system

Abstract

The system according to this embodiment aims to generate a correlation diagram of characters and settings as the reader progresses, and to quickly answer questions that arise during reading. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, a filtering unit, a reception unit, and an answering unit. The analysis unit analyzes the reading progress. The generation unit generates a correlation diagram of characters and settings based on the information analyzed by the analysis unit. The filtering unit filters the information based on the progress analyzed by the analysis unit. The reception unit receives questions. The answering unit generates answers to the questions received by the reception unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 conventional technology, there is a problem that it is difficult to grasp the characters and correlation relationships of a complex story, and there is no means to quickly solve the doubts during reading.

[0005] The system according to the embodiment aims to generate a correlation diagram of characters and settings according to the progress of reading and quickly answer the doubts during reading.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, a filtering unit, a reception unit, and an answering unit. The analysis unit analyzes the reading progress. The generation unit generates a correlation diagram of characters and settings based on the information analyzed by the analysis unit. The filtering unit filters the information based on the progress analyzed by the analysis unit. The reception unit receives questions. The answering unit generates answers to the questions received by the reception unit. [Effects of the Invention]

[0007] The system according to this embodiment generates a correlation diagram of characters and settings as the reader progresses, and can quickly answer questions that arise during reading. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable 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 reception 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 reception 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 book guide AI agent according to an embodiment of the present invention is a system for improving the reading experience. This system dynamically generates a relationship diagram of characters and settings in accordance with the reading progress, presents only information based on the current reading progress, and provides a spoiler prevention function that stops the reader from seeing further developments. It also accepts questions about the work through voice and chat interaction and provides instant answers. For example, the book guide AI agent analyzes the reading progress and automatically grasps the relationships between characters and settings. As the story progresses, if a new character appears, its profile and relationship with other characters are added to the relationship diagram. This allows the reader to easily grasp the complex relationships in the story. Next, the book guide AI agent analyzes the reader's reading progress and presents only information based on the current progress. For example, if the reader has read half of the story, it provides only information up to that point and does not display developments beyond that point. This allows the reader to enjoy reading with peace of mind without worrying about spoilers. Furthermore, the book guide AI agent accepts questions about the work through voice and chat interaction. When the reader inputs a question via voice or chat, the AI ​​provides an instant answer to that question. For example, the AI ​​can instantly answer questions such as, "Who is this character?" or "What is this setting?" This allows readers to quickly resolve questions while reading. This enables the book guide AI agent to deepen understanding of the story and enhance immersion. Readers can more easily grasp the characters and relationships in complex stories, improving their reading experience. Furthermore, preventing spoilers allows readers to enjoy the story's development. Additionally, information can be easily obtained through voice and chat interactions, making reading smoother.

[0029] The book guide AI agent according to this embodiment comprises an analysis unit, a generation unit, a filtering unit, a reception unit, and an answering unit. The analysis unit analyzes the reading progress. For example, the analysis unit analyzes the reading progress and grasps the relationships between characters and settings. By analyzing the reading progress and grasping the relationships between characters and settings, the analysis unit deepens the understanding of the story. The generation unit generates a correlation diagram of characters and settings based on the information analyzed by the analysis unit. For example, the generation unit generates a correlation diagram based on the information from the analysis unit. By generating a correlation diagram based on the information from the analysis unit, the generation unit makes it easier to grasp the complex relationships in the story. The filtering unit analyzes the reading progress. For example, the filtering unit filters information based on the reading progress. By filtering information based on the reading progress, the filtering unit prevents spoilers and improves the reading experience. The reception unit accepts questions. For example, the reception unit accepts questions from readers. By accepting questions from readers, the reception unit makes it easier to resolve questions that arise during reading. The answering unit generates answers based on information from the reception unit. For example, the answering unit generates answers based on information from the reception unit. By generating answers based on information from the reception unit, the answering unit provides immediate answers to the reader's questions. As a result, the book guide AI agent according to the embodiment improves the reading experience by generating a correlation diagram of characters and settings based on the reading progress, preventing spoilers, and providing immediate answers to questions.

[0030] The analysis unit analyzes the reader's reading progress. Specifically, it analyzes in detail how far the reader has read, which characters have appeared, and which plot points have been revealed. The analysis unit analyzes the reading progress and understands the relationships between characters and plot points. For example, it analyzes the actions and words of characters and changes in plot points to understand how they affect the overall story. By analyzing the reading progress and understanding the relationships between characters and plot points, the analysis unit deepens the reader's understanding of the story. This allows the reader to more deeply understand the complex structure and themes of the story. The analysis unit uses natural language processing technology to analyze the text and extract the emotions and motivations of the characters and the background of the plot points. This allows it to grasp subtle changes and foreshadowing that occur as the story progresses, improving the accuracy of the information provided to the reader. Furthermore, the analysis unit can provide individually customized analysis results, taking into account the reader's reading history and preferences. This allows readers to enjoy the story at their own pace while obtaining the necessary information at the appropriate time.

[0031] The generation unit generates character and setting correlation diagrams based on information analyzed by the analysis unit. Specifically, it generates correlation diagrams that visually represent the relationships between characters and changes in their settings. For example, the generation unit generates correlation diagrams based on information from the analysis unit. The correlation diagrams are designed to allow readers to understand the relationships between characters and the changes in their settings at a glance, making it easier to grasp the complex relationships in the story. The generation unit makes it easier to grasp the complex relationships in the story by generating correlation diagrams based on information from the analysis unit. The generation unit uses AI technology to automatically analyze the relationships between characters and changes in their settings and generates correlation diagrams. This allows readers to intuitively understand the changes in the relationships between characters and their settings as the story progresses. Furthermore, the generation unit dynamically updates the correlation diagrams according to the reader's progress, providing the latest information. This allows readers to always enjoy the story based on the latest information. In generating correlation diagrams, the generation unit uses colors, line thickness, icons, etc., to visually represent the importance of characters and the strength of their relationships. This allows readers to grasp important relationships and changes in settings at a glance.

[0032] The filtering unit analyzes the reader's reading progress. Specifically, it analyzes how far the reader has read and filters the information provided according to that progress. For example, the filtering unit filters information based on the reader's reading progress. This prevents spoilers for parts the reader hasn't yet read and improves the reading experience. The filtering unit prevents spoilers and improves the reading experience by filtering information based on the reader's reading progress. The filtering unit monitors the reader's reading progress in real time and dynamically adjusts the information provided. For example, when a reader reaches a specific chapter of the story, it provides only information related to that chapter and filters out information from there onward. This allows readers to enjoy reading with peace of mind without losing the surprises and emotions that come with the story's progression. Furthermore, the filtering unit can provide individually customized information by considering the reader's preferences and past reading history. This allows readers to enjoy the story at their own pace and obtain the necessary information at the right time. The filtering unit uses AI technology to accurately analyze the reader's reading progress and improve the accuracy of the information provided. This ensures that readers can always enjoy the story based on the latest information.

[0033] The reception desk accepts questions. Specifically, it accepts doubts and questions that readers may have while reading. For example, the reception desk accepts questions from readers. Readers may have questions about the actions of characters or the background of the setting while reading. By accepting questions from readers, the reception desk makes it easier to resolve doubts that arise while reading. In order to accept questions from readers, the reception desk uses natural language processing technology to analyze the questions from readers and provides information to generate appropriate answers. This allows readers to resolve doubts that arise while reading immediately and deepen their understanding of the story. Furthermore, the reception desk can record the reader's question history and provide individually customized answers based on past questions. This allows readers to enjoy the story at their own pace and obtain the necessary information at the right time. The reception desk provides multiple input methods, such as voice input and text input, to accept questions from readers. This allows readers to input questions according to their preferences and enjoy a more comfortable reading experience.

