system

Abstract

Provide a system. [Solution] means for receiving a book title or cover image as input from a user; A means for generating a summary using a generative AI model based on input data of the book; means for displaying said summary to a user; A way to recommend related books based on keywords, authors, and genres, a means for receiving evaluation comments and ratings as input from a user; a means for storing the evaluation comments and scores in a database and generating a popularity ranking; means for displaying the popularity ranking to a user; means for selecting and displaying advertising data to a user; A system including:

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] The present invention aims to solve problems such as the desire to read but not knowing which book to choose, the need to efficiently search for books within a limited time, and the difficulty of trying new genres or authors. It is also necessary to enrich the reading experience by allowing users to rate and grade books, making it easier to share information with other users. Another challenge is to realize monetization through advertising revenue and book purchase partnerships. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means. First, a means is provided for users to input book titles or cover images, allowing them to input information about books they are interested in into the system. Next, a means is provided for generating summaries using a generative AI model based on the input book data, and this summaries are provided to users, allowing them to quickly understand the contents of the book. The system also has a means for recommending related books based on keywords, authors, and genres, allowing users to easily discover new books and genres. Furthermore, a means is provided for users to input evaluation comments and ratings for books, and a means is provided for storing this evaluation data in a database and generating popularity rankings. Finally, a system is provided that includes a means for providing these rankings to users and a means for selecting and displaying advertising data to users. These means allow users to efficiently find books that match their interests and preferences, resulting in a more fulfilling reading experience.

[0006] A "book title" is the name used to identify a particular book, usually found on the cover or spine.

[0007] A "cover image" is image data that shows a visual representation of a book's cover, facilitating identification and recognition of the book.

[0008] A "generative AI model" is a type of artificial intelligence technology that uses advanced algorithms to generate text based on large amounts of data.

[0009] A "summary" is a short summary of the contents of a book, allowing a user to understand the outline of the book in a short amount of time.

[0010] "Keywords" are words or phrases used to search for specific information and indicate the content or theme of a book.

[0011] An "author" is a person who writes the text of a book and often owns the intellectual property rights to the book.

[0012] A "genre" is a category into which a book is classified based on its content or theme, such as fantasy, mystery, or non-fiction.

[0013] "Recommendation" is the act of suggesting highly relevant books based on information entered by the user.

[0014] An "evaluation comment" is a piece of writing in which a user writes their impressions or opinions after reading a book.

[0015] "Rating" is the act of a user assigning a rating to a book, which is usually expressed as a number of stars or points.

[0016] A "database" is a system that stores large amounts of data in an organized manner, making it easy to search and manage.

[0017] A "popularity ranking" is a list that ranks the popularity of multiple books based on evaluation comments and ratings.

[0018] "Advertising data" refers to data that includes information to advertise a particular product or service and is displayed to a user. [Brief explanation of the drawings]

[0019] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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, a 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), and an APU (Accelerated Processing Unit).

[0023] 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.

[0024] 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.

[0025] 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), Bluetooth (registered trademark), etc.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0031] 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.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] ---

[0041] The system of the present invention allows users to input information such as the book title, cover image, keywords, author, genre, etc., and provides book summaries and recommendations based on that information. The processing of the system's program is explained below in natural language.

[0042] First, the user uses the terminal to input the title and cover image of the book they want to read into the system. For example, the user may input the title "Harry Potter." The terminal then sends this input data to the server.

[0043] Based on the received data, the server sends a request to the generative AI model to generate a summary of the book. The generative AI model uses advanced algorithms based on large amounts of data to generate a summary that can be read in a few minutes. For example, a summary might be generated that reads, "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[0044] The server then sends the generated summary to the terminal, which then displays it to the user, allowing the user to quickly understand the contents of the book by reading the summary.

[0045] Furthermore, the server searches the database for related books based on the keywords, author, genre, etc. entered by the user, and generates a list of recommended books. The recommended book list is sent to the terminal and displayed to the user. For example, related books such as "The Lord of the Rings" and "The Chronicles of Narnia" are recommended.

[0046] Next, the user enters a rating and comment for the book they have just read. For example, they might comment, "This is an engrossing story!" and rate it five stars. The device then sends this rating data to the server. The server then stores the received rating data in a database and generates a popularity ranking based on this data. The generated popularity ranking is then sent back to the device and displayed to the user.

[0047] The server also selects relevant advertisements based on the user's interests and preferences and sends them to the device. For example, an advertisement for a book related to the "Fantasy" genre may be selected and displayed on the device. For example, an advertisement such as "Buy the Dragonlance series now" may be displayed.

[0048] In this way, the system of the present invention enriches the user's reading experience and enables them to efficiently find books by generating book summaries, recommending related books, storing evaluation data and generating popularity rankings, and displaying advertisements.

[0049] ---

[0050] The processing flow will be explained below.

[0051] ---

[0052] Step 1:

[0053] User: Enter the title or keywords of the book you want to read into the input form on your device. For example, enter "Harry Potter."

[0054] Step 2:

[0055] Terminal: Receives the entered book title and keywords and executes an API request to send them to the server.

[0056] Step 3:

[0057] Server: Based on the received book title and keywords, sends a summary generation request to the generative AI model.

[0058] Step 4:

[0059] Generative AI model: Generates a book summary based on the given book title and keywords. For example, it generates a summary such as "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[0060] Step 5:

[0061] Server: Sends the generated summary to the terminal.

[0062] Step 6:

[0063] Terminal: The generated summary is displayed to the user, who can then check the overview of the book.

[0064] Step 7:

[0065] Server: Searches the database for relevant books based on the keywords, author, and genre entered by the user.

[0066] Step 8:

[0067] Server: Generates a recommended book list from the search results by selecting highly relevant books.

[0068] Step 9:

[0069] Server: Sends the generated recommended book list to the terminal.

[0070] Step 10:

[0071] Terminal: Display a recommended book list to the user. For example, "The Lord of the Rings" and "The Chronicles of Narnia" are displayed as recommended books.

[0072] Step 11:

[0073] User: Enters a rating and comment for the book after reading it. For example, a user might comment, "This is an engrossing story!" and rate it five stars.

[0074] Step 12:

[0075] Device: Executes an API request to send the entered evaluation comments and scores to the server.

[0076] Step 13:

[0077] Server: Stores the received evaluation data in a database.

[0078] Step 14:

[0079] Server: Generates popularity rankings based on the saved rating data.

[0080] Step 15:

[0081] Server: Sends the generated popularity ranking to the device.

[0082] Step 16:

[0083] Device: Shows users a popularity ranking. For example, "Harry Potter" is displayed at the top of the popularity ranking.

[0084] Step 17:

[0085] Server: Selects relevant advertising data based on the user's interests and preferences.

[0086] Step 18:

[0087] Server: Sends the selected advertising data to the terminal.

[0088] Step 19:

[0089] Device: Shows relevant ads and book purchase links to users. For example, ads like "Buy the Dragonlance series now" may be displayed.

[0090] ---

[0091] Example 1

[0092] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0093] Many readers today want to efficiently find books that match their interests and understand summaries within the constraints of time and information. However, conventional search and recommendation systems have had difficulty efficiently retrieving the necessary information from the vast amount of book information available. It has also been difficult to reflect reader ratings and ranking information in real time and present popular books appropriately.

[0094] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0095] In this invention, the server includes means for receiving book information as input from a user, means for generating a summary based on the book information using a generative AI model, and means for displaying the summary to the user, thereby enabling users to efficiently obtain book summaries and find books that match their interests.

[0096] A "user" is a person who uses the system and inputs book information.

[0097] "Book information" refers to data such as the book title, cover image, keywords, author, genre, etc.

[0098] A "generative AI model" is an artificial intelligence model that uses advanced algorithms to generate summaries and recommendations based on input data.

[0099] A "summary" is a short sentence that abridges the contents of a book.

[0100] "Keywords" are words or phrases that indicate the content or characteristics of a book.

[0101] An "author" is a person who writes a book.

[0102] "Type" refers to the genre or category of the book.

[0103] "Recommendation" refers to providing related books that are suggested based on the user's interests.

[0104] An "evaluation comment" is a sentence that describes a user's impressions or opinions about a book.

[0105] A "rating" is a numerical value that a user gives to a book.

[0106] "Storage device" refers to a database or storage for storing evaluation data and book information.

[0107] "Popularity ranking" is a ranking calculated based on evaluation data and the popularity of books.

[0108] "Advertising information" is advertising data for products and services related to books.

[0109] "Control means" refers to a device or function that links each means and manages the operation of the entire system.

[0110] MODE FOR CARRYING OUT THE INVENTION

[0111] The system of the present invention uses a generative AI model to recommend summaries and related books, save evaluation comments, generate popularity rankings, and display advertisements based on user input of book information. The specific operation of the system is shown below.

[0112] User enters book information

[0113] A user uses a device (such as a smartphone or PC) to input information about the book they want to read, such as the title, cover image, keywords, author, and genre. For example, a user might input the title "Adventures at Magic School" into the device as book information. This information is then sent from the device to the server.

[0114] Generate a book summary

[0115] The server generates a summary by sending a prompt to the generative AI model based on the received book information. Specifically, the server passes the following prompt to the generative AI model:

[0116] Generate a summary of the book 'Adventures at Magic School'.

[0117] The generative AI model generates a summary based on this prompt, such as "Adventures at Wizarding School tells the story of a young wizard uncovering a secret," and returns it to the server, which then sends the summary to the device and displays it to the user.

[0118] Recommend a book

[0119] The server searches for and recommends related books based on the keywords, author, genre, etc. entered by the user. For example, the following prompt sentence is input to the generative AI model:

[0120] List five books related to 'wizard school adventures' in the fantasy genre.

