system

Abstract

The system according to this embodiment aims to efficiently provide information on the plot and characters based on the book data up to the point the user has read. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a presentation unit. The collection unit collects book data up to the point the user has read. The analysis unit analyzes the data collected by the collection unit and creates explanations of the plot, characters, and keywords. The presentation unit presents the explanations created by the analysis unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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, there is a problem that it is difficult to efficiently confirm the summary and character information when there is an interruption during reading or when reading a complex story.

[0005] The system according to the embodiment aims to efficiently provide the summary and character information based on the book data up to the location where the user has read.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, and a presentation unit. The collection unit collects book data up to the point the user has read. The analysis unit analyzes the data collected by the collection unit and creates explanations of the plot, characters, and keywords. The presentation unit presents the explanations created by the analysis unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently provide information on the plot and characters based on the book data up to the point the user has read. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The reading support system according to an embodiment of the present invention is a system for providing support to resume reading when it is interrupted. This reading support system collects book data up to the point the user has read, and a generating AI analyzes this data to create an explanation of the plot, characters, and keywords, which are then presented to the user. The reading support system is integrated into e-book services to differentiate them from competitors and promote service growth. First, the reading support system collects book data up to the point the user has read. At this time, it collects information such as which page the user has read, which characters have appeared, and which keywords are important. For example, if the user is reading a mystery novel, it collects information such as the culprit, the victim, and important evidence. Next, the reading support system uses a generating AI to analyze the collected data. Based on the collected data, the generating AI creates an explanation of the plot, characters, and keywords. For example, it concisely summarizes the plot up to the point the user has read and explains the relationships between characters and the meaning of important keywords. Finally, the reading support system presents the generated explanation to the user. The user can check the explanation created by the generating AI by tapping on character names or keywords within the app. For example, when a user taps on a character's name, such as "Member of the Mystery Club," detailed information about that character is displayed. This system ensures that even if a user interrupts their reading, they can obtain all the necessary information when they resume. It also eliminates the need to return to the character introductions at the beginning of the book or reread previous sections. This improves the reading experience and is expected to increase the number of users and purchase rates of the e-book service. As a result, the reading support system can efficiently collect, analyze, and present information up to the point the user has read.

[0029] The reading support system according to this embodiment comprises a collection unit, an analysis unit, and a presentation unit. The collection unit collects book data up to the point the user has read. The collection unit collects information such as which page the user has read, which characters have appeared, and which keywords are important. For example, if the user is reading a mystery novel, the collection unit can collect information such as the culprit, the victim, and important evidence. The analysis unit analyzes the data collected by the collection unit and creates explanations of the plot, characters, and keywords. For example, the analysis unit can concisely summarize the plot up to the point the user has read based on the collected data. For example, the analysis unit can explain the relationships between characters and the meaning of important keywords. The presentation unit presents the explanations created by the analysis unit to the user. For example, the presentation unit can display explanations created by the generating AI when the user taps on character names or keywords within the app. As a result, the reading support system according to this embodiment can efficiently collect, analyze, and present information up to the point the user has read.

[0030] The data collection unit collects book data up to the point the user has read. Specifically, it collects information such as which pages the user has read, which characters have appeared, and which keywords are important, through e-readers or dedicated apps. For example, e-readers can record the history of page scrolling and tapping, allowing for accurate tracking of the user's reading progress. Text analysis technology can also be used to identify the frequency and location of characters and keywords, extracting important information. Furthermore, if the user takes notes or highlights specific sections, this information is also collected and used for analysis. For example, if a user is reading a mystery novel, information such as the culprit, victim, and important evidence can be collected. This allows the data collection unit to gain a detailed understanding of the user's reading history and points of interest, which can be used for subsequent analysis and presentation. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and presentation units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit to create summaries, character descriptions, and keyword explanations. Specifically, based on the collected data, it can concisely summarize the plot up to the point the user has read. For example, it can use natural language processing techniques to summarize the text and extract important events and plot points. It can also utilize knowledge graphs and entity recognition techniques to explain the relationships between characters and the meaning of important keywords. For instance, by identifying the names and roles of characters and illustrating their relationships, it makes it easier for users to understand the progression of the story. Furthermore, it consults dictionary databases and glossaries to provide appropriate explanations for keywords. The analysis unit integrates this information to generate explanations that are useful to the user. For example, if a user is reading a mystery novel, it can explain the relationships between the culprit, victim, and important evidence to deepen their understanding of the story. The analysis unit uses generative AI to analyze this data and simulate multiple scenarios to identify the most likely explanation. This allows the analysis unit to quickly and accurately analyze the collected data and provide useful information to the user. In addition, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and pattern recognition. This allows the analysis unit to not only grasp the situation in real time but also to provide long-term reading support, improving the overall reliability and usefulness of the system.

