System

Abstract

An object of a system according to an embodiment is to enable a user to efficiently find a book suitable for the user.SOLUTION: A system includes a book summary provision unit, a similar book recommendation unit, and an evaluation unit. The book summary providing unit provides a summary of a book. The similar book recommendation unit recommends a similar book based on the summary provided by the book summary providing unit. The evaluation unit evaluates the book recommended by the similar book recommendation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem that it is difficult and time-consuming for users to find books that suit them.

[0005] The system according to the embodiment aims to enable users to efficiently find books that suit them. [Means for solving the problem]

[0006] The system according to the embodiment includes a book summary providing unit, a similar book recommendation unit, and an evaluation unit. The book summary providing unit provides a summary of the book. The similar book recommendation unit recommends similar books based on the summary provided by the book summary providing unit. The evaluation unit evaluates the books recommended by the similar book recommendation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to efficiently find books that suit them. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An application according to an embodiment of the present invention is an application that is familiar with books across a variety of genres and recommends books that users would like to read. This application allows users to provide summaries of books, recommend similar books, and rate books. This allows the application to easily find books that suit users and expand the enjoyment of reading.

[0029] An application according to an embodiment includes a book summary providing unit, a similar book recommendation unit, and an evaluation unit. The book summary providing unit provides a book summary. For example, the book summary providing unit uses a generation AI to analyze the contents of a book and generate a summary that can be read in a few minutes. The book summary providing unit can also analyze the contents and provide a summary when a user uploads an image of the book title or cover. For example, if a user uploads a cover image of "Harry Potter and the Philosopher's Stone," the generation AI analyzes the content and provides a summary such as "A story about Harry Potter attending a magic school and going on adventures with his friends." The similar book recommendation unit recommends similar books based on the summaries provided by the book summary providing unit. For example, when a user selects a favorite book title or a type of story they would like to read, the generation AI analyzes that information and recommends books with similar content. For example, if a user inputs "I want to read a fantasy novel," the generation AI would suggest similar books such as "The Lord of the Rings" and "The Chronicles of Narnia." The evaluation unit evaluates books recommended by the similar book recommendation unit. For example, a user can write evaluation comments and rate books they have read. This evaluation data is shared with other users and displayed as recommendations or popularity rankings. For example, if a user comments, "This book was very interesting," and rates it five stars, that evaluation is also displayed to other users. This allows the application according to the embodiment to easily find a book that suits the user and broaden the enjoyment of reading. For example, even people who do not usually read books can easily find books in genres or by authors that interest them. Furthermore, by referring to the opinions of other users through the evaluation function, a more satisfying reading experience can be provided.

[0030] The book summary providing unit can generate individually customized summaries by taking into account a user's past reading history and ratings. For example, the book summary providing unit uses a generation AI to analyze a user's past reading history and understand their preferences for specific genres and authors. Based on that information, the book summary providing unit provides the user with the optimal summary. For example, for a user who likes mystery novels, the unit generates a summary that emphasizes the mystery elements. The book summary providing unit also analyzes the characteristics of books that the user has previously rated highly and generates a summary that reflects those characteristics. For example, for a user who likes moving stories, the unit provides a summary that emphasizes moving scenes. The book summary providing unit also analyzes the user's rating comments, extracts parts that the user found particularly interesting, and reflects them in the summary. For example, if a user is interested in a particular character, the unit generates a summary that focuses on that character. This provides the user with the optimal summary, thereby improving their reading satisfaction.

[0031] In addition to the summary, the book summary providing unit can automatically generate the book's main themes and character relationship diagrams. For example, the book summary providing unit uses a generation AI to analyze the contents of the book and automatically generate the main themes and character relationship diagrams. For example, in the case of a fantasy novel, it illustrates the magical worldview and character relationships. In addition to the summary text, the book summary providing unit also uses the generation AI to provide a character relationship diagram, making it easier for users to grasp the overall picture of the story. For example, it generates a character relationship diagram and a power map. In addition, the book summary providing unit also uses the generation AI to automatically generate a theme map to visually display the book's main themes. For example, it provides a diagram showing the story's main events and themes in chronological order. This allows users to gain a deeper understanding of the book's contents.

[0032] The book summary providing unit has a function for providing summaries by audio, allowing the user to understand the content by listening. For example, the book summary providing unit adds a function for the generation AI to provide a book summary by audio, allowing the user to understand the content by listening. For example, the user can listen to the summary while commuting or exercising. When providing the summary by audio, the book summary providing unit also adjusts the tone and speed of the narration to suit the user's preferences. For example, the user can select a calm tone or a fast reading speed. The book summary providing unit also uses the audio summaries function to allow the user to understand the content of a book without using their eyes. For example, audio summaries are provided for visually impaired people. This allows the user to listen to the summary by ear and understand the content of the book without relying on their vision.

[0033] The book summary providing unit can provide summaries from multiple perspectives and present different interpretations and perspectives to the user. For example, the generation AI of the book summary providing unit provides book summaries from multiple perspectives and presents different interpretations and perspectives to the user. For example, summaries are provided from the perspective of the protagonist, the villain, and a third-person perspective. By providing summaries from multiple perspectives, the book summary providing unit also deepens the user's multifaceted understanding of the story. For example, summaries from the perspectives of different characters are compared. The book summary providing unit also generates book summaries from different perspectives and presents the user with various interpretations. For example, perspectives on the background and setting of the story are provided. This allows the user to understand the contents of the book from multiple perspectives.