[0034] The answering unit generates answers based on information from the reception unit. Specifically, it generates appropriate answers to readers' questions. For example, the answering unit generates answers based on information from the reception unit. By generating answers based on information from the reception unit, the answering unit provides immediate answers to readers' questions. The answering unit uses AI technology to generate the optimal answers to readers' questions. For example, it provides detailed answers based on the story's content to questions about the background of characters' actions or settings. This allows readers to deepen their understanding of the story. Furthermore, the answering unit can consider the reader's question history and provide answers based on past questions. This allows readers to enjoy the story at their own pace while obtaining the necessary information at the right time. The answering unit uses natural language processing technology to analyze questions and generate appropriate answers to readers' questions. This allows readers to instantly resolve questions they have while reading and deepen their understanding of the story. Furthermore, the answering unit can consider the reader's preferences and past reading history to provide individually customized answers. This allows readers to enjoy the story at their own pace while obtaining the necessary information at the right time.

[0035] The analysis unit can analyze the reading progress and understand the relationships between characters and settings. For example, the analysis unit analyzes the reading progress and understands the relationships between characters and settings. By analyzing the reading progress and understanding the relationships between characters and settings, the analysis unit deepens the understanding of the story. Relationships between characters and settings include, but are not limited to, parent-child relationships, antagonistic relationships, and interactions between settings. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input reading progress data into AI and have the AI ​​perform the task of understanding the relationships between characters and settings.

[0036] The generation unit can generate a correlation diagram based on information from the analysis unit. For example, the generation unit generates a correlation diagram based on information from the analysis unit. By generating a correlation diagram based on information from the analysis unit, the generation unit makes it easier to grasp the complex relationships in the story. The correlation diagram may include, but is not limited to, relationships between characters or relationships between settings. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information from the analysis unit into a generation AI and have the generation AI perform the generation of the correlation diagram.

[0037] The filtering unit can filter information based on the reading progress. For example, the filtering unit filters information based on the reading progress. By filtering information based on the reading progress, the filtering unit prevents spoilers and improves the reading experience. Reading progress includes, but is not limited to, the percentage of the book completed and the reading speed. Criteria for filtering information include, but is not limited to, criteria for preventing spoilers and filtering algorithms. Some or all of the above processing in the filtering unit may be performed using, for example, AI, or not using AI. For example, the filtering unit can input reading progress data into AI and have the AI ​​perform the information filtering.

[0038] The reception desk can receive questions from readers. For example, the reception desk receives questions from readers. By receiving questions from readers, the reception desk makes it easier for readers to resolve questions they have while reading. Questions include, but are not limited to, free-form questions and multiple-choice questions. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input readers' questions into AI and have the AI ​​perform the question reception.

[0039] The response unit can generate answers based on information from the reception unit. The response unit generates answers based on information from the reception unit, for example. By generating answers based on information from the reception unit, the response unit provides immediate answers to the reader's questions. Answers include, but are not limited to, text-based answers and automatically generated answers. Some or all of the above processing in the response unit may be performed, for example, using a generation AI, or without a generation AI. For example, the response unit can input information from the reception unit into a generation AI and have the generation AI generate answers.

[0040] The analysis unit can improve the accuracy of its analysis by referring to the reader's past reading history when analyzing reading progress. For example, the analysis unit predicts progress in a similar genre based on the genre of books the reader has read in the past. The analysis unit adjusts the reading speed based on the reader's past reading speed. The analysis unit predicts the relationships between new characters based on the types of characters the reader has liked in the past. In this way, the analysis accuracy is improved by referring to past reading history. Past reading history includes, but is not limited to, a list of books read and reading time. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the reader's past reading history data into AI and have the AI ​​perform the improvement of analysis accuracy.

[0041] The analysis unit can perform its analysis of reading progress while considering the reader's reading speed and concentration level. For example, if the reader is speed-reading, the analysis unit can quickly analyze the progress and present the next development early. If the reader is reading slowly, the analysis unit can analyze the progress slowly and provide detailed information. If the reader is concentrating, the analysis unit can analyze the progress in detail and provide deeper information. This improves the accuracy of the analysis by considering reading speed and concentration level. Reading speed and concentration level include, but are not limited to, reading time per page and methods for evaluating concentration level. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data on the reader's reading speed and concentration level into AI and have the AI ​​perform the analysis.

[0042] The analysis unit can perform analysis of reading progress while considering the reader's geographical location. For example, if the reader is traveling, the analysis unit will prioritize analyzing information relevant to that region. If the reader is at home, the analysis unit will provide information tailored to a relaxed environment. If the reader is using public transportation, the analysis unit will provide information that can be understood in a short time. This improves the accuracy of the analysis by considering geographical location. Geographical location information includes, but is not limited to, GPS data and location accuracy. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the reader's geographical location data into AI and have the AI ​​perform the analysis.

[0043] The analysis unit can analyze the reader's social media activity and incorporate relevant information into the analysis when analyzing reading progress. For example, the analysis unit can provide information related to the reader's comments based on the comments the reader shared on social media. The analysis unit can provide relevant information by referring to the content of posts from accounts the reader follows. The analysis unit can provide information related to reading groups the reader participates in on social media based on their activities. By analyzing social media activity, the accuracy of the analysis can be improved. Social media activity includes, but is not limited to, the content of posts and the number of likes. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the reader's social media activity data into AI and have the AI ​​perform the analysis.

[0044] The generation unit can adjust the level of detail in the relationship diagram based on the importance of the characters and settings when generating it. For example, the generation unit can generate a detailed relationship diagram that focuses on the main characters. The generation unit can add characters whose importance increases as the story progresses. The generation unit can generate a relationship diagram that includes detailed background information based on the importance of the settings. This deepens the understanding of the story by adjusting the level of detail in the relationship diagram based on the importance of the characters and settings. The importance of characters and settings includes, but is not limited to, the number of appearances and the degree of influence on the story. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input character and setting importance data into AI and have the AI ​​perform the adjustment of the level of detail in the relationship diagram.

[0045] The generation unit can apply different generation algorithms depending on the story category when generating correlation diagrams. For example, in the case of a fantasy story, the generation unit generates a correlation diagram that reflects magic and otherworldly settings. In the case of a mystery story, the generation unit generates a correlation diagram that emphasizes the relationships between events. In the case of a romance story, the generation unit generates a correlation diagram that emphasizes the emotional connections between characters. By applying different generation algorithms depending on the story category, the accuracy of the correlation diagram is improved. Story categories include, but are not limited to, genre and theme. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input story category data into AI and have the AI ​​perform the application of the generation algorithm.

[0046] The generation unit can determine the priority of the relationship diagram based on the frequency of appearance of characters and settings when generating the relationship diagram. For example, the generation unit generates a relationship diagram that prioritizes characters that appear frequently. The generation unit adds characters whose appearance frequency increases as the story progresses. The generation unit generates a relationship diagram that includes detailed background information based on the frequency of appearance of settings. This deepens the understanding of the story by determining the priority of the relationship diagram based on the frequency of appearance. Frequency of appearance includes, but is not limited to, the number of appearances and the importance of the scenes in which the character appears. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input character and setting frequency data into AI and have the AI ​​perform the determination of the priority of the relationship diagram.