[0032] The presentation unit presents explanations created by the analysis unit to the user. Specifically, by tapping character names or keywords within the app, the user can view explanations generated by the AI. For example, when a user taps a character's name, background information and relationships of that character will pop up. Similarly, tapping a keyword will display its meaning and related information. The presentation unit employs a user interface that is intuitive and easy to use, enabling users to quickly access the information they need. For example, information is displayed compactly using pop-up windows and tooltips, ensuring that the user's reading experience is not interrupted. Additionally, a voice assistant function can be added, allowing users to obtain information using voice commands. Furthermore, the presentation unit can collect user feedback and continuously improve the accuracy and effectiveness of its presentations. For example, users can evaluate the explanations or ask additional questions, allowing the system to learn from the feedback and make future presentations more appropriate. The presentation unit also supports multiple devices and platforms, enabling users to access information anywhere. For example, information can be seamlessly shared across different devices such as smartphones, tablets, and PCs, improving user convenience. This allows the display unit to provide information to the user quickly and accurately, enriching the reading experience.

[0033] The data collection unit can collect information such as which pages the user has read, which characters have appeared, and which keywords are important. For example, to determine which pages the user has read, the data collection unit can record page numbers and detect scroll positions. The data collection unit can also use name frequency and contextual analysis to identify characters. Furthermore, the data collection unit can consider frequency and contextual importance to evaluate the importance of keywords. In this way, the data collection unit can collect detailed information up to where the user has read. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's reading data into an AI, which can then analyze the data and collect the necessary information.

[0034] The analysis unit can create a concise summary based on the collected data. For example, the analysis unit can create a concise summary of the content up to the point the user has read, based on the collected data. The analysis unit can create a summary considering, for example, the length of the summary and the types of information to be included. This allows the analysis unit to concisely grasp the content up to the point the user has read. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the collected data into a generative AI, and the generative AI can analyze the data to create a summary.

[0035] The analysis unit can explain the relationships between the characters. For example, the analysis unit explains the relationships between the characters based on the collected data. For example, the analysis unit can explain relationships such as family relationships, friendships, and rivalries. In this way, the analysis unit can understand the relationships between the characters. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the collected data into a generative AI, and the generative AI can analyze the data and explain the relationships between the characters.

[0036] The analysis unit can explain the meaning of important keywords. For example, the analysis unit explains the meaning of important keywords based on collected data. For example, the analysis unit can select important keywords such as technical terms or key words in a story and explain their meaning. This allows the analysis unit to understand the meaning of important keywords. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input collected data into a generative AI, which can then analyze the data and explain the meaning of important keywords.

[0037] The presentation section can display explanations generated by a generative AI when the user taps on character names or keywords within the app. For example, if the user taps on a character name such as "Member of the Mystery Club," the presentation section will display detailed information about that character. For example, if the user taps on an important keyword, the presentation section will display the meaning of that keyword and related information. This allows the presentation section to easily check explanations. Some or all of the above processing in the presentation section may be performed using a generative AI, or it may be performed without a generative AI. For example, the presentation section can provide an interface for displaying explanations generated by a generative AI to the user.

[0038] The data collection unit can analyze the user's reading speed and determine the optimal timing for data collection. For example, if the user is speed reading, the data collection unit can collect a large amount of information in a short time. For example, if the user is reading slowly, the data collection unit can collect detailed information. For example, if the user is repeatedly interrupting their reading, the data collection unit can collect data frequently. This allows the data collection unit to collect data according to the user's reading speed. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's reading speed data into an AI, which can then analyze the data to determine the optimal timing for data collection.

[0039] The data collection unit can also collect data on similar books the user has read in the past by referring to the user's reading history during the collection process. For example, the data collection unit can collect information on works by the same author that the user has read in the past. For example, the data collection unit can collect information on works of the same genre that the user has read in the past. For example, the data collection unit can collect information on works of the same series that the user has read in the past. This enables the data collection unit to collect data based on past reading history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's reading history data into an AI, which can then analyze the data to collect information on similar books.

[0040] The data collection unit can prioritize collecting highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize collecting information related to that region. For example, if the user is traveling, the data collection unit can prioritize collecting information related to the travel destination. For example, if the user is at home, the data collection unit can prioritize collecting information related to home. This enables the data collection unit to collect data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into AI, and the AI ​​can analyze the data to collect highly relevant information.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, the data collection unit can analyze posts about reading that the user has shared on social media and collect relevant information. For example, the data collection unit can analyze posts from accounts that the user follows and collect relevant information. For example, the data collection unit can analyze the activities of reading groups that the user participates in and collect relevant information. This enables the data collection unit to collect data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into an AI, which can then analyze the data and collect relevant information.

