system

Abstract

The system according to this embodiment aims to improve the reading experience by providing users with explanations of difficult parts and related information while they are reading. [Solution] The system according to the embodiment comprises an analysis unit, an explanation unit, a provision unit, a promotion unit, a support unit, and a recommendation unit. The analysis unit analyzes the content of the book the user is reading in real time. The explanation unit explains difficult parts based on the content analyzed by the analysis unit. The provision unit provides relevant information based on the content analyzed by the analysis unit. The promotion unit facilitates discussion based on the content analyzed by the analysis unit. The support unit supports the formation of reading habits based on the content analyzed by the analysis unit. The recommendation unit recommends personalized books based on the content analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 prior art, it is difficult to understand difficult parts and obtain relevant information during reading, and there is room for improving the quality of the reading experience.

[0005] The system according to the embodiment aims to provide explanations of difficult parts and relevant information to the user during reading, thereby improving the reading experience.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, an explanation unit, a provision unit, a promotion unit, a support unit, and a recommendation unit. The analysis unit analyzes the content of the book the user is reading in real time. The explanation unit explains difficult parts based on the content analyzed by the analysis unit. The provision unit provides relevant information based on the content analyzed by the analysis unit. The promotion unit facilitates discussion based on the content analyzed by the analysis unit. The support unit supports the formation of reading habits based on the content analyzed by the analysis unit. The recommendation unit recommends personalized books based on the content analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve the reading experience by providing users with explanations of difficult parts and related information while they are 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 signed communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a 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 reading support system according to an embodiment of the present invention is a system that analyzes the content of a book being read by a user in real time, provides explanations of difficult parts and related information, and facilitates discussion. The reading support system analyzes the content of a book being read by a user in real time, provides explanations of difficult parts and related information, and facilitates discussion. It also supports reading in general, such as forming reading habits, managing reading lists, and recommending personalized books. For example, the reading support system analyzes the content of a book being read by a user in real time. For example, if a user encounters difficult terms or concepts, the reading support system will explain those parts in an easy-to-understand way. This allows the user to proceed with reading smoothly. Next, the reading support system provides the latest information and news related to the theme of the book. For example, if a user is reading a history book, the system can deepen the user's understanding by providing the latest research findings and news related to that era. Furthermore, the reading support system proposes questions and discussion themes based on the reading content and facilitates discussion. For example, if a user is reading a philosophy book, the system can propose discussion themes based on its content, allowing the user to exchange opinions with other readers. The reading support system also supports the formation of reading habits. For example, by providing reading schedule suggestions, progress tracking, and reminder functions, users can continue reading. Furthermore, the reading support system recommends the next book to read based on the user's interests and reading history. For instance, if a user enjoys mystery novels, the reading support system will recommend mystery novels to read next, enriching the user's reading experience. In this way, the reading support system is designed to enhance reading comprehension and enjoyment, and maximize knowledge absorption. Thus, the reading support system can revolutionize the user's reading experience, increasing comprehension and enjoyment, and maximizing knowledge absorption.

[0029] The reading support system according to this embodiment comprises an analysis unit, an explanation unit, a provision unit, a promotion unit, a support unit, and a recommendation unit. The analysis unit analyzes the content of the book being read by the user in real time. The analysis unit can perform analysis page by page, paragraph by paragraph, or sentence by sentence. The analysis unit can analyze the content of the book using natural language processing technology and extract important information. The analysis unit can analyze the content of the book using topic modeling and extract relevant information. The explanation unit explains difficult parts based on the content analyzed by the analysis unit. The explanation unit explains technical terms and abstract concepts in an easy-to-understand manner. The explanation unit can also explain difficult parts using diagrams and examples. The explanation unit can also explain difficult parts using videos and animations. The provision unit provides relevant information based on the content analyzed by the analysis unit. The provision unit provides the latest information and news related to the book's theme. The provision unit can also provide references and related articles related to the book's theme. The provisioning unit can, for example, provide videos and audio related to the book's theme. The facilitating unit facilitates discussion based on the analysis performed by the analytics unit. The facilitating unit can, for example, suggest questions and discussion topics based on the reading content. The facilitating unit can, for example, suggest how to conduct a discussion based on the reading content. The facilitating unit can, for example, record and share discussions based on the reading content. The support unit supports the formation of reading habits based on the analysis performed by the analytics unit. The support unit can, for example, provide reading schedule suggestions, progress management, and reminder functions. The support unit can, for example, provide motivational messages to support the formation of reading habits. The support unit can, for example, provide a function to share progress with friends and family to support the formation of reading habits. The recommendation unit recommends personalized books based on the analysis performed by the analytics unit. The recommendation unit recommends books to read next based on the user's interests and reading history. The recommendation unit can, for example, adjust the books recommended based on the user's emotions and reading speed.The recommendation system can, for example, customize the books it recommends based on the user's interests and trends. This allows the reading support system according to the embodiment to innovate the user's reading experience, enhance comprehension and enjoyment, and maximize knowledge absorption.

[0030] The analysis unit analyzes the content of books read by users in real time. For example, it can analyze page by page, paragraph by paragraph, or sentence by sentence. Specifically, the analysis unit acquires text data from books and analyzes the content using natural language processing (NLP) techniques. NLP techniques include morphological analysis, syntactic analysis, and semantic analysis, which are combined to analyze the book's content in detail. For example, morphological analysis is used to divide sentences into individual words and identify the part of speech of each word. Next, syntactic analysis is used to analyze the sentence structure and clarify relationships such as subject, predicate, and object. Furthermore, semantic analysis is used to understand the meaning of the sentence and extract important information. The analysis unit can also analyze the book's content using topic modeling and extract relevant information. Topic modeling classifies the book's content into multiple topics and identifies words and phrases related to each topic. This allows for an understanding of the book's overall theme and key points. Based on these analysis results, the analysis unit generates foundational data to provide users with important and relevant information.

[0031] The explanatory section explains complex parts based on the analysis conducted by the analysis section. For example, the explanatory section explains technical terms and abstract concepts in an easy-to-understand manner. Specifically, the explanatory section receives data provided by the analysis section and explains technical terms and abstract concepts in simple language. For example, it provides definitions and background information for technical terms to make them easier for users to understand. Furthermore, it explains abstract concepts using concrete examples and metaphors to allow users to understand them intuitively. The explanatory section can also explain complex parts using diagrams and examples. For example, it can illustrate complex processes and relationships with diagrams to make them easier to understand visually. It can also explain using concrete examples so that users can apply them to real-world situations. In addition, the explanatory section can explain complex parts using videos and animations. By using videos and animations, it can visually show dynamic processes and changes, allowing users to understand more deeply. In this way, the explanatory section can help users understand the content of the book more deeply and enhance the learning effect.

[0032] The information provision department provides relevant information based on the analysis conducted by the analysis department. Specifically, the information provision department provides the latest information and news related to the book's theme. For example, it collects and provides users with the latest research findings, technological trends, and social topics related to the book's content. The information provision department can also provide references and related articles related to the book's theme. For example, it introduces academic papers, specialized books, and related web articles to complement the book's content, enabling users to learn more deeply. Furthermore, the information provision department can also provide videos and audio related to the book's theme. For example, it provides lecture videos, interviews, and podcasts related to the book's content, enabling users to learn from multiple perspectives. In this way, the information provision department enables users to gain a broader understanding of the book's content and efficiently gather relevant information.

[0033] The Facilitation Unit promotes discussion based on the analysis performed by the Analysis Unit. Specifically, the Facilitation Unit suggests questions and discussion topics based on the reading content. For example, it may present questions about important points or debatable topics related to the book's content, providing users with a starting point for discussion. The Facilitation Unit can also suggest methods for conducting discussions based on the reading content. For example, it may provide guidelines and frameworks to support the discussion, enabling users to effectively advance the discussion. Furthermore, the Facilitation Unit can record and share discussions based on the reading content. For example, by recording the content of the discussion and sharing it with other users, it promotes knowledge sharing and exchange of opinions. This allows the Facilitation Unit to help users deepen their understanding of the book's content by thinking deeply about it and exchanging opinions with other users.

