in real time and converts the audio signal into text. This ensures that the user's question is accurately captured as text data. Furthermore, the reception desk uses natural language processing technology to analyze the text data in order to understand the intent of the user's utterance. For example, if a user asks, "What does this word mean?", the reception desk understands the user's intent to "find out the meaning of the word" and prepares to generate an appropriate answer. The reception desk can also collect information to provide a more appropriate answer by considering the context of the user's utterance and past dialogue history. This allows the reception desk to accurately receive the user's question and smoothly pass it on to the next processing step.

[0031] The generation unit generates answers to questions received by the reception unit. For example, the generation unit uses AI to generate answers to questions. The generation unit can generate appropriate answers to questions using, for example, natural language processing technology. Specifically, the generation unit uses a large-scale pre-trained language model to generate answers to user questions. This language model is trained on a large amount of text data and has the ability to generate appropriate answers to a variety of questions. For example, if a user asks, "What does this word mean?", the generation unit searches text data containing the meaning of that word and generates an appropriate answer. Furthermore, the generation unit can provide more specific and detailed answers by considering the context and intent of the user's question. For example, if a user asks, "What does this word mean?", and the word is used in a specific context, it can provide a meaning appropriate to that context. In addition, the generation unit can provide more personalized answers by considering the user's past question history and dialogue history. As a result, the generation unit can generate quick and appropriate answers to user questions and resolve user doubts.

[0032] The decision unit determines the user's level of understanding based on the answers generated by the generation unit. For example, the decision unit can evaluate the user's reaction to and understanding of the answers. Specifically, the decision unit monitors the user's reaction in real time and evaluates how well the user understands the generated answers. For example, if the user responds to the generated answer with "I understand" or "Tell me more," the decision unit analyzes the reaction and evaluates the user's level of understanding. The decision unit can also evaluate the user's level of understanding using quiz-style questions. For example, based on the answers provided by the generation unit, it can ask the user a related quiz and determine the user's level of understanding based on the accuracy of the answers and the rate of correct responses. Furthermore, the decision unit can analyze nonverbal information such as the user's tone of voice, facial expressions, and gestures to comprehensively evaluate the user's level of understanding. This allows the decision unit to accurately evaluate the user's level of understanding and appropriately adjust the information and supplementary explanations provided in the next step.

[0033] The display unit shows supplementary images based on the level of understanding determined by the judgment unit. For example, the display unit can display supplementary images when explaining complex concepts. Specifically, the display unit displays supplementary images such as diagrams, charts, and photographs according to the user's level of understanding. For example, if a user asks, "What does this word mean?" and the word is related to a scientific concept, the display unit can display a diagram or chart to visually explain that concept. Furthermore, if the display unit determines that the user's level of understanding is low, it can display more detailed supplementary images to help the user understand. For example, if a user asks, "What does this word mean?" and the word is a complex technical term, the display unit can display a detailed diagram or photograph to visually explain the meaning of that term. In addition, the display unit can dynamically adjust the content and format of the supplementary images it displays based on the user's reactions and feedback. This allows the display unit to provide appropriate supplementary information according to the user's level of understanding and resolve the user's questions.

[0034] The scanning unit can scan the contents of a book. For example, the scanning unit can scan the contents of a book using a scanner and save it as digital data. For example, the scanning unit can scan the contents of a book using a high-resolution scanner. The scanning unit can also adjust the scanning speed. For example, the scanning unit can set the scanning speed to high to complete the scan in a short time. Furthermore, the scanning unit can select the corresponding format. For example, the scanning unit can save the scanned data in formats such as PDF, image, or text. This allows the scanning unit to pre-scan the contents of a book, enabling the AI ​​to provide context-based answers. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input the scanned data into the AI, which can analyze the data and understand the context.

[0035] The learning unit can learn from references. For example, the learning unit can learn from references such as academic papers, books, and websites. For example, the learning unit can use AI to analyze references and understand the context. For example, the learning unit can use natural language processing technology to analyze the content of references and generate context-based answers. For example, the learning unit can extract relevant keywords and phrases and generate answers based on them. In this way, by learning from references, the learning unit can provide more detailed and accurate answers. Some or all of the above processing in the learning unit may be performed using generative AI, or it may be performed without generative AI. For example, the learning unit can input reference data into a generative AI, which can analyze the data and understand the context.

[0036] The memory unit can store the user's reading history. For example, the memory unit can store information such as the title of the book the user has read, the date it was completed, the reading time, and the number of pages read. For example, the memory unit can record the user's reading history using a database. For example, the memory unit can analyze the content of the book the user has read and store the relevant information. As a result, by storing the user's reading history, the memory unit can provide answers tailored to each individual user. Some or all of the above processing in the memory unit may be performed using AI or not. For example, the memory unit can input the user's reading history data into AI, and the AI ​​can analyze the data and store the relevant information.

[0037] The question memory unit can store past questions. For example, the question memory unit can store the content of questions the user has asked in the past, the date and time of the questions, and the content of the answers. For example, the question memory unit can record past questions using a database. For example, the question memory unit can analyze the content of questions the user has asked in the past and store related information. This makes it easier for the question memory unit to provide related information by storing past questions. Some or all of the above processing in the question memory unit may be performed using AI or not. For example, the question memory unit can input past question data into AI, and the AI ​​can analyze the data and store related information.

[0038] The generation unit can generate context-based answers. The generation unit can generate context-based answers using, for example, AI. The generation unit can analyze the context using, for example, natural language processing technology and generate appropriate answers. The generation unit can extract relevant keywords or phrases and generate answers based on them. In this way, the generation unit can provide more appropriate answers by generating context-based answers. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input context data into a generation AI, and the generation AI can analyze the data and generate an answer.

[0039] The decision unit can adjust the level of conversation based on the user's level of understanding. For example, the decision unit can use AI to evaluate the user's level of understanding and adjust the level of conversation. For example, the decision unit can determine the user's level of understanding based on the correct answer rate or accuracy of answers in quizzes and adjust the level of conversation. For example, the decision unit can explain things in simple terms to beginners and provide more detailed explanations to experts. In this way, the decision unit can support more effective learning by adjusting the level of conversation according to the user's level of understanding. Some or all of the above processing in the decision unit may be performed using generative AI or not. For example, the decision unit can input user understanding data into generative AI, and the generative AI can analyze the data and adjust the level of conversation.

[0040] The display unit can display supplementary images when explaining complex concepts. For example, the display unit can display supplementary images such as diagrams, charts, and photographs. The display unit can, for example, use AI to select and display appropriate supplementary images. For example, the display unit can help users understand complex concepts by displaying relevant supplementary images. Thus, the display unit can help users understand by displaying supplementary images. Some or all of the above processing in the display unit may be performed using AI, or without AI. For example, the display unit can input supplementary image data into AI, which can analyze the data, select appropriate supplementary images, and display them.

[0041] The reception desk can analyze a user's past question history and select the optimal reception method. For example, the reception desk can use AI to analyze a user's past question history and select the optimal reception method. For example, the reception desk can prioritize receiving questions that the user has frequently asked in the past. For example, the reception desk can prioritize suggesting question formats (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict questions to be asked at a specific time based on the user's past question history and receive them. In this way, the reception desk can provide the user with the optimal reception method by analyzing past question history. 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 past question history data into AI, and the AI ​​can analyze the data and select the optimal reception method.

[0042] The reception unit can filter questions based on the user's current reading status and areas of interest when receiving them. For example, the reception unit can use AI to analyze the user's current reading status and areas of interest and filter the questions. For example, the reception unit can prioritize receiving questions related to the content of the book the user is currently reading. For example, the reception unit can filter and receive relevant questions based on the user's areas of interest. For example, the reception unit can filter and receive questions based on topics the user has shown interest in in the past. In this way, the reception unit can prioritize receiving highly relevant questions by filtering them based on the user's reading status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's reading status data and areas of interest data into AI, and the AI ​​can analyze the data and filter the questions.

[0043] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location information. For example, the reception desk can use AI to analyze the user's geographical location information and prioritize receiving highly relevant questions. For example, if the user is in a specific region, the reception desk can prioritize receiving questions related to that region. For example, if the user is traveling, the reception desk can prioritize receiving questions related to their travel destination. For example, if the user is at home, the reception desk can prioritize receiving questions related to their home. In this way, the reception desk can prioritize receiving highly relevant questions by taking into account the user's 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 can input the user's geographical location information data into AI, and the AI ​​can analyze the data and prioritize receiving highly relevant questions.

[0044] The reception desk can analyze the user's social media activity when receiving a question and accept relevant questions. For example, the reception desk can use AI to analyze the user's social media activity and accept relevant questions. For example, the reception desk can accept relevant questions based on what the user has shared on social media. For example, the reception desk can accept questions based on topics the user follows on social media. For example, the reception desk can accept relevant questions based on groups the user participates in on social media. This allows the reception desk to prioritize accepting relevant questions by analyzing the user's 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 activity data into AI, which can analyze the data and accept relevant questions.