[0121] The generative AI model returns a list of related books, such as "The Magic Star" and "The Tale of the Dragon," to the server, which then sends this list to the device and displays it to the user.

[0122] User ratings and popularity ranking generation

[0123] After reading a book, the user inputs a rating and comment. For example, they might comment, "What a wonderful adventure story!" and rate it five stars. The device sends this rating data to the server, which stores it in a database. The server generates a popularity ranking based on the accumulated rating data, and sends information to the device, such as, "Magic School Adventure is currently ranked number one in popularity," to display to the user.

[0124] Displaying relevant ads

[0125] The server selects relevant advertisements based on the user's interests and preferences. For example, it uses a prompt such as "Show me advertisements for new books related to fantasy novels." The selected advertisement information is sent to the terminal and displayed to the user. For example, an advertisement such as "Buy the new book 'Wizard Training' now" is displayed.

[0126] In this way, the system performs various data processing and calculations based on the information entered by the user, efficiently providing book summaries and recommendations, and enriching the user's reading experience.

[0127] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0128] Processing Steps

[0129] Step 1:

[0130] The user enters book information into the device.

[0131] Input: Book title, cover image, keywords, author, genre, etc.

[0132] Specific behavior: The user uses the terminal to enter the title "Adventures at Magic School."

[0133] Output: The terminal sends the input data to the server in JSON format.

[0134] Step 2:

[0135] The server receives and analyzes the book information.

[0136] Input: Book information data in JSON format

[0137] Specific operation: The server parses the received JSON data and obtains the book title, cover image, keywords, author, genre, etc.

[0138] Output: Summary generation prompt "Generate a summary for the book 'Adventures at Wizarding School'."

[0139] Step 3:

[0140] The server sends a summary generation request to the generative AI model

[0141] Input: Summary generation prompt statement

[0142] Specific operation: The server sends a summary generation prompt to the generative AI model.

[0143] Output: The generative AI model generates a summary and sends it back to the server (e.g., "Adventures at Wizarding School tells the story of a young wizard who uncovers a secret.").

[0144] Step 4:

[0145] The server sends the generated summary to the device.

[0146] Input: Generated summary text

[0147] Specific operation: The server converts the summary text into JSON format and sends it to the terminal as an HTTP response.

[0148] Output: A summary is displayed on the user's terminal.

[0149] Step 5:

[0150] The server sends a request for related book recommendations to the generative AI model.

[0151] Input: Keywords, author, genre of book information

[0152] Specific behavior: The server sends the prompt "List five books in the fantasy genre that relate to 'wizard school adventures'" to the generative AI model.

[0153] Output: The generative AI model generates a list of related books and sends it back to the server (e.g., "Magic Star," "Tales of Dragons," etc.).

[0154] Step 6:

[0155] The server sends the recommended book list to the device.

[0156] Input: Related Book List

[0157] Specific operation: The server converts the book list into JSON format and sends it to the terminal as an HTTP response.

[0158] Output: A list of related books is displayed on the user's device.

[0159] Step 7:

[0160] Users enter evaluation comments and ratings for books

[0161] Input: Evaluation comments, score

[0162] What happens: A user comments, "A great adventure story!" and leaves a 5-star rating.

[0163] Output: The device sends the rating data in JSON format to the server.

[0164] Step 8:

[0165] The server stores the rating data in a database

[0166] Input: JSON data of evaluation comments and scores

[0167] Specific behavior: The server executes an SQL query to store the received rating data in a database.

[0168] Output: Evaluation data stored in a database

[0169] Step 9:

[0170] The server generates a popularity ranking and sends it to the device.

[0171] Input: Evaluation data from the database

[0172] Specific operation: The server aggregates the rating data from the database, generates a popularity ranking, converts the ranking data into JSON format, and sends it to the device as an HTTP response.

[0173] Output: The popularity ranking is displayed on the user's device.

[0174] Step 10:

[0175] The server selects relevant advertisements and sends them to the device.

[0176] Input: Prompt text based on user interests and preferences

[0177] Specific operation: The server sends a prompt such as "Show me advertisements for new books related to fantasy novels" to the generative AI model. The generated advertisement information is converted into JSON format and sent to the device as an HTTP response.

[0178] Output: The ad is displayed on the user's device.

[0179] The above are the specific processing steps of the system.

[0180] (Application example 1)

[0181] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0182] In the past, users had to manually input information to obtain book summaries and recommendations, which was inefficient. Furthermore, the collection of book review data and the display of advertisements were done separately, making it difficult to provide a comprehensive reading experience. The present invention aims to solve these problems by providing a system that allows users to efficiently obtain book information and provides related books and advertisements all at once.

[0183] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0184] In this invention, the server includes: means for receiving a book title or cover image as input from a user; means for generating a summary based on the book input data using a generative AI model; means for displaying the summary to the user; means for recommending related books based on keywords, authors, and genres; means for receiving evaluation comments and ratings as input from the user; means for storing the evaluation comments and ratings in a database and generating a popularity ranking; means for displaying the popularity ranking to the user; means for selecting advertising data and displaying it to the user; means for identifying the user's gaze point using eye-tracking technology and acquiring book information; and means for generating a book summary based on the book information acquired through the eye-tracking using a generative AI model and displaying it on a smart device. This allows users to intuitively acquire book information using their gaze and receive summaries, related book recommendations, evaluation data, and advertisements in a comprehensive manner.

[0185] "Book title" refers to the name of a book, which is usually printed on the cover or spine.

[0186] "Cover Image" refers to an image file containing visual data of the cover of a book.

[0187] A "generative AI model" is a model that uses artificial intelligence algorithms to generate text from input data.

[0188] A "summary" is a written summary of the contents of a book that can be understood concisely and quickly.

[0189] "Means for displaying to the user" refers to a method for displaying the generated information on a terminal screen or the like so that the user can visually confirm it.

[0190] "Keywords" are words or phrases that characterize the content of a book.

[0191] An "author" is a person who writes a book.

[0192] A "genre" is a classification that indicates what theme or category a book belongs to.

[0193] "Recommendation methods" refer to methods that recommend related books based on a user's interests and past behavior.

[0194] "Evaluation comments" are impressions and opinions entered by users about books they have read.

[0195] "Rating" refers to the user's evaluation of a book they have read, expressed as a number or number of stars.

[0196] A "database" is a system that efficiently stores and manages data and allows you to quickly search for the information you need.

[0197] "Popularity ranking" is a list of books ranked by popularity based on user ratings and comments.

[0198] "Advertising data" refers to information created for the purpose of advertising a specific product.

[0199] "Eye tracking technology" refers to technology that tracks the movement of a user's eyes and identifies their point of gaze.

[0200] "Smart devices" refers to electronic devices with internet connectivity and high-performance computing capabilities, including smart glasses and smartphones.

[0201] The system of the present invention comprises multiple means for providing book summaries and related book recommendations. Each component of the system and its operation are described in detail below.

[0202] Corresponding measures and technologies

[0203] 1. Input Method

[0204] A user inputs the book title or cover image using smart glasses or other smart devices (e.g., smartphones). The smart glasses are equipped with a camera and eye-tracking technology, which are used to identify the gaze point and obtain book information.

[0205] 2. Summary generation method using generative AI models

[0206] Input data (book title and cover image) is sent to the server, and a generative AI model (e.g., OpenAI's GPT-4) receives it and generates a summary. An example prompt might be "Write a brief summary of the following book: Harry Potter." The generated summary is sent from the server to the device and displayed to the user.

[0207] 3. Recommendation methods

[0208] The server searches the database for related books based on the input keywords, author, and genre, and obtains recommendation information. This information is also sent to the terminal and displayed to the user. For example, "The Lord of the Rings" and "The Chronicles of Narnia" are recommended.

[0209] 4. Evaluation Receipt Method

[0210] It also includes a means for receiving user input such as a rating and comment on the book after reading it. For example, a user might comment, "An engrossing story!" and give it a five-star rating.

[0211] 5. Means for generating and displaying popularity rankings

[0212] The server stores the received rating data in a database and generates a popularity ranking based on this data. The generated popularity ranking is then sent back to the terminal and displayed to the user.

[0213] 6. Advertisement Selection Method

[0214] The system also includes a means for selecting and displaying relevant advertising data to users based on their interests and preferences. For example, an advertisement such as "Buy the Dragonlance series now" may be displayed.

[0215] Hardware and Software

[0216] Smart devices: Smart glasses and smartphones with eye tracking

[0217] Server: Uses a Python (registered trademark)-based framework (e.g., Flask)

[0218] Generative AI model: OpenAI's GPT-4

[0219] Database: MongoDB, etc.

[0220] Specific examples

[0221] The user puts on the smart glasses and directs their gaze at the book cover. Eye-tracking technology identifies the point of gaze and sends that information to the server. The server then sends prompts to the generative AI model based on the book cover image, generating a summary. Related books are then recommended, and all information is displayed on the smart glasses' display. This allows the user to easily obtain summaries and recommended information with just eye-gaze interaction.

[0222] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0223] Step 1:

[0224] A user puts on the smart glasses and looks at the cover of a book.

[0225] Specific operation: The smart glasses' built-in camera tracks the user's gaze direction and captures an image of the book cover.

[0226] Input: User gaze and book cover image.

[0227] Output: Send the gaze information and cover image data to the server.

[0228] Step 2:

[0229] The terminal transmits the cover image data to the server.

[0230] Specific operation: The smart glasses upload the processed cover image data to the server.

[0231] Input: Cover image data.

[0232] Output: The cover image data is saved on the server.

[0233] Step 3:

[0234] The server analyzes the cover image data and extracts the book title.

[0235] Specific operation: The server uses a machine learning algorithm to perform character recognition on the cover image and identify the title.