[0034] The support unit assists in forming reading habits based on the analysis performed by the analytics unit. Specifically, the support unit provides reading schedule suggestions, progress management, and reminder functions. For example, it suggests an appropriate reading plan based on the user's reading goals and schedule, and manages their progress. It also uses a reminder function to notify users of reading times, supporting the establishment of reading habits. Furthermore, the support unit can provide messages that boost motivation to support the formation of reading habits. For example, it can send encouraging messages and messages that convey a sense of accomplishment according to the user's reading progress to maintain their motivation. The support unit can also provide a progress sharing function with friends and family to support the formation of reading habits. For example, by sharing reading progress with friends and family and encouraging each other, it promotes the establishment of reading habits. In this way, the support unit supports users in continuously enjoying reading and forming reading habits.

[0035] The recommendation team recommends personalized books based on the analysis performed by the analytics team. Specifically, the recommendation team recommends books to read next based on the user's interests and reading history. For example, it analyzes the genres, themes, and ratings of books the user has read in the past and recommends similar books. The recommendation team can also adjust its recommendations based on the user's mood and reading speed. For example, if the user wants to relax, it will recommend lighter books, and if they want to concentrate and learn, it will recommend books with more specialized content. Furthermore, the recommendation team can customize its recommendations based on the user's interests and current trends. For example, it can recommend books related to current social trends and the user's interests, providing books that are likely to interest the user. This makes it easier for the recommendation team to find books that users will always be interested in, enriching the reading experience.

[0036] The explanation section can explain difficult terms and concepts based on the content of the book the user is reading. For example, the explanation section can explain technical terms and abstract concepts in an easy-to-understand way. For example, the explanation section can also explain difficult parts using diagrams and examples. For example, the explanation section can also explain difficult parts using videos and animations. In this way, by explaining difficult terms and concepts based on the content of the book the user is reading, the user's understanding can be deepened. Some or all of the above processing in the explanation section may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the explanation section can input the content of the book the user is reading into a generative AI and have the generative AI perform explanations of difficult terms and concepts.

[0037] The information provider can provide the latest information and news related to the book's theme. For example, the information provider can provide the latest research findings and news articles related to the book's theme. The information provider can also provide references and related articles related to the book's theme. The information provider can also provide videos and audio related to the book's theme. This allows for a deeper understanding of the user by providing the latest information and news related to the book's theme. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input the latest information related to the book's theme into a generative AI and have the generative AI provide the relevant information.

[0038] The facilitator can suggest questions and discussion topics based on the reading content and facilitate the discussion. For example, the facilitator can suggest questions and discussion topics based on the reading content. The facilitator can also suggest methods for conducting a discussion based on the reading content. The facilitator can also record and share discussions based on the reading content. This allows for a deeper understanding of the user by suggesting questions and discussion topics based on the reading content and facilitating the discussion. Some or all of the above processing in the facilitator may be performed using, for example, a generative AI, or without a generative AI. For example, the facilitator can input questions and discussion topics based on the reading content into a generative AI and have the generative AI facilitate the discussion.

[0039] The support unit can provide reading schedule suggestions, progress management, and reminder functions. For example, the support unit can provide reading schedule suggestions, progress management, and reminder functions. The support unit can also provide motivational messages to support the formation of reading habits. The support unit can also provide a progress sharing function with friends and family to support the formation of reading habits. This allows users to continue reading by providing reading schedule suggestions, progress management, and reminder functions. Some or all of the above-described processes in the support unit may be performed using, for example, a generative AI, or not. For example, the support unit can input reading schedule suggestions, progress management, and reminder functions into a generative AI and have the generative AI execute processes to support the formation of reading habits.

[0040] The recommendation system can recommend books to read next based on the user's interests and reading history. For example, the recommendation system can recommend books based on the user's interests and reading history. The recommendation system can also adjust the books it recommends based on the user's emotions and reading speed. Furthermore, it can customize the books it recommends based on the user's interests and current trends. This enriches the user's reading experience by recommending books based on their interests and reading history. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or without one. For example, the recommendation system can input the user's interests and reading history into a generative AI and have the generative AI recommend books to read next.

[0041] The analysis unit can apply different analysis algorithms depending on the genre of the book. For example, in the case of a novel, the analysis unit applies an algorithm that analyzes the flow of the story and the relationships between characters. For example, in the case of an academic book, the analysis unit applies an algorithm that specializes in the analysis of technical terms and theories. For example, in the case of a self-help book, the analysis unit applies an algorithm that specializes in the analysis of practical advice and case studies. By applying the appropriate analysis algorithm according to the genre of the book, the accuracy of the analysis results is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the genre of the book into a generative AI and have the generative AI execute the application of an appropriate analysis algorithm.

[0042] The analysis unit can provide analysis results in real time according to the user's reading speed. For example, if the user is speed-reading, the analysis unit provides analysis results that highlight the key points. For example, if the user is reading slowly, the analysis unit provides detailed analysis results. For example, if the user stops midway, the analysis unit provides analysis results indicating the next point to read. In this way, by providing analysis results in real time according to the user's reading speed, information tailored to the user can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's reading speed into the generative AI and have the generative AI perform the provision of analysis results in real time.

[0043] The analysis unit can analyze other related media (videos, audio, etc.) based on the content of the book. For example, the analysis unit can analyze documentary videos related to the content of the book and provide relevant information. For example, the analysis unit can analyze podcasts related to the content of the book and provide relevant information. For example, the analysis unit can analyze interview audio related to the content of the book and provide relevant information. In this way, by analyzing other related media based on the content of the book, the user can be provided with multifaceted information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the content of the book into a generative AI and have the generative AI perform the analysis of other related media.

[0044] The analysis unit can take the user's reading history into consideration and compare it with past analysis results. For example, the analysis unit can compare the analysis results of books the user has read in the past with the analysis results of books currently being read. For example, the analysis unit can compare the analysis results of books on the same theme from the user's reading history. For example, the analysis unit can show the differences between past and current analysis results based on the user's reading history. This allows the system to provide information tailored to the user by comparing past analysis results while considering the user's reading history. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's reading history into a generative AI and have the generative AI perform a comparison with past analysis results.

[0045] The explanatory section can add diagrams and examples to explanations of difficult terms and concepts. For example, the explanatory section can add relevant diagrams to explanations of difficult terms. For example, the explanatory section can add concrete examples to explanations of difficult concepts. For example, the explanatory section can add videos or animations to explanations of difficult terms and concepts. By adding diagrams and examples to explanations of difficult terms and concepts, the user's understanding can be deepened. Some or all of the above processing in the explanatory section may be performed using, for example, a generative AI, or without a generative AI. For example, the explanatory section can input the diagrams and examples necessary for explaining difficult terms and concepts into a generative AI and have the generative AI add the diagrams and examples.

[0046] The commentary section can incorporate the opinions and explanations of experts related to the book's content. For example, the commentary section may incorporate interviews with experts related to the book's content. For example, the commentary section may incorporate papers or articles by experts related to the book's content. For example, the commentary section may incorporate the content of lectures or seminars by experts related to the book's content. This allows users to deepen their understanding by incorporating the opinions and explanations of experts related to the book's content. Some or all of the above processing in the commentary section may be performed using, for example, a generative AI, or without a generative AI. For example, the commentary section can input the opinions and explanations of experts related to the book's content into a generative AI and have the generative AI incorporate the opinions and explanations of experts.

[0047] The commentary section can refer to other related books and materials during the commentary process. For example, the commentary section may refer to the contents of other related books during the commentary process. For example, the commentary section may refer to related academic papers and articles during the commentary process. For example, the commentary section may refer to related online materials and databases during the commentary process. This allows the user to deepen their understanding by referring to other related books and materials during the commentary process. Some or all of the above processing in the commentary section may be performed using, for example, a generative AI, or without a generative AI. For example, the commentary section may input other related books and materials into a generative AI and have the generative AI provide the information to be referenced.

[0048] The explanation unit can customize the explanation content based on the user's learning history. For example, the explanation unit provides appropriate explanation content based on the user's past learning history. For example, the explanation unit provides explanation content that matches the user's level of understanding based on their learning history. For example, the explanation unit provides explanations on related topics based on the user's learning history. In this way, by customizing the explanation content based on the user's learning history, explanations suitable for the user can be provided. Some or all of the above processing in the explanation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the explanation unit can input the user's learning history into a generative AI and have the generative AI perform the customization of the explanation content.