[0045] The generation unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the generation unit can use AI to evaluate the importance of the question and adjust the level of detail in the answer. For example, the generation unit can evaluate the importance of the question based on the content of the question and the user's level of interest and adjust the level of detail in the answer. For example, the generation unit can generate detailed answers for important questions. For example, the generation unit can generate concise answers for general questions. For example, the generation unit can generate answers with adjusted levels of detail according to the user's level of understanding. In this way, the generation unit can provide more appropriate answers by adjusting the level of detail in the answer based on the importance of the question. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input question importance data into AI, and the AI ​​can analyze the data and adjust the level of detail in the answer.

[0046] The generation unit can apply different answer algorithms depending on the question category when generating answers. For example, the generation unit can use AI to analyze the question category and apply an appropriate answer algorithm. For example, for scientific questions, the generation unit can apply a specialized algorithm to generate an answer. For example, for historical questions, the generation unit can refer to a historical database to generate an answer. For example, for technical questions, the generation unit can generate an answer based on technical literature. In this way, the generation unit can provide more appropriate answers by applying different answer algorithms depending on the question category. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input question category data into a generation AI, and the generation AI can analyze the data and apply an appropriate answer algorithm.

[0047] The generation unit can determine the priority of answers based on when the questions were submitted when generating answers. For example, the generation unit can use AI to analyze when the questions were submitted and determine the priority of the answers. For example, the generation unit can prioritize answers to recently submitted questions. For example, the generation unit can prioritize generating answers to questions that have remained unanswered for a long time. For example, the generation unit can provide answers at the optimal time based on the user's schedule. In this way, the generation unit can provide answers at a more appropriate time by determining the priority of answers based on when the questions were submitted. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input question submission data into AI, and the AI ​​can analyze the data to determine the priority of the answers.

[0048] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the generation unit can use AI to analyze the relevance of the questions and adjust the order of the answers. For example, the generation unit can prioritize answers that are highly relevant to the questions. For example, the generation unit can postpone answers that are less relevant to the questions. For example, the generation unit can generate answers in order of relevance based on the category of the questions. In this way, the generation unit can prioritize providing more relevant answers by adjusting the order of answers based on the relevance of the questions. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input question relevance data into AI, and the AI ​​can analyze the data and adjust the order of the answers.

[0049] The judgment unit can improve the accuracy of its judgment by referring to the user's past comprehension data when determining comprehension. The judgment unit can improve the accuracy of its judgment by, for example, using AI to analyze the user's past comprehension data. The judgment unit can determine the current comprehension level based on, for example, the user's past quiz results and learning history. The judgment unit can improve the accuracy of its comprehension judgment by, for example, analyzing the user's past comprehension data. The judgment unit can make the optimal comprehension judgment by referring to the user's past comprehension data. In this way, the judgment unit can improve the accuracy of its comprehension judgment by referring to past comprehension data. Some or all of the above processing in the judgment unit may be performed using AI or not using AI. For example, the judgment unit can input the user's past comprehension data into AI, and the AI ​​can analyze the data to improve the accuracy of its judgment.

[0050] The judgment unit can make a judgment by considering the user's attribute information when determining the level of understanding. For example, the judgment unit can use AI to analyze the user's attribute information and make a judgment on the level of understanding. For example, the judgment unit can make a judgment on the level of understanding based on the user's age. For example, the judgment unit can make a judgment on the level of understanding based on the user's occupation. For example, the judgment unit can make a judgment on the level of understanding based on the user's educational background. In this way, the judgment unit can make a more appropriate judgment on the level of understanding by considering the user's attribute information. Some or all of the above processing in the judgment unit may be performed using AI or not using AI. For example, the judgment unit can input user attribute information data into AI, and the AI ​​can analyze the data and make a judgment on the level of understanding.

[0051] The decision-making unit can make decisions regarding the level of understanding by considering the geographical distribution of users. For example, the decision-making unit can use AI to analyze the geographical distribution of users and make decisions regarding the level of understanding. For example, if a user is in a specific region, the decision-making unit can make decisions regarding the level of understanding by considering the characteristics of that region. For example, if a user is traveling, the decision-making unit can make decisions regarding the level of understanding by considering the characteristics of the travel destination. For example, if a user is at home, the decision-making unit can make decisions regarding the level of understanding by considering the geographical distribution of users. In this way, the decision-making unit can make more appropriate decisions regarding the level of understanding by considering the geographical distribution of users. Some or all of the above processing in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input the geographical distribution data of users into AI, and the AI ​​can analyze the data and make decisions regarding the level of understanding.

[0052] The judgment unit can improve the accuracy of its judgment by referring to relevant literature when determining the level of understanding. The judgment unit can improve the accuracy of its judgment by, for example, using AI to analyze relevant literature. The judgment unit can improve the accuracy of its judgment by, for example, referring to relevant literature. The judgment unit can make the optimal judgment of the level of understanding based on relevant literature. The judgment unit can improve the accuracy of its judgment by, for example, analyzing relevant literature. In this way, the judgment unit can improve the accuracy of its judgment of the level of understanding by referring to relevant literature. Some or all of the above processing in the judgment unit may be performed using AI or not using AI. For example, the judgment unit can input relevant literature data into AI, and the AI ​​can analyze the data to improve the accuracy of its judgment.

[0053] The display unit can select the optimal display method when displaying supplemental images by referring to the user's past viewing history. The display unit can, for example, use AI to analyze the user's past viewing history and select the optimal display method. The display unit can, for example, display the optimal supplemental image based on the user's past viewing history. The display unit can, for example, analyze the user's past viewing history and select the optimal display method. The display unit can, for example, refer to the user's past viewing history to display the optimal supplemental image. In this way, the display unit can display the optimal supplemental image by referring to the past viewing history. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the user's past viewing history data into AI, and the AI ​​can analyze the data to select the optimal display method.

[0054] The display unit can display supplementary images while considering the user's attribute information. For example, the display unit can use AI to analyze the user's attribute information and display supplementary images. For example, the display unit can display appropriate supplementary images based on the user's age. For example, the display unit can display relevant supplementary images based on the user's occupation. For example, the display unit can display supplementary images that are easy to understand based on the user's educational background. In this way, the display unit can display more appropriate supplementary images by considering the user's attribute information. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input user attribute information data into AI, and the AI ​​can analyze the data and display supplementary images.

[0055] The display unit can select the optimal display method when displaying supplementary images, taking into account the user's geographical location information. For example, the display unit can use AI to analyze the user's geographical location information and select the optimal display method. For example, if the user is in a specific region, the display unit can display supplementary images related to that region. For example, if the user is traveling, the display unit can display supplementary images related to the travel destination. For example, if the user is at home, the display unit can display supplementary images related to the home. In this way, the display unit can display the optimal supplementary image by taking into account the user's geographical location information. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the user's geographical location information data into AI, and the AI ​​can analyze the data and select the optimal display method.

[0056] The display unit can improve the accuracy of the display by referring to related literature when displaying supplementary images. The display unit can improve the accuracy of the display by, for example, using AI to analyze related literature. The display unit can improve the accuracy of the supplementary image by, for example, referring to related literature. The display unit can display the optimal supplementary image based on related literature. The display unit can improve the accuracy of the supplementary image by, for example, analyzing related literature. In this way, the display unit can improve the accuracy of the supplementary image by referring to related literature. Some or all of the above processing in the display unit may be performed using AI or not using AI. For example, the display unit can input related literature data into AI, and the AI ​​can analyze the data to improve the accuracy of the display.

[0057] The scanning unit can select the optimal scanning method by referring to the user's past scanning history during scanning. The scanning unit can, for example, use AI to analyze the user's past scanning history and select the optimal scanning method. The scanning unit can, for example, select the optimal scanning method based on the user's past scanning history. The scanning unit can, for example, analyze the user's past scanning history and select the optimal scanning method. The scanning unit can, for example, refer to the user's past scanning history to select the optimal scanning method. In this way, the scanning unit can select the optimal scanning method by referring to past scanning history. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input the user's past scanning history data into AI, and the AI ​​can analyze the data to select the optimal scanning method.

[0058] The scanning unit can select the optimal scanning method by considering the user's geographical location information during scanning. For example, the scanning unit can use AI to analyze the user's geographical location information and select the optimal scanning method. For example, if the user is in a specific region, the scanning unit can prioritize scanning items related to that region. For example, if the user is traveling, the scanning unit can prioritize scanning items related to the travel destination. For example, if the user is at home, the scanning unit can prioritize scanning items related to the home. In this way, the scanning unit can select the optimal scanning method by considering the user's geographical location information. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input the user's geographical location data into AI, and the AI ​​can analyze the data to select the optimal scanning method.