[0047] The generation unit can improve the accuracy of the relationship diagram by referring to related literature for characters and settings when generating the diagram. For example, the generation unit can add detailed profiles to the relationship diagram based on related literature for characters. The generation unit can reflect background information in the relationship diagram by referring to related literature for settings. The generation unit improves the accuracy of the relationship diagram by adding information from related literature as the story progresses. In this way, the accuracy of the relationship diagram is improved by referring to related literature. Related literature includes, but is not limited to, bibliographies and citations. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input data on related literature for characters and settings into AI and have the AI ​​perform the improvement of the accuracy of the relationship diagram.

[0048] The filtering unit can improve filtering accuracy by referring to the reader's past reading history during the filtering process. For example, the filtering unit prioritizes providing information of similar genres based on the genres of books the reader has read in the past. The filtering unit adjusts the level of detail of the information based on the reader's past reading speed. The filtering unit provides relevant information based on the types of characters the reader has liked in the past. In this way, filtering accuracy is improved by referring to past reading history. Past reading history includes, but is not limited to, a list of books read and reading time. Some or all of the above processing in the filtering unit may be performed using AI, for example, or not using AI. For example, the filtering unit can input the reader's past reading history data into AI and have the AI ​​perform the improvement of filtering accuracy.

[0049] The filtering unit can apply different filtering algorithms depending on the progress of the story during filtering. For example, in the early stages of the story, the filtering unit applies a filtering algorithm that provides basic information. In the middle stages of the story, the filtering unit applies a filtering algorithm that provides detailed information. In the later stages of the story, the filtering unit applies a filtering algorithm that highlights important information. This improves the accuracy of filtering by applying different filtering algorithms depending on the progress of the story. The progress of the story includes, but is not limited to, chapter progress, page count, etc. Some or all of the above processing in the filtering unit may be performed using, for example, AI, or not using AI. For example, the filtering unit can input story progress data into AI and have the AI ​​perform the application of filtering algorithms.

[0050] The filtering unit can perform filtering while considering the reader's geographical location information. For example, if the reader is traveling, the filtering unit will prioritize providing information relevant to that region. If the reader is at home, the filtering unit will provide information suited to a relaxed environment. If the reader is using public transport, the filtering unit will provide information that can be understood in a short time. This improves the accuracy of filtering by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and location accuracy. Some or all of the above processing in the filtering unit may be performed using, for example, AI, or not using AI. For example, the filtering unit can input the reader's geographical location data into AI and have the AI ​​perform the filtering.

[0051] The filtering unit can analyze the reader's social media activity during filtering and reflect relevant information in the filtering process. For example, the filtering unit can provide information related to the reader's comments shared on social media. The filtering unit can provide relevant information by referring to the content of posts from accounts the reader follows. The filtering unit can provide information related to reading groups the reader participates in on social media. This improves the accuracy of filtering by analyzing social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the reader's social media activity data into AI and have the AI ​​perform the filtering.

[0052] The reception desk can improve the accuracy of receiving questions by referring to the reader's past question history when a question is received. For example, the reception desk can prioritize receiving relevant questions based on the topics the reader has frequently asked in the past. The reception desk can quickly provide appropriate answers by referring to the reader's past question history. The reception desk can automatically suggest relevant information based on the topics the reader has asked in the past. This improves the accuracy of receiving questions by referring to the past question history. Past question history includes, but is not limited to, past question content and answer history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the reader's past question history data into AI and have the AI ​​perform the improvement of reception accuracy.

[0053] The reception desk can select the optimal reception method when receiving a question, taking into account the reader's device information. For example, if the reader is using a smartphone, the reception desk will prioritize voice input. If the reader is using a tablet, the reception desk will prioritize touch input. If the reader is using a personal computer, the reception desk will prioritize keyboard input. In this way, the reception desk optimizes the reception method by taking device information into account. Device information includes, but is not limited to, the type of device and the OS version. Some or all of the processing described above in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the reader's device information data into an AI and have the AI ​​select the optimal reception method.

[0054] The answering unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the answering unit provides detailed answers to important questions. For general questions, the answering unit provides concise answers. For urgent questions, the answering unit provides quick answers. In this way, appropriate answers are provided by adjusting the level of detail in the answer based on the importance of the question. The importance of a question includes, but is not limited to, the content of the question and the level of interest to the reader. Some or all of the above processing in the answering unit may be performed using, for example, AI, or not using AI. For example, the answering unit can input question importance data into AI and have the AI ​​perform the adjustment of the level of detail in the answer.

[0055] The answering unit can apply different answering algorithms depending on the question category when generating answers. For example, the answering unit can apply a specialized algorithm to technical questions to generate answers. For general questions, it can apply a concise algorithm to generate answers. For emotional questions, it can apply an emotionally sensitive algorithm to generate answers. In this way, by applying different answering algorithms depending on the question category, it provides appropriate answers. Question categories include, but are not limited to, technical questions and narrative questions. Some or all of the above processing in the answering unit may be performed using AI, for example, or not using AI. For example, the answering unit can input question category data into AI and have the AI ​​perform the application of answering algorithms.

[0056] The answering unit can determine the priority of answers based on when the questions were submitted when generating answers. For example, the answering unit will prioritize answers to recently submitted questions. The answering unit will prioritize answers to questions that have remained unanswered for a long time. The answering unit will provide answers quickly to urgent questions. By prioritizing answers based on when the questions were submitted, the answering unit can provide answers at the appropriate time. The question submission date includes, but is not limited to, the date and time the question was submitted and the progress of the story. Some or all of the above processing in the answering unit may be performed using AI, for example, or not using AI. For example, the answering unit can input question submission date data into AI and have the AI ​​perform the determination of answer priorities.

[0057] The answering unit can improve the accuracy of its answers by referring to relevant literature related to the question during answer generation. For example, the answering unit can provide detailed answers based on literature related to the question. The answering unit can provide expert answers by referring to research papers related to the question. The answering unit can provide reliable answers based on books related to the question. In this way, the accuracy of the answers is improved by referring to relevant literature. Relevant literature includes, but is not limited to, bibliographies and citations. Some or all of the above processing in the answering unit may be performed using AI, for example, or not using AI. For example, the answering unit can input the relevant literature data for the question into AI and have the AI ​​perform the task of improving the accuracy of the answers.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The book guide AI agent can analyze readers' reading habits and provide recommendations tailored to their reading rhythm. For example, if the analysis unit finds that a reader tends to read during certain times of the day, it can send notifications encouraging reading during those times. If a reader concentrates their reading on weekends, it can suggest a reading plan suited to the weekend. Furthermore, if a reader prefers a particular genre, it can provide information on new releases related to that genre. This allows the AI ​​to provide an optimal reading experience based on the reader's reading habits.

[0060] The book guide AI agent can analyze a reader's reading speed and provide information tailored to that speed. For example, if a reader is speed-reading, the analysis unit can provide concise summaries of key points. If a reader is reading slowly, it can provide more detailed information. Furthermore, if a reader pauses at a particular section, it can provide supplementary information related to that section. By providing information according to the reader's reading speed, the AI ​​agent can improve the reading experience.

[0061] The book guide AI agent can analyze a reader's reading history and provide recommendations based on that history. For example, the analysis unit can recommend new releases in similar genres based on the genres of books the reader has read in the past. It can also predict the relationships between new characters based on the types of characters the reader has liked in the past. Furthermore, it can predict the reader's reading progress based on the completion rate of books they have read in the past. This allows the AI ​​to provide optimal recommendations based on past reading history.

[0062] The book guide AI agent can analyze a reader's reading environment and provide information tailored to that environment. For example, if a reader is reading at home, the analysis unit can provide information suited to a relaxed environment. If the reader is using public transportation, it can provide information that can be understood in a short time. Furthermore, if the reader is traveling, it can prioritize providing information relevant to that region. In this way, by providing information tailored to the reading environment, the reading experience can be improved.