[0049] The information provider can provide the latest research findings and statistical data related to the book's theme. For example, the information provider can provide the latest research papers related to the book's theme. For example, the information provider can provide the latest statistical data related to the book's theme. For example, the information provider can provide the latest news articles related to the book's theme. By providing the latest research findings and statistical data related to the book's theme, the user's understanding can be deepened. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input the latest research findings and statistical data related to the book's theme into a generative AI and have the generative AI perform the information provision.

[0050] The information provider may include related videos and podcasts in the information it provides. For example, the provider may provide videos related to the book's theme. For example, the provider may provide podcasts related to the book's theme. For example, the provider may provide interview videos related to the book's theme. By including videos and podcasts related to the information provided, the provider can offer users a multifaceted view of the information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the provider may input related videos and podcasts into a generative AI and have the generative AI perform the information provision.

[0051] The information provider can provide information on events and seminars related to the book's content. For example, the information provider can provide information on online seminars related to the book's theme. For example, the information provider can provide information on events related to the book's theme. For example, the information provider can provide information on workshops related to the book's theme. By providing information on events and seminars related to the book's content, the user's understanding can be deepened. Some or all of the above processing in the information provider can be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input information on events and seminars related to the book's content into a generative AI and have the generative AI provide the information.

[0052] The information provider can customize the information it provides based on the user's areas of interest. For example, the provider provides relevant information based on the user's areas of interest. For example, the provider provides relevant information based on the user's past reading history. For example, the provider provides relevant information based on the user's current interests. In this way, by customizing the information provided based on the user's areas of interest, it is possible to provide information that is suitable for the user. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can input the user's areas of interest into a generative AI and have the generative AI perform the information customization.

[0053] The facilitator can provide additional questions or points of discussion depending on the progress of the discussion. For example, if the discussion is stalled, the facilitator can provide additional questions. For example, if the discussion is lively, the facilitator can provide points of discussion. For example, if the discussion is nearing its end, the facilitator can provide concluding questions. In this way, the discussion can proceed smoothly by providing additional questions or points of discussion according to the progress of the discussion. Some or all of the above processing in the facilitator may be performed using, for example, a generative AI, or without a generative AI. For example, the facilitator can input the progress of the discussion into a generative AI and have the generative AI provide additional questions or points of discussion.

[0054] The facilitator can record the content of the discussion so that it can be referenced later. For example, the facilitator can record the content of the discussion in text format. For example, the facilitator can record the content of the discussion in audio format. For example, the facilitator can record the content of the discussion in video format. This allows users to review the content of the discussion by recording it and making it available for later reference. Some or all of the above processing in the facilitator may be performed using, for example, a generative AI, or without a generative AI. For example, the facilitator can input the content of the discussion into a generative AI and have the generative AI perform the recording and referencing functions.

[0055] The facilitator can introduce relevant online forums and communities based on the content of the discussion. For example, the facilitator can introduce online forums related to the content of the discussion. For example, the facilitator can introduce communities related to the content of the discussion. For example, the facilitator can introduce social networking groups related to the content of the discussion. This allows users to engage in deeper discussions by introducing relevant online forums and communities based on the content of the discussion. Some or all of the above processing in the facilitator may be performed using, for example, a generative AI, or without a generative AI. For example, the facilitator can input the content of the discussion into a generative AI and have the generative AI introduce relevant online forums and communities.

[0056] The facilitator can share the content of the discussion with other users and obtain feedback. For example, the facilitator can share the content of the discussion with other users and solicit their opinions. For example, the facilitator can share the content of the discussion with other users and obtain feedback. For example, the facilitator can share the content of the discussion with other users and conduct further discussions. This allows users to engage in deeper discussions by sharing the content of the discussion with other users and obtaining feedback. Some or all of the above processing in the facilitator may be performed using, for example, a generative AI, or without a generative AI. For example, the facilitator can input the content of the discussion into a generative AI and have the generative AI perform the sharing and feedback functions.

[0057] The support unit can provide messages to boost motivation according to the user's reading progress. For example, if the user is behind in their reading progress, the support unit will provide encouraging messages. For example, if the user is making good progress, the support unit will provide messages of praise. For example, if the user's reading progress has stalled, the support unit will provide messages to encourage them to resume reading. By providing messages to boost motivation according to the user's reading progress, the support unit can help the user continue reading. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input reading progress data into a generative AI and have the generative AI provide messages to boost motivation.

[0058] The support unit can set reminders tailored to the user's daily rhythm to support the formation of a reading habit. For example, the support unit can set reading reminders according to the user's daily rhythm. For example, the support unit can suggest reading times according to the user's daily rhythm. For example, the support unit can provide advice on forming a reading habit according to the user's daily rhythm. In this way, by setting reminders tailored to the user's daily rhythm to support the formation of a reading habit, the user can continue to read. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input the user's daily rhythm data into a generative AI and have the generative AI set the reminders.

[0059] The support unit can take into account the user's other schedules and appointments when proposing a reading schedule. For example, the support unit can refer to the user's calendar information and propose a reading schedule. For example, the support unit can adjust the reading time to match the user's schedule. For example, the support unit can set reading reminders, taking into account the user's other schedules. In this way, by taking into account the user's other schedules and appointments when proposing a reading schedule, it can provide a reading schedule that is suitable for the user. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the user's calendar information into a generative AI and have the generative AI execute the reading schedule proposal.

[0060] The support unit can provide features to share the user's progress with friends and family to support the formation of a reading habit. For example, the support unit can provide a function to share the user's reading progress with friends and encourage each other. For example, the support unit can provide a function to share the user's reading progress with family and receive support. For example, the support unit can provide a function to share the user's reading progress on social media and receive feedback. By providing a function to share the user's progress with friends and family to support the formation of a reading habit, the user can continue reading. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input the user's reading progress data into a generative AI and have the generative AI execute the progress sharing function.

[0061] The recommendation system can recommend books that are highly relevant to books the user has read in the past, based on the user's reading history. For example, the recommendation system may recommend books by the same author as books the user has read in the past. For example, the recommendation system may recommend books on the same theme as books the user has read in the past. For example, the recommendation system may recommend books in a series related to books the user has read in the past. In this way, by recommending highly relevant books based on the user's reading history, the system can provide the user with books that are suitable for them. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation system can input the user's reading history into a generative AI and have the generative AI perform the task of recommending highly relevant books.

[0062] The recommendation section can provide reviews and ratings from other users for the books it recommends. For example, the recommendation section can display reviews from other users for the books it recommends. For example, the recommendation section can display ratings from other users for the books it recommends. For example, the recommendation section can display comments from other users for the books it recommends. By providing reviews and ratings from other users for the books it recommends, users can use this information as a reference when choosing books. Some or all of the above processing in the recommendation section may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation section can input reviews and ratings from other users into a generative AI and have the generative AI provide the information.

[0063] The recommendation section can introduce movies and documentaries related to the recommended book. For example, the recommendation section can introduce movies related to the recommended book. For example, the recommendation section can introduce documentaries related to the recommended book. For example, the recommendation section can introduce television programs related to the recommended book. In this way, by introducing movies and documentaries related to the recommended book, the user can be provided with multifaceted information. Some or all of the above processing in the recommendation section may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation section can input related movies and documentaries into a generative AI and have the generative AI provide the information.

[0064] The recommendation system can customize the books it recommends based on the user's current interests and trends. For example, the recommendation system can recommend relevant books based on the user's current interests. For example, the recommendation system can recommend relevant books based on the user's current trends. For example, the recommendation system can recommend relevant books based on the user's current interests. This allows the system to provide books that are suitable for the user by customizing the books it recommends based on the user's current interests and trends. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation system can input the user's current interests and trends into a generative AI and have the generative AI perform the book customization.

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

[0066] The analysis unit can adjust the timing of the analysis based on the user's reading speed. For example, if the user is speed-reading, the analysis unit can perform a concise analysis, while if the user is reading slowly, it can perform a more detailed analysis. Furthermore, if the user stops reading midway, the analysis unit can provide an analysis result indicating the next point to read. In this way, by adjusting the timing of the analysis according to the user's reading speed, the system can provide analysis results tailored to the user.