[0034] The similar book recommendation unit can extract parts of recommended books that are likely to be of particular interest to the user and provide them as previews. For example, the similar book recommendation unit analyzes the contents of books recommended by a generation AI and extracts parts that are likely to be of particular interest to the user and provides them as previews. For example, it extracts the climax scene of the story. The similar book recommendation unit also identifies parts that are likely to be of particular interest to the user based on the user's past reading history and ratings, and provides those parts as previews. For example, it extracts parts related to the user's favorite themes or characters. The similar book recommendation unit also builds a system that automatically extracts parts of recommended books that are likely to be of interest to the user and provides them as previews. For example, it extracts the beginning of a story or an important scene. This allows the user to select a book by providing previews of parts that are likely to be of interest to the user.

[0035] The similar book recommendation unit expands the recommendation function to recommend not only books but also related movies, dramas, podcasts, etc. The similar book recommendation unit adds a function that enables the generation AI to recommend not only books but also related movies, dramas, podcasts, etc. For example, if a user has read a fantasy novel, it will suggest movies and dramas in the same genre. The similar book recommendation unit also builds a system that recommends related content based on the user's reading history and ratings. For example, it will suggest podcasts related to the user's favorite themes. The similar book recommendation unit also expands the recommendation function to comprehensively suggest related content in addition to books. For example, it will provide a list of movies and dramas related to the books the user has read. This allows users to enjoy related content other than books.

[0036] The similar book recommendation unit can provide not only purchase links for recommended books, but also library lending information and free sample links for e-books. For example, the similar book recommendation unit adds a function that provides not only purchase links for books recommended by the generation AI, but also library lending information and free sample links for e-books. For example, it allows users to preview books before purchasing them. The similar book recommendation unit also builds a system that provides library lending information in addition to purchase links for recommended books. For example, it allows users to borrow books from nearby libraries. The similar book recommendation unit also provides free sample links for e-books, allowing users to preview parts of books. For example, it provides the beginning of a recommended book for free. This allows users to preview books and check lending information before purchasing them.

[0037] The rating function can analyze a user's reading habits based on rating data and provide individually customized rating criteria. For example, the rating function analyzes a user's reading habits based on the user's rating data and provides individually customized rating criteria. For example, rating criteria are set for specific genres or authors. The rating function also analyzes rating data and builds a system that understands a user's reading habits. For example, rating criteria are set based on the characteristics of books that users have given high ratings to. The rating function also develops a system that provides individually customized rating criteria based on the user's rating data. For example, rating criteria are automatically set according to the user's preferences. This allows for more appropriate ratings by providing rating criteria that are according to the user's reading habits.

[0038] The rating function allows the generation AI to automatically generate reply comments in response to book ratings, promoting communication between users. For example, the rating function allows the generation AI to automatically generate reply comments in response to user rating comments, promoting communication between users. For example, it automatically generates a message of thanks in response to a rating comment. The rating function also allows the generation AI to automatically generate reply comments in response to rating comments, providing related books and information. For example, it recommends books related to the rating comment. The rating function also allows the generation AI to automatically generate reply comments in response to user rating comments, building a system that promotes dialogue between users. For example, it automatically generates questions and opinions in response to rating comments. This promotes communication between users and leads to more active exchange of opinions about books.

[0039] The rating function extends the book rating function to automatically display articles and reviews related to books rated by users. The rating function, for example, extends the book rating function to build a system that automatically displays articles and reviews related to books rated by users. For example, blog articles and expert reviews related to rating comments are displayed. The rating function also adds a function that automatically collects and displays articles and reviews related to books rated by users. For example, news articles and interviews related to rating comments are displayed. The rating function also extends the book rating function to develop a system that automatically displays information related to books rated by users. For example, background information about books related to rating comments and interviews with the authors are displayed. This automatically displays information related to books rated by users, deepening their understanding of the books.

[0040] The rating function can provide information on reading events and autograph sessions that may interest users based on rating data. For example, the rating function builds a system that provides information on reading events and autograph sessions that may interest users based on rating data. For example, it provides information on autograph sessions for authors that users have given high ratings to. The rating function also adds a function that analyzes user rating data and provides event information that may interest users. For example, it provides information on reading events and workshops related to rating comments. The rating function also develops a system that provides event information that may interest users based on rating data. For example, it automatically displays event information that corresponds to the user's rating tendencies. This enriches the reading experience by providing event information that may interest users.

[0041] The function related to advertising revenue and affiliate revenue can optimize the timing of ad display based on a user's reading habits and app usage. The function related to advertising revenue and affiliate revenue, for example, analyzes a user's reading habits and app usage and builds a system that optimizes the timing of ad display. For example, an ad is displayed immediately after a user finishes reading. The function related to advertising revenue and affiliate revenue also adds a function that adjusts the timing of ad display based on a user's usage. For example, an ad is displayed immediately after a user opens an app or just before a user closes it. The function related to advertising revenue and affiliate revenue also develops a system that optimizes the timing of ad display based on a user's reading habits. For example, if a user uses an app during a specific time period, an ad is displayed during that time period. This improves advertising effectiveness by optimizing the timing of ad display based on a user's reading habits and app usage.

[0042] The advertising revenue and affiliate revenue function can use generation AI to automatically generate customized advertisements based on a user's interests. For example, the advertising revenue and affiliate revenue function builds a system in which generation AI analyzes a user's interests and automatically generates customized advertisements based on that information. For example, it generates advertisements related to a user's favorite genres or themes. In addition, the advertising revenue and affiliate revenue function adds a function in which generation AI automatically generates customized advertisements based on a user's past behavioral data. For example, it generates advertisements related to books the user has previously purchased. In addition, the advertising revenue and affiliate revenue function uses generation AI to develop a system that generates customized advertisements in real time based on a user's interests. For example, it generates advertisements related to books the user is currently reading. This automatically generates customized advertisements based on a user's interests and concerns, thereby improving advertising effectiveness.