[0236] Input: Cover image data.

[0237] Output: The book title.

[0238] Step 4:

[0239] The server sends prompts to the generative AI model to generate summaries.

[0240] How it works: The server generates a prompt containing the book title and sends it to a generative AI model (e.g., GPT-4), which then generates a summary based on the prompt.

[0241] Input: Book title and prompt (e.g., "Write a brief summary of the following book: Harry Potter").

[0242] Output: The generated summary text.

[0243] Step 5:

[0244] The server sends the generated summary to the terminal for display to the user.

[0245] Specific operation: The generated summary text is sent to the terminal for display on the smart glasses display.

[0246] Input: The generated summary text.

[0247] Output: A summary is displayed on the smart glasses display.

[0248] Step 6:

[0249] The server recommends related books based on keywords, authors, and genres.

[0250] What happens: The server searches its database to identify books related to the entered title.

[0251] Input: Book title, keywords, author, genre.

[0252] Output: A list of related books.

[0253] Step 7:

[0254] The server sends a list of related books to the terminal and displays it to the user.

[0255] Specific operation: A list of related books is sent to the terminal for display on the smart glasses display.

[0256] Input: A list of related books.

[0257] Output: A list of related books is displayed on the smart glasses display.

[0258] Step 8:

[0259] The user inputs evaluation comments and ratings for the books he or she has read.

[0260] Specific operation: The user inputs evaluation comments and scores using smart glasses or a smartphone.

[0261] Input: Rating information such as rating comments and star ratings.

[0262] Output: Evaluation comments and score information are sent to the server.

[0263] Step 9:

[0264] The server stores the evaluation comments and scores in a database and generates a popularity ranking.

[0265] Specific operation: The server records evaluation comments and scoring data in a database and generates popularity rankings based on that.

[0266] Input: Evaluation comments and scoring data.

[0267] Output: Popularity ranking.

[0268] Step 10:

[0269] The server transmits the popularity ranking to the terminal and displays it to the user.

[0270] Specific operation: The generated popularity ranking is sent to the terminal for display on the smart glasses display.

[0271] Input: Popularity Rankings.

[0272] Output: The popularity ranking is displayed on the smart glasses display.

[0273] Step 11:

[0274] The server selects the advertisement data and displays it to the user.

[0275] Specific operation: The server identifies relevant advertisements based on the user's interests and past behavior data and sends them to the device.

[0276] Input: User interest and behavior data, advertising data.

[0277] Output: The advertisement is displayed on the smart glasses display.

[0278] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0279] ---

[0280] This invention combines an emotion engine with a system that provides book summaries and recommendations based on input information such as book titles, cover images, keywords, authors, and genres, recognizing user emotions and optimizing recommendations and advertisement display. The system's program processing is explained below in natural language.

[0281] First, the user uses the terminal to input the title and cover image of the book they want to read into the system. For example, the user inputs the title "Harry Potter." The terminal then sends this input data to the server.

[0282] Based on the received data, the server sends a request to the generative AI model to generate a summary of the book. The generative AI model uses advanced algorithms based on large amounts of data to generate a summary that can be read in a few minutes. For example, a summary might be generated that reads, "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[0283] The server then sends the generated summary to the terminal, which then displays it to the user, allowing the user to quickly understand the contents of the book by reading the summary.

[0284] Furthermore, the server searches the database for related books based on the keywords, authors, genres, etc. entered by the user, and generates a list of recommended books. At this time, an emotion engine can be used to recognize emotions from the user's input and behavior. For example, if a user enters keywords such as "moving" or "fun," the server will select recommended books that match that emotion. The display order of recommended books may also be adjusted based on the analysis results of the emotion engine.

[0285] The recommended books are then sent to the device and displayed to the user. For example, books related to "The Lord of the Rings" and "The Chronicles of Narnia" are recommended.

[0286] Users input their evaluation comments and ratings for books they have read. For example, they might comment, "This is an engrossing story!" and rate it five stars. The device then sends this evaluation data to the server. The server then stores the received evaluation data in a database and generates a popularity ranking based on this data. The stored evaluation data can also be used for sentiment analysis and may be incorporated as emotional feedback for the user.

[0287] The generated popularity ranking is sent back to the device and displayed to the user. For example, "Harry Potter" appears at the top of the popularity ranking.

[0288] The server then selects relevant advertising data based on the user's interests and preferences. The emotion engine analyzes the user's emotions in real time and reflects them in the advertisement selection. For example, if the user is feeling "fun," an advertisement for a book that matches that emotion may be selected.

[0289] The selected advertising data is sent to the device and displayed to the user. For example, an advertisement such as "Purchase the Dragonlance series now" is displayed.

[0290] In this way, the system of the present invention combines an emotion engine with book summary generation, related book recommendation, evaluation data storage and popularity ranking generation, and advertisement display to realize optimal recommendations and advertisement display based on the user's emotions, enriching the reading experience and enabling efficient book discovery.

[0291] ---

[0292] The processing flow will be explained below.

[0293] ---

[0294] Step 1:

[0295] User: Enter the title or keywords of the book you want to read into the input form on your device. For example, enter "Harry Potter."

[0296] Step 2:

[0297] Terminal: Receives the entered book title and keywords and executes an API request to send them to the server.

[0298] Step 3:

[0299] Server: Based on the received book title and keywords, sends a summary generation request to the generative AI model.

[0300] Step 4:

[0301] Generative AI model: Generates a book summary based on the given book title and keywords. For example, it generates a summary such as "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[0302] Step 5:

[0303] Server: Sends the generated summary to the terminal.

[0304] Step 6:

[0305] Terminal: The generated summary is displayed to the user, allowing the user to see an overview of the book.

[0306] Step 7:

[0307] Server: Searches the database for relevant books based on the keywords, author, and genre entered by the user.

[0308] Step 8:

[0309] Server: Generates a list of recommended books from the search results that are highly relevant. In this process, an emotion engine is used to recognize emotions from the user's input and behavior, and books that fit those emotions are selected.

[0310] Step 9:

[0311] Server: Analyzes user sentiment and adjusts the order in which recommended books are displayed.

[0312] Step 10:

[0313] Server: Sends the generated recommended book list to the terminal.

[0314] Step 11:

[0315] Terminal: Display a recommended book list to the user. For example, "The Lord of the Rings" and "The Chronicles of Narnia" are displayed as recommended books.

[0316] Step 12:

[0317] User: Enters a rating and comment for the book after reading it. For example, a user might comment, "This is an engrossing story!" and rate it five stars.

[0318] Step 13:

[0319] Device: Executes an API request to send the entered evaluation comments and scores to the server.

[0320] Step 14:

[0321] Server: Stores the received evaluation data in a database.

[0322] Step 15:

[0323] Server: Generates popularity rankings based on the saved rating data. Sentiment analysis is also used to capture user sentiment feedback.

[0324] Step 16:

[0325] Server: Sends the generated popularity ranking to the device.

[0326] Step 17:

[0327] Device: Shows users a popularity ranking. For example, "Harry Potter" is displayed at the top of the popularity ranking.

[0328] Step 18:

[0329] Server: Selects relevant advertising data based on the user's interests and preferences. In this process, it uses an emotion engine to analyze user emotions in real time and reflects this information in the selection of ads.

[0330] Step 19:

[0331] Server: Sends the selected advertising data to the terminal.

[0332] Step 20:

[0333] Device: Shows relevant ads and book purchase links to users. For example, ads like "Buy the Dragonlance series now" may be displayed.

[0334] ---

[0335] The above is the specific processing flow for carrying out the invention.

[0336] Example 2

[0337] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0338] Current book summarization and recommendation systems provide uniform results without considering user emotions, making it difficult to improve user experience. Furthermore, there is no way to efficiently display advertisements that match the user's interests and emotions, limiting the effectiveness of advertising. Furthermore, despite using a large amount of data, there is a lack of technology that utilizes user emotions to optimize recommendations and advertisements.

[0339] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving a book name or image as input from a user, a means for generating a summary based on the input data of the book using a generative AI model, a means for recommending related books based on keywords, authors, and genres, and a means for analyzing user emotions by combining an emotion recognition engine and optimizing recommendations and advertisement display. This makes it possible to optimize recommendations and advertisement display based on user emotions.

[0340] "User" refers to the person who operates the system to input information and receive results.

[0341] "Book" refers to any medium provided as reading material, including e-books and printed books.

[0342] "Name" refers to the title or title used to identify a book.

[0343] "Image" refers to the book cover and related visual data.

[0344] A "generative AI model" refers to artificial intelligence technology that uses machine learning to summarize and recommend books.

[0345] A "summary" is a brief summary of the book's contents.

[0346] "Keywords" refer to important words and phrases that indicate the content and characteristics of a book.

[0347] "Author" refers to the person who wrote the book.

[0348] "Genre" refers to a classification category based on the content or theme of a book.

[0349] "Recommendation" refers to the act of suggesting related books, items, etc. to a user.

[0350] An "emotion recognition engine" refers to a system that analyzes a user's emotions and reflects the results in other processes.

[0351] "Advertising" refers to information that encourages users to purchase products or use services.

[0352] "Database" refers to a recording device that systematically stores information and makes it possible to search and use it as needed.

[0353] "Evaluation comments" refer to the impressions and opinions that users give about a book.

[0354] "Scoring" refers to the score or rating a user gives to a book.

[0355] This invention combines an emotion recognition engine with a system that inputs book information (title, cover image, author name, genre, etc.) and provides book summaries and recommendations based on that information, thereby analyzing and reflecting the user's emotions and optimizing recommendations and advertisement display.

[0356] The system operates as follows.