[0059] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can use AI to analyze past learning data and optimize the learning algorithm. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and optimize the learning algorithm. For example, the learning unit can select the optimal learning algorithm by referring to past learning data. Thus, the learning unit can optimize the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI or without AI. For example, the learning unit can input past learning data into AI, which can then analyze the data and optimize the learning algorithm.

[0060] The learning unit can weight the training data based on the submission timing of the scan data during training. For example, the learning unit can use AI to analyze the submission timing of the scan data and weight the training data. For example, the learning unit can prioritize training on recently submitted scan data. For example, the learning unit can weight scan data that has not been trained for a long period of time. For example, the learning unit can weight the training data based on the submission timing of the scan data. This allows the learning unit to perform more appropriate training by weighting the training data based on the submission timing of the scan data. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input scan data submission timing data into AI, and the AI ​​can analyze the data and weight the training data.

[0061] The memory unit can optimize its memory algorithm by referring to past memory data during memory storage. For example, the memory unit can use AI to analyze past memory data and optimize the memory algorithm. For example, the memory unit can select the optimal memory algorithm based on past memory data. For example, the memory unit can analyze past memory data and optimize the memory algorithm. For example, the memory unit can select the optimal memory algorithm by referring to past memory data. Thus, the memory unit can optimize its memory algorithm by referring to past memory data. Some or all of the above processing in the memory unit may be performed using AI or without AI. For example, the memory unit can input past memory data into AI, which can then analyze the data and optimize the memory algorithm.

[0062] The memory unit can weight the stored data based on when the reading history was submitted. For example, the memory unit can use AI to analyze the submission timing of the reading history and weight the stored data. For example, the memory unit can prioritize storing recently submitted reading history entries. For example, the memory unit can weight reading history entries that have not been stored for a long period of time. For example, the memory unit can weight the stored data based on when the reading history was submitted. This allows the memory unit to store data more appropriately by weighting it based on when the reading history was submitted. Some or all of the above processing in the memory unit may be performed using AI or not. For example, the memory unit can input reading history submission timing data into AI, which can then analyze the data and weight the stored data.

[0063] The question memory unit can optimize its memory algorithm by referring to past question memory data when storing questions. The question memory unit can optimize its memory algorithm by, for example, using AI to analyze past question memory data. The question memory unit can select the optimal memory algorithm based on past question memory data. The question memory unit can optimize its memory algorithm by, for example, analyzing past question memory data. The question memory unit can select the optimal memory algorithm by referring to past question memory data. In this way, the question memory unit can optimize its memory algorithm by referring to past question memory data. Some or all of the above processing in the question memory unit may be performed using AI or not. For example, the question memory unit can input past question memory data into AI, and the AI ​​can analyze the data to optimize the memory algorithm.

[0064] The question memory unit can weight the stored data based on when the questions were submitted. For example, the question memory unit can use AI to analyze the submission timing of questions and weight the stored data. For example, the question memory unit can prioritize storing recently submitted questions. For example, the question memory unit can weight questions that have not been stored for a long period of time. For example, the question memory unit can weight the stored data based on when the questions were submitted. This allows the question memory unit to store more appropriate information by weighting the stored data based on when the questions were submitted. Some or all of the above processing in the question memory unit may be performed using AI or not. For example, the question memory unit can input question submission timing data into AI, and the AI ​​can analyze the data and weight the stored data.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] Voice-activated AI assistant systems can estimate a user's reading speed and adjust the timing of responses based on that estimate. For example, if a user is speed-reading, the AI ​​assistant can provide answers quickly. Conversely, if a user is reading slowly, the response can be delayed. It is also possible to adjust the level of detail in the responses according to the reading speed. For example, a concise answer can be provided to a speed-reading user, while a detailed explanation can be given to a slow-reading user. This can make the user's reading experience smoother and support more efficient learning.

[0067] Voice-activated AI assistant systems can detect a user's reading posture and provide advice to maintain proper posture. For example, if a user is reading in the same position for an extended period, the AI ​​assistant can prompt them to change their posture. It can also display images or videos demonstrating correct posture if the user is reading in an inappropriate position. Furthermore, it can record the user's posture data and compare it to past data to support posture improvement. This allows for a comfortable reading experience while maintaining the user's health.

[0068] A voice-activated AI assistant system can analyze the ambient sounds a user is listening to while reading and provide advice to create an appropriate reading environment. For example, if the surroundings are noisy, the AI ​​assistant can suggest moving to a quieter location. It can also play appropriate ambient sounds (such as nature sounds or classical music) to enhance concentration. Furthermore, it can record ambient sound data and provide feedback by comparing it with past data to offer the optimal reading environment. This allows users to enjoy reading in a more comfortable environment and improve their learning effectiveness.

[0069] A voice-activated AI assistant system can track a user's gaze while reading and evaluate their reading progress based on the eye-tracking data. For example, if a user lingers on a particular page or paragraph for an extended period, the AI ​​assistant can determine that the section is difficult and provide additional explanations. It can also identify areas of particular interest to the user based on eye-tracking data and provide relevant information. Furthermore, it can record eye-tracking data, compare it to past data to evaluate reading progress, and offer appropriate advice. This allows for a more personalized reading experience and supports effective learning.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The reception desk receives questions from users. For example, if a user asks, "What does this word mean?", the reception desk can receive that question. Step 2: The generation unit generates answers to the questions received by the reception unit. For example, the generation unit uses AI to generate answers to questions. The generation unit can, for example, use natural language processing technology to generate appropriate answers to questions. Step 3: The judgment unit determines the user's level of understanding based on the answers generated by the generation unit. For example, the judgment unit can evaluate the user's reaction to and understanding of the answers. For example, the judgment unit can determine the user's level of understanding based on the quiz's correct answer rate and the accuracy of the answers. Step 4: The display unit displays supplementary images based on the level of understanding determined by the judgment unit. For example, the display unit can display supplementary images when explaining complex concepts. The display unit can display supplementary images such as diagrams, charts, or photographs.

[0072] (Example of form 2) The voice-interactive AI assistant system according to an embodiment of the present invention is a system that resolves questions arising during reading in real time. This system is activated when a user has a question while reading and says "Book" to an offline smartphone. The user can ask questions and discuss in a natural conversation. This AI assistant pre-scans the contents of the book and references, and the AI ​​learns from them to provide context-based answers. It also has a function to remember the user's reading history and past questions and adjust the level of conversation according to the individual user's level of understanding. Furthermore, it is possible to display supplementary images in addition to voice when explaining complex concepts, enabling multimodal support. For example, when a user has a question while reading, they say "Book" to an offline smartphone. In this case, the AI ​​assistant can be activated even if the smartphone is offline. For example, if the user asks, "What does this word mean?", the AI ​​assistant will explain the meaning of the word. Next, the AI ​​assistant pre-scans the contents of the book and references, and the AI ​​learns from them. As a result, the AI ​​assistant can provide context-based answers. For example, if a particular term is defined based on prior research, it can understand that definition and then answer. Furthermore, the AI ​​assistant remembers the user's reading history and past questions, adjusting the conversation level to suit each user's level of understanding. For example, it can explain things in simple terms for beginners and provide more detailed explanations for experts. It can also provide relevant information based on questions the user has asked in the past. In addition to voice, the AI ​​assistant can also display supplementary images when explaining complex concepts, enabling multimodal functionality. For example, when explaining specialized topics or models, it can display diagrams and graphs to aid user understanding. This system allows for real-time resolution of questions during reading, improving the user's learning experience. For example, it can provide an efficient learning method for curious working professionals and highly motivated undergraduate and graduate students. Moreover, the AI ​​assistant can appropriately explain terms and concepts that are difficult to understand in context, deepening comprehension.Furthermore, because it can be used offline, it can be utilized even in locations without an internet connection. This allows the voice-activated AI assistant system to resolve questions in real time while reading, improving the user's learning experience.

[0073] The voice-interactive AI assistant system according to this embodiment comprises a reception unit, a generation unit, a judgment unit, and a display unit. The reception unit receives questions from the user. For example, if the user asks, "What does this word mean?", the reception unit can receive the question. The generation unit generates an answer to the question received by the reception unit. For example, the generation unit generates an answer to the question using AI. The generation unit can generate an appropriate answer to the question using, for example, natural language processing technology. The judgment unit determines the user's level of understanding based on the answer generated by the generation unit. For example, the judgment unit can evaluate the user's reaction to the answer and their level of understanding. The judgment unit can determine the user's level of understanding based, for example, the correct answer rate of a quiz or the accuracy of the answer. The display unit displays supplementary images based on the level of understanding determined by the judgment unit. For example, the display unit can display supplementary images when explaining complex concepts. The display unit can display supplementary images such as diagrams, charts, or photographs. As a result, the voice-interactive AI assistant system according to this embodiment can resolve questions while reading by providing real-time answers to user questions and displaying supplementary information according to the user's level of understanding.