[0043] The function related to advertising revenue and affiliate revenue can also handle advertisements for products and services other than books in order to increase advertising revenue and affiliate revenue. The function related to advertising revenue and affiliate revenue, for example, builds a system that also handles advertisements for products and services other than books in order to increase advertising revenue and affiliate revenue. For example, advertisements for goods and accessories related to books are displayed. The function related to advertising revenue and affiliate revenue also adds a function to increase advertising revenue by handling advertisements for products and services other than books. For example, advertisements for cafes and events related to reading are displayed. The function related to advertising revenue and affiliate revenue also develops a system that handles advertisements for products and services other than books in order to increase advertising revenue and affiliate revenue. For example, advertisements for apps and devices related to reading are displayed. In this way, by handling advertisements for products and services other than books, advertising revenue and affiliate revenue can be increased.

[0044] The function related to advertising revenue and affiliate income can also suggest goods and accessories related to books purchased by users through affiliate links. The function related to advertising revenue and affiliate income, for example, builds a system that suggests goods and accessories related to books purchased by users through affiliate links. For example, it suggests character goods and related products of the book. Furthermore, the function related to advertising revenue and affiliate income adds a function that suggests goods and accessories related to books purchased by users. For example, it suggests accessories and decorations related to the theme of the book. Furthermore, the function related to advertising revenue and affiliate income develops a system that suggests products related to books purchased by users through affiliate links. For example, it suggests goods and accessories related to the content of the book. This allows for an increase in affiliate income by suggesting goods and accessories related to books purchased by users.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] In addition to the summary, the book summary provider can automatically generate the book's main themes and character relationship diagrams. For example, the generation AI analyzes the contents of the book and automatically generates the main themes and character relationship diagrams. In the case of fantasy novels, it illustrates the magical worldview and character relationships. In addition to the summary, the book summary provider also provides a character relationship diagram, making it easier for users to grasp the overall picture of the story. It can also generate character relationship diagrams and power maps. In addition, the book summary provider automatically generates a theme map to visually display the book's main themes. It can also provide a diagram showing the story's main events and themes in chronological order. This allows users to gain a deeper understanding of the book's contents.

[0047] The book summary providing unit can have a function to provide audio summaries, allowing users to understand the content by listening. For example, the generation AI can add a function to provide audio summaries of books, allowing users to understand the content by listening. The user can listen to the summaries while commuting or exercising. The book summary providing unit can also adjust the tone and speed of the narration when providing audio summaries to suit the user's preferences. A calm tone or a fast reading speed can be selected. The book summary providing unit can also use the audio summaries function to allow users to understand the content of a book without using their eyes. Audio summaries can also be provided for visually impaired people. This allows users to listen to the summaries by ear and understand the content of a book without relying on their vision.

[0048] The book summary providing unit can provide summaries from multiple perspectives, presenting different interpretations and perspectives to the user. For example, the generation AI can provide book summaries from multiple perspectives, presenting different interpretations and perspectives to the user. It can also provide summaries from the protagonist's perspective, the villain's perspective, and a third-person perspective. By providing summaries from multiple perspectives, the book summary providing unit can deepen the user's understanding of the story from multiple angles. It can also compare summaries from the perspectives of different characters. The book summary providing unit can also generate book summaries from different perspectives, presenting the user with various interpretations. It can also provide perspectives on the background and setting of the story. This allows the user to understand the contents of the book from multiple angles.

[0049] The similar book recommendation unit can extract parts of recommended books that are likely to be of interest to the user and provide them as previews. For example, the generation AI can analyze the contents of a recommended book and extract parts that are likely to be of interest to the user and provide them as previews. It can also extract the climax scene of the story. The similar book recommendation unit can also identify parts that are likely to be of interest to the user based on the user's past reading history and ratings, and provide those parts as previews. It can also extract parts related to the user's favorite themes or characters. The similar book recommendation unit can also build a system that automatically extracts parts of recommended books that are likely to be of interest to the user and provides them as previews. It can also extract the beginning of a story or important scenes. This can provide previews of parts that are likely to interest the user, helping them choose a book.

[0050] The similar book recommendation unit expands the recommendation function to recommend not only books but also related movies, dramas, podcasts, etc. For example, the generative AI can add a function to recommend not only books but also related movies, dramas, podcasts, etc. For a user who has read a fantasy novel, it can also suggest movies and dramas in the same genre. The similar book recommendation unit can also build a system that recommends related content based on the user's reading history and ratings. It can also suggest podcasts related to the user's favorite themes. The similar book recommendation unit can also expand the recommendation function to suggest not only books but also related content in general. It can also provide a list of movies and dramas related to the books the user has read. This allows users to enjoy related content other than books.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The book summary provider provides a book summary. For example, the book summary provider uses a generation AI to analyze the contents of a book and generate a summary that can be read in a few minutes. The book summary provider can also analyze the contents and provide a summary when a user uploads an image of the book title or cover. For example, if a user uploads an image of the cover of "Harry Potter and the Philosopher's Stone," the generation AI will analyze the contents and provide a summary such as "The story of Harry Potter enrolling in a magic school and going on adventures with his friends." Step 2: The similar book recommendation unit recommends similar books based on the summaries provided by the book summary provider. For example, if a user selects their favorite book titles and the type of stories they would like to read, the generation AI analyzes that information and recommends books with similar content. For example, if a user inputs "I want to read fantasy novels," the generation AI will suggest similar books such as "The Lord of the Rings" and "The Chronicles of Narnia." Step 3: The evaluation unit evaluates the books recommended by the similar book recommendation unit. For example, users can leave evaluation comments and ratings for books they have read. This evaluation data is shared with other users and displayed as recommendations and popularity rankings. For example, if a user comments, "This book was very interesting," and rates it five stars, that evaluation will be displayed to other users.