[0067] The explanation section can customize its explanation methods based on the user's learning style. For example, it can use diagrams and videos extensively for visual learners, and provide audio explanations for auditory learners. It can also provide concrete examples and practice problems for practical learners. By providing explanations tailored to the user's learning style, it can enhance their understanding.

[0068] The service can customize relevant information based on the user's areas of interest. For example, if a user is interested in science, it can provide the latest research findings and news related to science. Similarly, if a user is interested in history, it can provide references and articles related to history. This enriches the reading experience by providing information tailored to the user's interests.

[0069] The discussion facilitator can suggest discussion topics based on the user's reading history. For example, it can suggest discussion topics related to books the user has read in the past. It can also suggest discussion topics based on topics the user is interested in. This allows for deeper discussions by suggesting discussion topics based on the user's reading history.

[0070] The support team can suggest reading schedules based on the user's lifestyle. For example, if a user has a morning-oriented lifestyle, the support team can suggest a reading schedule for the morning hours. Similarly, if a user has a night-owl lifestyle, the support team can suggest a reading schedule for the evening hours. By suggesting reading schedules tailored to the user's lifestyle, the support team can help them develop a reading habit.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The analysis unit analyzes the content of the book the user is reading in real time. The analysis unit can analyze page by page, paragraph by paragraph, and sentence by sentence, using natural language processing technology and topic models to analyze the content of the book and extract important and relevant information. Step 2: The explanation section explains the difficult parts based on the analysis performed by the analysis section. The explanation section explains technical terms and abstract concepts in an easy-to-understand manner, and can also use diagrams, examples, videos, and animations to explain difficult parts. Step 3: The provision department provides relevant information based on the analysis conducted by the analysis department. The provision department can provide the latest information, news, references, related articles, videos, and audio related to the book's theme. Step 4: The facilitator facilitates the discussion based on the analysis performed by the analysis unit. The facilitator can suggest questions and discussion topics based on the reading material, propose methods for conducting the discussion, and record and share the discussion. Step 5: The support unit supports the formation of reading habits based on the analysis performed by the analysis unit. The support unit can provide reading schedule suggestions, progress management, reminder functions, motivational messages, and progress sharing functions with friends and family. Step 6: The recommendation team recommends personalized books based on the analysis performed by the analytics team. The recommendation team recommends the next book to read based on the user's interests, reading history, emotions, reading speed, concerns, and trends.

[0073] (Example of form 2) The reading support system according to an embodiment of the present invention is a system that analyzes the content of a book being read by a user in real time, provides explanations of difficult parts and related information, and facilitates discussion. The reading support system analyzes the content of a book being read by a user in real time, provides explanations of difficult parts and related information, and facilitates discussion. It also supports reading in general, such as forming reading habits, managing reading lists, and recommending personalized books. For example, the reading support system analyzes the content of a book being read by a user in real time. For example, if a user encounters difficult terms or concepts, the reading support system will explain those parts in an easy-to-understand way. This allows the user to proceed with reading smoothly. Next, the reading support system provides the latest information and news related to the theme of the book. For example, if a user is reading a history book, the system can deepen the user's understanding by providing the latest research findings and news related to that era. Furthermore, the reading support system proposes questions and discussion themes based on the reading content and facilitates discussion. For example, if a user is reading a philosophy book, the system can propose discussion themes based on its content, allowing the user to exchange opinions with other readers. The reading support system also supports the formation of reading habits. For example, by providing reading schedule suggestions, progress tracking, and reminder functions, users can continue reading. Furthermore, the reading support system recommends the next book to read based on the user's interests and reading history. For instance, if a user enjoys mystery novels, the reading support system will recommend mystery novels to read next, enriching the user's reading experience. In this way, the reading support system is designed to enhance reading comprehension and enjoyment, and maximize knowledge absorption. Thus, the reading support system can revolutionize the user's reading experience, increasing comprehension and enjoyment, and maximizing knowledge absorption.

[0074] The reading support system according to this embodiment comprises an analysis unit, an explanation unit, a provision unit, a promotion unit, a support unit, and a recommendation unit. The analysis unit analyzes the content of the book being read by the user in real time. The analysis unit can perform analysis page by page, paragraph by paragraph, or sentence by sentence. The analysis unit can analyze the content of the book using natural language processing technology and extract important information. The analysis unit can analyze the content of the book using topic modeling and extract relevant information. The explanation unit explains difficult parts based on the content analyzed by the analysis unit. The explanation unit explains technical terms and abstract concepts in an easy-to-understand manner. The explanation unit can also explain difficult parts using diagrams and examples. The explanation unit can also explain difficult parts using videos and animations. The provision unit provides relevant information based on the content analyzed by the analysis unit. The provision unit provides the latest information and news related to the book's theme. The provision unit can also provide references and related articles related to the book's theme. The provisioning unit can, for example, provide videos and audio related to the book's theme. The facilitating unit facilitates discussion based on the analysis performed by the analytics unit. The facilitating unit can, for example, suggest questions and discussion topics based on the reading content. The facilitating unit can, for example, suggest how to conduct a discussion based on the reading content. The facilitating unit can, for example, record and share discussions based on the reading content. The support unit supports the formation of reading habits based on the analysis performed by the analytics unit. The support unit can, for example, provide reading schedule suggestions, progress management, and reminder functions. The support unit can, for example, provide motivational messages to support the formation of reading habits. The support unit can, for example, provide a function to share progress with friends and family to support the formation of reading habits. The recommendation unit recommends personalized books based on the analysis performed by the analytics unit. The recommendation unit recommends books to read next based on the user's interests and reading history. The recommendation unit can, for example, adjust the books recommended based on the user's emotions and reading speed.The recommendation system can, for example, customize the books it recommends based on the user's interests and trends. This allows the reading support system according to the embodiment to innovate the user's reading experience, enhance comprehension and enjoyment, and maximize knowledge absorption.

[0075] The analysis unit analyzes the content of books read by users in real time. For example, it can analyze page by page, paragraph by paragraph, or sentence by sentence. Specifically, the analysis unit acquires text data from books and analyzes the content using natural language processing (NLP) techniques. NLP techniques include morphological analysis, syntactic analysis, and semantic analysis, which are combined to analyze the book's content in detail. For example, morphological analysis is used to divide sentences into individual words and identify the part of speech of each word. Next, syntactic analysis is used to analyze the sentence structure and clarify relationships such as subject, predicate, and object. Furthermore, semantic analysis is used to understand the meaning of the sentence and extract important information. The analysis unit can also analyze the book's content using topic modeling and extract relevant information. Topic modeling classifies the book's content into multiple topics and identifies words and phrases related to each topic. This allows for an understanding of the book's overall theme and key points. Based on these analysis results, the analysis unit generates foundational data to provide users with important and relevant information.

[0076] The explanatory section explains complex parts based on the analysis conducted by the analysis section. For example, the explanatory section explains technical terms and abstract concepts in an easy-to-understand manner. Specifically, the explanatory section receives data provided by the analysis section and explains technical terms and abstract concepts in simple language. For example, it provides definitions and background information for technical terms to make them easier for users to understand. Furthermore, it explains abstract concepts using concrete examples and metaphors to allow users to understand them intuitively. The explanatory section can also explain complex parts using diagrams and examples. For example, it can illustrate complex processes and relationships with diagrams to make them easier to understand visually. It can also explain using concrete examples so that users can apply them to real-world situations. In addition, the explanatory section can explain complex parts using videos and animations. By using videos and animations, it can visually show dynamic processes and changes, allowing users to understand more deeply. In this way, the explanatory section can help users understand the content of the book more deeply and enhance the learning effect.

[0077] The information provision department provides relevant information based on the analysis conducted by the analysis department. Specifically, the information provision department provides the latest information and news related to the book's theme. For example, it collects and provides users with the latest research findings, technological trends, and social topics related to the book's content. The information provision department can also provide references and related articles related to the book's theme. For example, it introduces academic papers, specialized books, and related web articles to complement the book's content, enabling users to learn more deeply. Furthermore, the information provision department can also provide videos and audio related to the book's theme. For example, it provides lecture videos, interviews, and podcasts related to the book's content, enabling users to learn from multiple perspectives. In this way, the information provision department enables users to gain a broader understanding of the book's content and efficiently gather relevant information.