[0357] First, the user uses the device to enter information about the book they want to read. Specifically, they enter the book title "Harry Potter" into the input form on the device, and also enter a cover image, author name, genre, etc. as needed. The entered data is sent to the server as an HTTP request.

[0358] The server analyzes the received data and confirms that the book title is "Harry Potter." It then sends a summary generation request to the generative AI model. Specifically, it sends the generative AI model a prompt message saying, "Please generate a summary for Harry Potter." The generative AI model uses its internal database and algorithms to generate a summary, returning, for example, a summary such as, "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[0359] The generated summary is sent to the terminal via the server, where it is analyzed and displayed to the user, allowing the user to quickly grasp the contents of the book.

[0360] Furthermore, the server searches a database based on information such as keywords, authors, and genres entered by the user, and recommends related books. At this time, an emotion recognition engine is used to recognize emotions from the user's input data and past behavior, and the order in which recommended books are displayed is adjusted based on this. For example, if a user enters the keyword "moving," books that match that emotion will be recommended first. The recommendation results are sent to the device in JSON format, and the device displays a list of recommended books to the user. Specifically, "The Lord of the Rings" and "The Chronicles of Narnia" are recommended.

[0361] Users input their evaluation comments and ratings for books they have read. For example, they can enter a comment like "What an engrossing story!" and a five-star rating. This evaluation data is sent from the device to the server. The server stores the received evaluation data in a database and generates a popularity ranking based on this. The evaluation data is also used as emotional feedback by an emotion recognition engine.

[0362] The generated popularity ranking is sent back to the device and displayed to the user. For example, "Harry Potter" appears at the top of the popularity ranking.

[0363] Finally, the server selects advertising data based on the user's interests and preferences, and uses an emotion recognition engine to select advertisements that match the user's emotions. For example, if the user feels "fun," an advertisement for a book that matches that emotion will be selected. The selected advertising data is sent to the device and displayed to the user. For example, an advertisement such as "Buy the Dragonlance series now" will be displayed.

[0364] In this way, the system generates book summaries, recommends related books, stores rating data and generates popularity rankings, and combines an emotion recognition engine with ad display to achieve optimal recommendations and ad display based on the user's emotions. The hardware required is a user device (smartphone, tablet, PC, etc.) and a server, while the software used is a generative AI model and an emotion recognition engine. Examples of prompts for the generative AI model include "Generate a summary of the following book: 'Harry Potter'" and "Recommend books that the user will find inspiring."

[0365] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0366] Step 1:

[0367] The user enters information about the book they want to read.

[0368] Input: Book title, cover image, author name, genre.

[0369] Operation: The user enters book information into the input form on the device and presses the submit button.

[0370] Output: The input data is sent to the server as an HTTP request.

[0371] Step 2:

[0372] The server analyzes the received data and sends a summary generation request to the generative AI model.

[0373] Input: Data entered, such as the title of a book, "Harry Potter."

[0374] How it works: The server parses the incoming data, checks the book title, and then sends a request to the generative AI model with the prompt "Generate a summary of Harry Potter."

[0375] Output: The prompt is sent to the generative AI model.

[0376] Step 3:

[0377] A generative AI model generates a book summary.

[0378] Input: The prompt sent by the server: "Generate a summary of Harry Potter."

[0379] How it works: The generative AI model uses its internal database and algorithms to generate summaries.

[0380] Output: The generated summary (e.g. "Harry Potter is a fantasy novel about adventures at the magical school Hogwarts.") is sent back to the server.

[0381] Step 4:

[0382] The server sends the generated summary to the terminal.

[0383] Input: Summary data returned from the generative AI model.

[0384] How it works: The server parses the abstract data and generates an HTTP response to send back to the user's device.

[0385] Output: Summary data is sent to the terminal.

[0386] Step 5:

[0387] The terminal displays the generated summary to the user.

[0388] Input: Abstract data received from the server.

[0389] Actions: The device parses the summary data and updates the HTML for display in the user interface.

[0390] Output: User can see the summary.

[0391] Step 6:

[0392] The server recommends related books based on the user's keywords, authors, genres, etc.

[0393] Input: Keywords, author, and genre entered by the user.

[0394] How it works: The server searches the database to retrieve relevant books. It then uses an emotion recognition engine to recognize emotions from the user's input data and past behavior, and adjusts the order of recommended books accordingly.

[0395] Output: The recommended book list is sent to the device in JSON format.

[0396] Step 7:

[0397] The device displays a list of recommended books to the user.

[0398] Input: A list of recommended books received from the server.

[0399] How it works: The device parses the recommended books and displays them in a list format in the user interface.

[0400] Output: The user can see the recommended books list.

[0401] Step 8:

[0402] The user enters evaluation comments and ratings.

[0403] Input: Evaluation comments and ratings for books you have read.

[0404] How it works: The user enters a rating (e.g., 5 stars) or a rating comment (e.g., "A captivating story!") into the input form on their device and presses the submit button.

[0405] Output: The rating data is sent to the server as an HTTP request.

[0406] Step 9:

[0407] A server stores the rating data and generates a popularity ranking.

[0408] Input: Rating data received from users.

[0409] How it works: The server analyzes the rating data and stores it in a database. It then performs statistical processing based on the stored rating data to generate popularity rankings. It also uses the data as emotional feedback through an emotion recognition engine.

[0410] Output: Popularity ranking data is generated and sent to the device.

[0411] Step 10:

[0412] The terminal displays the generated popularity ranking to the user.

[0413] Input: Popularity ranking data received from the server.

[0414] Operation: The device analyzes the popularity ranking data and displays it in the user interface.

[0415] Output: User can check the popularity ranking.

[0416] Step 11:

[0417] The server selects relevant advertising data and transmits it to the terminal.

[0418] Input: Data based on user interests and preferences, analysis results of emotion recognition engine.

[0419] How it works: The server selects relevant advertising data based on the user's interests and preferences. It uses an emotion recognition engine to select ads that match the user's emotions.

[0420] Output: The selected advertising data is sent to the device.

[0421] Step 12:

[0422] The terminal displays the advertisement to the user.

[0423] Input: Advertisement data received from the server.

[0424] Operation: The device parses the advertising data and displays the advertisement in the user interface.

[0425] Output: User can see the ad.

[0426] (Application example 2)

[0427] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0428] Conventional book recommendation systems only summarize and recommend books based on static data without considering user emotions, making it difficult to provide optimal recommendations and display advertisements based on individual users' emotions and interests. For this reason, a system that can effectively enhance users' reading experience and purchasing motivation has been sought.

[0429] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a book title or cover image as input from a user, means for generating a summary using a generative AI model based on the input data of the book, means for displaying the summary to the user, and means for optimizing a recommended book list and advertisement display based on the analysis results of the emotion engine. This makes it possible to recommend books and display advertisements that match the user's emotions and interests.

[0430] The "means for receiving a book title or cover image as input from a user" refers to a device or interface that allows a user to input the title or cover image of a book that the user wants to read into the system.

[0431] "Means for generating a summary using a generative AI model based on input data of a book" refers to a method or system that automatically generates a summary of a book using a generative AI model based on input information about the book.

[0432] The "means for displaying the summary to the user" refers to a device or interface for providing the generated book summary to the user.

[0433] "Means for recommending related books based on keywords, authors, and genres" refers to a method or system that suggests related books based on keywords, authors, and genres entered by a user.

[0434] The "means for receiving evaluation comments and ratings as input from the user" refers to a device or interface that allows the user to input comments and ratings for the book that they have read.

[0435] "Means for storing the evaluation comments and ratings in a database and generating a popularity ranking" refers to a method or system for storing evaluation comments and ratings from users in a database and creating a popularity ranking of books based on the stored evaluation comments and ratings.

[0436] The "means for displaying the popularity ranking to the user" refers to a device or interface for providing the generated popularity ranking to the user.

[0437] The "means for selecting advertising data and displaying it to the user" refers to a method or system for selecting appropriate advertising data and displaying it to the user.

[0438] The "means including an emotion engine that recognizes emotions from user input and behavior" refers to algorithms or software that analyzes a user's text input and behavior data and recognizes their emotional state.

[0439] "Means for optimizing the recommended book list and advertisement display based on the analysis results of the emotion engine" refers to a method or system for presenting optimal book recommendations and advertisements to users based on the results of analysis by the emotion engine.

[0440] This invention is a system that uses an emotion engine to generate book summaries, recommendations, and optimize advertisement display based on book information entered by the user. This system receives book titles or cover images as input from the user, processes them with a generative AI model to generate summaries, and also uses the emotion engine to make recommendations and display advertisements that match the user's emotions.

[0441] First, a user enters information about the book they want to read, such as the title, cover image, keywords, author, and genre, through a smartphone application. This information is sent from the device to a server. The server then sends a summary creation request to the generative AI model based on the received book information. For example, a prompt sentence such as "Please provide a summary of Harry Potter" is entered into the generative AI model. The generative AI model then generates a readable summary in a few minutes based on a large database and returns the summary to the server.

[0442] The generated summary is then sent from the server to the terminal and displayed to the user. By looking at this summary, the user can quickly understand the contents of the book. The server also searches a database for related books based on the keywords, author, genre, etc. entered by the user, and generates a list of recommended books. Here, an emotion engine is utilized to recognize emotions from the user's input and behavior. For example, if a user enters a keyword such as "moving story," books that match that emotion will be recommended.

[0443] Furthermore, after users finish reading a book, they can enter their evaluation comments and ratings. For example, they can comment, "This is an engrossing story!" and rate it five stars. This evaluation data is sent to the server and stored in a database. The server then generates a popularity ranking based on this evaluation data and displays it to the user.