[0063] The book guide AI agent can analyze a reader's reading speed and provide information tailored to that speed. For example, if a reader is speed-reading, the analysis unit can provide concise summaries of key points. If a reader is reading slowly, it can provide more detailed information. Furthermore, if a reader pauses at a particular section, it can provide supplementary information related to that section. By providing information according to the reader's reading speed, the AI ​​agent can improve the reading experience.

[0064] The book guide AI agent can analyze a reader's reading history and provide recommendations based on that history. For example, the analysis unit can recommend new releases in similar genres based on the genres of books the reader has read in the past. It can also predict the relationships between new characters based on the types of characters the reader has liked in the past. Furthermore, it can predict the reader's reading progress based on the completion rate of books they have read in the past. This allows the AI ​​to provide optimal recommendations based on past reading history.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The analysis unit analyzes the reading progress. For example, the analysis unit analyzes the reading progress to understand the relationships between characters and settings. This allows for a deeper understanding of the story. Step 2: The generation unit generates a correlation diagram of characters and settings based on the information analyzed by the analysis unit. For example, the generation unit makes it easier to understand the complex relationships in the story by generating a correlation diagram based on the information from the analysis unit. Step 3: The filtering unit analyzes the reading progress and filters information based on the progress analyzed by the analysis unit. For example, the filtering unit prevents spoilers and improves the reading experience by filtering information based on the reading progress. Step 4: The reception desk accepts questions. The reception desk makes it easier for readers to resolve their questions while reading, for example, by accepting readers' questions. Step 5: The response unit generates answers based on information from the reception unit. The response unit provides immediate answers to readers' questions, for example, by generating answers based on information from the reception unit.

[0067] (Example of form 2) The book guide AI agent according to an embodiment of the present invention is a system for improving the reading experience. This system dynamically generates a relationship diagram of characters and settings in accordance with the reading progress, presents only information based on the current reading progress, and provides a spoiler prevention function that stops the reader from seeing further developments. It also accepts questions about the work through voice and chat interaction and provides instant answers. For example, the book guide AI agent analyzes the reading progress and automatically grasps the relationships between characters and settings. As the story progresses, if a new character appears, its profile and relationship with other characters are added to the relationship diagram. This allows the reader to easily grasp the complex relationships in the story. Next, the book guide AI agent analyzes the reader's reading progress and presents only information based on the current progress. For example, if the reader has read half of the story, it provides only information up to that point and does not display developments beyond that point. This allows the reader to enjoy reading with peace of mind without worrying about spoilers. Furthermore, the book guide AI agent accepts questions about the work through voice and chat interaction. When the reader inputs a question via voice or chat, the AI ​​provides an instant answer to that question. For example, the AI ​​can instantly answer questions such as, "Who is this character?" or "What is this setting?" This allows readers to quickly resolve questions while reading. This enables the book guide AI agent to deepen understanding of the story and enhance immersion. Readers can more easily grasp the characters and relationships in complex stories, improving their reading experience. Furthermore, preventing spoilers allows readers to enjoy the story's development. Additionally, information can be easily obtained through voice and chat interactions, making reading smoother.

[0068] The book guide AI agent according to this embodiment comprises an analysis unit, a generation unit, a filtering unit, a reception unit, and an answering unit. The analysis unit analyzes the reading progress. For example, the analysis unit analyzes the reading progress and grasps the relationships between characters and settings. By analyzing the reading progress and grasping the relationships between characters and settings, the analysis unit deepens the understanding of the story. The generation unit generates a correlation diagram of characters and settings based on the information analyzed by the analysis unit. For example, the generation unit generates a correlation diagram based on the information from the analysis unit. By generating a correlation diagram based on the information from the analysis unit, the generation unit makes it easier to grasp the complex relationships in the story. The filtering unit analyzes the reading progress. For example, the filtering unit filters information based on the reading progress. By filtering information based on the reading progress, the filtering unit prevents spoilers and improves the reading experience. The reception unit accepts questions. For example, the reception unit accepts questions from readers. By accepting questions from readers, the reception unit makes it easier to resolve questions that arise during reading. The answering unit generates answers based on information from the reception unit. For example, the answering unit generates answers based on information from the reception unit. By generating answers based on information from the reception unit, the answering unit provides immediate answers to the reader's questions. As a result, the book guide AI agent according to the embodiment improves the reading experience by generating a correlation diagram of characters and settings based on the reading progress, preventing spoilers, and providing immediate answers to questions.

[0069] The analysis unit analyzes the reader's reading progress. Specifically, it analyzes in detail how far the reader has read, which characters have appeared, and which plot points have been revealed. The analysis unit analyzes the reading progress and understands the relationships between characters and plot points. For example, it analyzes the actions and words of characters and changes in plot points to understand how they affect the overall story. By analyzing the reading progress and understanding the relationships between characters and plot points, the analysis unit deepens the reader's understanding of the story. This allows the reader to more deeply understand the complex structure and themes of the story. The analysis unit uses natural language processing technology to analyze the text and extract the emotions and motivations of the characters and the background of the plot points. This allows it to grasp subtle changes and foreshadowing that occur as the story progresses, improving the accuracy of the information provided to the reader. Furthermore, the analysis unit can provide individually customized analysis results, taking into account the reader's reading history and preferences. This allows readers to enjoy the story at their own pace while obtaining the necessary information at the appropriate time.

[0070] The generation unit generates character and setting correlation diagrams based on information analyzed by the analysis unit. Specifically, it generates correlation diagrams that visually represent the relationships between characters and changes in their settings. For example, the generation unit generates correlation diagrams based on information from the analysis unit. The correlation diagrams are designed to allow readers to understand the relationships between characters and the changes in their settings at a glance, making it easier to grasp the complex relationships in the story. The generation unit makes it easier to grasp the complex relationships in the story by generating correlation diagrams based on information from the analysis unit. The generation unit uses AI technology to automatically analyze the relationships between characters and changes in their settings and generates correlation diagrams. This allows readers to intuitively understand the changes in the relationships between characters and their settings as the story progresses. Furthermore, the generation unit dynamically updates the correlation diagrams according to the reader's progress, providing the latest information. This allows readers to always enjoy the story based on the latest information. In generating correlation diagrams, the generation unit uses colors, line thickness, icons, etc., to visually represent the importance of characters and the strength of their relationships. This allows readers to grasp important relationships and changes in settings at a glance.

[0071] The filtering unit analyzes the reader's reading progress. Specifically, it analyzes how far the reader has read and filters the information provided according to that progress. For example, the filtering unit filters information based on the reader's reading progress. This prevents spoilers for parts the reader hasn't yet read and improves the reading experience. The filtering unit prevents spoilers and improves the reading experience by filtering information based on the reader's reading progress. The filtering unit monitors the reader's reading progress in real time and dynamically adjusts the information provided. For example, when a reader reaches a specific chapter of the story, it provides only information related to that chapter and filters out information from there onward. This allows readers to enjoy reading with peace of mind without losing the surprises and emotions that come with the story's progression. Furthermore, the filtering unit can provide individually customized information by considering the reader's preferences and past reading history. This allows readers to enjoy the story at their own pace and obtain the necessary information at the right time. The filtering unit uses AI technology to accurately analyze the reader's reading progress and improve the accuracy of the information provided. This ensures that readers can always enjoy the story based on the latest information.