[0053] (Example 2) An application according to an embodiment of the present invention is an application that is familiar with books across a variety of genres and recommends books that users would like to read. This application allows users to provide summaries of books, recommend similar books, and rate books. This allows the application to easily find books that suit users and expand the enjoyment of reading.

[0054] An application according to an embodiment includes a book summary providing unit, a similar book recommendation unit, and an evaluation unit. The book summary providing unit provides a book summary. For example, the book summary providing unit uses a generation AI to analyze the contents of a book and generate a summary that can be read in a few minutes. The book summary providing unit can also analyze the contents and provide a summary when a user uploads an image of the book title or cover. For example, if a user uploads a cover image of "Harry Potter and the Philosopher's Stone," the generation AI analyzes the content and provides a summary such as "A story about Harry Potter attending a magic school and going on adventures with his friends." The similar book recommendation unit recommends similar books based on the summaries provided by the book summary providing unit. For example, when a user selects a favorite book title or a type of story they would like to read, the generation AI analyzes that information and recommends books with similar content. For example, if a user inputs "I want to read a fantasy novel," the generation AI would suggest similar books such as "The Lord of the Rings" and "The Chronicles of Narnia." The evaluation unit evaluates books recommended by the similar book recommendation unit. For example, a user can write evaluation comments and rate books they have read. This evaluation data is shared with other users and displayed as recommendations or popularity rankings. For example, if a user comments, "This book was very interesting," and rates it five stars, that evaluation is also displayed to other users. This allows the application according to the embodiment to easily find a book that suits the user and broaden the enjoyment of reading. For example, even people who do not usually read books can easily find books in genres or by authors that interest them. Furthermore, by referring to the opinions of other users through the evaluation function, a more satisfying reading experience can be provided.

[0055] The book summary providing unit can generate individually customized summaries by taking into account a user's past reading history and ratings. For example, the book summary providing unit uses a generation AI to analyze a user's past reading history and understand their preferences for specific genres and authors. Based on that information, the book summary providing unit provides the user with the optimal summary. For example, for a user who likes mystery novels, the unit generates a summary that emphasizes the mystery elements. The book summary providing unit also analyzes the characteristics of books that the user has previously rated highly and generates a summary that reflects those characteristics. For example, for a user who likes moving stories, the unit provides a summary that emphasizes moving scenes. The book summary providing unit also analyzes the user's rating comments, extracts parts that the user found particularly interesting, and reflects them in the summary. For example, if a user is interested in a particular character, the unit generates a summary that focuses on that character. This provides the user with the optimal summary, thereby improving their reading satisfaction.

[0056] In addition to the summary, the book summary providing unit can automatically generate the book's main themes and character relationship diagrams. For example, the book summary providing unit uses a generation AI to analyze the contents of the book and automatically generate the main themes and character relationship diagrams. For example, in the case of a fantasy novel, it illustrates the magical worldview and character relationships. In addition to the summary text, the book summary providing unit also uses the generation AI to provide a character relationship diagram, making it easier for users to grasp the overall picture of the story. For example, it generates a character relationship diagram and a power map. In addition, the book summary providing unit also uses the generation AI to automatically generate a theme map to visually display the book's main themes. For example, it provides a diagram showing the story's main events and themes in chronological order. This allows users to gain a deeper understanding of the book's contents.

[0057] The book summary providing unit can use the emotion estimation function to analyze the emotional response of the user when reading the summary and provide a summary that elicits positive emotions. For example, the book summary providing unit can use the emotion estimation function to analyze the emotional response of the user when reading the summary in real time and provide a summary that elicits positive emotions. For example, it can emphasize moving scenes. The book summary providing unit can also dynamically adjust the content of the summary based on the user's emotional response data to elicit positive emotions. For example, it can emphasize elements that make the user feel joy. The book summary providing unit can also use the emotion estimation function to calculate an emotion score when the user reads the summary and optimize the content of the summary based on the score. For example, it can emphasize parts with a high emotion score. This allows the user to read the summary with positive emotions.

[0058] The book summary providing unit has a function for providing summaries by audio, allowing the user to understand the content by listening. For example, the book summary providing unit adds a function for the generation AI to provide a book summary by audio, allowing the user to understand the content by listening. For example, the user can listen to the summary while commuting or exercising. When providing the summary by audio, the book summary providing unit also adjusts the tone and speed of the narration to suit the user's preferences. For example, the user can select a calm tone or a fast reading speed. The book summary providing unit also uses the audio summaries function to allow the user to understand the content of a book without using their eyes. For example, audio summaries are provided for visually impaired people. This allows the user to listen to the summary by ear and understand the content of the book without relying on their vision.

[0059] The book summary providing unit can provide summaries from multiple perspectives and present different interpretations and perspectives to the user. For example, the generation AI of the book summary providing unit provides book summaries from multiple perspectives and presents different interpretations and perspectives to the user. For example, summaries are provided from the perspective of the protagonist, the villain, and a third-person perspective. By providing summaries from multiple perspectives, the book summary providing unit also deepens the user's multifaceted understanding of the story. For example, summaries from the perspectives of different characters are compared. The book summary providing unit also generates book summaries from different perspectives and presents the user with various interpretations. For example, perspectives on the background and setting of the story are provided. This allows the user to understand the contents of the book from multiple perspectives.