[0074] The reception desk receives questions from users. For example, if a user asks, "What does this word mean?", the reception desk can receive that question. Specifically, the reception desk uses speech recognition technology to convert the user's utterance into text data. The speech recognition technology uses an algorithm that analyzes the user's utterance in real time and converts the audio signal into text. This ensures that the user's question is accurately captured as text data. Furthermore, the reception desk uses natural language processing technology to analyze the text data in order to understand the intent of the user's utterance. For example, if a user asks, "What does this word mean?", the reception desk understands the user's intent to "find out the meaning of the word" and prepares to generate an appropriate answer. The reception desk can also collect information to provide a more appropriate answer by considering the context of the user's utterance and past dialogue history. This allows the reception desk to accurately receive the user's question and smoothly pass it on to the next processing step.

[0075] The generation unit generates answers to questions received by the reception unit. For example, the generation unit uses AI to generate answers to questions. The generation unit can generate appropriate answers to questions using, for example, natural language processing technology. Specifically, the generation unit uses a large-scale pre-trained language model to generate answers to user questions. This language model is trained on a large amount of text data and has the ability to generate appropriate answers to a variety of questions. For example, if a user asks, "What does this word mean?", the generation unit searches text data containing the meaning of that word and generates an appropriate answer. Furthermore, the generation unit can provide more specific and detailed answers by considering the context and intent of the user's question. For example, if a user asks, "What does this word mean?", and the word is used in a specific context, it can provide a meaning appropriate to that context. In addition, the generation unit can provide more personalized answers by considering the user's past question history and dialogue history. As a result, the generation unit can generate quick and appropriate answers to user questions and resolve user doubts.

[0076] The decision unit determines the user's level of understanding based on the answers generated by the generation unit. For example, the decision unit can evaluate the user's reaction to and understanding of the answers. Specifically, the decision unit monitors the user's reaction in real time and evaluates how well the user understands the generated answers. For example, if the user responds to the generated answer with "I understand" or "Tell me more," the decision unit analyzes the reaction and evaluates the user's level of understanding. The decision unit can also evaluate the user's level of understanding using quiz-style questions. For example, based on the answers provided by the generation unit, it can ask the user a related quiz and determine the user's level of understanding based on the accuracy of the answers and the rate of correct responses. Furthermore, the decision unit can analyze nonverbal information such as the user's tone of voice, facial expressions, and gestures to comprehensively evaluate the user's level of understanding. This allows the decision unit to accurately evaluate the user's level of understanding and appropriately adjust the information and supplementary explanations provided in the next step.

[0077] The display unit shows supplementary images based on the level of understanding determined by the judgment unit. For example, the display unit can display supplementary images when explaining complex concepts. Specifically, the display unit displays supplementary images such as diagrams, charts, and photographs according to the user's level of understanding. For example, if a user asks, "What does this word mean?" and the word is related to a scientific concept, the display unit can display a diagram or chart to visually explain that concept. Furthermore, if the display unit determines that the user's level of understanding is low, it can display more detailed supplementary images to help the user understand. For example, if a user asks, "What does this word mean?" and the word is a complex technical term, the display unit can display a detailed diagram or photograph to visually explain the meaning of that term. In addition, the display unit can dynamically adjust the content and format of the supplementary images it displays based on the user's reactions and feedback. This allows the display unit to provide appropriate supplementary information according to the user's level of understanding and resolve the user's questions.

[0078] The scanning unit can scan the contents of a book. For example, the scanning unit can scan the contents of a book using a scanner and save it as digital data. For example, the scanning unit can scan the contents of a book using a high-resolution scanner. The scanning unit can also adjust the scanning speed. For example, the scanning unit can set the scanning speed to high to complete the scan in a short time. Furthermore, the scanning unit can select the corresponding format. For example, the scanning unit can save the scanned data in formats such as PDF, image, or text. This allows the scanning unit to pre-scan the contents of a book, enabling the AI ​​to provide context-based answers. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input the scanned data into the AI, which can analyze the data and understand the context.

[0079] The learning unit can learn from references. For example, the learning unit can learn from references such as academic papers, books, and websites. For example, the learning unit can use AI to analyze references and understand the context. For example, the learning unit can use natural language processing technology to analyze the content of references and generate context-based answers. For example, the learning unit can extract relevant keywords and phrases and generate answers based on them. In this way, by learning from references, the learning unit can provide more detailed and accurate answers. Some or all of the above processing in the learning unit may be performed using generative AI, or it may be performed without generative AI. For example, the learning unit can input reference data into a generative AI, which can analyze the data and understand the context.

[0080] The memory unit can store the user's reading history. For example, the memory unit can store information such as the title of the book the user has read, the date it was completed, the reading time, and the number of pages read. For example, the memory unit can record the user's reading history using a database. For example, the memory unit can analyze the content of the book the user has read and store the relevant information. As a result, by storing the user's reading history, the memory unit can provide answers tailored to each individual user. Some or all of the above processing in the memory unit may be performed using AI or not. For example, the memory unit can input the user's reading history data into AI, and the AI ​​can analyze the data and store the relevant information.

[0081] The question memory unit can store past questions. For example, the question memory unit can store the content of questions the user has asked in the past, the date and time of the questions, and the content of the answers. For example, the question memory unit can record past questions using a database. For example, the question memory unit can analyze the content of questions the user has asked in the past and store related information. This makes it easier for the question memory unit to provide related information by storing past questions. Some or all of the above processing in the question memory unit may be performed using AI or not. For example, the question memory unit can input past question data into AI, and the AI ​​can analyze the data and store related information.

[0082] The generation unit can generate context-based answers. The generation unit can generate context-based answers using, for example, AI. The generation unit can analyze the context using, for example, natural language processing technology and generate appropriate answers. The generation unit can extract relevant keywords or phrases and generate answers based on them. In this way, the generation unit can provide more appropriate answers by generating context-based answers. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input context data into a generation AI, and the generation AI can analyze the data and generate an answer.

[0083] The decision unit can adjust the level of conversation based on the user's level of understanding. For example, the decision unit can use AI to evaluate the user's level of understanding and adjust the level of conversation. For example, the decision unit can determine the user's level of understanding based on the correct answer rate or accuracy of answers in quizzes and adjust the level of conversation. For example, the decision unit can explain things in simple terms to beginners and provide more detailed explanations to experts. In this way, the decision unit can support more effective learning by adjusting the level of conversation according to the user's level of understanding. Some or all of the above processing in the decision unit may be performed using generative AI or not. For example, the decision unit can input user understanding data into generative AI, and the generative AI can analyze the data and adjust the level of conversation.

[0084] The display unit can display supplementary images when explaining complex concepts. For example, the display unit can display supplementary images such as diagrams, charts, and photographs. The display unit can, for example, use AI to select and display appropriate supplementary images. For example, the display unit can help users understand complex concepts by displaying relevant supplementary images. Thus, the display unit can help users understand by displaying supplementary images. Some or all of the above processing in the display unit may be performed using AI, or without AI. For example, the display unit can input supplementary image data into AI, which can analyze the data, select appropriate supplementary images, and display them.

[0085] The reception unit can estimate the user's emotions and adjust the timing of question reception based on the estimated user emotions. The reception unit can estimate the user's emotions and adjust the timing of question reception, for example, using AI. The reception unit can estimate the user's emotions and adjust the timing of question reception, for example, using facial recognition technology. The reception unit can estimate the user's emotions and adjust the timing of question reception, for example, using voice analysis technology. The reception unit can estimate the user's emotions and adjust the timing of question reception, for example, using text analysis technology. In this way, the reception unit can achieve smoother interaction by adjusting the timing of question reception 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 unit may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generating AI, which can then analyze the data and adjust the timing of question reception.

[0086] The reception desk can analyze a user's past question history and select the optimal reception method. For example, the reception desk can use AI to analyze a user's past question history and select the optimal reception method. For example, the reception desk can prioritize receiving questions that the user has frequently asked in the past. For example, the reception desk can prioritize suggesting question formats (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict questions to be asked at a specific time based on the user's past question history and receive them. In this way, the reception desk can provide the user with the optimal reception method by analyzing past question history. 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 past question history data into AI, and the AI ​​can analyze the data and select the optimal reception method.

[0087] The reception unit can filter questions based on the user's current reading status and areas of interest when receiving them. For example, the reception unit can use AI to analyze the user's current reading status and areas of interest and filter the questions. For example, the reception unit can prioritize receiving questions related to the content of the book the user is currently reading. For example, the reception unit can filter and receive relevant questions based on the user's areas of interest. For example, the reception unit can filter and receive questions based on topics the user has shown interest in in the past. In this way, the reception unit can prioritize receiving highly relevant questions by filtering them based on the user's reading status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's reading status data and areas of interest data into AI, and the AI ​​can analyze the data and filter the questions.