[0072] The reception desk accepts questions. Specifically, it accepts doubts and questions that readers may have while reading. For example, the reception desk accepts questions from readers. Readers may have questions about the actions of characters or the background of the setting while reading. By accepting questions from readers, the reception desk makes it easier to resolve doubts that arise while reading. In order to accept questions from readers, the reception desk uses natural language processing technology to analyze the questions from readers and provides information to generate appropriate answers. This allows readers to resolve doubts that arise while reading immediately and deepen their understanding of the story. Furthermore, the reception desk can record the reader's question history and provide individually customized answers based on past questions. This allows readers to enjoy the story at their own pace and obtain the necessary information at the right time. The reception desk provides multiple input methods, such as voice input and text input, to accept questions from readers. This allows readers to input questions according to their preferences and enjoy a more comfortable reading experience.

[0073] The answering unit generates answers based on information from the reception unit. Specifically, it generates appropriate answers to readers' questions. For example, the answering unit generates answers based on information from the reception unit. By generating answers based on information from the reception unit, the answering unit provides immediate answers to readers' questions. The answering unit uses AI technology to generate the optimal answers to readers' questions. For example, it provides detailed answers based on the story's content to questions about the background of characters' actions or settings. This allows readers to deepen their understanding of the story. Furthermore, the answering unit can consider the reader's question history and provide answers based on past questions. This allows readers to enjoy the story at their own pace while obtaining the necessary information at the right time. The answering unit uses natural language processing technology to analyze questions and generate appropriate answers to readers' questions. This allows readers to instantly resolve questions they have while reading and deepen their understanding of the story. Furthermore, the answering unit can consider the reader's preferences and past reading history to provide individually customized answers. This allows readers to enjoy the story at their own pace while obtaining the necessary information at the right time.

[0074] The analysis unit can analyze the reading progress and understand the relationships between characters and settings. For example, the analysis unit analyzes the reading progress and understands the relationships between characters and settings. By analyzing the reading progress and understanding the relationships between characters and settings, the analysis unit deepens the understanding of the story. Relationships between characters and settings include, but are not limited to, parent-child relationships, antagonistic relationships, and interactions between settings. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input reading progress data into AI and have the AI ​​perform the task of understanding the relationships between characters and settings.

[0075] The generation unit can generate a correlation diagram based on information from the analysis unit. For example, the generation unit generates a correlation diagram based on information from the analysis unit. By generating a correlation diagram based on information from the analysis unit, the generation unit makes it easier to grasp the complex relationships in the story. The correlation diagram may include, but is not limited to, relationships between characters or relationships between settings. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information from the analysis unit into a generation AI and have the generation AI perform the generation of the correlation diagram.

[0076] The filtering unit can filter information based on the reading progress. For example, the filtering unit filters information based on the reading progress. By filtering information based on the reading progress, the filtering unit prevents spoilers and improves the reading experience. Reading progress includes, but is not limited to, the percentage of the book completed and the reading speed. Criteria for filtering information include, but is not limited to, criteria for preventing spoilers and filtering algorithms. Some or all of the above processing in the filtering unit may be performed using, for example, AI, or not using AI. For example, the filtering unit can input reading progress data into AI and have the AI ​​perform the information filtering.

[0077] The reception desk can receive questions from readers. For example, the reception desk receives questions from readers. By receiving questions from readers, the reception desk makes it easier for readers to resolve questions they have while reading. Questions include, but are not limited to, free-form questions and multiple-choice questions. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input readers' questions into AI and have the AI ​​perform the question reception.

[0078] The response unit can generate answers based on information from the reception unit. The response unit generates answers based on information from the reception unit, for example. By generating answers based on information from the reception unit, the response unit provides immediate answers to the reader's questions. Answers include, but are not limited to, text-based answers and automatically generated answers. Some or all of the above processing in the response unit may be performed, for example, using a generation AI, or without a generation AI. For example, the response unit can input information from the reception unit into a generation AI and have the generation AI generate answers.

[0079] The analysis unit can estimate the reader's emotions and adjust the reading progression analysis method based on the estimated emotions. For example, if the reader is excited, the analysis unit will quickly analyze the reading progression and present the next development early. If the reader is relaxed, the analysis unit will slowly analyze the reading progression and provide detailed information. If the reader is tired, the analysis unit will simplify the reading progression and analyze only the main points. In this way, the reading experience is individually optimized by adjusting the reading progression analysis method based on the reader's emotions. The estimation of the reader's emotions 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the reader's emotion data into the generative AI and have the generative AI perform the adjustment of the reading progression analysis method.

[0080] The analysis unit can improve the accuracy of its analysis by referring to the reader's past reading history when analyzing reading progress. For example, the analysis unit predicts progress in a similar genre based on the genre of books the reader has read in the past. The analysis unit adjusts the reading speed based on the reader's past reading speed. The analysis unit predicts the relationships between new characters based on the types of characters the reader has liked in the past. In this way, the analysis accuracy is improved by referring to past reading history. Past reading history includes, but is not limited to, a list of books read and reading time. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the reader's past reading history data into AI and have the AI ​​perform the improvement of analysis accuracy.

[0081] The analysis unit can perform its analysis of reading progress while considering the reader's reading speed and concentration level. For example, if the reader is speed-reading, the analysis unit can quickly analyze the progress and present the next development early. If the reader is reading slowly, the analysis unit can analyze the progress slowly and provide detailed information. If the reader is concentrating, the analysis unit can analyze the progress in detail and provide deeper information. This improves the accuracy of the analysis by considering reading speed and concentration level. Reading speed and concentration level include, but are not limited to, reading time per page and methods for evaluating concentration level. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data on the reader's reading speed and concentration level into AI and have the AI ​​perform the analysis.

[0082] The analysis unit can estimate the reader's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the reader is excited, the analysis unit displays the results in a visually stimulating format. If the reader is relaxed, the analysis unit displays the results in a calm format. If the reader is tired, the analysis unit displays the results in a concise and easy-to-read format. This individually optimizes the reading experience by adjusting how the analysis results are displayed based on the reader's emotions. The estimation of the reader's emotions is achieved using an emotion estimation function, for example, using 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 processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the reader's emotion data into the generative AI and have the generative AI adjust how the analysis results are displayed.

[0083] The analysis unit can perform analysis of reading progress while considering the reader's geographical location. For example, if the reader is traveling, the analysis unit will prioritize analyzing information relevant to that region. If the reader is at home, the analysis unit will provide information tailored to a relaxed environment. If the reader is using public transportation, the analysis unit will provide information that can be understood in a short time. This improves the accuracy of the analysis by considering geographical location. Geographical location information includes, but is not limited to, GPS data and location accuracy. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the reader's geographical location data into AI and have the AI ​​perform the analysis.

[0084] The analysis unit can analyze the reader's social media activity and incorporate relevant information into the analysis when analyzing reading progress. For example, the analysis unit can provide information related to the reader's comments based on the comments the reader shared on social media. The analysis unit can provide relevant information by referring to the content of posts from accounts the reader follows. The analysis unit can provide information related to reading groups the reader participates in on social media based on their activities. By analyzing social media activity, the accuracy of the analysis can be improved. Social media activity includes, but is not limited to, the content of posts and the number of likes. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the reader's social media activity data into AI and have the AI ​​perform the analysis.

[0085] The generation unit can estimate the reader's emotions and adjust the correlation diagram generation method based on the estimated emotions. For example, if the reader is excited, the generation unit generates a visually stimulating correlation diagram. If the reader is relaxed, the generation unit generates a calmly designed correlation diagram. If the reader is tired, the generation unit generates a simple and highly visible correlation diagram. This individually optimizes the reading experience by adjusting the correlation diagram generation method based on the reader's emotions. The estimation of the reader's emotions is achieved 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the reader's emotion data into the generative AI and have the generative AI perform the adjustment of the correlation diagram generation method.