[0042] The analysis unit can apply different analysis algorithms depending on the genre of the book during analysis. For example, in the case of a mystery novel, the analysis unit can apply an algorithm that emphasizes information about the culprit and evidence. For example, in the case of a romance novel, the analysis unit can apply an algorithm that emphasizes information about the relationships between characters. For example, in the case of science fiction, the analysis unit can apply an algorithm that emphasizes information about technology and science. This allows the analysis unit to perform analysis according to the genre of the book. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input book genre data into a generative AI, and the generative AI can analyze the data and apply an appropriate algorithm.

[0043] The analysis unit can improve the accuracy of its analysis by referring to the user's reading history during the analysis process, based on past reading trends. For example, the analysis unit can improve the accuracy of its analysis by analyzing the trends of works by the same author that the user has read in the past. For example, the analysis unit can improve the accuracy of its analysis by analyzing the trends of works of the same genre that the user has read in the past. For example, the analysis unit can improve the accuracy of its analysis by analyzing the trends of works in the same series that the user has read in the past. This enables the analysis unit to perform analysis based on past reading trends. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's reading history data into a generative AI, and the generative AI can analyze the data to improve the accuracy of the analysis.

[0044] The analysis unit can determine the priority of analysis based on the publication date of the books during the analysis process. For example, in the case of the most recent books, the analysis unit prioritizes the analysis of the latest information. For example, in the case of classic books, the analysis unit can consider the historical background during the analysis. For example, in the case of reprinted books, the analysis unit can consider additional information added at the time of reprinting during the analysis. This enables the analysis unit to perform analysis based on the publication date of the books. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input book publication date data into a generating AI, and the generating AI can analyze the data to determine the priority of analysis.

[0045] The analysis unit can improve the accuracy of its analysis by referring to related literature in the book during the analysis process. For example, the analysis unit can refer to the book's bibliography and reflect relevant information in the analysis. For example, the analysis unit can refer to other literature written by the book's author and reflect it in the analysis. For example, the analysis unit can refer to literature related to the book's genre and reflect it in the analysis. This enables the analysis unit to perform analysis based on related literature. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input related literature data into a generating AI, and the generating AI can analyze the data to improve the accuracy of the analysis.

[0046] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit can prioritize display methods that the user has preferred to use in the past. For example, the display unit can exclude display methods that the user has avoided in the past. For example, the display unit can suggest the optimal display method based on the user's past operation history. In this way, the display unit provides the optimal display method based on past operation history. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input user operation history data into AI, and the AI ​​can analyze the data to select the optimal display method.

[0047] The presentation unit can, at the time of presentation, refer to the user's reading history and also present information on similar books the user has read in the past. For example, the presentation unit can present information on works by the same author that the user has read in the past. For example, the presentation unit can present information on works of the same genre that the user has read in the past. For example, the presentation unit can present information on works of the same series that the user has read in the past. This enables the presentation unit to present information based on the user's past reading history. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the user's reading history data into AI, and the AI ​​can analyze the data and present information on similar books.

[0048] The display unit can select the optimal display method based on the user's device information at the time of presentation. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the display unit provides the optimal display method based on the user's device information. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the user's device information into AI, and the AI ​​can analyze the data and select the optimal display method.

[0049] The presentation unit can analyze the user's social media activity and present relevant information at the time of presentation. For example, the presentation unit can analyze posts about reading that the user has shared on social media and present relevant information. For example, the presentation unit can analyze posts from accounts that the user follows and present relevant information. For example, the presentation unit can analyze the activities of reading groups that the user participates in and present relevant information. This enables the presentation unit to present information based on the user's social media activity. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the user's social media data into AI, which can then analyze the data and present relevant information.

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

[0051] A reading support system can learn a user's reading habits and provide individually optimized reading support. For example, if a user often reads at night, the system can automatically select a display mode suitable for nighttime reading. Furthermore, if a user prefers a particular genre, the system can prioritize collecting and analyzing information related to that genre. Additionally, if a user prefers the works of a specific author, the system can provide information about other works by that author. In this way, the reading support system can provide individually optimized support based on the user's reading habits.

[0052] A reading support system can monitor a user's reading speed in real time and suggest an optimal reading pace. For example, if a user is speed reading, the system can provide a lot of information in a short time. Conversely, if a user is reading slowly, the system can provide detailed information. Furthermore, if a user repeatedly interrupts their reading, the system can frequently collect data and provide appropriate information when they resume reading. In this way, the reading support system can suggest an optimal reading pace tailored to the user's reading speed.

[0053] The reading support system can provide highly relevant information based on the user's geographical location. For example, if a user is in a specific region, it can prioritize providing books and information related to that region. If the user is traveling, it can provide books and information related to their travel destination. Furthermore, if the user is at home, it can provide books and information related to their home. In this way, the reading support system can provide highly relevant information based on the user's geographical location.