[0444] Furthermore, the server selects relevant advertising data based on the user's interests and preferences. At this time, an emotion engine analyzes the user's emotions in real time and reflects them in the advertisement selection. For example, if the user is feeling "fun," an advertisement for a book that matches that emotion will be selected and displayed on the device. For example, an advertisement such as "Buy the Dragon Adventure series now" will be displayed.

[0445] In this invention, the smartphone functions as the user interface, and data input from the device is processed on a backend server. The server is responsible for data processing and storage, while the generative AI model and emotion engine perform advanced algorithmic processing. Specific software used includes React Native (mobile app development) on the front end, Node.js and Express (data processing) on ​​the back end, MongoDB for the database, Microsoft® Azure® Cognitive Services for emotion analysis, and OpenAI GPT-4 for the generative AI model.

[0446] Specific examples of prompt sentences are as follows:

[0447] "Please provide a summary of Harry Potter. Also, please provide related books that contain inspirational elements."

[0448] In this way, the present invention enables optimal book recommendations and advertisement display based on the user's emotions, providing the user with a highly satisfying reading experience.

[0449] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0450] Step 1:

[0451] Users use the smartphone interface to input information about the book they want to read, such as the title, cover image, keywords, author, and genre. This input data is then sent from the device to the server.

[0452] Step 2:

[0453] The server analyzes the book input data received from the device, formats it as needed, creates a prompt for the generative AI model based on the formatted data, and sends a summary generation request.

[0454] Input: Book title, cover image, keywords, author, genre, and other information

[0455] Data processing: Prompt sentence creation for generative AI models

[0456] Output: Prompt: "Please provide a summary of the book title."

[0457] Step 3:

[0458] The generative AI model generates a summary of the book based on the prompt received from the server, and the generated summary is sent back to the server.

[0459] Input: prompt statement

[0460] Data computation: Applying summary generation algorithms

[0461] Output: Generated book summary

[0462] Step 4:

[0463] The server sends the generated summary to the terminal and displays it to the user.

[0464] Input: Generated book summary

[0465] Data processing: Formatting and sending summary data

[0466] Output: Book summary displayed on terminal

[0467] Step 5:

[0468] The server searches the database for related books based on the keywords, authors, and genres entered by the user, and generates a list of recommended books. It uses an emotion engine to recognize emotions from the user's input and behavior, and optimizes the ranking of recommendations based on these emotions.

[0469] Input: User's keywords, author, and genre information

[0470] Data Computing: Database Search and Emotion Recognition

[0471] Output: Recommended book list

[0472] Step 6:

[0473] The server sends the recommended book list to the terminal and displays it to the user, who can then view the recommended books.

[0474] Input: Recommended book list

[0475] Data processing: Formatting and sending the recommendation list

[0476] Output: Recommended book list displayed on the device

[0477] Step 7:

[0478] Users input evaluation comments and ratings for the books they have read, and the evaluation comments and rating data are sent from the terminal to the server.

[0479] Input: User's evaluation comments, ratings

[0480] Data transmission: Sending evaluation data to the server

[0481] Output: Evaluation comments and scoring data saved on the server

[0482] Step 8:

[0483] The server stores the received evaluation comments and scoring data in a database and generates popularity rankings based on this data.

[0484] Input: Evaluation comments, scoring data

[0485] Data arithmetic: Analysis of evaluation data and generation of rankings

[0486] Output: Popularity ranking

[0487] Step 9:

[0488] The server transmits the generated popularity ranking to the terminal and displays it to the user.

[0489] Input: Popularity Ranking

[0490] Data processing: Formatting and sending ranking data

[0491] Output: Popular rankings displayed on the device

[0492] Step 10:

[0493] The server selects relevant advertising data based on the user's interests and emotions, and uses an emotion engine to analyze the user's real-time emotions and reflect them in the selection of advertisements.

[0494] Input: User sentiment analysis results

[0495] Data calculation: Selection of advertising data

[0496] Output: Highly relevant advertising data

[0497] Step 11:

[0498] The server sends the selected advertisement data to the terminal and displays it to the user. For example, an advertisement saying "Buy the Dragon Adventure series now" is displayed.

[0499] Input: Selected advertising data

[0500] Data processing: Formatting and sending advertising data

[0501] Output: Ad data displayed on the device

[0502] This program is a system that generates book summaries based on book information entered by the user, and uses an emotion engine to optimize recommended books and advertisements according to the user's emotions, thereby providing users with an effective and satisfying service.

[0503] 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.

[0504] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0505] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0506] [Second embodiment]

[0507] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0508] 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.

[0509] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0510] 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.

[0511] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0512] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0513] 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.

[0514] 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.

[0515] 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 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.

[0516] 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.

[0517] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0518] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0519] ---

[0520] The system of the present invention allows users to input information such as the book title, cover image, keywords, author, genre, etc., and provides book summaries and recommendations based on that information. The processing of the system's program is explained below in natural language.

[0521] First, the user uses the terminal to input the title and cover image of the book they want to read into the system. For example, the user may input the title "Harry Potter." The terminal then sends this input data to the server.

[0522] Based on the received data, the server sends a request to the generative AI model to generate a summary of the book. The generative AI model uses advanced algorithms based on large amounts of data to generate a summary that can be read in a few minutes. For example, a summary might be generated that reads, "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[0523] The server then sends the generated summary to the terminal, which then displays it to the user, allowing the user to quickly understand the contents of the book by reading the summary.

[0524] Furthermore, the server searches the database for related books based on the keywords, author, genre, etc. entered by the user, and generates a list of recommended books. The recommended book list is sent to the terminal and displayed to the user. For example, related books such as "The Lord of the Rings" and "The Chronicles of Narnia" are recommended.

[0525] Next, the user enters a rating and comment for the book they have just read. For example, they might comment, "This is an engrossing story!" and rate it five stars. The device then sends this rating data to the server. The server then stores the received rating data in a database and generates a popularity ranking based on this data. The generated popularity ranking is then sent back to the device and displayed to the user.

[0526] The server also selects relevant advertisements based on the user's interests and preferences and sends them to the device. For example, an advertisement for a book related to the "Fantasy" genre may be selected and displayed on the device. For example, an advertisement such as "Buy the Dragonlance series now" may be displayed.

[0527] In this way, the system of the present invention enriches the user's reading experience and enables them to efficiently find books by generating book summaries, recommending related books, storing evaluation data and generating popularity rankings, and displaying advertisements.

[0528] ---

[0529] The processing flow will be explained below.

[0530] ---

[0531] Step 1:

[0532] User: Enter the title or keywords of the book you want to read into the input form on your device. For example, enter "Harry Potter."

[0533] Step 2:

[0534] Terminal: Receives the entered book title and keywords and executes an API request to send them to the server.

[0535] Step 3:

[0536] Server: Based on the received book title and keywords, sends a summary generation request to the generative AI model.

[0537] Step 4:

[0538] Generative AI model: Generates a book summary based on the given book title and keywords. For example, it generates a summary such as "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[0539] Step 5:

[0540] Server: Sends the generated summary to the terminal.

[0541] Step 6:

[0542] Terminal: The generated summary is displayed to the user, who can then check the overview of the book.

[0543] Step 7:

[0544] Server: Searches the database for relevant books based on the keywords, author, and genre entered by the user.

[0545] Step 8:

[0546] Server: Generates a recommended book list from the search results by selecting highly relevant books.

[0547] Step 9:

[0548] Server: Sends the generated recommended book list to the terminal.

[0549] Step 10:

[0550] Terminal: Display a recommended book list to the user. For example, "The Lord of the Rings" and "The Chronicles of Narnia" are displayed as recommended books.

[0551] Step 11:

[0552] User: Enters a rating and comment for the book after reading it. For example, a user might comment, "This is an engrossing story!" and rate it five stars.

[0553] Step 12:

[0554] Device: Executes an API request to send the entered evaluation comments and scores to the server.

[0555] Step 13:

[0556] Server: Stores the received evaluation data in a database.

[0557] Step 14:

[0558] Server: Generates popularity rankings based on the saved rating data.

[0559] Step 15:

[0560] Server: Sends the generated popularity ranking to the device.

[0561] Step 16:

[0562] Device: Shows users a popularity ranking. For example, "Harry Potter" is displayed at the top of the popularity ranking.

[0563] Step 17:

[0564] Server: Selects relevant advertising data based on the user's interests and preferences.

[0565] Step 18:

[0566] Server: Sends the selected advertising data to the terminal.

[0567] Step 19:

[0568] Device: Shows relevant ads and book purchase links to users. For example, ads like "Buy the Dragonlance series now" may be displayed.

[0569] ---

[0570] Example 1

[0571] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0572] Many readers today want to efficiently find books that match their interests and understand summaries within the constraints of time and information. However, conventional search and recommendation systems have had difficulty efficiently retrieving the necessary information from the vast amount of book information available. It has also been difficult to reflect reader ratings and ranking information in real time and present popular books appropriately.

[0573] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0574] In this invention, the server includes means for receiving book information as input from a user, means for generating a summary based on the book information using a generative AI model, and means for displaying the summary to the user, thereby enabling users to efficiently obtain book summaries and find books that match their interests.

[0575] A "user" is a person who uses the system and inputs book information.

[0576] "Book information" refers to data such as the book title, cover image, keywords, author, genre, etc.

[0577] A "generative AI model" is an artificial intelligence model that uses advanced algorithms to generate summaries and recommendations based on input data.

[0578] A "summary" is a short sentence that abridges the contents of a book.

[0579] "Keywords" are words or phrases that indicate the content or characteristics of a book.

[0580] An "author" is a person who writes a book.

[0581] "Type" refers to the genre or category of the book.

[0582] "Recommendation" refers to providing related books that are suggested based on the user's interests.

[0583] An "evaluation comment" is a sentence that describes a user's impressions or opinions about a book.