[0088] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated emotions. The reception unit can, for example, use AI to estimate the user's emotions and determine the priority of questions. The reception unit can, for example, use facial recognition technology to estimate the user's emotions and determine the priority of questions. The reception unit can, for example, use voice analysis technology to estimate the user's emotions and determine the priority of questions. The reception unit can, for example, use text analysis technology to estimate the user's emotions and determine the priority of questions. In this way, the reception unit can prioritize receiving more appropriate questions by determining the priority of questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI, and the generative AI can analyze the data to determine the priority of questions.

[0089] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location information. For example, the reception desk can use AI to analyze the user's geographical location information and prioritize receiving highly relevant questions. For example, if the user is in a specific region, the reception desk can prioritize receiving questions related to that region. For example, if the user is traveling, the reception desk can prioritize receiving questions related to their travel destination. For example, if the user is at home, the reception desk can prioritize receiving questions related to their home. In this way, the reception desk can prioritize receiving highly relevant questions by taking into account the user's 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 can input the user's geographical location information data into AI, and the AI ​​can analyze the data and prioritize receiving highly relevant questions.

[0090] The reception desk can analyze the user's social media activity when receiving a question and accept relevant questions. For example, the reception desk can use AI to analyze the user's social media activity and accept relevant questions. For example, the reception desk can accept relevant questions based on what the user has shared on social media. For example, the reception desk can accept questions based on topics the user follows on social media. For example, the reception desk can accept relevant questions based on groups the user participates in on social media. This allows the reception desk to prioritize accepting relevant questions by analyzing the user's 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 activity data into AI, which can analyze the data and accept relevant questions.

[0091] The generation unit can estimate the user's emotions and adjust the way the response is expressed based on the estimated user's emotions. The generation unit can, for example, use AI to estimate the user's emotions and adjust the way the response is expressed. The generation unit can, for example, use facial recognition technology to estimate the user's emotions and adjust the way the response is expressed. The generation unit can, for example, use speech analysis technology to estimate the user's emotions and adjust the way the response is expressed. The generation unit can, for example, use text analysis technology to estimate the user's emotions and adjust the way the response is expressed. As a result, the generation unit can provide a more appropriate response by adjusting the way the response is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processes in the generation unit may be performed using a generation AI or not using a generation AI. For example, the generation unit can input user emotion data into the generation AI, which can then analyze the data and adjust how the response is expressed.

[0092] The generation unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the generation unit can use AI to evaluate the importance of the question and adjust the level of detail in the answer. For example, the generation unit can evaluate the importance of the question based on the content of the question and the user's level of interest and adjust the level of detail in the answer. For example, the generation unit can generate detailed answers for important questions. For example, the generation unit can generate concise answers for general questions. For example, the generation unit can generate answers with adjusted levels of detail according to the user's level of understanding. In this way, the generation unit can provide more appropriate answers by adjusting the level of detail in the answer based on the importance of the question. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input question importance data into AI, and the AI ​​can analyze the data and adjust the level of detail in the answer.

[0093] The generation unit can apply different answer algorithms depending on the question category when generating answers. For example, the generation unit can use AI to analyze the question category and apply an appropriate answer algorithm. For example, for scientific questions, the generation unit can apply a specialized algorithm to generate an answer. For example, for historical questions, the generation unit can refer to a historical database to generate an answer. For example, for technical questions, the generation unit can generate an answer based on technical literature. In this way, the generation unit can provide more appropriate answers by applying different answer algorithms depending on the question category. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input question category data into a generation AI, and the generation AI can analyze the data and apply an appropriate answer algorithm.

[0094] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. The generation unit can estimate the user's emotions and adjust the length of the response using, for example, AI. The generation unit can estimate the user's emotions and adjust the length of the response using, for example, facial recognition technology. The generation unit can estimate the user's emotions and adjust the length of the response using, for example, speech analysis technology. The generation unit can estimate the user's emotions and adjust the length of the response using, for example, text analysis technology. In this way, the generation unit can provide a more appropriate response by adjusting the length of the response according to 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI, and the generation AI can analyze the data and adjust the length of the response.

[0095] The generation unit can determine the priority of answers based on when the questions were submitted when generating answers. For example, the generation unit can use AI to analyze when the questions were submitted and determine the priority of the answers. For example, the generation unit can prioritize answers to recently submitted questions. For example, the generation unit can prioritize generating answers to questions that have remained unanswered for a long time. For example, the generation unit can provide answers at the optimal time based on the user's schedule. In this way, the generation unit can provide answers at a more appropriate time by determining the priority of answers based on when the questions were submitted. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input question submission data into AI, and the AI ​​can analyze the data to determine the priority of the answers.

[0096] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the generation unit can use AI to analyze the relevance of the questions and adjust the order of the answers. For example, the generation unit can prioritize answers that are highly relevant to the questions. For example, the generation unit can postpone answers that are less relevant to the questions. For example, the generation unit can generate answers in order of relevance based on the category of the questions. In this way, the generation unit can prioritize providing more relevant answers by adjusting the order of answers based on the relevance of the questions. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input question relevance data into AI, and the AI ​​can analyze the data and adjust the order of the answers.

[0097] The decision unit can estimate the user's emotions and adjust the comprehension criteria based on the estimated user emotions. The decision unit can, for example, use AI to estimate the user's emotions and adjust the comprehension criteria. The decision unit can, for example, use facial recognition technology to estimate the user's emotions and adjust the comprehension criteria. The decision unit can, for example, use speech analysis technology to estimate the user's emotions and adjust the comprehension criteria. The decision unit can, for example, use text analysis technology to estimate the user's emotions and adjust the comprehension criteria. As a result, the decision unit can make a more appropriate comprehension judgment by adjusting the comprehension criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the decision unit may be performed using AI or not using AI. For example, the decision-making unit can input user emotion data into a generating AI, which can then analyze the data and adjust the criteria for determining the level of understanding.

[0098] The judgment unit can improve the accuracy of its judgment by referring to the user's past comprehension data when determining comprehension. The judgment unit can improve the accuracy of its judgment by, for example, using AI to analyze the user's past comprehension data. The judgment unit can determine the current comprehension level based on, for example, the user's past quiz results and learning history. The judgment unit can improve the accuracy of its comprehension judgment by, for example, analyzing the user's past comprehension data. The judgment unit can make the optimal comprehension judgment by referring to the user's past comprehension data. In this way, the judgment unit can improve the accuracy of its comprehension judgment by referring to past comprehension data. Some or all of the above processing in the judgment unit may be performed using AI or not using AI. For example, the judgment unit can input the user's past comprehension data into AI, and the AI ​​can analyze the data to improve the accuracy of its judgment.

[0099] The judgment unit can make a judgment by considering the user's attribute information when determining the level of understanding. For example, the judgment unit can use AI to analyze the user's attribute information and make a judgment on the level of understanding. For example, the judgment unit can make a judgment on the level of understanding based on the user's age. For example, the judgment unit can make a judgment on the level of understanding based on the user's occupation. For example, the judgment unit can make a judgment on the level of understanding based on the user's educational background. In this way, the judgment unit can make a more appropriate judgment on the level of understanding by considering the user's attribute information. Some or all of the above processing in the judgment unit may be performed using AI or not using AI. For example, the judgment unit can input user attribute information data into AI, and the AI ​​can analyze the data and make a judgment on the level of understanding.

[0100] The decision unit can estimate the user's emotions and adjust the display method of the level of understanding based on the estimated user emotions. The decision unit can, for example, use AI to estimate the user's emotions and adjust the display method of the level of understanding. The decision unit can, for example, use facial recognition technology to estimate the user's emotions and adjust the display method of the level of understanding. The decision unit can, for example, use voice analysis technology to estimate the user's emotions and adjust the display method of the level of understanding. The decision unit can, for example, use text analysis technology to estimate the user's emotions and adjust the display method of the level of understanding. As a result, the decision unit can provide a more appropriate display by adjusting the display method of the level of understanding according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the decision unit may be performed using AI or not using AI. For example, the decision-making unit can input user emotion data into a generating AI, which can then analyze the data and adjust how the level of understanding is displayed.

[0101] The decision-making unit can make decisions regarding the level of understanding by considering the geographical distribution of users. For example, the decision-making unit can use AI to analyze the geographical distribution of users and make decisions regarding the level of understanding. For example, if a user is in a specific region, the decision-making unit can make decisions regarding the level of understanding by considering the characteristics of that region. For example, if a user is traveling, the decision-making unit can make decisions regarding the level of understanding by considering the characteristics of the travel destination. For example, if a user is at home, the decision-making unit can make decisions regarding the level of understanding by considering the geographical distribution of users. In this way, the decision-making unit can make more appropriate decisions regarding the level of understanding by considering the geographical distribution of users. Some or all of the above processing in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input the geographical distribution data of users into AI, and the AI ​​can analyze the data and make decisions regarding the level of understanding.