[0054] A reading support system can analyze a user's social media activity and provide relevant information. For example, it can analyze reading-related posts a user shares on social media and provide relevant books and information. It can also analyze posts from accounts a user follows and provide relevant books and information. Furthermore, it can analyze the activities of reading groups a user participates in and provide relevant books and information. In this way, the reading support system can provide relevant information based on the user's social media activity.

[0055] The reading support system can refer to the user's reading history and provide information on similar books they have read in the past. For example, it can provide information on works by the same author the user has read in the past. It can also provide information on works of the same genre the user has read in the past. Furthermore, it can provide information on works of the same series the user has read in the past. In this way, the reading support system can provide information on similar books based on the user's past reading history.

[0056] The reading support system can provide the optimal display method based on the user's device information. For example, if the user is using a smartphone, it can provide a display method adapted to the screen size. If the user is using a tablet, it can provide a display method optimized for the larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, the reading support system can provide the optimal display method based on the user's device information.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects book data up to the point the user has read. Specifically, it collects information such as which page the user has read, which characters have appeared, and which keywords are important. For example, if the user is reading a mystery novel, it can collect information such as the culprit, the victim, and important evidence. Step 2: The analysis unit analyzes the data collected by the collection unit and creates summaries, character descriptions, and keyword explanations. Specifically, based on the collected data, it can create a concise summary of the plot up to the point the user has read. It can also explain the relationships between characters and the meaning of important keywords. Step 3: The presentation unit presents the explanation created by the analysis unit to the user. Specifically, the user can tap on character names or keywords within the app to display the explanation created by the generation AI.

[0059] (Example of form 2) The reading support system according to an embodiment of the present invention is a system for providing support to resume reading when it is interrupted. This reading support system collects book data up to the point the user has read, and a generating AI analyzes this data to create an explanation of the plot, characters, and keywords, which are then presented to the user. The reading support system is integrated into e-book services to differentiate them from competitors and promote service growth. First, the reading support system collects book data up to the point the user has read. At this time, it collects information such as which page the user has read, which characters have appeared, and which keywords are important. For example, if the user is reading a mystery novel, it collects information such as the culprit, the victim, and important evidence. Next, the reading support system uses a generating AI to analyze the collected data. Based on the collected data, the generating AI creates an explanation of the plot, characters, and keywords. For example, it concisely summarizes the plot up to the point the user has read and explains the relationships between characters and the meaning of important keywords. Finally, the reading support system presents the generated explanation to the user. The user can check the explanation created by the generating AI by tapping on character names or keywords within the app. For example, when a user taps on a character's name, such as "Member of the Mystery Club," detailed information about that character is displayed. This system ensures that even if a user interrupts their reading, they can obtain all the necessary information when they resume. It also eliminates the need to return to the character introductions at the beginning of the book or reread previous sections. This improves the reading experience and is expected to increase the number of users and purchase rates of the e-book service. As a result, the reading support system can efficiently collect, analyze, and present information up to the point the user has read.

[0060] The reading support system according to this embodiment comprises a collection unit, an analysis unit, and a presentation unit. The collection unit collects book data up to the point the user has read. The collection unit collects information such as which page the user has read, which characters have appeared, and which keywords are important. For example, if the user is reading a mystery novel, the collection unit can collect information such as the culprit, the victim, and important evidence. The analysis unit analyzes the data collected by the collection unit and creates explanations of the plot, characters, and keywords. For example, the analysis unit can concisely summarize the plot up to the point the user has read based on the collected data. For example, the analysis unit can explain the relationships between characters and the meaning of important keywords. The presentation unit presents the explanations created by the analysis unit to the user. For example, the presentation unit can display explanations created by the generating AI when the user taps on character names or keywords within the app. As a result, the reading support system according to this embodiment can efficiently collect, analyze, and present information up to the point the user has read.