[0060] The book summary providing unit can use the emotion estimation function to monitor the user's emotions in real time while reading the summary and dynamically adjust the content of the summary. The book summary providing unit, for example, uses the emotion estimation function to monitor the user's emotions in real time while reading the summary and dynamically adjust the content of the summary. For example, if the user is excited, action scenes are emphasized. The book summary providing unit also builds a system that analyzes the user's emotional reactions in real time and adjusts the content of the summary. For example, if the user is moved, moving scenes are emphasized. The book summary providing unit also uses the emotion estimation function to dynamically adjust the content of the summary according to changes in the user's emotions. For example, if the user loses interest, interesting elements are added. This allows the content of the summary to be dynamically adjusted according to the user's emotions.

[0061] The similar book recommendation unit can make recommendations taking into account not only the user's reading history and ratings, but also the user's current mood and emotions. For example, the similar book recommendation unit uses a generation AI to analyze the user's current mood and emotions and make recommendations based on that information. For example, when a user feels like relaxing, it recommends books with relaxing content. The similar book recommendation unit also builds a system that recommends optimal books based on the user's emotional data. For example, when a user is feeling sad, it suggests books with uplifting content. The similar book recommendation unit also uses an emotion estimation function to analyze the user's current emotional state in real time and makes recommendations based on the results. For example, when a user is excited, it recommends action-packed books. This allows it to recommend optimal books according to the user's mood and emotions.

[0062] The similar book recommendation unit can extract parts of recommended books that are likely to be of particular interest to the user and provide them as previews. For example, the similar book recommendation unit analyzes the contents of books recommended by a generation AI and extracts parts that are likely to be of particular interest to the user and provides them as previews. For example, it extracts the climax scene of the story. The similar book recommendation unit also identifies parts that are likely to be of particular interest to the user based on the user's past reading history and ratings, and provides those parts as previews. For example, it extracts parts related to the user's favorite themes or characters. The similar book recommendation unit also builds a system that automatically extracts parts of recommended books that are likely to be of interest to the user and provides them as previews. For example, it extracts the beginning of a story or an important scene. This allows the user to select a book by providing previews of parts that are likely to be of interest to the user.

[0063] The similar book recommendation unit uses the emotion estimation function to analyze the emotions a user has toward a recommended book and can prioritize recommend books that the user can easily empathize with. The similar book recommendation unit, for example, uses the emotion estimation function to analyze the emotions a user has toward a recommended book in real time and prioritize recommends books that the user can easily empathize with. For example, it prioritizes suggesting books that move the user. The similar book recommendation unit also identifies books that the user can easily empathize with based on the user's emotional response data and builds a system that prioritizes recommending those books. For example, it suggests books that the user finds enjoyable. The similar book recommendation unit also uses the emotion estimation function to analyze the emotions a user has toward a recommended book and optimizes the recommendation content based on the results. For example, it prioritizes recommending books that excite the user. This improves the reading experience by preferentially recommending books that the user can easily empathize with.

[0064] The similar book recommendation unit expands the recommendation function to recommend not only books but also related movies, dramas, podcasts, etc. The similar book recommendation unit adds a function that enables the generation AI to recommend not only books but also related movies, dramas, podcasts, etc. For example, if a user has read a fantasy novel, it will suggest movies and dramas in the same genre. The similar book recommendation unit also builds a system that recommends related content based on the user's reading history and ratings. For example, it will suggest podcasts related to the user's favorite themes. The similar book recommendation unit also expands the recommendation function to comprehensively suggest related content in addition to books. For example, it will provide a list of movies and dramas related to the books the user has read. This allows users to enjoy related content other than books.

[0065] The similar book recommendation unit can provide not only purchase links for recommended books, but also library lending information and free sample links for e-books. For example, the similar book recommendation unit adds a function that provides not only purchase links for books recommended by the generation AI, but also library lending information and free sample links for e-books. For example, it allows users to preview books before purchasing them. The similar book recommendation unit also builds a system that provides library lending information in addition to purchase links for recommended books. For example, it allows users to borrow books from nearby libraries. The similar book recommendation unit also provides free sample links for e-books, allowing users to preview parts of books. For example, it provides the beginning of a recommended book for free. This allows users to preview books and check lending information before purchasing them.

[0066] The similar book recommendation unit can use the emotion estimation function to monitor the user's emotional response to the recommended book in real time and dynamically adjust the recommendation content. The similar book recommendation unit, for example, uses the emotion estimation function to monitor the user's emotional response to the recommended book in real time and dynamically adjust the recommendation content. For example, if the user is excited, an action-packed book is additionally suggested. The similar book recommendation unit also builds a system that dynamically adjusts the recommendation content based on the user's emotional response data. For example, if the user is moved, an inspiring book is additionally recommended. The similar book recommendation unit also uses the emotion estimation function to adjust the recommendation content in real time according to changes in the user's emotions. For example, if the user loses interest, an additional book with interesting elements is suggested. This makes it possible to dynamically adjust the recommendation content according to the user's emotions.

[0067] When a user rates a book, the rating function can automatically analyze the emotional nuances of the rating comments using the emotion estimation function, and display comments that other users can easily empathize with. The rating function, for example, uses the emotion estimation function to automatically analyze the emotional nuances of the comments when the user rates a book. For example, comments with positive emotions are preferentially displayed. The rating function also performs emotional analysis of the user's rating comments, and builds a system that displays comments that other users can easily empathize with. For example, moving comments are displayed prominently. The rating function also uses the emotion estimation function to analyze the emotional nuances of the rating comments, and automatically selects comments that other users can easily empathize with. For example, comments with emotions of joy or surprise are preferentially displayed. This allows rating comments that users can easily empathize with to be displayed, which is helpful for other users.