[0078] The Facilitation Unit promotes discussion based on the analysis performed by the Analysis Unit. Specifically, the Facilitation Unit suggests questions and discussion topics based on the reading content. For example, it may present questions about important points or debatable topics related to the book's content, providing users with a starting point for discussion. The Facilitation Unit can also suggest methods for conducting discussions based on the reading content. For example, it may provide guidelines and frameworks to support the discussion, enabling users to effectively advance the discussion. Furthermore, the Facilitation Unit can record and share discussions based on the reading content. For example, by recording the content of the discussion and sharing it with other users, it promotes knowledge sharing and exchange of opinions. This allows the Facilitation Unit to help users deepen their understanding of the book's content by thinking deeply about it and exchanging opinions with other users.

[0079] The support unit assists in forming reading habits based on the analysis performed by the analytics unit. Specifically, the support unit provides reading schedule suggestions, progress management, and reminder functions. For example, it suggests an appropriate reading plan based on the user's reading goals and schedule, and manages their progress. It also uses a reminder function to notify users of reading times, supporting the establishment of reading habits. Furthermore, the support unit can provide messages that boost motivation to support the formation of reading habits. For example, it can send encouraging messages and messages that convey a sense of accomplishment according to the user's reading progress to maintain their motivation. The support unit can also provide a progress sharing function with friends and family to support the formation of reading habits. For example, by sharing reading progress with friends and family and encouraging each other, it promotes the establishment of reading habits. In this way, the support unit supports users in continuously enjoying reading and forming reading habits.

[0080] The recommendation team recommends personalized books based on the analysis performed by the analytics team. Specifically, the recommendation team recommends books to read next based on the user's interests and reading history. For example, it analyzes the genres, themes, and ratings of books the user has read in the past and recommends similar books. The recommendation team can also adjust its recommendations based on the user's mood and reading speed. For example, if the user wants to relax, it will recommend lighter books, and if they want to concentrate and learn, it will recommend books with more specialized content. Furthermore, the recommendation team can customize its recommendations based on the user's interests and current trends. For example, it can recommend books related to current social trends and the user's interests, providing books that are likely to interest the user. This makes it easier for the recommendation team to find books that users will always be interested in, enriching the reading experience.

[0081] The explanation section can explain difficult terms and concepts based on the content of the book the user is reading. For example, the explanation section can explain technical terms and abstract concepts in an easy-to-understand way. For example, the explanation section can also explain difficult parts using diagrams and examples. For example, the explanation section can also explain difficult parts using videos and animations. In this way, by explaining difficult terms and concepts based on the content of the book the user is reading, the user's understanding can be deepened. Some or all of the above processing in the explanation section may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the explanation section can input the content of the book the user is reading into a generative AI and have the generative AI perform explanations of difficult terms and concepts.

[0082] The information provider can provide the latest information and news related to the book's theme. For example, the information provider can provide the latest research findings and news articles related to the book's theme. The information provider can also provide references and related articles related to the book's theme. The information provider can also provide videos and audio related to the book's theme. This allows for a deeper understanding of the user by providing the latest information and news related to the book's theme. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input the latest information related to the book's theme into a generative AI and have the generative AI provide the relevant information.

[0083] The facilitator can suggest questions and discussion topics based on the reading content and facilitate the discussion. For example, the facilitator can suggest questions and discussion topics based on the reading content. The facilitator can also suggest methods for conducting a discussion based on the reading content. The facilitator can also record and share discussions based on the reading content. This allows for a deeper understanding of the user by suggesting questions and discussion topics based on the reading content and facilitating the discussion. Some or all of the above processing in the facilitator may be performed using, for example, a generative AI, or without a generative AI. For example, the facilitator can input questions and discussion topics based on the reading content into a generative AI and have the generative AI facilitate the discussion.

[0084] The support unit can provide reading schedule suggestions, progress management, and reminder functions. For example, the support unit can provide reading schedule suggestions, progress management, and reminder functions. The support unit can also provide motivational messages to support the formation of reading habits. The support unit can also provide a progress sharing function with friends and family to support the formation of reading habits. This allows users to continue reading by providing reading schedule suggestions, progress management, and reminder functions. Some or all of the above-described processes in the support unit may be performed using, for example, a generative AI, or not. For example, the support unit can input reading schedule suggestions, progress management, and reminder functions into a generative AI and have the generative AI execute processes to support the formation of reading habits.

[0085] The recommendation system can recommend books to read next based on the user's interests and reading history. For example, the recommendation system can recommend books based on the user's interests and reading history. The recommendation system can also adjust the books it recommends based on the user's emotions and reading speed. Furthermore, it can customize the books it recommends based on the user's interests and current trends. This enriches the user's reading experience by recommending books based on their interests and reading history. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or without one. For example, the recommendation system can input the user's interests and reading history into a generative AI and have the generative AI recommend books to read next.

[0086] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated emotions. For example, if the user is excited, the analysis unit performs a detailed analysis to facilitate a deeper understanding. For example, if the user is tired, the analysis unit performs a concise analysis to reduce the burden. For example, if the user is relaxed, the analysis unit performs a balanced analysis to provide appropriate information. In this way, by adjusting the depth of the analysis according to the user's emotions, the analysis unit can provide analysis results that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the depth of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the genre of the book. For example, in the case of a novel, the analysis unit applies an algorithm that analyzes the flow of the story and the relationships between characters. For example, in the case of an academic book, the analysis unit applies an algorithm that specializes in the analysis of technical terms and theories. For example, in the case of a self-help book, the analysis unit applies an algorithm that specializes in the analysis of practical advice and case studies. By applying the appropriate analysis algorithm according to the genre of the book, the accuracy of the analysis results is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the genre of the book into a generative AI and have the generative AI execute the application of an appropriate analysis algorithm.

[0088] The analysis unit can provide analysis results in real time according to the user's reading speed. For example, if the user is speed-reading, the analysis unit provides analysis results that highlight the key points. For example, if the user is reading slowly, the analysis unit provides detailed analysis results. For example, if the user stops midway, the analysis unit provides analysis results indicating the next point to read. In this way, by providing analysis results in real time according to the user's reading speed, information tailored to the user can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's reading speed into the generative AI and have the generative AI perform the provision of analysis results in real time.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, information suitable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0090] The analysis unit can analyze other related media (videos, audio, etc.) based on the content of the book. For example, the analysis unit can analyze documentary videos related to the content of the book and provide relevant information. For example, the analysis unit can analyze podcasts related to the content of the book and provide relevant information. For example, the analysis unit can analyze interview audio related to the content of the book and provide relevant information. In this way, by analyzing other related media based on the content of the book, the user can be provided with multifaceted information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the content of the book into a generative AI and have the generative AI perform the analysis of other related media.

[0091] The analysis unit can take the user's reading history into consideration and compare it with past analysis results. For example, the analysis unit can compare the analysis results of books the user has read in the past with the analysis results of books currently being read. For example, the analysis unit can compare the analysis results of books on the same theme from the user's reading history. For example, the analysis unit can show the differences between past and current analysis results based on the user's reading history. This allows the system to provide information tailored to the user by comparing past analysis results while considering the user's reading history. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's reading history into a generative AI and have the generative AI perform a comparison with past analysis results.

[0092] The commentary unit can estimate the user's emotions and adjust the way the commentary is presented based on the estimated emotions. For example, if the user is nervous, the commentary unit will use simple and easy-to-understand language. If the user is relaxed, the commentary unit will use language that includes detailed explanations. If the user is excited, the commentary unit will use visually stimulating language. In this way, by adjusting the way the commentary is presented according to the user's emotions, it is possible to provide a commentary that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the commentary unit may be performed using a generative AI, or not using a generative AI. For example, the commentary unit can input user emotion data into a generative AI and have the generative AI adjust the way the commentary is presented.

[0093] The explanatory section can add diagrams and examples to explanations of difficult terms and concepts. For example, the explanatory section can add relevant diagrams to explanations of difficult terms. For example, the explanatory section can add concrete examples to explanations of difficult concepts. For example, the explanatory section can add videos or animations to explanations of difficult terms and concepts. By adding diagrams and examples to explanations of difficult terms and concepts, the user's understanding can be deepened. Some or all of the above processing in the explanatory section may be performed using, for example, a generative AI, or without a generative AI. For example, the explanatory section can input the diagrams and examples necessary for explaining difficult terms and concepts into a generative AI and have the generative AI add the diagrams and examples.