[0061] The data collection unit collects book data up to the point the user has read. Specifically, it collects information such as which pages the user has read, which characters have appeared, and which keywords are important, through e-readers or dedicated apps. For example, e-readers can record the history of page scrolling and tapping, allowing for accurate tracking of the user's reading progress. Text analysis technology can also be used to identify the frequency and location of characters and keywords, extracting important information. Furthermore, if the user takes notes or highlights specific sections, this information is also collected and used for analysis. For example, if a user is reading a mystery novel, information such as the culprit, victim, and important evidence can be collected. This allows the data collection unit to gain a detailed understanding of the user's reading history and points of interest, which can be used for subsequent analysis and presentation. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and presentation units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0062] The analysis unit analyzes the data collected by the collection unit to create summaries, character descriptions, and keyword explanations. Specifically, based on the collected data, it can concisely summarize the plot up to the point the user has read. For example, it can use natural language processing techniques to summarize the text and extract important events and plot points. It can also utilize knowledge graphs and entity recognition techniques to explain the relationships between characters and the meaning of important keywords. For instance, by identifying the names and roles of characters and illustrating their relationships, it makes it easier for users to understand the progression of the story. Furthermore, it consults dictionary databases and glossaries to provide appropriate explanations for keywords. The analysis unit integrates this information to generate explanations that are useful to the user. For example, if a user is reading a mystery novel, it can explain the relationships between the culprit, victim, and important evidence to deepen their understanding of the story. The analysis unit uses generative AI to analyze this data and simulate multiple scenarios to identify the most likely explanation. This allows the analysis unit to quickly and accurately analyze the collected data and provide useful information to the user. In addition, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and pattern recognition. This allows the analysis unit to not only grasp the situation in real time but also to provide long-term reading support, improving the overall reliability and usefulness of the system.

[0063] The presentation unit presents explanations created by the analysis unit to the user. Specifically, by tapping character names or keywords within the app, the user can view explanations generated by the AI. For example, when a user taps a character's name, background information and relationships of that character will pop up. Similarly, tapping a keyword will display its meaning and related information. The presentation unit employs a user interface that is intuitive and easy to use, enabling users to quickly access the information they need. For example, information is displayed compactly using pop-up windows and tooltips, ensuring that the user's reading experience is not interrupted. Additionally, a voice assistant function can be added, allowing users to obtain information using voice commands. Furthermore, the presentation unit can collect user feedback and continuously improve the accuracy and effectiveness of its presentations. For example, users can evaluate the explanations or ask additional questions, allowing the system to learn from the feedback and make future presentations more appropriate. The presentation unit also supports multiple devices and platforms, enabling users to access information anywhere. For example, information can be seamlessly shared across different devices such as smartphones, tablets, and PCs, improving user convenience. This allows the display unit to provide information to the user quickly and accurately, enriching the reading experience.

[0064] The data collection unit can collect information such as which pages the user has read, which characters have appeared, and which keywords are important. For example, to determine which pages the user has read, the data collection unit can record page numbers and detect scroll positions. The data collection unit can also use name frequency and contextual analysis to identify characters. Furthermore, the data collection unit can consider frequency and contextual importance to evaluate the importance of keywords. In this way, the data collection unit can collect detailed information up to where the user has read. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's reading data into an AI, which can then analyze the data and collect the necessary information.

[0065] The analysis unit can create a concise summary based on the collected data. For example, the analysis unit can create a concise summary of the content up to the point the user has read, based on the collected data. The analysis unit can create a summary considering, for example, the length of the summary and the types of information to be included. This allows the analysis unit to concisely grasp the content up to the point the user has read. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the collected data into a generative AI, and the generative AI can analyze the data to create a summary.

[0066] The analysis unit can explain the relationships between the characters. For example, the analysis unit explains the relationships between the characters based on the collected data. For example, the analysis unit can explain relationships such as family relationships, friendships, and rivalries. In this way, the analysis unit can understand the relationships between the characters. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the collected data into a generative AI, and the generative AI can analyze the data and explain the relationships between the characters.

[0067] The analysis unit can explain the meaning of important keywords. For example, the analysis unit explains the meaning of important keywords based on collected data. For example, the analysis unit can select important keywords such as technical terms or key words in a story and explain their meaning. This allows the analysis unit to understand the meaning of important keywords. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input collected data into a generative AI, which can then analyze the data and explain the meaning of important keywords.

[0068] The presentation section can display explanations generated by a generative AI when the user taps on character names or keywords within the app. For example, if the user taps on a character name such as "Member of the Mystery Club," the presentation section will display detailed information about that character. For example, if the user taps on an important keyword, the presentation section will display the meaning of that keyword and related information. This allows the presentation section to easily check explanations. Some or all of the above processing in the presentation section may be performed using a generative AI, or it may be performed without a generative AI. For example, the presentation section can provide an interface for displaying explanations generated by a generative AI to the user.

[0069] The data collection unit can estimate the user's emotions and adjust the type of data collected based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting information on important action scenes and climaxes. If the user is relaxed, the data collection unit may collect detailed background information and character inner thoughts. If the user is tired, the data collection unit may collect concise and to-the-point information. This enables the data collection unit to collect data in accordance with the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can then analyze the data and adjust the type of data collected.