[0068] The rating function can analyze a user's reading habits based on rating data and provide individually customized rating criteria. For example, the rating function analyzes a user's reading habits based on the user's rating data and provides individually customized rating criteria. For example, rating criteria are set for specific genres or authors. The rating function also analyzes rating data and builds a system that understands a user's reading habits. For example, rating criteria are set based on the characteristics of books that users have given high ratings to. The rating function also develops a system that provides individually customized rating criteria based on the user's rating data. For example, rating criteria are automatically set according to the user's preferences. This allows for more appropriate ratings by providing rating criteria that are according to the user's reading habits.

[0069] The rating function allows the generation AI to automatically generate reply comments in response to book ratings, promoting communication between users. For example, the rating function allows the generation AI to automatically generate reply comments in response to user rating comments, promoting communication between users. For example, it automatically generates a message of thanks in response to a rating comment. The rating function also allows the generation AI to automatically generate reply comments in response to rating comments, providing related books and information. For example, it recommends books related to the rating comment. The rating function also allows the generation AI to automatically generate reply comments in response to user rating comments, building a system that promotes dialogue between users. For example, it automatically generates questions and opinions in response to rating comments. This promotes communication between users and leads to more active exchange of opinions about books.

[0070] The rating function extends the book rating function to automatically display articles and reviews related to books rated by users. The rating function, for example, extends the book rating function to build a system that automatically displays articles and reviews related to books rated by users. For example, blog articles and expert reviews related to rating comments are displayed. The rating function also adds a function that automatically collects and displays articles and reviews related to books rated by users. For example, news articles and interviews related to rating comments are displayed. The rating function also extends the book rating function to develop a system that automatically displays information related to books rated by users. For example, background information about books related to rating comments and interviews with the authors are displayed. This automatically displays information related to books rated by users, deepening their understanding of the books.

[0071] The rating function can provide information on reading events and autograph sessions that may interest users based on rating data. For example, the rating function builds a system that provides information on reading events and autograph sessions that may interest users based on rating data. For example, it provides information on autograph sessions for authors that users have given high ratings to. The rating function also adds a function that analyzes user rating data and provides event information that may interest users. For example, it provides information on reading events and workshops related to rating comments. The rating function also develops a system that provides event information that may interest users based on rating data. For example, it automatically displays event information that corresponds to the user's rating tendencies. This enriches the reading experience by providing event information that may interest users.

[0072] The evaluation function uses the emotion estimation function to monitor the emotions of users when they enter evaluation comments in real time, and can provide comment entry support that elicits positive emotions. For example, the evaluation function uses the emotion estimation function to monitor the emotions of users when they enter evaluation comments in real time, and can provide comment entry support that elicits positive emotions. For example, if the user has negative emotions, the evaluation function suggests positive expressions. In addition, the evaluation function analyzes the user's emotional reactions in real time, and builds a system that provides comment entry support that elicits positive emotions. For example, if the user is moved, the evaluation function suggests moving expressions. In addition, the evaluation function uses the emotion estimation function to develop a system that provides comment entry support according to changes in the user's emotions. For example, if the user is excited, the evaluation function suggests comments that express excitement. This allows the user to enter evaluation comments with positive emotions.

[0073] The function related to advertising revenue and affiliate revenue can use an emotion estimation function to analyze a user's emotional response when viewing an advertisement and prioritize displaying advertisements that the user is likely to empathize with emotionally. The function related to advertising revenue and affiliate revenue, for example, uses the emotion estimation function to analyze a user's emotional response when viewing an advertisement in real time and prioritize displaying advertisements that the user is likely to empathize with emotionally. For example, advertisements that bring joy to the user are prioritized. The function related to advertising revenue and affiliate revenue also builds a system that identifies advertisements that the user is likely to empathize with emotionally based on user emotional response data and prioritizes displaying those advertisements. For example, advertisements that excite the user are prioritized. The function related to advertising revenue and affiliate revenue also uses the emotion estimation function to develop a system that dynamically adjusts the content of advertisements displayed in accordance with changes in the user's emotions. For example, if the user is emotional, emotional advertisements are prioritized. This prioritizes displaying advertisements that the user is likely to empathize with emotionally, thereby improving advertising effectiveness.

[0074] The function related to advertising revenue and affiliate revenue can optimize the timing of ad display based on a user's reading habits and app usage. The function related to advertising revenue and affiliate revenue, for example, analyzes a user's reading habits and app usage and builds a system that optimizes the timing of ad display. For example, an ad is displayed immediately after a user finishes reading. The function related to advertising revenue and affiliate revenue also adds a function that adjusts the timing of ad display based on a user's usage. For example, an ad is displayed immediately after a user opens an app or just before a user closes it. The function related to advertising revenue and affiliate revenue also develops a system that optimizes the timing of ad display based on a user's reading habits. For example, if a user uses an app during a specific time period, an ad is displayed during that time period. This improves advertising effectiveness by optimizing the timing of ad display based on a user's reading habits and app usage.

[0075] The advertising revenue and affiliate revenue function can use generation AI to automatically generate customized advertisements based on a user's interests. For example, the advertising revenue and affiliate revenue function builds a system in which generation AI analyzes a user's interests and automatically generates customized advertisements based on that information. For example, it generates advertisements related to a user's favorite genres or themes. In addition, the advertising revenue and affiliate revenue function adds a function in which generation AI automatically generates customized advertisements based on a user's past behavioral data. For example, it generates advertisements related to books the user has previously purchased. In addition, the advertising revenue and affiliate revenue function uses generation AI to develop a system that generates customized advertisements in real time based on a user's interests. For example, it generates advertisements related to books the user is currently reading. This automatically generates customized advertisements based on a user's interests and concerns, thereby improving advertising effectiveness.