[0102] The judgment unit can improve the accuracy of its judgment by referring to relevant literature when determining the level of understanding. The judgment unit can improve the accuracy of its judgment by, for example, using AI to analyze relevant literature. The judgment unit can improve the accuracy of its judgment by, for example, referring to relevant literature. The judgment unit can make the optimal judgment of the level of understanding based on relevant literature. The judgment unit can improve the accuracy of its judgment by, for example, analyzing relevant literature. In this way, the judgment unit can improve the accuracy of its judgment of the level of understanding by referring to relevant literature. Some or all of the above processing in the judgment unit may be performed using AI or not using AI. For example, the judgment unit can input relevant literature data into AI, and the AI ​​can analyze the data to improve the accuracy of its judgment.

[0103] The display unit can estimate the user's emotions and adjust the display method of supplementary images based on the estimated user emotions. The display unit can estimate the user's emotions using, for example, AI and adjust the display method of supplementary images. The display unit can estimate the user's emotions using, for example, facial expression recognition technology and adjust the display method of supplementary images. The display unit can estimate the user's emotions using, for example, voice analysis technology and adjust the display method of supplementary images. The display unit can estimate the user's emotions using, for example, text analysis technology and adjust the display method of supplementary images. As a result, the display unit can provide a more appropriate display by adjusting the display method of supplementary images according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI or not using AI. For example, the display unit can input user emotion data into a generating AI, which can then analyze the data and adjust how supplementary images are displayed.

[0104] The display unit can select the optimal display method when displaying supplemental images by referring to the user's past viewing history. The display unit can, for example, use AI to analyze the user's past viewing history and select the optimal display method. The display unit can, for example, display the optimal supplemental image based on the user's past viewing history. The display unit can, for example, analyze the user's past viewing history and select the optimal display method. The display unit can, for example, refer to the user's past viewing history to display the optimal supplemental image. In this way, the display unit can display the optimal supplemental image by referring to the past viewing history. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the user's past viewing history data into AI, and the AI ​​can analyze the data to select the optimal display method.

[0105] The display unit can display supplementary images while considering the user's attribute information. For example, the display unit can use AI to analyze the user's attribute information and display supplementary images. For example, the display unit can display appropriate supplementary images based on the user's age. For example, the display unit can display relevant supplementary images based on the user's occupation. For example, the display unit can display supplementary images that are easy to understand based on the user's educational background. In this way, the display unit can display more appropriate supplementary images by considering the user's attribute information. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input user attribute information data into AI, and the AI ​​can analyze the data and display supplementary images.

[0106] The display unit can estimate the user's emotions and adjust the display order of supplementary images based on the estimated user emotions. The display unit can estimate the user's emotions using, for example, AI and adjust the display order of supplementary images. The display unit can estimate the user's emotions using, for example, facial expression recognition technology and adjust the display order of supplementary images. The display unit can estimate the user's emotions using, for example, voice analysis technology and adjust the display order of supplementary images. The display unit can estimate the user's emotions using, for example, text analysis technology and adjust the display order of supplementary images. As a result, the display unit can provide a more appropriate display by adjusting the display order of supplementary images according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI or not using AI. For example, the display unit can input user emotion data into a generating AI, which can then analyze the data and adjust the display order of supplementary images.

[0107] The display unit can select the optimal display method when displaying supplementary images, taking into account the user's geographical location information. For example, the display unit can use AI to analyze the user's geographical location information and select the optimal display method. For example, if the user is in a specific region, the display unit can display supplementary images related to that region. For example, if the user is traveling, the display unit can display supplementary images related to the travel destination. For example, if the user is at home, the display unit can display supplementary images related to the home. In this way, the display unit can display the optimal supplementary image by taking into account the user's geographical location information. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the user's geographical location information data into AI, and the AI ​​can analyze the data and select the optimal display method.

[0108] The display unit can improve the accuracy of the display by referring to related literature when displaying supplementary images. The display unit can improve the accuracy of the display by, for example, using AI to analyze related literature. The display unit can improve the accuracy of the supplementary image by, for example, referring to related literature. The display unit can display the optimal supplementary image based on related literature. The display unit can improve the accuracy of the supplementary image by, for example, analyzing related literature. In this way, the display unit can improve the accuracy of the supplementary image by referring to related literature. Some or all of the above processing in the display unit may be performed using AI or not using AI. For example, the display unit can input related literature data into AI, and the AI ​​can analyze the data to improve the accuracy of the display.

[0109] The scanning unit can estimate the user's emotions and adjust the scanning timing based on the estimated emotions. For example, the scanning unit can use AI to estimate the user's emotions and adjust the scanning timing. For example, the scanning unit can use facial recognition technology to estimate the user's emotions and adjust the scanning timing. For example, the scanning unit can use voice analysis technology to estimate the user's emotions and adjust the scanning timing. For example, the scanning unit can use text analysis technology to estimate the user's emotions and adjust the scanning timing. This allows the scanning unit to perform more appropriate scans by adjusting the scanning timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, 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 scanning unit may be performed using AI or not. For example, the scanning unit can input user emotion data into the generative AI, which can analyze the data and adjust the scanning timing.

[0110] The scanning unit can select the optimal scanning method by referring to the user's past scanning history during scanning. The scanning unit can, for example, use AI to analyze the user's past scanning history and select the optimal scanning method. The scanning unit can, for example, select the optimal scanning method based on the user's past scanning history. The scanning unit can, for example, analyze the user's past scanning history and select the optimal scanning method. The scanning unit can, for example, refer to the user's past scanning history to select the optimal scanning method. In this way, the scanning unit can select the optimal scanning method by referring to past scanning history. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input the user's past scanning history data into AI, and the AI ​​can analyze the data to select the optimal scanning method.

[0111] The scanning unit can estimate the user's emotions and determine the scanning priority based on the estimated emotions. For example, the scanning unit can use AI to estimate the user's emotions and determine the scanning priority. For example, the scanning unit can use facial recognition technology to estimate the user's emotions and determine the scanning priority. For example, the scanning unit can use speech analysis technology to estimate the user's emotions and determine the scanning priority. For example, the scanning unit can use text analysis technology to estimate the user's emotions and determine the scanning priority. This allows the scanning unit to perform more appropriate scans by determining the scanning priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the scanning unit may be performed using AI or not. For example, the scanning unit can input user emotion data into a generative AI, which can then analyze the data and determine the scanning priority.

[0112] The scanning unit can select the optimal scanning method by considering the user's geographical location information during scanning. For example, the scanning unit can use AI to analyze the user's geographical location information and select the optimal scanning method. For example, if the user is in a specific region, the scanning unit can prioritize scanning items related to that region. For example, if the user is traveling, the scanning unit can prioritize scanning items related to the travel destination. For example, if the user is at home, the scanning unit can prioritize scanning items related to the home. In this way, the scanning unit can select the optimal scanning method by considering the user's geographical location information. Some or all of the above processing in the scanning unit may be performed using AI or not. For example, the scanning unit can input the user's geographical location data into AI, and the AI ​​can analyze the data to select the optimal scanning method.

[0113] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, the learning unit can use AI to estimate the user's emotions and select training data. For example, the learning unit can use facial recognition technology to estimate the user's emotions and select training data. For example, the learning unit can use speech analysis technology to estimate the user's emotions and select training data. For example, the learning unit can use text analysis technology to estimate the user's emotions and select training data. This allows the learning unit to perform more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the learning unit may be performed using AI or not using AI. For example, the learning unit can input user emotion data into a generative AI, and the generative AI can analyze the data and select training data.

[0114] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can use AI to analyze past learning data and optimize the learning algorithm. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and optimize the learning algorithm. For example, the learning unit can select the optimal learning algorithm by referring to past learning data. Thus, the learning unit can optimize the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI or without AI. For example, the learning unit can input past learning data into AI, which can then analyze the data and optimize the learning algorithm.

[0115] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user emotions. The learning unit can, for example, use AI to estimate the user's emotions and adjust the learning frequency. The learning unit can, for example, use facial recognition technology to estimate the user's emotions and adjust the learning frequency. The learning unit can, for example, use speech analysis technology to estimate the user's emotions and adjust the learning frequency. The learning unit can, for example, use text analysis technology to estimate the user's emotions and adjust the learning frequency. This allows the learning unit to perform more appropriate learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using AI or not using AI. For example, the learning unit can input user emotion data into a generative AI, and the generative AI can analyze the data and adjust the learning frequency.

[0116] The learning unit can weight the training data based on the submission timing of the scan data during training. For example, the learning unit can use AI to analyze the submission timing of the scan data and weight the training data. For example, the learning unit can prioritize training on recently submitted scan data. For example, the learning unit can weight scan data that has not been trained for a long period of time. For example, the learning unit can weight the training data based on the submission timing of the scan data. This allows the learning unit to perform more appropriate training by weighting the training data based on the submission timing of the scan data. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input scan data submission timing data into AI, and the AI ​​can analyze the data and weight the training data.