[0584] A "rating" is a numerical value that a user gives to a book.

[0585] "Storage device" refers to a database or storage for storing evaluation data and book information.

[0586] "Popularity ranking" is a ranking calculated based on evaluation data and the popularity of books.

[0587] "Advertising information" is advertising data for products and services related to books.

[0588] "Control means" refers to a device or function that links each means and manages the operation of the entire system.

[0589] MODE FOR CARRYING OUT THE INVENTION

[0590] The system of the present invention uses a generative AI model to recommend summaries and related books, save evaluation comments, generate popularity rankings, and display advertisements based on user input of book information. The specific operation of the system is shown below.

[0591] User enters book information

[0592] A user uses a device (such as a smartphone or PC) to input information about the book they want to read, such as the title, cover image, keywords, author, and genre. For example, a user might input the title "Adventures at Magic School" into the device as book information. This information is then sent from the device to the server.

[0593] Generate a book summary

[0594] The server generates a summary by sending a prompt to the generative AI model based on the received book information. Specifically, the server passes the following prompt to the generative AI model:

[0595] Generate a summary of the book 'Adventures at Magic School'.

[0596] The generative AI model generates a summary based on this prompt, such as "Adventures at Wizarding School tells the story of a young wizard uncovering a secret," and returns it to the server, which then sends the summary to the device and displays it to the user.

[0597] Recommend a book

[0598] The server searches for and recommends related books based on the keywords, author, genre, etc. entered by the user. For example, the following prompt sentence is input to the generative AI model:

[0599] List five books related to 'wizard school adventures' in the fantasy genre.

[0600] The generative AI model returns a list of related books, such as "The Magic Star" and "The Tale of the Dragon," to the server, which then sends this list to the device and displays it to the user.

[0601] User ratings and popularity ranking generation

[0602] After reading a book, the user inputs a rating and comment. For example, they might comment, "What a wonderful adventure story!" and rate it five stars. The device sends this rating data to the server, which stores it in a database. The server generates a popularity ranking based on the accumulated rating data, and sends information to the device, such as, "Magic School Adventure is currently ranked number one in popularity," to display to the user.

[0603] Displaying relevant ads

[0604] The server selects relevant advertisements based on the user's interests and preferences. For example, it uses a prompt such as "Show me advertisements for new books related to fantasy novels." The selected advertisement information is sent to the terminal and displayed to the user. For example, an advertisement such as "Buy the new book 'Wizard Training' now" is displayed.

[0605] In this way, the system performs various data processing and calculations based on the information entered by the user, efficiently providing book summaries and recommendations, and enriching the user's reading experience.

[0606] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0607] Processing Steps

[0608] Step 1:

[0609] The user enters book information into the device.

[0610] Input: Book title, cover image, keywords, author, genre, etc.

[0611] Specific behavior: The user uses the terminal to enter the title "Adventures at Magic School."

[0612] Output: The terminal sends the input data to the server in JSON format.

[0613] Step 2:

[0614] The server receives and analyzes the book information.

[0615] Input: Book information data in JSON format

[0616] Specific operation: The server parses the received JSON data and obtains the book title, cover image, keywords, author, genre, etc.

[0617] Output: Summary generation prompt "Generate a summary for the book 'Adventures at Wizarding School'."

[0618] Step 3:

[0619] The server sends a summary generation request to the generative AI model

[0620] Input: Summary generation prompt statement

[0621] Specific operation: The server sends a summary generation prompt to the generative AI model.

[0622] Output: The generative AI model generates a summary and sends it back to the server (e.g., "Adventures at Wizarding School tells the story of a young wizard who uncovers a secret.").

[0623] Step 4:

[0624] The server sends the generated summary to the device.

[0625] Input: Generated summary text

[0626] Specific operation: The server converts the summary text into JSON format and sends it to the terminal as an HTTP response.

[0627] Output: A summary is displayed on the user's terminal.

[0628] Step 5:

[0629] The server sends a request for related book recommendations to the generative AI model.

[0630] Input: Keywords, author, genre of book information

[0631] Specific behavior: The server sends the prompt "List five books in the fantasy genre that relate to 'wizard school adventures'" to the generative AI model.

[0632] Output: The generative AI model generates a list of related books and sends it back to the server (e.g., "Magic Star," "Tales of Dragons," etc.).

[0633] Step 6:

[0634] The server sends the recommended book list to the device.

[0635] Input: Related Book List

[0636] Specific operation: The server converts the book list into JSON format and sends it to the terminal as an HTTP response.

[0637] Output: A list of related books is displayed on the user's device.

[0638] Step 7:

[0639] Users enter evaluation comments and ratings for books

[0640] Input: Evaluation comments, score

[0641] What happens: A user comments, "A great adventure story!" and leaves a 5-star rating.

[0642] Output: The device sends the rating data in JSON format to the server.

[0643] Step 8:

[0644] The server stores the rating data in a database

[0645] Input: JSON data of evaluation comments and scores

[0646] Specific behavior: The server executes an SQL query to store the received rating data in a database.

[0647] Output: Evaluation data stored in a database

[0648] Step 9:

[0649] The server generates a popularity ranking and sends it to the device.

[0650] Input: Evaluation data from the database

[0651] Specific operation: The server aggregates the rating data from the database, generates a popularity ranking, converts the ranking data into JSON format, and sends it to the device as an HTTP response.

[0652] Output: The popularity ranking is displayed on the user's device.

[0653] Step 10:

[0654] The server selects relevant advertisements and sends them to the device.

[0655] Input: Prompt text based on user interests and preferences

[0656] Specific operation: The server sends a prompt such as "Show me advertisements for new books related to fantasy novels" to the generative AI model. The generated advertisement information is converted into JSON format and sent to the device as an HTTP response.

[0657] Output: The ad is displayed on the user's device.

[0658] The above are the specific processing steps of the system.

[0659] (Application example 1)

[0660] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0661] In the past, users had to manually input information to obtain book summaries and recommendations, which was inefficient. Furthermore, the collection of book review data and the display of advertisements were done separately, making it difficult to provide a comprehensive reading experience. The present invention aims to solve these problems by providing a system that allows users to efficiently obtain book information and provides related books and advertisements all at once.

[0662] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0663] In this invention, the server includes: means for receiving a book title or cover image as input from a user; means for generating a summary based on the book input data using a generative AI model; means for displaying the summary to the user; means for recommending related books based on keywords, authors, and genres; means for receiving evaluation comments and ratings as input from the user; means for storing the evaluation comments and ratings in a database and generating a popularity ranking; means for displaying the popularity ranking to the user; means for selecting advertising data and displaying it to the user; means for identifying the user's gaze point using eye-tracking technology and acquiring book information; and means for generating a book summary based on the book information acquired through the eye-tracking using a generative AI model and displaying it on a smart device. This allows users to intuitively acquire book information using their gaze and receive summaries, related book recommendations, evaluation data, and advertisements in a comprehensive manner.

[0664] "Book title" refers to the name of a book, which is usually printed on the cover or spine.

[0665] "Cover Image" refers to an image file containing visual data of the cover of a book.

[0666] A "generative AI model" is a model that uses artificial intelligence algorithms to generate text from input data.

[0667] A "summary" is a written summary of the contents of a book that can be understood concisely and quickly.

[0668] "Means for displaying to the user" refers to a method for displaying the generated information on a terminal screen or the like so that the user can visually confirm it.

[0669] "Keywords" are words or phrases that characterize the content of a book.

[0670] An "author" is a person who writes a book.

[0671] A "genre" is a classification that indicates what theme or category a book belongs to.

[0672] "Recommendation methods" refer to methods that recommend related books based on a user's interests and past behavior.

[0673] "Evaluation comments" are impressions and opinions entered by users about books they have read.

[0674] "Rating" refers to the user's evaluation of a book they have read, expressed as a number or number of stars.

[0675] A "database" is a system that efficiently stores and manages data and allows you to quickly search for the information you need.

[0676] "Popularity ranking" is a list of books ranked by popularity based on user ratings and comments.

[0677] "Advertising data" refers to information created for the purpose of advertising a specific product.

[0678] "Eye tracking technology" refers to technology that tracks the movement of a user's eyes and identifies their point of gaze.

[0679] "Smart devices" refers to electronic devices with internet connectivity and high-performance computing capabilities, including smart glasses and smartphones.

[0680] The system of the present invention comprises multiple means for providing book summaries and related book recommendations. Each component of the system and its operation are described in detail below.

[0681] Corresponding measures and technologies

[0682] 1. Input Method

[0683] A user inputs the book title or cover image using smart glasses or other smart devices (e.g., smartphones). The smart glasses are equipped with a camera and eye-tracking technology, which are used to identify the gaze point and obtain book information.

[0684] 2. Summary generation method using generative AI models

[0685] Input data (book title and cover image) is sent to the server, and a generative AI model (e.g., OpenAI's GPT-4) receives it and generates a summary. An example prompt might be, "Write a brief summary of the following book: Harry Potter." The generated summary is sent from the server to the device and displayed to the user.

[0686] 3. Recommendation methods

[0687] The server searches the database for related books based on the input keywords, author, and genre, and obtains recommendation information. This information is also sent to the terminal and displayed to the user. For example, "The Lord of the Rings" and "The Chronicles of Narnia" are recommended.

[0688] 4. Evaluation Receipt Method

[0689] It also includes a means for receiving user input such as a rating and comment on the book after reading it. For example, a user might comment, "An engrossing story!" and give it a five-star rating.

[0690] 5. Means for generating and displaying popularity rankings

[0691] The server stores the received rating data in a database and generates a popularity ranking based on this data. The generated popularity ranking is then sent back to the terminal and displayed to the user.