[0094] The commentary section can incorporate the opinions and explanations of experts related to the book's content. For example, the commentary section may incorporate interviews with experts related to the book's content. For example, the commentary section may incorporate papers or articles by experts related to the book's content. For example, the commentary section may incorporate the content of lectures or seminars by experts related to the book's content. This allows users to deepen their understanding by incorporating the opinions and explanations of experts related to the book's content. Some or all of the above processing in the commentary section may be performed using, for example, a generative AI, or without a generative AI. For example, the commentary section can input the opinions and explanations of experts related to the book's content into a generative AI and have the generative AI incorporate the opinions and explanations of experts.

[0095] The commentary unit can estimate the user's emotions and adjust the level of detail in the commentary based on the estimated emotions. For example, if the user is nervous, the commentary unit provides a concise commentary. For example, if the user is relaxed, the commentary unit provides a detailed commentary. For example, if the user is excited, the commentary unit provides a visually stimulating commentary. In this way, by adjusting the level of detail in the commentary according to the user's emotions, a commentary suitable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the commentary unit may be performed using a generative AI, or not using a generative AI. For example, the commentary unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the level of detail in the commentary.

[0096] The commentary section can refer to other related books and materials during the commentary process. For example, the commentary section may refer to the contents of other related books during the commentary process. For example, the commentary section may refer to related academic papers and articles during the commentary process. For example, the commentary section may refer to related online materials and databases during the commentary process. This allows the user to deepen their understanding by referring to other related books and materials during the commentary process. Some or all of the above processing in the commentary section may be performed using, for example, a generative AI, or without a generative AI. For example, the commentary section may input other related books and materials into a generative AI and have the generative AI provide the information to be referenced.

[0097] The explanation unit can customize the explanation content based on the user's learning history. For example, the explanation unit provides appropriate explanation content based on the user's past learning history. For example, the explanation unit provides explanation content that matches the user's level of understanding based on their learning history. For example, the explanation unit provides explanations on related topics based on the user's learning history. In this way, by customizing the explanation content based on the user's learning history, explanations suitable for the user can be provided. Some or all of the above processing in the explanation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the explanation unit can input the user's learning history into a generative AI and have the generative AI perform the customization of the explanation content.

[0098] The information provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is excited, the information provider will prioritize providing the latest information. For example, if the user is tired, the information provider will prioritize providing concise information. For example, if the user is relaxed, the information provider will provide balanced information. In this way, by determining the priority of the information to be provided according to the user's emotions, information appropriate to the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using a generative AI, or not using a generative AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform the determination of information priority.

[0099] The information provider can provide the latest research findings and statistical data related to the book's theme. For example, the information provider can provide the latest research papers related to the book's theme. For example, the information provider can provide the latest statistical data related to the book's theme. For example, the information provider can provide the latest news articles related to the book's theme. By providing the latest research findings and statistical data related to the book's theme, the user's understanding can be deepened. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input the latest research findings and statistical data related to the book's theme into a generative AI and have the generative AI perform the information provision.

[0100] The information provider may include related videos and podcasts in the information it provides. For example, the provider may provide videos related to the book's theme. For example, the provider may provide podcasts related to the book's theme. For example, the provider may provide interview videos related to the book's theme. By including videos and podcasts related to the information provided, the provider can offer users a multifaceted view of the information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the provider may input related videos and podcasts into a generative AI and have the generative AI perform the information provision.

[0101] The information provider can estimate the user's emotions and adjust the format of the information provided based on the estimated emotions. For example, if the user is nervous, the information provider will provide information in a simple and easily readable format. For example, if the user is relaxed, the information provider will provide information in a format that includes detailed information. For example, if the user is in a hurry, the information provider will provide information in a format that gets straight to the point. In this way, by adjusting the format of the information provided according to the user's emotions, information suitable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using a generative AI, or not using a generative AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the information format.

[0102] The information provider can provide information on events and seminars related to the book's content. For example, the information provider can provide information on online seminars related to the book's theme. For example, the information provider can provide information on events related to the book's theme. For example, the information provider can provide information on workshops related to the book's theme. By providing information on events and seminars related to the book's content, the user's understanding can be deepened. Some or all of the above processing in the information provider can be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input information on events and seminars related to the book's content into a generative AI and have the generative AI provide the information.

[0103] The information provider can customize the information it provides based on the user's areas of interest. For example, the provider provides relevant information based on the user's areas of interest. For example, the provider provides relevant information based on the user's past reading history. For example, the provider provides relevant information based on the user's current interests. In this way, by customizing the information provided based on the user's areas of interest, it is possible to provide information that is suitable for the user. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can input the user's areas of interest into a generative AI and have the generative AI perform the information customization.

[0104] The facilitator can estimate the user's emotions and adjust the discussion topic based on the estimated emotions. For example, if the user is excited, the facilitator can suggest a stimulating topic. For example, if the user is tired, the facilitator can suggest a relaxing topic. For example, if the user is relaxed, the facilitator can suggest a balanced topic. In this way, by adjusting the discussion topic according to the user's emotions, a discussion suitable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the facilitator may be performed using a generative AI, or not using a generative AI. For example, the facilitator can input user emotion data into a generative AI and have the generative AI adjust the discussion topic.

[0105] The facilitator can provide additional questions or points of discussion depending on the progress of the discussion. For example, if the discussion is stalled, the facilitator can provide additional questions. For example, if the discussion is lively, the facilitator can provide points of discussion. For example, if the discussion is nearing its end, the facilitator can provide concluding questions. In this way, the discussion can proceed smoothly by providing additional questions or points of discussion according to the progress of the discussion. Some or all of the above processing in the facilitator may be performed using, for example, a generative AI, or without a generative AI. For example, the facilitator can input the progress of the discussion into a generative AI and have the generative AI provide additional questions or points of discussion.

[0106] The facilitator can record the content of the discussion so that it can be referenced later. For example, the facilitator can record the content of the discussion in text format. For example, the facilitator can record the content of the discussion in audio format. For example, the facilitator can record the content of the discussion in video format. This allows users to review the content of the discussion by recording it and making it available for later reference. Some or all of the above processing in the facilitator may be performed using, for example, a generative AI, or without a generative AI. For example, the facilitator can input the content of the discussion into a generative AI and have the generative AI perform the recording and referencing functions.

[0107] The facilitator can estimate the user's emotions and adjust the discussion's progression based on the estimated emotions. For example, if the user is tense, the facilitator can suggest a relaxing progression. If the user is relaxed, the facilitator can suggest a lively progression. If the user is excited, the facilitator can suggest a balanced progression. By adjusting the discussion's progression according to the user's emotions, a discussion suitable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the facilitator may be performed using a generative AI, or not. For example, the facilitator can input user emotion data into a generative AI and have the generative AI adjust the discussion's progression.

[0108] The facilitator can introduce relevant online forums and communities based on the content of the discussion. For example, the facilitator can introduce online forums related to the content of the discussion. For example, the facilitator can introduce communities related to the content of the discussion. For example, the facilitator can introduce social networking groups related to the content of the discussion. This allows users to engage in deeper discussions by introducing relevant online forums and communities based on the content of the discussion. Some or all of the above processing in the facilitator may be performed using, for example, a generative AI, or without a generative AI. For example, the facilitator can input the content of the discussion into a generative AI and have the generative AI introduce relevant online forums and communities.

[0109] The facilitator can share the content of the discussion with other users and obtain feedback. For example, the facilitator can share the content of the discussion with other users and solicit their opinions. For example, the facilitator can share the content of the discussion with other users and obtain feedback. For example, the facilitator can share the content of the discussion with other users and conduct further discussions. This allows users to engage in deeper discussions by sharing the content of the discussion with other users and obtaining feedback. Some or all of the above processing in the facilitator may be performed using, for example, a generative AI, or without a generative AI. For example, the facilitator can input the content of the discussion into a generative AI and have the generative AI perform the sharing and feedback functions.