[0070] The data collection unit can analyze the user's reading speed and determine the optimal timing for data collection. For example, if the user is speed reading, the data collection unit can collect a large amount of information in a short time. For example, if the user is reading slowly, the data collection unit can collect detailed information. For example, if the user is repeatedly interrupting their reading, the data collection unit can collect data frequently. This allows the data collection unit to collect data according to the user's reading speed. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's reading speed data into an AI, which can then analyze the data to determine the optimal timing for data collection.

[0071] The data collection unit can also collect data on similar books the user has read in the past by referring to the user's reading history during the collection process. For example, the data collection unit can collect information on works by the same author that the user has read in the past. For example, the data collection unit can collect information on works of the same genre that the user has read in the past. For example, the data collection unit can collect information on works of the same series that the user has read in the past. This enables the data collection unit to collect data based on past reading history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's reading history data into an AI, which can then analyze the data to collect information on similar books.

[0072] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting information on important action scenes and climaxes. If the user is relaxed, the data collection unit may prioritize collecting detailed background information and character inner monologues. If the user is tired, the data collection unit may prioritize collecting concise and to-the-point information. This allows the data collection unit to prioritize data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can analyze the data and determine the priority of data to collect.

[0073] The data collection unit can prioritize collecting highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize collecting information related to that region. For example, if the user is traveling, the data collection unit can prioritize collecting information related to the travel destination. For example, if the user is at home, the data collection unit can prioritize collecting information related to home. This enables the data collection unit to collect data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into AI, and the AI ​​can analyze the data to collect highly relevant information.

[0074] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, the data collection unit can analyze posts about reading that the user has shared on social media and collect relevant information. For example, the data collection unit can analyze posts from accounts that the user follows and collect relevant information. For example, the data collection unit can analyze the activities of reading groups that the user participates in and collect relevant information. This enables the data collection unit to collect data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into an AI, which can then analyze the data and collect relevant information.

[0075] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is excited, the analysis unit can generate analysis results with visually stimulating effects. For example, if the user is relaxed, the analysis unit can generate analysis results with calming colors. For example, if the user is tired, the analysis unit can generate concise and highly visible analysis results. This allows the analysis unit to present analysis results in a way that reflects the user's emotions. 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 processes in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI, which can then analyze the data and adjust the presentation of the analysis results.

[0076] The analysis unit can apply different analysis algorithms depending on the genre of the book during analysis. For example, in the case of a mystery novel, the analysis unit can apply an algorithm that emphasizes information about the culprit and evidence. For example, in the case of a romance novel, the analysis unit can apply an algorithm that emphasizes information about the relationships between characters. For example, in the case of science fiction, the analysis unit can apply an algorithm that emphasizes information about technology and science. This allows the analysis unit to perform analysis according to the genre of the book. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input book genre data into a generative AI, and the generative AI can analyze the data and apply an appropriate algorithm.

[0077] The analysis unit can improve the accuracy of its analysis by referring to the user's reading history during the analysis process, based on past reading trends. For example, the analysis unit can improve the accuracy of its analysis by analyzing the trends of works by the same author that the user has read in the past. For example, the analysis unit can improve the accuracy of its analysis by analyzing the trends of works of the same genre that the user has read in the past. For example, the analysis unit can improve the accuracy of its analysis by analyzing the trends of works in the same series that the user has read in the past. This enables the analysis unit to perform analysis based on past reading trends. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's reading history data into a generative AI, and the generative AI can analyze the data to improve the accuracy of the analysis.

[0078] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis results based on the estimated emotions. For example, if the user is excited, the analysis unit can provide detailed analysis results. For example, if the user is relaxed, the analysis unit can provide concise analysis results. For example, if the user is tired, the analysis unit can provide concise analysis results. This allows the analysis unit to adjust the level of detail in the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI, and the generative AI can analyze the data and adjust the level of detail in the analysis results.

[0079] The analysis unit can determine the priority of analysis based on the publication date of the books during the analysis process. For example, in the case of the most recent books, the analysis unit prioritizes the analysis of the latest information. For example, in the case of classic books, the analysis unit can consider the historical background during the analysis. For example, in the case of reprinted books, the analysis unit can consider additional information added at the time of reprinting during the analysis. This enables the analysis unit to perform analysis based on the publication date of the books. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input book publication date data into a generating AI, and the generating AI can analyze the data to determine the priority of analysis.

[0080] The analysis unit can improve the accuracy of its analysis by referring to related literature in the book during the analysis process. For example, the analysis unit can refer to the book's bibliography and reflect relevant information in the analysis. For example, the analysis unit can refer to other literature written by the book's author and reflect it in the analysis. For example, the analysis unit can refer to literature related to the book's genre and reflect it in the analysis. This enables the analysis unit to perform analysis based on related literature. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input related literature data into a generating AI, and the generating AI can analyze the data to improve the accuracy of the analysis.