[0076] The function related to advertising revenue and affiliate revenue can also handle advertisements for products and services other than books in order to increase advertising revenue and affiliate revenue. The function related to advertising revenue and affiliate revenue, for example, builds a system that also handles advertisements for products and services other than books in order to increase advertising revenue and affiliate revenue. For example, advertisements for goods and accessories related to books are displayed. The function related to advertising revenue and affiliate revenue also adds a function to increase advertising revenue by handling advertisements for products and services other than books. For example, advertisements for cafes and events related to reading are displayed. The function related to advertising revenue and affiliate revenue also develops a system that handles advertisements for products and services other than books in order to increase advertising revenue and affiliate revenue. For example, advertisements for apps and devices related to reading are displayed. In this way, by handling advertisements for products and services other than books, advertising revenue and affiliate revenue can be increased.

[0077] The function related to advertising revenue and affiliate income can also suggest goods and accessories related to books purchased by users through affiliate links. The function related to advertising revenue and affiliate income, for example, builds a system that suggests goods and accessories related to books purchased by users through affiliate links. For example, it suggests character goods and related products of the book. Furthermore, the function related to advertising revenue and affiliate income adds a function that suggests goods and accessories related to books purchased by users. For example, it suggests accessories and decorations related to the theme of the book. Furthermore, the function related to advertising revenue and affiliate income develops a system that suggests products related to books purchased by users through affiliate links. For example, it suggests goods and accessories related to the content of the book. This allows for an increase in affiliate income by suggesting goods and accessories related to books purchased by users.

[0078] The advertising revenue and affiliate revenue functions use an emotion estimation function to monitor users' emotional reactions in real time when they view advertisements, and dynamically adjust the advertisement content. For example, the advertising revenue and affiliate revenue functions use the emotion estimation function to build a system that monitors users' emotional reactions in real time when they view advertisements and dynamically adjusts the advertisement content. For example, if a user is excited, an action-packed advertisement is displayed. Furthermore, the advertising revenue and affiliate revenue functions add a function that dynamically adjusts advertisement content based on the user's emotional reaction data. For example, if a user is moved, an emotional advertisement is displayed. Furthermore, the advertising revenue and affiliate revenue functions use the emotion estimation function to develop a system that adjusts advertisement content in real time in response to changes in the user's emotions. For example, if a user loses interest, an advertisement with elements that pique the user's interest is displayed. This dynamically adjusts advertisement content in response to the user's emotions, thereby improving the effectiveness of advertisements.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] In addition to the summary, the book summary provider can automatically generate the book's main themes and character relationship diagrams. For example, the generation AI analyzes the contents of the book and automatically generates the main themes and character relationship diagrams. In the case of fantasy novels, it illustrates the magical worldview and character relationships. In addition to the summary, the book summary provider also provides a character relationship diagram, making it easier for users to grasp the overall picture of the story. It can also generate character relationship diagrams and power maps. In addition, the book summary provider automatically generates a theme map to visually display the book's main themes. It can also provide a diagram showing the story's main events and themes in chronological order. This allows users to gain a deeper understanding of the book's contents.

[0081] The book summary providing unit can have a function to provide audio summaries, allowing users to understand the content by listening. For example, the generation AI can add a function to provide audio summaries of books, allowing users to understand the content by listening. The user can listen to the summaries while commuting or exercising. The book summary providing unit can also adjust the tone and speed of the narration when providing audio summaries to suit the user's preferences. A calm tone or a fast reading speed can be selected. The book summary providing unit can also use the audio summaries function to allow users to understand the content of a book without using their eyes. Audio summaries can also be provided for visually impaired people. This allows users to listen to the summaries by ear and understand the content of a book without relying on their vision.

[0082] The book summary providing unit can provide summaries from multiple perspectives, presenting different interpretations and perspectives to the user. For example, the generation AI can provide book summaries from multiple perspectives, presenting different interpretations and perspectives to the user. It can also provide summaries from the protagonist's perspective, the villain's perspective, and a third-person perspective. By providing summaries from multiple perspectives, the book summary providing unit can deepen the user's understanding of the story from multiple angles. It can also compare summaries from the perspectives of different characters. The book summary providing unit can also generate book summaries from different perspectives, presenting the user with various interpretations. It can also provide perspectives on the background and setting of the story. This allows the user to understand the contents of the book from multiple angles.

[0083] The book summary providing unit can use the emotion estimation function to analyze the emotional response of the user when reading the summary and provide a summary that elicits positive emotions. For example, the emotion estimation function can be used to analyze the emotional response of the user when reading the summary in real time and provide a summary that elicits positive emotions. Moving scenes can also be emphasized. The book summary providing unit can also dynamically adjust the content of the summary based on the user's emotional response data to elicit positive emotions. Elements that bring joy to the user can also be emphasized. The book summary providing unit can also use the emotion estimation function to calculate an emotion score when the user reads the summary and optimize the content of the summary based on that score. Parts with a high emotion score can also be emphasized. This allows the user to read the summary with positive emotions.

[0084] The book summary providing unit can use the emotion estimation function to monitor the user's emotions in real time while reading the summary and dynamically adjust the content of the summary. For example, the emotion estimation function can be used to monitor the user's emotions in real time while reading the summary and dynamically adjust the content of the summary. If the user is excited, action scenes can be emphasized. The book summary providing unit can also build a system that analyzes the user's emotional reactions in real time and adjusts the content of the summary. If the user is moved, moving scenes can be emphasized. The book summary providing unit can also use the emotion estimation function to dynamically adjust the content of the summary according to changes in the user's emotions. If the user loses interest, interesting elements can be added. This allows the content of the summary to be dynamically adjusted according to the user's emotions.