[0110] The support unit can estimate the user's emotions and adjust the suggested reading schedule based on the estimated emotions. For example, if the user is excited, the support unit will suggest an energetic reading schedule. If the user is tired, the support unit will suggest a relaxing reading schedule. If the user is relaxed, the support unit will suggest a balanced reading schedule. In this way, by adjusting the suggested reading schedule according to the user's emotions, a reading schedule suitable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using a generative AI, or not using a generative AI. For example, the support unit can input the user's emotion data into a generative AI and have the generative AI adjust the suggested reading schedule.

[0111] The support unit can provide messages to boost motivation according to the user's reading progress. For example, if the user is behind in their reading progress, the support unit will provide encouraging messages. For example, if the user is making good progress, the support unit will provide messages of praise. For example, if the user's reading progress has stalled, the support unit will provide messages to encourage them to resume reading. By providing messages to boost motivation according to the user's reading progress, the support unit can help the user continue reading. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input reading progress data into a generative AI and have the generative AI provide messages to boost motivation.

[0112] The support unit can set reminders tailored to the user's daily rhythm to support the formation of a reading habit. For example, the support unit can set reading reminders according to the user's daily rhythm. For example, the support unit can suggest reading times according to the user's daily rhythm. For example, the support unit can provide advice on forming a reading habit according to the user's daily rhythm. In this way, by setting reminders tailored to the user's daily rhythm to support the formation of a reading habit, the user can continue to read. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input the user's daily rhythm data into a generative AI and have the generative AI set the reminders.

[0113] The support unit can estimate the user's emotions and adjust the reading progress management method based on the estimated emotions. For example, if the user is nervous, the support unit provides a simple progress management method. For example, if the user is relaxed, the support unit provides a detailed progress management method. For example, if the user is excited, the support unit provides a visually stimulating progress management method. This allows the support unit to provide progress management that is appropriate for the user by adjusting the reading progress management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 support unit may be performed using a generative AI, for example, or without a generative AI. For example, the support unit can input the user's emotion data into a generative AI and have the generative AI adjust the reading progress management method.

[0114] The support unit can take into account the user's other schedules and appointments when proposing a reading schedule. For example, the support unit can refer to the user's calendar information and propose a reading schedule. For example, the support unit can adjust the reading time to match the user's schedule. For example, the support unit can set reading reminders, taking into account the user's other schedules. In this way, by taking into account the user's other schedules and appointments when proposing a reading schedule, it can provide a reading schedule that is suitable for the user. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the user's calendar information into a generative AI and have the generative AI execute the reading schedule proposal.

[0115] The support unit can provide features to share the user's progress with friends and family to support the formation of a reading habit. For example, the support unit can provide a function to share the user's reading progress with friends and encourage each other. For example, the support unit can provide a function to share the user's reading progress with family and receive support. For example, the support unit can provide a function to share the user's reading progress on social media and receive feedback. By providing a function to share the user's progress with friends and family to support the formation of a reading habit, the user can continue reading. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input the user's reading progress data into a generative AI and have the generative AI execute the progress sharing function.

[0116] The recommendation system can estimate the user's emotions and adjust the genre of books it recommends based on those emotions. For example, if the user is excited, the recommendation system will recommend books in an exciting genre. If the user is tired, the recommendation system will recommend books in a relaxing genre. If the user is relaxed, the recommendation system will recommend books in a balanced genre. In this way, by adjusting the genre of books recommended according to the user's emotions, it is possible to recommend books that are suitable for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using a generative AI, or not using a generative AI. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI perform the adjustment of book genres.

[0117] The recommendation system can recommend books that are highly relevant to books the user has read in the past, based on the user's reading history. For example, the recommendation system may recommend books by the same author as books the user has read in the past. For example, the recommendation system may recommend books on the same theme as books the user has read in the past. For example, the recommendation system may recommend books in a series related to books the user has read in the past. In this way, by recommending highly relevant books based on the user's reading history, the system can provide the user with books that are suitable for them. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation system can input the user's reading history into a generative AI and have the generative AI perform the task of recommending highly relevant books.

[0118] The recommendation section can provide reviews and ratings from other users for the books it recommends. For example, the recommendation section can display reviews from other users for the books it recommends. For example, the recommendation section can display ratings from other users for the books it recommends. For example, the recommendation section can display comments from other users for the books it recommends. By providing reviews and ratings from other users for the books it recommends, users can use this information as a reference when choosing books. Some or all of the above processing in the recommendation section may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation section can input reviews and ratings from other users into a generative AI and have the generative AI provide the information.

[0119] The recommendation system can estimate the user's emotions and adjust the order of recommended books based on those emotions. For example, if the user is excited, the recommendation system will prioritize recommending stimulating books. If the user is tired, the recommendation system will prioritize recommending relaxing books. If the user is relaxed, the recommendation system will prioritize recommending balanced books. By adjusting the order of recommended books according to the user's emotions, the system can provide books that are suitable for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using a generative AI, or not. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI adjust the order of books.

[0120] The recommendation section can introduce movies and documentaries related to the recommended book. For example, the recommendation section can introduce movies related to the recommended book. For example, the recommendation section can introduce documentaries related to the recommended book. For example, the recommendation section can introduce television programs related to the recommended book. In this way, by introducing movies and documentaries related to the recommended book, the user can be provided with multifaceted information. Some or all of the above processing in the recommendation section may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation section can input related movies and documentaries into a generative AI and have the generative AI provide the information.

[0121] The recommendation system can customize the books it recommends based on the user's current interests and trends. For example, the recommendation system can recommend relevant books based on the user's current interests. For example, the recommendation system can recommend relevant books based on the user's current trends. For example, the recommendation system can recommend relevant books based on the user's current interests. This allows the system to provide books that are suitable for the user by customizing the books it recommends based on the user's current interests and trends. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation system can input the user's current interests and trends into a generative AI and have the generative AI perform the book customization.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The analysis unit can adjust the timing of the analysis based on the user's reading speed. For example, if the user is speed-reading, the analysis unit can perform a concise analysis, while if the user is reading slowly, it can perform a more detailed analysis. Furthermore, if the user stops reading midway, the analysis unit can provide an analysis result indicating the next point to read. In this way, by adjusting the timing of the analysis according to the user's reading speed, the system can provide analysis results tailored to the user.

[0124] The explanation section can customize its explanation methods based on the user's learning style. For example, it can use diagrams and videos extensively for visual learners, and provide audio explanations for auditory learners. It can also provide concrete examples and practice problems for practical learners. By providing explanations tailored to the user's learning style, it can enhance their understanding.

[0125] The service can customize relevant information based on the user's areas of interest. For example, if a user is interested in science, it can provide the latest research findings and news related to science. Similarly, if a user is interested in history, it can provide references and articles related to history. This enriches the reading experience by providing information tailored to the user's interests.

[0126] The discussion facilitator can suggest discussion topics based on the user's reading history. For example, it can suggest discussion topics related to books the user has read in the past. It can also suggest discussion topics based on topics the user is interested in. This allows for deeper discussions by suggesting discussion topics based on the user's reading history.

[0127] The support team can suggest reading schedules based on the user's lifestyle. For example, if a user has a morning-oriented lifestyle, the support team can suggest a reading schedule for the morning hours. Similarly, if a user has a night-owl lifestyle, the support team can suggest a reading schedule for the evening hours. By suggesting reading schedules tailored to the user's lifestyle, the support team can help them develop a reading habit.

[0128] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on those emotions. For example, if the user is excited, a detailed analysis can be performed to facilitate a deeper understanding. Conversely, if the user is tired, a concise analysis can be performed to reduce the burden. This allows for an improved reading experience by providing analysis results tailored to the user's emotions.

[0129] The explanation section can estimate the user's emotions and adjust the way the explanation is presented based on those emotions. For example, if the user is nervous, simple and easy-to-understand language can be used; if the user is relaxed, language with detailed explanations can be used. Furthermore, if the user is excited, visually stimulating language can be used. This allows for improved comprehension by providing explanations tailored to the user's emotions.

[0130] The information delivery system can estimate the user's emotions and prioritize the information provided based on those emotions. For example, if the user is excited, it can prioritize providing the latest information; if the user is tired, it can prioritize providing concise information. Furthermore, if the user is relaxed, it can provide balanced information. This allows for an improved reading experience by providing information tailored to the user's emotions.