[0081] The presentation unit can estimate the user's emotions and adjust the display method of the information presented based on the estimated user emotions. For example, if the user is excited, the presentation unit can provide a display method with visually stimulating effects. For example, if the user is relaxed, the presentation unit can provide a display method with calming colors. For example, if the user is tired, the presentation unit can provide a concise and highly visible display method. This enables the presentation unit to display information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input user emotion data into AI, and the AI ​​can analyze the data and adjust the display method.

[0082] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit can prioritize display methods that the user has preferred to use in the past. For example, the display unit can exclude display methods that the user has avoided in the past. For example, the display unit can suggest the optimal display method based on the user's past operation history. In this way, the display unit provides the optimal display method based on past operation history. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input user operation history data into AI, and the AI ​​can analyze the data to select the optimal display method.

[0083] The presentation unit can, at the time of presentation, refer to the user's reading history and also present information on similar books the user has read in the past. For example, the presentation unit can present information on works by the same author that the user has read in the past. For example, the presentation unit can present information on works of the same genre that the user has read in the past. For example, the presentation unit can present information on works of the same series that the user has read in the past. This enables the presentation unit to present information based on the user's past reading history. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the user's reading history data into AI, and the AI ​​can analyze the data and present information on similar books.

[0084] The presentation unit can estimate the user's emotions and determine the priority of information to present based on the estimated emotions. For example, if the user is excited, the presentation unit may prioritize presenting information about important action scenes or climaxes. If the user is relaxed, the presentation unit may prioritize presenting detailed background information or character inner monologues. If the user is tired, the presentation unit may prioritize presenting concise and to-the-point information. This allows the presentation unit to prioritize information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 presentation unit may be performed using AI or not. For example, the presentation unit can input user emotion data into an AI, which can analyze the data to determine the priority of information.

[0085] The display unit can select the optimal display method based on the user's device information at the time of presentation. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the display unit provides the optimal display method based on the user's device information. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the user's device information into AI, and the AI ​​can analyze the data and select the optimal display method.

[0086] The presentation unit can analyze the user's social media activity and present relevant information at the time of presentation. For example, the presentation unit can analyze posts about reading that the user has shared on social media and present relevant information. For example, the presentation unit can analyze posts from accounts that the user follows and present relevant information. For example, the presentation unit can analyze the activities of reading groups that the user participates in and present relevant information. This enables the presentation unit to present information based on the user's social media activity. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the user's social media data into AI, which can then analyze the data and present relevant information.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] A reading support system can learn a user's reading habits and provide individually optimized reading support. For example, if a user often reads at night, the system can automatically select a display mode suitable for nighttime reading. Furthermore, if a user prefers a particular genre, the system can prioritize collecting and analyzing information related to that genre. Additionally, if a user prefers the works of a specific author, the system can provide information about other works by that author. In this way, the reading support system can provide individually optimized support based on the user's reading habits.

[0089] A reading support system can estimate a user's emotions and support their reading based on those emotions. For example, if a user is feeling down, the system can suggest encouraging messages or books with positive content. If a user is excited, the system can prioritize providing information about action scenes or climaxes. Furthermore, if a user is relaxed, the system can provide detailed background information or character inner monologues. In this way, a reading support system can provide reading support tailored to the user's emotions.

[0090] A reading support system can monitor a user's reading speed in real time and suggest an optimal reading pace. For example, if a user is speed reading, the system can provide a lot of information in a short time. Conversely, if a user is reading slowly, the system can provide detailed information. Furthermore, if a user repeatedly interrupts their reading, the system can frequently collect data and provide appropriate information when they resume reading. In this way, the reading support system can suggest an optimal reading pace tailored to the user's reading speed.

[0091] The reading support system can provide highly relevant information based on the user's geographical location. For example, if a user is in a specific region, it can prioritize providing books and information related to that region. If the user is traveling, it can provide books and information related to their travel destination. Furthermore, if the user is at home, it can provide books and information related to their home. In this way, the reading support system can provide highly relevant information based on the user's geographical location.

[0092] A reading support system can analyze a user's social media activity and provide relevant information. For example, it can analyze reading-related posts a user shares on social media and provide relevant books and information. It can also analyze posts from accounts a user follows and provide relevant books and information. Furthermore, it can analyze the activities of reading groups a user participates in and provide relevant books and information. In this way, the reading support system can provide relevant information based on the user's social media activity.

[0093] The reading support system can estimate the user's emotions and adjust the presentation of the analysis results based on those emotions. For example, if the user is excited, the system can provide analysis results with visually stimulating effects. If the user is relaxed, the system can provide analysis results in calming colors. Furthermore, if the user is tired, the system can provide concise and easily readable analysis results. In this way, the reading support system can provide analysis results presented in a way that suits the user's emotions.