[0086] The generation unit can adjust the level of detail in the relationship diagram based on the importance of the characters and settings when generating it. For example, the generation unit can generate a detailed relationship diagram that focuses on the main characters. The generation unit can add characters whose importance increases as the story progresses. The generation unit can generate a relationship diagram that includes detailed background information based on the importance of the settings. This deepens the understanding of the story by adjusting the level of detail in the relationship diagram based on the importance of the characters and settings. The importance of characters and settings includes, but is not limited to, the number of appearances and the degree of influence on the story. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input character and setting importance data into AI and have the AI ​​perform the adjustment of the level of detail in the relationship diagram.

[0087] The generation unit can apply different generation algorithms depending on the story category when generating correlation diagrams. For example, in the case of a fantasy story, the generation unit generates a correlation diagram that reflects magic and otherworldly settings. In the case of a mystery story, the generation unit generates a correlation diagram that emphasizes the relationships between events. In the case of a romance story, the generation unit generates a correlation diagram that emphasizes the emotional connections between characters. By applying different generation algorithms depending on the story category, the accuracy of the correlation diagram is improved. Story categories include, but are not limited to, genre and theme. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input story category data into AI and have the AI ​​perform the application of the generation algorithm.

[0088] The generation unit can estimate the reader's emotions and adjust the display method of the correlation diagram based on the estimated emotions. For example, if the reader is excited, the generation unit provides a visually stimulating display method. If the reader is relaxed, the generation unit provides a calm display method. If the reader is tired, the generation unit provides a simple and easy-to-read display method. This individually optimizes the reading experience by adjusting the display method of the correlation diagram based on the reader's emotions. The estimation of the reader's emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the reader's emotion data into the generation AI and have the generation AI perform the adjustment of the display method of the correlation diagram.

[0089] The generation unit can determine the priority of the relationship diagram based on the frequency of appearance of characters and settings when generating the relationship diagram. For example, the generation unit generates a relationship diagram that prioritizes characters that appear frequently. The generation unit adds characters whose appearance frequency increases as the story progresses. The generation unit generates a relationship diagram that includes detailed background information based on the frequency of appearance of settings. This deepens the understanding of the story by determining the priority of the relationship diagram based on the frequency of appearance. Frequency of appearance includes, but is not limited to, the number of appearances and the importance of the scenes in which the character appears. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input character and setting frequency data into AI and have the AI ​​perform the determination of the priority of the relationship diagram.

[0090] The generation unit can improve the accuracy of the relationship diagram by referring to related literature for characters and settings when generating the diagram. For example, the generation unit can add detailed profiles to the relationship diagram based on related literature for characters. The generation unit can reflect background information in the relationship diagram by referring to related literature for settings. The generation unit improves the accuracy of the relationship diagram by adding information from related literature as the story progresses. In this way, the accuracy of the relationship diagram is improved by referring to related literature. Related literature includes, but is not limited to, bibliographies and citations. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input data on related literature for characters and settings into AI and have the AI ​​perform the improvement of the accuracy of the relationship diagram.

[0091] The filtering unit can estimate the reader's emotions and adjust the filtering criteria based on the estimated emotions. For example, if the reader is excited, the filtering unit provides detailed information. If the reader is relaxed, the filtering unit provides calm information. If the reader is tired, the filtering unit provides concise information. In this way, the reading experience is individually optimized by adjusting the filtering criteria based on the reader's emotions. The estimation of the reader's emotions 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 filtering unit may be performed using AI, for example, or not using AI. For example, the filtering unit can input the reader's emotion data into the generative AI and have the generative AI perform the adjustment of the filtering criteria.

[0092] The filtering unit can improve filtering accuracy by referring to the reader's past reading history during the filtering process. For example, the filtering unit prioritizes providing information of similar genres based on the genres of books the reader has read in the past. The filtering unit adjusts the level of detail of the information based on the reader's past reading speed. The filtering unit provides relevant information based on the types of characters the reader has liked in the past. In this way, filtering accuracy is improved by referring to past reading history. Past reading history includes, but is not limited to, a list of books read and reading time. Some or all of the above processing in the filtering unit may be performed using AI, for example, or not using AI. For example, the filtering unit can input the reader's past reading history data into AI and have the AI ​​perform the improvement of filtering accuracy.

[0093] The filtering unit can apply different filtering algorithms depending on the progress of the story during filtering. For example, in the early stages of the story, the filtering unit applies a filtering algorithm that provides basic information. In the middle stages of the story, the filtering unit applies a filtering algorithm that provides detailed information. In the later stages of the story, the filtering unit applies a filtering algorithm that highlights important information. This improves the accuracy of filtering by applying different filtering algorithms depending on the progress of the story. The progress of the story includes, but is not limited to, chapter progress, page count, etc. Some or all of the above processing in the filtering unit may be performed using, for example, AI, or not using AI. For example, the filtering unit can input story progress data into AI and have the AI ​​perform the application of filtering algorithms.

[0094] The filtering unit can estimate the reader's emotions and adjust the display method of the filtering results based on the estimated emotions. For example, if the reader is excited, the filtering unit provides a visually stimulating display method. If the reader is relaxed, the filtering unit provides a calm display method. If the reader is tired, the filtering unit provides a simple and easy-to-read display method. This individually optimizes the reading experience by adjusting the display method of the filtering results based on the reader's emotions. The estimation of the reader's emotions is achieved using an emotion estimation function, for example, using 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 processing in the filtering unit may be performed using AI, for example, or not using AI. For example, the filtering unit can input the reader's emotion data into the generative AI and have the generative AI adjust the display method of the filtering results.

[0095] The filtering unit can perform filtering while considering the reader's geographical location information. For example, if the reader is traveling, the filtering unit will prioritize providing information relevant to that region. If the reader is at home, the filtering unit will provide information suited to a relaxed environment. If the reader is using public transport, the filtering unit will provide information that can be understood in a short time. This improves the accuracy of filtering by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and location accuracy. Some or all of the above processing in the filtering unit may be performed using, for example, AI, or not using AI. For example, the filtering unit can input the reader's geographical location data into AI and have the AI ​​perform the filtering.

[0096] The filtering unit can analyze the reader's social media activity during filtering and reflect relevant information in the filtering process. For example, the filtering unit can provide information related to the reader's comments shared on social media. The filtering unit can provide relevant information by referring to the content of posts from accounts the reader follows. The filtering unit can provide information related to reading groups the reader participates in on social media. This improves the accuracy of filtering by analyzing social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the reader's social media activity data into AI and have the AI ​​perform the filtering.

[0097] The reception desk can estimate the reader's emotions and adjust the way questions are received based on the estimated emotions. For example, if the reader is excited, the reception desk will quickly receive questions and provide immediate answers. If the reader is relaxed, the reception desk will receive detailed questions and provide careful answers. If the reader is tired, the reception desk will receive concise questions and provide concise answers. This individually optimizes the reading experience by adjusting the way questions are received based on the reader's emotions. The estimation of the reader's emotions 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 processing in the reception desk may be performed using AI or not. For example, the reception desk can input the reader's emotion data into the generative AI and have the generative AI adjust the way questions are received.