[0117] The memory unit can estimate the user's emotions and select memory data based on the estimated user emotions. The memory unit can estimate the user's emotions and select memory data using, for example, AI. The memory unit can estimate the user's emotions and select memory data using, for example, facial recognition technology. The memory unit can estimate the user's emotions and select memory data using, for example, voice analysis technology. The memory unit can estimate the user's emotions and select memory data using, for example, text analysis technology. As a result, the memory unit can perform more appropriate memorization by selecting memory data according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the memory unit may be performed using AI or not using AI. For example, the memory unit can input user emotion data into a generative AI, and the generative AI can analyze the data and select memory data.

[0118] The memory unit can optimize its memory algorithm by referring to past memory data during memory storage. For example, the memory unit can use AI to analyze past memory data and optimize the memory algorithm. For example, the memory unit can select the optimal memory algorithm based on past memory data. For example, the memory unit can analyze past memory data and optimize the memory algorithm. For example, the memory unit can select the optimal memory algorithm by referring to past memory data. Thus, the memory unit can optimize its memory algorithm by referring to past memory data. Some or all of the above processing in the memory unit may be performed using AI or without AI. For example, the memory unit can input past memory data into AI, which can then analyze the data and optimize the memory algorithm.

[0119] The memory unit can estimate the user's emotions and adjust the frequency of memory based on the estimated user emotions. The memory unit can estimate the user's emotions and adjust the frequency of memory using, for example, AI. The memory unit can estimate the user's emotions and adjust the frequency of memory using, for example, facial recognition technology. The memory unit can estimate the user's emotions and adjust the frequency of memory using, for example, voice analysis technology. The memory unit can estimate the user's emotions and adjust the frequency of memory using, for example, text analysis technology. In this way, the memory unit can perform more appropriate memory by adjusting the frequency of memory according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the memory unit may be performed using AI or not using AI. For example, the memory unit can input user emotion data into a generative AI, and the generative AI can analyze the data and adjust the frequency of memory.

[0120] The memory unit can weight the stored data based on when the reading history was submitted. For example, the memory unit can use AI to analyze the submission timing of the reading history and weight the stored data. For example, the memory unit can prioritize storing recently submitted reading history entries. For example, the memory unit can weight reading history entries that have not been stored for a long period of time. For example, the memory unit can weight the stored data based on when the reading history was submitted. This allows the memory unit to store data more appropriately by weighting it based on when the reading history was submitted. Some or all of the above processing in the memory unit may be performed using AI or not. For example, the memory unit can input reading history submission timing data into AI, which can then analyze the data and weight the stored data.

[0121] The question memory unit can estimate the user's emotions and select question memory data based on the estimated user emotions. For example, the question memory unit can use AI to estimate the user's emotions and select question memory data. For example, the question memory unit can use facial recognition technology to estimate the user's emotions and select question memory data. For example, the question memory unit can use speech analysis technology to estimate the user's emotions and select question memory data. For example, the question memory unit can use text analysis technology to estimate the user's emotions and select question memory data. As a result, the question memory unit can perform more appropriate memorization by selecting question memory data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question memory unit may be performed using AI or not using AI. For example, the question memory unit can input user emotion data into a generating AI, which can then analyze the data and select question memory data.

[0122] The question memory unit can optimize its memory algorithm by referring to past question memory data when storing questions. The question memory unit can optimize its memory algorithm by, for example, using AI to analyze past question memory data. The question memory unit can select the optimal memory algorithm based on past question memory data. The question memory unit can optimize its memory algorithm by, for example, analyzing past question memory data. The question memory unit can select the optimal memory algorithm by referring to past question memory data. In this way, the question memory unit can optimize its memory algorithm by referring to past question memory data. Some or all of the above processing in the question memory unit may be performed using AI or not. For example, the question memory unit can input past question memory data into AI, and the AI ​​can analyze the data to optimize the memory algorithm.

[0123] The question memory unit can estimate the user's emotions and adjust the frequency of question recall based on the estimated user emotions. The question memory unit can estimate the user's emotions and adjust the frequency of question recall using, for example, AI. The question memory unit can estimate the user's emotions and adjust the frequency of question recall using, for example, facial recognition technology. The question memory unit can estimate the user's emotions and adjust the frequency of question recall using, for example, speech analysis technology. The question memory unit can estimate the user's emotions and adjust the frequency of question recall using, for example, text analysis technology. As a result, the question memory unit can perform more appropriate recall by adjusting the frequency of question recall according 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 question memory unit may be performed using AI or not using AI. For example, the question memory unit can input user emotion data into a generating AI, which can then analyze the data and adjust the frequency of question memory.

[0124] The question memory unit can weight the stored data based on when the questions were submitted. For example, the question memory unit can use AI to analyze the submission timing of questions and weight the stored data. For example, the question memory unit can prioritize storing recently submitted questions. For example, the question memory unit can weight questions that have not been stored for a long period of time. For example, the question memory unit can weight the stored data based on when the questions were submitted. This allows the question memory unit to store more appropriate information by weighting the stored data based on when the questions were submitted. Some or all of the above processing in the question memory unit may be performed using AI or not. For example, the question memory unit can input question submission timing data into AI, and the AI ​​can analyze the data and weight the stored data.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] Voice-activated AI assistant systems can estimate a user's reading speed and adjust the timing of responses based on that estimate. For example, if a user is speed-reading, the AI ​​assistant can provide answers quickly. Conversely, if a user is reading slowly, the response can be delayed. It is also possible to adjust the level of detail in the responses according to the reading speed. For example, a concise answer can be provided to a speed-reading user, while a detailed explanation can be given to a slow-reading user. This can make the user's reading experience smoother and support more efficient learning.

[0127] Voice-activated AI assistant systems can detect a user's reading posture and provide advice to maintain proper posture. For example, if a user is reading in the same position for an extended period, the AI ​​assistant can prompt them to change their posture. It can also display images or videos demonstrating correct posture if the user is reading in an inappropriate position. Furthermore, it can record the user's posture data and compare it to past data to support posture improvement. This allows for a comfortable reading experience while maintaining the user's health.

[0128] Voice-activated AI assistant systems can estimate a user's level of concentration while reading and adjust the frequency of interaction accordingly. For example, if a user is highly focused, the AI ​​assistant can ask fewer questions and offer fewer suggestions. Conversely, if a user is not focused, the AI ​​assistant can actively ask questions and provide interesting information. It can also suggest short breaks if it detects a decline in concentration. This helps maintain the user's focus and supports effective learning.

[0129] A voice-activated AI assistant system can estimate a user's emotions while reading and evaluate their reading progress based on those emotions. For example, if a user is feeling stressed while reading, the AI ​​assistant can suggest slowing down their reading pace. Conversely, if the user is enjoying reading, it can support them in maintaining their pace. Furthermore, based on emotional data, it can identify sections the user is particularly interested in or finds difficult to understand, and provide appropriate advice. This allows for a more personalized reading experience and supports more effective learning.

[0130] A voice-activated AI assistant system can analyze the ambient sounds a user is listening to while reading and provide advice to create an appropriate reading environment. For example, if the surroundings are noisy, the AI ​​assistant can suggest moving to a quieter location. It can also play appropriate ambient sounds (such as nature sounds or classical music) to enhance concentration. Furthermore, it can record ambient sound data and provide feedback by comparing it with past data to offer the optimal reading environment. This allows users to enjoy reading in a more comfortable environment and improve their learning effectiveness.

[0131] A voice-activated AI assistant system can track a user's gaze while reading and evaluate their reading progress based on the eye-tracking data. For example, if a user lingers on a particular page or paragraph for an extended period, the AI ​​assistant can determine that the section is difficult and provide additional explanations. It can also identify areas of particular interest to the user based on eye-tracking data and provide relevant information. Furthermore, it can record eye-tracking data, compare it to past data to evaluate reading progress, and offer appropriate advice. This allows for a more personalized reading experience and supports effective learning.

[0132] A voice-activated AI assistant system can estimate a user's emotions while reading and provide support to maintain their reading motivation based on those emotions. For example, if a user is feeling tired while reading, the AI ​​assistant can suggest a short break. Conversely, if the user is highly motivated, it can encourage them to continue reading. Furthermore, based on emotional data, it can provide information related to topics the user is particularly interested in, thereby increasing their reading motivation. This can enrich the user's reading experience and support more effective learning.

[0133] A voice-activated AI assistant system can estimate a user's emotions while reading and evaluate their reading progress based on those emotions. For example, if a user is feeling stressed while reading, the AI ​​assistant can suggest slowing down their reading pace. Conversely, if the user is enjoying reading, it can support them in maintaining their pace. Furthermore, based on emotional data, it can identify sections the user is particularly interested in or finds difficult to understand, and provide appropriate advice. This allows for a more personalized reading experience and supports more effective learning.