[0094] The reading support system can refer to the user's reading history and provide information on similar books they have read in the past. For example, it can provide information on works by the same author the user has read in the past. It can also provide information on works of the same genre the user has read in the past. Furthermore, it can provide information on works of the same series the user has read in the past. In this way, the reading support system can provide information on similar books based on the user's past reading history.

[0095] The reading support system can estimate the user's emotions and adjust the level of detail in the analysis results based on those emotions. For example, if the user is excited, it can provide detailed analysis results. If the user is relaxed, it can provide concise analysis results. Furthermore, if the user is tired, it can provide analysis results that focus on the essentials. In this way, the reading support system can provide analysis results with a level of detail that matches the user's emotions.

[0096] The reading support system can provide the optimal display method based on the user's device information. For example, if the user is using a smartphone, it can provide a display method adapted to the screen size. If the user is using a tablet, it can provide a display method optimized for the larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, the reading support system can provide the optimal display method based on the user's device information.

[0097] A reading support system can estimate the user's emotions and prioritize the information presented based on those emotions. For example, if the user is excited, it can prioritize providing information about important action scenes and climaxes. If the user is relaxed, it can prioritize providing detailed background information and character inner monologues. Furthermore, if the user is tired, it can prioritize providing concise and to-the-point information. In this way, the reading support system can provide information prioritization tailored to the user's emotions.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The data collection unit collects book data up to the point the user has read. Specifically, it collects information such as which page the user has read, which characters have appeared, and which keywords are important. For example, if the user is reading a mystery novel, it can collect information such as the culprit, the victim, and important evidence. Step 2: The analysis unit analyzes the data collected by the collection unit and creates summaries, character descriptions, and keyword explanations. Specifically, based on the collected data, it can create a concise summary of the plot up to the point the user has read. It can also explain the relationships between characters and the meaning of important keywords. Step 3: The presentation unit presents the explanation created by the analysis unit to the user. Specifically, the user can tap on character names or keywords within the app to display the explanation created by the generation AI.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements described above, including the collection unit, analysis unit, and presentation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user reading data using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A transmits the collected data to the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to create explanations of the plot, characters, and keywords. The presentation unit is implemented in the control unit 46A of the smart device 14, and presents the generated explanations to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the collection unit, analysis unit, and presentation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's reading data using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A transmits the collected data to the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to create explanations of the plot, characters, and keywords. The presentation unit is implemented in the control unit 46A of the smart glasses 214, and presents the generated explanations to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the collection unit, analysis unit, and presentation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user reading data using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to create explanations of the plot, characters, and keywords. The presentation unit is implemented in the control unit 46A of the headset terminal 314 and presents the generated explanations to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, and presentation unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user reading data using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to create explanations of the plot, characters, and keywords. The presentation unit is implemented in, for example, the control unit 46A of the robot 414 and presents the generated explanations to the user. 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) A collection unit that collects book data up to the point the user has read, The data collected by the aforementioned collection unit is analyzed by the analysis unit, which then creates an explanation of the plot, characters, and keywords. The system includes a presentation unit that presents an explanation created by the analysis unit to the user. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects information such as which pages the user has read, which characters have appeared, and which keywords are important. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, summarize the plot concisely. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, This explains the relationships between the characters. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, This explains the meaning of important keywords. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is, When a user taps on a character's name or keyword within the app, the AI ​​generates an explanation that is displayed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's reading speed and determine the optimal timing for data collection. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the system also references the user's reading history to collect data on similar books they have read in the past. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the genre of the book. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the system references the user's reading history to improve the accuracy of the analysis based on past reading trends. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the level of detail in the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on the publication date of the books. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature in books to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is, When presenting information, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is, When presenting books, the system also references the user's reading history and displays information about similar books they have read in the past. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, It estimates the user's emotions and determines the priority of the information presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is, When presenting the information, the system selects the optimal display method based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is, When presenting information, the system analyzes the user's social media activity and displays relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects book data up to the point the user has read, The data collected by the aforementioned collection unit is analyzed by the analysis unit, which then creates an explanation of the plot, characters, and keywords. The system includes a presentation unit that presents an explanation created by the analysis unit to the user. A system characterized by the following features.

2. The aforementioned collection unit is The system collects information such as which pages the user has read, which characters have appeared, and which keywords are important. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, summarize the plot concisely. The system according to feature 1.

4. The aforementioned analysis unit, This explains the relationships between the characters. The system according to feature 1.

5. The aforementioned analysis unit, This explains the meaning of important keywords. The system according to feature 1.

6. The aforementioned display unit is, When a user taps on a character's name or keyword within the app, the AI ​​generates an explanation that is displayed. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's reading speed and determine the optimal timing for data collection. The system according to feature 1.

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