[0098] The reception desk can improve the accuracy of receiving questions by referring to the reader's past question history when a question is received. For example, the reception desk can prioritize receiving relevant questions based on the topics the reader has frequently asked in the past. The reception desk can quickly provide appropriate answers by referring to the reader's past question history. The reception desk can automatically suggest relevant information based on the topics the reader has asked in the past. This improves the accuracy of receiving questions by referring to the past question history. Past question history includes, but is not limited to, past question content and answer history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the reader's past question history data into AI and have the AI ​​perform the improvement of reception accuracy.

[0099] The reception desk can estimate the reader's emotions and prioritize questions based on those emotions. For example, if the reader is excited, the reception desk will prioritize that question. If the reader is relaxed, the reception desk will prioritize questions according to their content. If the reader is tired, the reception desk will prioritize concise questions. This optimizes the reading experience individually by prioritizing questions based on the reader's emotions. The estimation of the reader's emotions 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 processing described above in the reception desk may be performed using AI or not. For example, the reception desk can input the reader's emotion data into a generative AI and have the generative AI determine the priority of questions.

[0100] The reception desk can select the optimal reception method when receiving a question, taking into account the reader's device information. For example, if the reader is using a smartphone, the reception desk will prioritize voice input. If the reader is using a tablet, the reception desk will prioritize touch input. If the reader is using a personal computer, the reception desk will prioritize keyboard input. In this way, the reception desk optimizes the reception method by taking device information into account. Device information includes, but is not limited to, the type of device and the OS version. Some or all of the processing described above in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the reader's device information data into an AI and have the AI ​​select the optimal reception method.

[0101] The response unit can estimate the reader's emotions and adjust the way it presents its responses based on those emotions. For example, if the reader is excited, the response unit may provide a visually stimulating response. If the reader is relaxed, the response unit may provide a calm response. If the reader is tired, the response unit may provide a concise and easily readable response. This individually optimizes the reading experience by adjusting the way the responses are presented based on the reader's emotions. The estimation of the reader's emotions 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 processing described above in the response unit may be performed using AI or not. For example, the response unit can input the reader's emotion data into the generative AI and have the generative AI adjust the way the responses are presented.

[0102] The answering unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the answering unit provides detailed answers to important questions. For general questions, the answering unit provides concise answers. For urgent questions, the answering unit provides quick answers. In this way, appropriate answers are provided by adjusting the level of detail in the answer based on the importance of the question. The importance of a question includes, but is not limited to, the content of the question and the level of interest to the reader. Some or all of the above processing in the answering unit may be performed using, for example, AI, or not using AI. For example, the answering unit can input question importance data into AI and have the AI ​​perform the adjustment of the level of detail in the answer.

[0103] The answering unit can apply different answering algorithms depending on the question category when generating answers. For example, the answering unit can apply a specialized algorithm to technical questions to generate answers. For general questions, it can apply a concise algorithm to generate answers. For emotional questions, it can apply an emotionally sensitive algorithm to generate answers. In this way, by applying different answering algorithms depending on the question category, it provides appropriate answers. Question categories include, but are not limited to, technical questions and narrative questions. Some or all of the above processing in the answering unit may be performed using AI, for example, or not using AI. For example, the answering unit can input question category data into AI and have the AI ​​perform the application of answering algorithms.

[0104] The response unit can estimate the reader's emotions and adjust how the response is displayed based on the estimated emotions. For example, if the reader is excited, the response unit may provide the response in a visually stimulating way. If the reader is relaxed, the response unit may provide the response in a calm way. If the reader is tired, the response unit may provide the response in a concise and easily readable way. This individually optimizes the reading experience by adjusting how the response is displayed based on the reader's emotions. The estimation of the reader's emotions is achieved using an emotion estimation function, for example, using 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 processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input the reader's emotion data into the generative AI and have the generative AI adjust how the response is displayed.

[0105] The answering unit can determine the priority of answers based on when the questions were submitted when generating answers. For example, the answering unit will prioritize answers to recently submitted questions. The answering unit will prioritize answers to questions that have remained unanswered for a long time. The answering unit will provide answers quickly to urgent questions. By prioritizing answers based on when the questions were submitted, the answering unit can provide answers at the appropriate time. The question submission date includes, but is not limited to, the date and time the question was submitted and the progress of the story. Some or all of the above processing in the answering unit may be performed using AI, for example, or not using AI. For example, the answering unit can input question submission date data into AI and have the AI ​​perform the determination of answer priorities.

[0106] The answering unit can improve the accuracy of its answers by referring to relevant literature related to the question during answer generation. For example, the answering unit can provide detailed answers based on literature related to the question. The answering unit can provide expert answers by referring to research papers related to the question. The answering unit can provide reliable answers based on books related to the question. In this way, the accuracy of the answers is improved by referring to relevant literature. Relevant literature includes, but is not limited to, bibliographies and citations. Some or all of the above processing in the answering unit may be performed using AI, for example, or not using AI. For example, the answering unit can input the relevant literature data for the question into AI and have the AI ​​perform the task of improving the accuracy of the answers.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The book guide AI agent can analyze readers' reading habits and provide recommendations tailored to their reading rhythm. For example, if the analysis unit finds that a reader tends to read during certain times of the day, it can send notifications encouraging reading during those times. If a reader concentrates their reading on weekends, it can suggest a reading plan suited to the weekend. Furthermore, if a reader prefers a particular genre, it can provide information on new releases related to that genre. This allows the AI ​​to provide an optimal reading experience based on the reader's reading habits.

[0109] The book guide AI agent can estimate the reader's emotions and adjust the reading pace based on those emotions. For example, if the reader is excited by the story's development, the analysis unit can present the next chapter earlier. If the reader is relaxed, it can provide detailed background information. Furthermore, if the reader is tired, it can present only the key points concisely. In this way, the reading experience can be individually optimized by adjusting the reading pace based on the reader's emotions.

[0110] The book guide AI agent can analyze a reader's reading speed and provide information tailored to that speed. For example, if a reader is speed-reading, the analysis unit can provide concise summaries of key points. If a reader is reading slowly, it can provide more detailed information. Furthermore, if a reader pauses at a particular section, it can provide supplementary information related to that section. By providing information according to the reader's reading speed, the AI ​​agent can improve the reading experience.

[0111] The book guide AI agent can analyze a reader's reading history and provide recommendations based on that history. For example, the analysis unit can recommend new releases in similar genres based on the genres of books the reader has read in the past. It can also predict the relationships between new characters based on the types of characters the reader has liked in the past. Furthermore, it can predict the reader's reading progress based on the completion rate of books they have read in the past. This allows the AI ​​to provide optimal recommendations based on past reading history.

[0112] The book guide AI agent can estimate the reader's emotions and support their reading based on those emotions. For example, if the reader is excited by the story's development, the analysis unit can suggest the next chapter earlier. If the reader is relaxed, it can provide detailed background information. Furthermore, if the reader is tired, it can present only the key points concisely. In this way, the reading experience can be individually optimized by supporting the reader's reading based on their emotions.

[0113] The book guide AI agent can analyze a reader's reading environment and provide information tailored to that environment. For example, if a reader is reading at home, the analysis unit can provide information suited to a relaxed environment. If the reader is using public transportation, it can provide information that can be understood in a short time. Furthermore, if the reader is traveling, it can prioritize providing information relevant to that region. In this way, by providing information tailored to the reading environment, the reading experience can be improved.

[0114] The book guide AI agent can estimate the reader's emotions and adjust the reading pace based on those emotions. For example, if the reader is excited by the story's development, the analysis unit can present the next chapter earlier. If the reader is relaxed, it can provide detailed background information. Furthermore, if the reader is tired, it can present only the key points concisely. In this way, the reading experience can be individually optimized by adjusting the reading pace based on the reader's emotions.