[0134] A voice-activated AI assistant system can estimate a user's emotions while reading and evaluate their reading progress based on those emotions. For example, if a user is feeling stressed while reading, the AI ​​assistant can suggest slowing down their reading pace. Conversely, if the user is enjoying reading, it can support them in maintaining their pace. Furthermore, based on emotional data, it can identify sections the user is particularly interested in or finds difficult to understand, and provide appropriate advice. This allows for a more personalized reading experience and supports more effective learning.

[0135] A voice-activated AI assistant system can estimate a user's emotions while reading and evaluate their reading progress based on those emotions. For example, if a user is feeling stressed while reading, the AI ​​assistant can suggest slowing down their reading pace. Conversely, if the user is enjoying reading, it can support them in maintaining their pace. Furthermore, based on emotional data, it can identify sections the user is particularly interested in or finds difficult to understand, and provide appropriate advice. This allows for a more personalized reading experience and supports more effective learning.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The reception desk receives questions from users. For example, if a user asks, "What does this word mean?", the reception desk can receive that question. Step 2: The generation unit generates answers to the questions received by the reception unit. For example, the generation unit uses AI to generate answers to questions. The generation unit can, for example, use natural language processing technology to generate appropriate answers to questions. Step 3: The judgment unit determines the user's level of understanding based on the answers generated by the generation unit. For example, the judgment unit can evaluate the user's reaction to and understanding of the answers. For example, the judgment unit can determine the user's level of understanding based on the quiz's correct answer rate and the accuracy of the answers. Step 4: The display unit displays supplementary images based on the level of understanding determined by the judgment unit. For example, the display unit can display supplementary images when explaining complex concepts. The display unit can display supplementary images such as diagrams, charts, or photographs.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the reception unit, generation unit, judgment unit, display unit, scanning unit, learning unit, memory unit, question memory unit, and emotion estimation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives questions from the user. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates answers to questions. The judgment unit is implemented by the identification processing unit 290 of the data processing device 12 and determines the user's level of understanding. The display unit is implemented by the display 40A of the smart device 14 and displays supplementary images. The scanning unit is implemented by the camera 42 of the smart device 14 and scans the contents of a book. The learning unit is implemented by the identification processing unit 290 of the data processing device 12 and learns references. The memory unit is implemented by the database 24 of the data processing device 12 and stores the user's reading history. The question storage unit is implemented, for example, by the database 24 of the data processing device 12, and stores past questions. The emotion estimation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and estimates the user's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the reception unit, generation unit, judgment unit, display unit, scanning unit, learning unit, memory unit, question memory unit, and emotion estimation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives questions from the user. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates answers to questions. The judgment unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines the user's level of understanding. The display unit is implemented by the display of the smart glasses 214 and displays supplementary images. The scanning unit is implemented by the camera 42 of the smart glasses 214 and scans the contents of a book. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12 and learns references. The memory unit is implemented by the database 24 of the data processing unit 12 and stores the user's reading history. The question storage unit is implemented, for example, by the database 24 of the data processing device 12, and stores past questions. The emotion estimation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and estimates the user's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the reception unit, generation unit, judgment unit, display unit, scanning unit, learning unit, memory unit, question memory unit, and emotion estimation 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 control unit 46A of the headset terminal 314 and receives questions from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates answers to questions. The judgment unit is implemented by the specific processing unit 290 of the data processing unit 12 and determines the user's level of understanding. The display unit is implemented by the display 343 of the headset terminal 314 and displays supplementary images. The scanning unit is implemented by the camera 42 of the headset terminal 314 and scans the contents of a book. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns references. The memory unit is implemented by the database 24 of the data processing unit 12 and stores the user's reading history. The question storage unit is implemented, for example, by the database 24 of the data processing device 12, and stores past questions. The emotion estimation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and estimates the user's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] Each of the multiple elements described above, including the reception unit, generation unit, judgment unit, display unit, scanning unit, learning unit, memory unit, question memory unit, and emotion estimation 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 control unit 46A of the robot 414 and receives questions from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates answers to questions. The judgment unit is implemented by the specific processing unit 290 of the data processing unit 12 and determines the user's level of understanding. The display unit is implemented by the display of the robot 414 and displays supplementary images. The scanning unit is implemented by the camera 42 of the robot 414 and scans the contents of a book. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns references. The memory unit is implemented by the database 24 of the data processing unit 12 and stores the user's reading history. The question storage unit is implemented, for example, by the database 24 of the data processing device 12, and stores past questions. The emotion estimation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and estimates the user's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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."

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] (Note 1) A reception desk that handles questions from users, A generation unit that generates answers to questions received by the reception unit, A determination unit that determines the user's level of understanding based on the answer generated by the generation unit, The system includes a display unit that displays a supplementary image based on the level of understanding determined by the aforementioned determination unit. A system characterized by the following features. (Note 2) It has a scanning unit that scans the contents of books. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a learning section for studying reference materials. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has a memory unit that stores the user's reading history. The system described in Appendix 1, characterized by the features described herein. (Note 5) It is equipped with a question memory unit that stores past questions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate context-based answers The system described in Appendix 1, characterized by the features described herein. (Note 7) The unit that makes the determination said, Adjust the conversation level based on the user's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned display unit is When explaining complex concepts, display supplementary images. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of question submissions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of handling inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When a question is submitted, it is filtered based on the user's current reading status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the questions to be asked based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving questions, the system prioritizes accepting questions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When receiving a question, the system analyzes the user's social media activity and selects relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating answers, adjust the level of detail in the answers based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating answers, different answer algorithms are applied depending on the question category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating answers, the system prioritizes answers based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating answers, the order of answers is adjusted based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The unit that makes the determination said, The system estimates the user's emotions and adjusts the criteria for determining comprehension based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The unit that makes the determination said, When assessing comprehension levels, the system improves accuracy by referencing the user's past comprehension data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The unit that makes the determination said, When assessing comprehension, the user's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The unit that makes the determination said, The system estimates the user's emotions and adjusts how the level of understanding is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The unit that makes the determination said, When assessing comprehension levels, the geographical distribution of users should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The unit that makes the determination said, When assessing understanding, refer to relevant literature to improve the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is It estimates the user's emotions and adjusts how supplemental images are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is When displaying supplementary images, the system selects the optimal display method by referring to the user's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is When displaying supplementary images, the display should take into account the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is It estimates the user's emotions and adjusts the display order of supplementary images based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned display unit is When displaying supplementary images, the optimal display method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned display unit is When displaying supplementary images, we improve the accuracy of the display by referring to related literature. The system described in Appendix 1, characterized by the features described herein. (Note 33) The scanning unit is It estimates the user's emotions and adjusts the timing of scans based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The scanning unit is During scanning, the system selects the optimal scanning method by referring to the user's past scan history. The system described in Appendix 2, characterized by the features described herein. (Note 35) The scanning unit is It estimates the user's emotions and determines the priority of scans based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The scanning unit is During scanning, the system selects the optimal scanning method by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned learning unit, During training, the training data is weighted based on when the scanned data was submitted. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned storage unit is The system estimates the user's emotions and selects memory data based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned storage unit is During memory storage, the memory algorithm is optimized by referencing past memory data. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned storage unit is It estimates the user's emotions and adjusts the frequency of memories based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned storage unit is During memorization, the memorized data is weighted based on when the reading history was submitted. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned question storage unit is The system estimates the user's emotions and selects question memory data based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 46) The aforementioned question storage unit is When memorizing questions, the memory algorithm is optimized by referring to past question memory data. The system described in Appendix 5, characterized by the features described herein. (Note 47) The aforementioned question storage unit is The system estimates the user's emotions and adjusts the frequency of question recall based on the estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 48) The aforementioned question storage unit is When memorizing questions, the memorized data is weighted based on when the questions were submitted. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0210] 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 handles questions from users, A generation unit that generates answers to questions received by the reception unit, A determination unit that determines the user's level of understanding based on the answer generated by the generation unit, The system includes a display unit that displays a supplementary image based on the level of understanding determined by the aforementioned determination unit. A system characterized by the following features.

2. It has a scanning unit that scans the contents of books. The system according to feature 1.

3. It includes a learning section for studying reference materials. The system according to feature 1.

4. It has a memory unit that stores the user's reading history. The system according to feature 1.

5. It is equipped with a question memory unit that stores past questions. The system according to feature 1.

6. The generating unit is Generate context-based answers The system according to feature 1.

7. The unit that makes the determination said, Adjust the conversation level based on the user's level of understanding. The system according to feature 1.

8. The aforementioned display unit is When explaining complex concepts, display supplementary images. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of question submissions based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of handling inquiries. The system according to feature 1.

Images