[0692] 6. Advertisement Selection Method

[0693] The system also includes a means for selecting and displaying relevant advertising data to users based on their interests and preferences. For example, an advertisement such as "Buy the Dragonlance series now" may be displayed.

[0694] Hardware and Software

[0695] Smart devices: Smart glasses and smartphones with eye tracking

[0696] Server: Uses a Python-based framework (e.g., Flask)

[0697] Generative AI model: OpenAI's GPT-4

[0698] Database: MongoDB, etc.

[0699] Specific examples

[0700] The user puts on the smart glasses and directs their gaze at the book cover. Eye-tracking technology identifies the point of gaze and sends that information to the server. The server then sends prompts to the generative AI model based on the book cover image, generating a summary. Related books are then recommended, and all information is displayed on the smart glasses' display. This allows the user to easily obtain summaries and recommended information with just eye-gaze interaction.

[0701] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0702] Step 1:

[0703] A user puts on the smart glasses and looks at the cover of a book.

[0704] Specific operation: The smart glasses' built-in camera tracks the user's gaze direction and captures an image of the book cover.

[0705] Input: User gaze and book cover image.

[0706] Output: Send the gaze information and cover image data to the server.

[0707] Step 2:

[0708] The terminal transmits the cover image data to the server.

[0709] Specific operation: The smart glasses upload the processed cover image data to the server.

[0710] Input: Cover image data.

[0711] Output: The cover image data is saved on the server.

[0712] Step 3:

[0713] The server analyzes the cover image data and extracts the book title.

[0714] Specific operation: The server uses a machine learning algorithm to perform character recognition on the cover image and identify the title.

[0715] Input: Cover image data.

[0716] Output: The book title.

[0717] Step 4:

[0718] The server sends prompts to the generative AI model to generate summaries.

[0719] How it works: The server generates a prompt containing the book title and sends it to a generative AI model (e.g., GPT-4), which then generates a summary based on the prompt.

[0720] Input: Book title and prompt (e.g., "Write a brief summary of the following book: Harry Potter").

[0721] Output: The generated summary text.

[0722] Step 5:

[0723] The server sends the generated summary to the terminal for display to the user.

[0724] Specific operation: The generated summary text is sent to the terminal for display on the smart glasses display.

[0725] Input: The generated summary text.

[0726] Output: A summary is displayed on the smart glasses display.

[0727] Step 6:

[0728] The server recommends related books based on keywords, authors, and genres.

[0729] What happens: The server searches its database to identify books related to the entered title.

[0730] Input: Book title, keywords, author, genre.

[0731] Output: A list of related books.

[0732] Step 7:

[0733] The server sends a list of related books to the terminal and displays it to the user.

[0734] Specific operation: A list of related books is sent to the terminal for display on the smart glasses display.

[0735] Input: A list of related books.

[0736] Output: A list of related books is displayed on the smart glasses display.

[0737] Step 8:

[0738] The user inputs evaluation comments and ratings for the books he or she has read.

[0739] Specific operation: The user inputs evaluation comments and scores using smart glasses or a smartphone.

[0740] Input: Rating information such as rating comments and star ratings.

[0741] Output: Evaluation comments and score information are sent to the server.

[0742] Step 9:

[0743] The server stores the evaluation comments and scores in a database and generates a popularity ranking.

[0744] Specific operation: The server records evaluation comments and scoring data in a database and generates popularity rankings based on that.

[0745] Input: Evaluation comments and scoring data.

[0746] Output: Popularity ranking.

[0747] Step 10:

[0748] The server transmits the popularity ranking to the terminal and displays it to the user.

[0749] Specific operation: The generated popularity ranking is sent to the terminal for display on the smart glasses display.

[0750] Input: Popularity Rankings.

[0751] Output: The popularity ranking is displayed on the smart glasses display.

[0752] Step 11:

[0753] The server selects the advertisement data and displays it to the user.

[0754] Specific operation: The server identifies relevant advertisements based on the user's interests and past behavior data and sends them to the device.

[0755] Input: User interest and behavior data, advertising data.

[0756] Output: The advertisement is displayed on the smart glasses display.

[0757] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0758] ---

[0759] This invention combines an emotion engine with a system that provides book summaries and recommendations based on input information such as book titles, cover images, keywords, authors, and genres, recognizing user emotions and optimizing recommendations and advertisement display. The system's program processing is explained below in natural language.

[0760] First, the user uses the terminal to input the title and cover image of the book they want to read into the system. For example, the user inputs the title "Harry Potter." The terminal then sends this input data to the server.

[0761] Based on the received data, the server sends a request to the generative AI model to generate a summary of the book. The generative AI model uses advanced algorithms based on large amounts of data to generate a summary that can be read in a few minutes. For example, a summary might be generated that reads, "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[0762] The server then sends the generated summary to the terminal, which then displays it to the user, allowing the user to quickly understand the contents of the book by reading the summary.

[0763] Furthermore, the server searches the database for related books based on the keywords, authors, genres, etc. entered by the user, and generates a list of recommended books. At this time, an emotion engine can be used to recognize emotions from the user's input and behavior. For example, if a user enters keywords such as "moving" or "fun," the server will select recommended books that match that emotion. The display order of recommended books may also be adjusted based on the analysis results of the emotion engine.

[0764] The recommended books are then sent to the device and displayed to the user. For example, books related to "The Lord of the Rings" and "The Chronicles of Narnia" are recommended.

[0765] Users input their evaluation comments and ratings for books they have read. For example, they might comment, "This is an engrossing story!" and rate it five stars. The device then sends this evaluation data to the server. The server then stores the received evaluation data in a database and generates a popularity ranking based on this data. The stored evaluation data can also be used for sentiment analysis and may be incorporated as emotional feedback for the user.

[0766] The generated popularity ranking is sent back to the device and displayed to the user. For example, "Harry Potter" appears at the top of the popularity ranking.

[0767] The server then selects relevant advertising data based on the user's interests and preferences. The emotion engine analyzes the user's emotions in real time and reflects them in the advertisement selection. For example, if the user is feeling "fun," an advertisement for a book that matches that emotion may be selected.

[0768] The selected advertising data is sent to the device and displayed to the user. For example, an advertisement such as "Purchase the Dragonlance series now" is displayed.

[0769] In this way, the system of the present invention combines an emotion engine with book summary generation, related book recommendation, evaluation data storage and popularity ranking generation, and advertisement display to realize optimal recommendations and advertisement display based on the user's emotions, enriching the reading experience and enabling efficient book discovery.

[0770] ---

[0771] The processing flow will be explained below.

[0772] ---

[0773] Step 1:

[0774] User: Enter the title or keywords of the book you want to read into the input form on your device. For example, enter "Harry Potter."

[0775] Step 2:

[0776] Terminal: Receives the entered book title and keywords and executes an API request to send them to the server.

[0777] Step 3:

[0778] Server: Based on the received book title and keywords, sends a summary generation request to the generative AI model.

[0779] Step 4:

[0780] Generative AI model: Generates a book summary based on the given book title and keywords. For example, it generates a summary such as "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[0781] Step 5:

[0782] Server: Sends the generated summary to the terminal.

[0783] Step 6:

[0784] Terminal: The generated summary is displayed to the user, allowing the user to see an overview of the book.

[0785] Step 7:

[0786] Server: Searches the database for relevant books based on the keywords, author, and genre entered by the user.

[0787] Step 8:

[0788] Server: Generates a list of recommended books from the search results that are highly relevant. In this process, an emotion engine is used to recognize emotions from the user's input and behavior, and books that fit those emotions are selected.

[0789] Step 9:

[0790] Server: Analyzes user sentiment and adjusts the order in which recommended books are displayed.

[0791] Step 10:

[0792] Server: Sends the generated recommended book list to the terminal.

[0793] Step 11:

[0794] Terminal: Display a recommended book list to the user. For example, "The Lord of the Rings" and "The Chronicles of Narnia" are displayed as recommended books.

[0795] Step 12:

[0796] User: Enters a rating and comment for the book after reading it. For example, a user might comment, "This is an engrossing story!" and rate it five stars.

[0797] Step 13:

[0798] Device: Executes an API request to send the entered evaluation comments and scores to the server.

[0799] Step 14:

[0800] Server: Stores the received evaluation data in a database.

[0801] Step 15:

[0802] Server: Generates popularity rankings based on the saved rating data. Sentiment analysis is also used to capture user sentiment feedback.

[0803] Step 16:

[0804] Server: Sends the generated popularity ranking to the device.

[0805] Step 17:

[0806] Device: Shows users a popularity ranking. For example, "Harry Potter" is displayed at the top of the popularity ranking.

[0807] Step 18:

[0808] Server: Selects relevant advertising data based on the user's interests and preferences. In this process, it uses an emotion engine to analyze user emotions in real time and reflects this information in the selection of ads.

[0809] Step 19:

[0810] Server: Sends the selected advertising data to the terminal.

[0811] Step 20:

[0812] Device: Shows relevant ads and book purchase links to users. For example, ads like "Buy the Dragonlance series now" may be displayed.

[0813] ---

[0814] The above is the specific processing flow for carrying out the invention.

[0815] Example 2

[0816] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0817] Current book summarization and recommendation systems provide uniform results without considering user emotions, making it difficult to improve user experience. Furthermore, there is no way to efficiently display advertisements that match the user's interests and emotions, limiting the effectiveness of advertising. Furthermore, despite using a large amount of data, there is a lack of technology that utilizes user emotions to optimize recommendations and advertisements.

[0818] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving a book name or image as input from a user, a means for generating a summary based on the input data of the book using a generative AI model, a means for recommending related books based on keywords, authors, and genres, and a means for analyzing user emotions by combining an emotion recognition engine and optimizing recommendations and advertisement display. This makes it possible to optimize recommendations and advertisement display based on user emotions.