[0115] The book guide AI agent can analyze a reader's reading speed and provide information tailored to that speed. For example, if a reader is speed-reading, the analysis unit can provide concise summaries of key points. If a reader is reading slowly, it can provide more detailed information. Furthermore, if a reader pauses at a particular section, it can provide supplementary information related to that section. By providing information according to the reader's reading speed, the AI ​​agent can improve the reading experience.

[0116] The book guide AI agent can analyze a reader's reading history and provide recommendations based on that history. For example, the analysis unit can recommend new releases in similar genres based on the genres of books the reader has read in the past. It can also predict the relationships between new characters based on the types of characters the reader has liked in the past. Furthermore, it can predict the reader's reading progress based on the completion rate of books they have read in the past. This allows the AI ​​to provide optimal recommendations based on past reading history.

[0117] The book guide AI agent can estimate the reader's emotions and support their reading based on those emotions. For example, if the reader is excited by the story's development, the analysis unit can suggest the next chapter earlier. If the reader is relaxed, it can provide detailed background information. Furthermore, if the reader is tired, it can present only the key points concisely. In this way, the reading experience can be individually optimized by supporting the reader's reading based on their emotions.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The analysis unit analyzes the reading progress. For example, the analysis unit analyzes the reading progress to understand the relationships between characters and settings. This allows for a deeper understanding of the story. Step 2: The generation unit generates a correlation diagram of characters and settings based on the information analyzed by the analysis unit. For example, the generation unit makes it easier to understand the complex relationships in the story by generating a correlation diagram based on the information from the analysis unit. Step 3: The filtering unit analyzes the reading progress and filters information based on the progress analyzed by the analysis unit. For example, the filtering unit prevents spoilers and improves the reading experience by filtering information based on the reading progress. Step 4: The reception desk accepts questions. The reception desk makes it easier for readers to resolve their questions while reading, for example, by accepting readers' questions. Step 5: The response unit generates answers based on information from the reception unit. The response unit provides immediate answers to readers' questions, for example, by generating answers based on information from the reception unit.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the analysis unit, generation unit, filtering unit, reception unit, and response unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes the reading progress. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a correlation diagram of characters and settings based on the analyzed information. The filtering unit is implemented by the control unit 46A of the smart device 14 and filters information based on the reading progress. The reception unit is implemented by the control unit 46A of the smart device 14 and receives questions from the reader. The response unit is implemented by the specific processing unit 290 of the data processing device 12 and generates answers based on the information from the reception unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the analysis unit, generation unit, filtering unit, reception unit, and answering unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the reading progress. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a correlation diagram of characters and settings based on the analyzed information. The filtering unit is implemented by the control unit 46A of the smart glasses 214 and filters information based on the reading progress. The reception unit is implemented by the control unit 46A of the smart glasses 214 and receives questions from the reader. The answering unit is implemented by the specific processing unit 290 of the data processing device 12 and generates answers based on the information from the reception unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the analysis unit, generation unit, filtering unit, reception unit, and response unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the reading progress. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a correlation diagram of characters and settings based on the analyzed information. The filtering unit is implemented by the control unit 46A of the headset terminal 314 and filters information based on the reading progress. The reception unit is implemented by the control unit 46A of the headset terminal 314 and receives questions from the reader. The response unit is implemented by the specific processing unit 290 of the data processing device 12 and generates answers based on the information from the reception unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the analysis unit, generation unit, filtering unit, reception unit, and answering unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 and analyzes the reading progress. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a correlation diagram of characters and settings based on the analyzed information. The filtering unit is implemented by the control unit 46A of the robot 414 and filters information based on the reading progress. The reception unit is implemented by the control unit 46A of the robot 414 and receives questions from the reader. The answering unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates answers based on the information from the reception unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] (Note 1) An analysis unit that analyzes reading progress, A generation unit generates a correlation diagram of characters and settings based on the information analyzed by the aforementioned analysis unit, An analysis unit that analyzes the reading progress, A filtering unit that filters information based on the progress analyzed by the aforementioned analysis unit, The reception desk that takes questions, The system includes a response unit that generates answers to questions received by the reception unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze the reading process to understand the relationships between characters and settings. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is A correlation diagram is generated based on information from the analysis unit. The system described in Appendix 1, characterized by the features described herein. (Note 4) The filtering unit is Filter information based on reading progress. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is We accept questions from readers. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned response section is, The response is generated based on the information from the reception department. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We estimate the reader's emotions and adjust the reading progress analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing reading progress, we improve the accuracy of the analysis by referring to the reader's past reading history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing reading progress, the analysis takes into account the reader's reading speed and concentration level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the reader's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing reading progress, the analysis takes into account the reader's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing reading progress, we analyze readers' social media activity and incorporate relevant information into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We estimate the reader's emotions and adjust the method of generating the correlation diagram based on the estimated reader emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating the relationship diagram, adjust the level of detail in the diagram based on the importance of the characters and settings. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating correlation diagrams, different generation algorithms are applied depending on the category of the story. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The system estimates the reader's emotions and adjusts how the correlation diagram is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a correlation diagram, the priority of the diagram is determined based on the frequency of appearance of characters and settings. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating correlation diagrams, we improve the accuracy of the diagrams by referring to relevant literature related to the characters and settings. The system described in Appendix 1, characterized by the features described herein. (Note 19) The filtering unit is We estimate the reader's emotions and adjust the filtering criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The filtering unit is During filtering, the system improves filtering accuracy by referencing the reader's past reading history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The filtering unit is During filtering, different filtering algorithms are applied depending on the progress of the story. The system described in Appendix 1, characterized by the features described herein. (Note 22) The filtering unit is It estimates the reader's emotions and adjusts how the filtered results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The filtering unit is When filtering, the reader's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The filtering unit is During filtering, analyze the reader's social media activity and incorporate relevant information into the filtering process. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reception unit is We estimate the reader's emotions and adjust the way we ask questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reception unit is When receiving questions, we improve the accuracy of the question submission process by referring to the reader's past question history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reception unit is Infer the reader's emotions and prioritize questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reception unit is When receiving questions, the most suitable method of submission will be selected, taking into account the reader's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned response section is, We estimate the reader's emotions and adjust the way we express our responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned response section 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 31) The aforementioned response section 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 32) The aforementioned response section is, We estimate the reader's emotions and adjust how the responses are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned response section 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 34) The aforementioned response section is, When generating answers, we refer to relevant literature related to the question to improve the accuracy of the answers. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0192] 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. An analysis unit that analyzes reading progress, A generation unit generates a correlation diagram of characters and settings based on the information analyzed by the aforementioned analysis unit, An analysis unit that analyzes the reading progress, A filtering unit that filters information based on the progress analyzed by the aforementioned analysis unit, The reception desk that takes questions, The system includes a response unit that generates answers to questions received by the reception unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze the reading process to understand the relationships between characters and settings. The system according to feature 1.

3. The generating unit is A correlation diagram is generated based on the information from the aforementioned analysis unit. The system according to feature 1.

4. The filtering unit is Filter information based on reading progress. The system according to feature 1.

5. The aforementioned reception unit is We accept questions from readers. The system according to feature 1.

6. The aforementioned response section is, The response is generated based on the information from the aforementioned reception unit. The system according to feature 1.

7. The aforementioned analysis unit, We estimate the reader's emotions and adjust the reading progress analysis method based on the estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing reading progress, we improve the accuracy of the analysis by referring to the reader's past reading history. The system according to feature 1.

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