[0131] The facilitator can estimate the user's emotions and adjust the discussion topic based on those emotions. For example, if the user is excited, it can suggest a stimulating topic; if the user is tired, it can suggest a relaxing topic. It can also suggest a balanced topic if the user is relaxed. This allows for more engaging discussions by providing a discussion tailored to the user's emotions.

[0132] The support unit can estimate the user's emotions and adjust the suggested reading schedule based on those emotions. For example, if the user is excited, it can suggest an energetic reading schedule; if the user is tired, it can suggest a relaxing reading schedule. It can also suggest a balanced reading schedule if the user is relaxed. This allows the system to support the formation of reading habits by providing reading schedules tailored to the user's emotions.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The analysis unit analyzes the content of the book the user is reading in real time. The analysis unit can analyze page by page, paragraph by paragraph, and sentence by sentence, using natural language processing technology and topic models to analyze the content of the book and extract important and relevant information. Step 2: The explanation section explains the difficult parts based on the analysis performed by the analysis section. The explanation section explains technical terms and abstract concepts in an easy-to-understand manner, and can also use diagrams, examples, videos, and animations to explain difficult parts. Step 3: The provision department provides relevant information based on the analysis conducted by the analysis department. The provision department can provide the latest information, news, references, related articles, videos, and audio related to the book's theme. Step 4: The facilitator facilitates the discussion based on the analysis performed by the analysis unit. The facilitator can suggest questions and discussion topics based on the reading material, propose methods for conducting the discussion, and record and share the discussion. Step 5: The support unit supports the formation of reading habits based on the analysis performed by the analysis unit. The support unit can provide reading schedule suggestions, progress management, reminder functions, motivational messages, and progress sharing functions with friends and family. Step 6: The recommendation team recommends personalized books based on the analysis performed by the analytics team. The recommendation team recommends the next book to read based on the user's interests, reading history, emotions, reading speed, concerns, and trends.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the analysis unit, explanation unit, provision unit, promotion unit, support unit, and recommendation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes the contents of the book in real time. The explanation unit is implemented by the specific processing unit 290 of the data processing unit 12 and explains difficult parts. The provision unit is implemented by the control unit 46A of the smart device 14 and provides relevant information. The promotion unit is implemented by the specific processing unit 290 of the data processing unit 12 and facilitates discussion. The support unit is implemented by the control unit 46A of the smart device 14 and supports the formation of reading habits. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends personalized books. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the analysis unit, explanation unit, provision unit, promotion unit, support unit, and recommendation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and analyzes the contents of the book in real time. The explanation unit is implemented by the specific processing unit 290 of the data processing unit 12 and explains difficult parts. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides relevant information. The promotion unit is implemented by the specific processing unit 290 of the data processing unit 12 and facilitates discussion. The support unit is implemented by the control unit 46A of the smart glasses 214 and supports the formation of reading habits. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends personalized books. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the analysis unit, explanation unit, provision unit, promotion unit, support unit, and recommendation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and analyzes the contents of the book in real time. The explanation unit is implemented by the specific processing unit 290 of the data processing unit 12 and explains difficult parts. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides relevant information. The promotion unit is implemented by the specific processing unit 290 of the data processing unit 12 and facilitates discussion. The support unit is implemented by the control unit 46A of the headset terminal 314 and supports the formation of reading habits. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends personalized books. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] Each of the multiple elements described above, including the analysis unit, explanation unit, provision unit, promotion unit, support unit, and recommendation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and analyzes the contents of the book in real time. The explanation unit is implemented by the specific processing unit 290 of the data processing unit 12 and explains difficult parts. The provision unit is implemented by the control unit 46A of the robot 414 and provides relevant information. The promotion unit is implemented by the specific processing unit 290 of the data processing unit 12 and facilitates discussion. The support unit is implemented by the control unit 46A of the robot 414 and supports the formation of reading habits. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends personalized books. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] (Note 1) An analysis unit that analyzes the content of books that users are reading in real time, Based on the analysis performed by the aforementioned analysis unit, an explanatory unit explains the difficult parts, A providing unit that provides related information based on the content analyzed by the aforementioned analysis unit, A facilitator unit that facilitates discussion based on the content analyzed by the aforementioned analysis unit, A support unit that supports the formation of reading habits based on the content analyzed by the aforementioned analysis unit, The system includes a recommendation unit that recommends personalized books based on the content analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned explanatory section is, This tool explains complex terms and concepts based on the content of the books the user is reading. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Provides the latest information and news related to the book's theme. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned promotion unit is Suggest questions and discussion topics based on the reading material and facilitate the discussion. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit is It offers reading schedule suggestions, progress tracking, and reminder functions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned recommendation department, Based on the user's interests and reading history, we recommend books they should read next. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts the depth of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, Apply different analysis algorithms depending on the genre of the book. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, Provides real-time analysis results based on the user's reading speed. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, Based on the content of the book, analyze other related media. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The analysis results are compared with past results, taking into account the user's reading history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned explanatory section is, The system estimates the user's emotions and adjusts the way explanations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned explanatory section is, Add diagrams and examples to explain complex terms and concepts. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned explanatory section is, Incorporate expert opinions and explanations related to the book's content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned explanatory section is, It estimates the user's emotions and adjusts the level of detail in the explanation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned explanatory section is, When explaining, refer to other related books and materials. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned explanatory section is, Customize the explanation content based on the user's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, This book provides the latest research findings and statistical data related to the book's theme. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The information provided will include related videos and podcasts. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the format of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, Provides information on events and seminars related to the book's content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, Customize the information provided based on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned promotion unit is It estimates the user's emotions and adjusts the discussion topic based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned promotion unit is Depending on the progress of the discussion, provide additional questions and points for discussion. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned promotion unit is Record the content of the discussion so that it can be referenced later. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned promotion unit is It estimates the user's emotions and adjusts the discussion's progression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned promotion unit is Based on the discussion, we will introduce relevant online forums and communities. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned promotion unit is Share the discussion with other users and get feedback. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned support unit is It estimates the user's emotions and adjusts reading schedule suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned support unit is Provides motivational messages based on reading progress. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned support unit is To support the formation of reading habits, set reminders that are tailored to the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit is It estimates the user's emotions and adjusts how reading progress is managed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned support unit is When suggesting a reading schedule, take into account the user's other schedules and appointments. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned support unit is To support the development of reading habits, we provide a feature that allows users to share their progress with friends and family. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned recommendation department, It estimates the user's emotions and adjusts the genre of books recommended based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned recommendation department, Based on the user's reading history, the system recommends books that are highly relevant to books they have read in the past. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned recommendation department, Provide reviews and ratings from other users for recommended books. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned recommendation department, It estimates the user's emotions and adjusts the order of recommended books based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned recommendation department, This section introduces movies and documentaries related to the recommended books. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned recommendation department, Customize recommended books based on the user's current interests and trends. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0207] 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 the content of books that users are reading in real time, Based on the analysis performed by the aforementioned analysis unit, an explanatory unit explains the difficult parts, A providing unit that provides related information based on the content analyzed by the aforementioned analysis unit, A facilitator unit that facilitates discussion based on the content analyzed by the aforementioned analysis unit, A support unit that supports the formation of reading habits based on the content analyzed by the aforementioned analysis unit, The system includes a recommendation unit that recommends personalized books based on the content analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned explanatory section is, This tool explains complex terms and concepts based on the content of the books the user is reading. The system according to feature 1.

3. The aforementioned supply unit is, Provides the latest information and news related to the book's theme. The system according to feature 1.

4. The aforementioned promotion unit is Suggest questions and discussion topics based on the reading material and facilitate the discussion. The system according to feature 1.

5. The aforementioned support unit is It offers reading schedule suggestions, progress tracking, and reminder functions. The system according to feature 1.

6. The aforementioned recommendation department, Based on the user's interests and reading history, we recommend books they should read next. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts the depth of the analysis based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, Apply different analysis algorithms depending on the genre of the book. The system according to feature 1.

9. The aforementioned analysis unit, Provides real-time analysis results based on the user's reading speed. The system according to feature 1.

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