[0085] The similar book recommendation unit can make recommendations based not only on the user's reading history and ratings, but also on the user's current mood and emotions. For example, the generation AI can analyze the user's current mood and emotions and make recommendations based on that information. When a user feels like relaxing, it can recommend books with relaxing content. The similar book recommendation unit can also build a system that recommends optimal books based on the user's emotional data. When a user is feeling sad, it can suggest books with uplifting content. The similar book recommendation unit can also use an emotion estimation function to analyze the user's current emotional state in real time and make recommendations based on the results. When a user is excited, it can recommend action-packed books. This allows the system to recommend optimal books based on the user's mood and emotions.

[0086] The similar book recommendation unit can extract parts of recommended books that are likely to be of interest to the user and provide them as previews. For example, the generation AI can analyze the contents of a recommended book and extract parts that are likely to be of interest to the user and provide them as previews. It can also extract the climax scene of the story. The similar book recommendation unit can also identify parts that are likely to be of interest to the user based on the user's past reading history and ratings, and provide those parts as previews. It can also extract parts related to the user's favorite themes or characters. The similar book recommendation unit can also build a system that automatically extracts parts of recommended books that are likely to be of interest to the user and provides them as previews. It can also extract the beginning of a story or important scenes. This can provide previews of parts that are likely to interest the user, helping them choose a book.

[0087] The similar book recommendation unit expands the recommendation function to recommend not only books but also related movies, dramas, podcasts, etc. For example, the generative AI can add a function to recommend not only books but also related movies, dramas, podcasts, etc. For a user who has read a fantasy novel, it can also suggest movies and dramas in the same genre. The similar book recommendation unit can also build a system that recommends related content based on the user's reading history and ratings. It can also suggest podcasts related to the user's favorite themes. The similar book recommendation unit can also expand the recommendation function to suggest not only books but also related content in general. It can also provide a list of movies and dramas related to the books the user has read. This allows users to enjoy related content other than books.

[0088] The similar book recommendation unit can use the emotion estimation function to analyze the emotions a user has toward a recommended book and prioritize recommend books that the user can easily empathize with. For example, the emotion estimation function can be used to analyze the emotions a user has toward a recommended book in real time and prioritize recommend books that the user can easily empathize with. It can also prioritize suggesting books that move the user. The similar book recommendation unit can also build a system that identifies books that the user can easily empathize with emotionally based on the user's emotional response data and prioritizes recommending those books. It can also suggest books that the user finds enjoyable. The similar book recommendation unit can also use the emotion estimation function to analyze the emotions a user has toward a recommended book and optimize the recommendation content based on the results. It can also prioritize recommending books that the user finds exciting. This improves the reading experience by prioritizing recommendations of books that the user can easily empathize with emotionally,

[0089] When a user rates a book, the rating function can automatically analyze the emotional nuances of the rating comments using the emotion estimation function, and display comments that other users can easily empathize with. For example, the emotion estimation function can be used to automatically analyze the emotional nuances of the comments when a user rates a book. Comments with positive emotions can also be preferentially displayed. The rating function can also emotionally analyze users' rating comments and build a system that displays comments that other users can easily empathize with. Moving comments can also be displayed prominently. The rating function can also analyze the emotional nuances of the rating comments using the emotion estimation function, and automatically select comments that other users can easily empathize with. Comments with emotions such as joy and surprise can also be preferentially displayed. This allows rating comments that users can easily empathize with to be displayed, which can be helpful to other users.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The book summary provider provides a book summary. For example, the book summary provider uses a generation AI to analyze the contents of a book and generate a summary that can be read in a few minutes. The book summary provider can also analyze the contents and provide a summary when a user uploads an image of the book title or cover. For example, if a user uploads an image of the cover of "Harry Potter and the Philosopher's Stone," the generation AI will analyze the contents and provide a summary such as "The story of Harry Potter enrolling in a magic school and going on adventures with his friends." Step 2: The similar book recommendation unit recommends similar books based on the summaries provided by the book summary provider. For example, if a user selects their favorite book titles and the type of stories they would like to read, the generation AI analyzes that information and recommends books with similar content. For example, if a user inputs "I want to read fantasy novels," the generation AI will suggest similar books such as "The Lord of the Rings" and "The Chronicles of Narnia." Step 3: The evaluation unit evaluates the books recommended by the similar book recommendation unit. For example, users can leave evaluation comments and ratings for books they have read. This evaluation data is shared with other users and displayed as recommendations and popularity rankings. For example, if a user comments, "This book was very interesting," and rates it five stars, that evaluation will be displayed to other users.

[0092] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0106] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0143] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0144] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0148] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0150] 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.

[0151] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0152] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0153] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0156] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a book summary providing unit that provides a summary of a book; a similar book recommendation unit that recommends similar books based on the summaries provided by the book summary providing unit; an evaluation unit that evaluates the books recommended by the similar book recommendation unit. A system characterized by:

2. The book summary providing unit Generates personalized summaries based on the user's past reading history and ratings 2. The system of claim 1.

3. The book summary providing unit In addition to the summary, a relationship diagram of the main themes and characters of the book is automatically generated.

2. The system of claim 1.

4. The book summary providing unit Analyze the emotional response of users when reading the summary and provide summaries that evoke positive emotions.

2. The system of claim 1.

5. The book summary providing unit a function of providing the summary by voice; Allow users to understand the content by listening 2. The system of claim 1.

Images