[0819] "User" refers to the person who operates the system to input information and receive results.

[0820] "Book" refers to any medium provided as reading material, including e-books and printed books.

[0821] "Name" refers to the title or title used to identify a book.

[0822] "Image" refers to the book cover and related visual data.

[0823] A "generative AI model" refers to artificial intelligence technology that uses machine learning to summarize and recommend books.

[0824] A "summary" is a brief summary of the book's contents.

[0825] "Keywords" refer to important words and phrases that indicate the content and characteristics of a book.

[0826] "Author" refers to the person who wrote the book.

[0827] "Genre" refers to a classification category based on the content or theme of a book.

[0828] "Recommendation" refers to the act of suggesting related books, items, etc. to a user.

[0829] An "emotion recognition engine" refers to a system that analyzes a user's emotions and reflects the results in other processes.

[0830] "Advertising" refers to information that encourages users to purchase products or use services.

[0831] "Database" refers to a recording device that systematically stores information and makes it possible to search and use it as needed.

[0832] "Evaluation comments" refer to the impressions and opinions that users give about a book.

[0833] "Scoring" refers to the score or rating a user gives to a book.

[0834] This invention combines an emotion recognition engine with a system that inputs book information (title, cover image, author name, genre, etc.) and provides book summaries and recommendations based on that information, thereby analyzing and reflecting the user's emotions and optimizing recommendations and advertisement display.

[0835] The system operates as follows.

[0836] First, the user uses the device to enter information about the book they want to read. Specifically, they enter the book title "Harry Potter" into the input form on the device, and also enter a cover image, author name, genre, etc. as needed. The entered data is sent to the server as an HTTP request.

[0837] The server analyzes the received data and confirms that the book title is "Harry Potter." It then sends a summary generation request to the generative AI model. Specifically, it sends the generative AI model a prompt message saying, "Please generate a summary for Harry Potter." The generative AI model uses its internal database and algorithms to generate a summary, returning, for example, a summary such as, "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[0838] The generated summary is sent to the terminal via the server, where it is analyzed and displayed to the user, allowing the user to quickly grasp the contents of the book.

[0839] Furthermore, the server searches a database based on information such as keywords, authors, and genres entered by the user, and recommends related books. At this time, an emotion recognition engine is used to recognize emotions from the user's input data and past behavior, and the order in which recommended books are displayed is adjusted based on this. For example, if a user enters the keyword "moving," books that match that emotion will be recommended first. The recommendation results are sent to the device in JSON format, and the device displays a list of recommended books to the user. Specifically, "The Lord of the Rings" and "The Chronicles of Narnia" are recommended.

[0840] Users input their evaluation comments and ratings for books they have read. For example, they can enter a comment like "What an engrossing story!" and a five-star rating. This evaluation data is sent from the device to the server. The server stores the received evaluation data in a database and generates a popularity ranking based on this. The evaluation data is also used as emotional feedback by an emotion recognition engine.

[0841] The generated popularity ranking is sent back to the device and displayed to the user. For example, "Harry Potter" appears at the top of the popularity ranking.

[0842] Finally, the server selects advertising data based on the user's interests and preferences, and uses an emotion recognition engine to select advertisements that match the user's emotions. For example, if the user feels "fun," an advertisement for a book that matches that emotion will be selected. The selected advertising data is sent to the device and displayed to the user. For example, an advertisement such as "Buy the Dragonlance series now" will be displayed.

[0843] In this way, the system generates book summaries, recommends related books, stores rating data and generates popularity rankings, and combines an emotion recognition engine with ad display to achieve optimal recommendations and ad display based on the user's emotions. The hardware required is a user device (smartphone, tablet, PC, etc.) and a server, while the software used is a generative AI model and an emotion recognition engine. Examples of prompts for the generative AI model include "Generate a summary of the following book: 'Harry Potter'" and "Recommend books that the user will find inspiring."

[0844] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0845] Step 1:

[0846] The user enters information about the book they want to read.

[0847] Input: Book title, cover image, author name, genre.

[0848] Operation: The user enters book information into the input form on the device and presses the submit button.

[0849] Output: The input data is sent to the server as an HTTP request.

[0850] Step 2:

[0851] The server analyzes the received data and sends a summary generation request to the generative AI model.

[0852] Input: Data entered, such as the title of a book, "Harry Potter."

[0853] How it works: The server parses the incoming data, checks the book title, and then sends a request to the generative AI model with the prompt "Generate a summary of Harry Potter."

[0854] Output: The prompt is sent to the generative AI model.

[0855] Step 3:

[0856] A generative AI model generates a book summary.

[0857] Input: The prompt sent by the server: "Generate a summary of Harry Potter."

[0858] How it works: The generative AI model uses its internal database and algorithms to generate summaries.

[0859] Output: The generated summary (e.g. "Harry Potter is a fantasy novel about adventures at the magical school Hogwarts.") is sent back to the server.

[0860] Step 4:

[0861] The server sends the generated summary to the terminal.

[0862] Input: Summary data returned from the generative AI model.

[0863] How it works: The server parses the abstract data and generates an HTTP response to send back to the user's device.

[0864] Output: Summary data is sent to the terminal.

[0865] Step 5:

[0866] The terminal displays the generated summary to the user.

[0867] Input: Abstract data received from the server.

[0868] Actions: The device parses the summary data and updates the HTML for display in the user interface.

[0869] Output: User can see the summary.

[0870] Step 6:

[0871] The server recommends related books based on the user's keywords, authors, genres, etc.

[0872] Input: Keywords, author, and genre entered by the user.

[0873] How it works: The server searches the database to retrieve relevant books. It then uses an emotion recognition engine to recognize emotions from the user's input data and past behavior, and adjusts the order of recommended books accordingly.

[0874] Output: The recommended book list is sent to the device in JSON format.

[0875] Step 7:

[0876] The device displays a list of recommended books to the user.

[0877] Input: A list of recommended books received from the server.

[0878] How it works: The device parses the recommended books and displays them in a list format in the user interface.

[0879] Output: The user can see the recommended books list.

[0880] Step 8:

[0881] The user enters evaluation comments and ratings.

[0882] Input: Evaluation comments and ratings for books you have read.

[0883] How it works: The user enters a rating (e.g., 5 stars) or a rating comment (e.g., "A captivating story!") into the input form on their device and presses the submit button.

[0884] Output: The rating data is sent to the server as an HTTP request.

[0885] Step 9:

[0886] A server stores the rating data and generates a popularity ranking.

[0887] Input: Rating data received from users.

[0888] How it works: The server analyzes the rating data and stores it in a database. It then performs statistical processing based on the stored rating data to generate popularity rankings. It also uses the data as emotional feedback through an emotion recognition engine.

[0889] Output: Popularity ranking data is generated and sent to the device.

[0890] Step 10:

[0891] The terminal displays the generated popularity ranking to the user.

[0892] Input: Popularity ranking data received from the server.

[0893] Operation: The device analyzes the popularity ranking data and displays it in the user interface.

[0894] Output: User can check the popularity ranking.

[0895] Step 11:

[0896] The server selects relevant advertising data and transmits it to the terminal.

[0897] Input: Data based on user interests and preferences, analysis results of emotion recognition engine.

[0898] How it works: The server selects relevant advertising data based on the user's interests and preferences. It uses an emotion recognition engine to select ads that match the user's emotions.

[0899] Output: The selected advertising data is sent to the device.

[0900] Step 12:

[0901] The terminal displays the advertisement to the user.

[0902] Input: Advertisement data received from the server.

[0903] Operation: The device parses the advertising data and displays the advertisement in the user interface.

[0904] Output: User can see the ad.

[0905] (Application example 2)

[0906] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0907] Conventional book recommendation systems only summarize and recommend books based on static data without considering user emotions, making it difficult to provide optimal recommendations and display advertisements based on individual users' emotions and interests. For this reason, a system that can effectively enhance users' reading experience and purchasing motivation has been sought.

[0908] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a book title or cover image as input from a user, means for generating a summary using a generative AI model based on the input data of the book, means for displaying the summary to the user, and means for optimizing a recommended book list and advertisement display based on the analysis results of the emotion engine. This makes it possible to recommend books and display advertisements that match the user's emotions and interests.

[0909] The "means for receiving a book title or cover image as input from a user" refers to a device or interface that allows a user to input the title or cover image of a book that the user wants to read into the system.

[0910] "Means for generating a summary using a generative AI model based on input data of a book" refers to a method or system that automatically generates a summary of a book using a generative AI model based on input information about the book.

[0911] The "means for displaying the summary to the user" refers to a device or interface for providing the generated book summary to the user.

[0912] "Means for recommending related books based on keywords, authors, and genres" refers to a method or system that suggests related books based on keywords, authors, and genres entered by a user.

[0913] The "means for receiving evaluation comments and ratings as input from the user" refers to a device or interface that allows the user to input comments and ratings for the book that they have read.

[0914] "Means for storing the evaluation comments and ratings in a database and generating a popularity ranking" refers to a method or system for storing evaluation comments and ratings from users in a database and creating a popularity ranking of books based on the stored evaluation comments and ratings.

[0915] The "means for displaying the popularity ranking to the user" refers to a device or interface for providing the generated popularity ranking to the user.

[0916] The "means for selecting advertising data and displaying it to the user" refers to a method or system for selecting appropriate advertising data and displaying it to the user.

[0917] The "means including an emotion engine that recognizes emotions from user input and behavior" refers to algorithms or software that analyzes a user's text input and behavior data and recognizes their emotional state.

[0918] "Means for optimizing the recommended book list and advertisement display based on the analysis results of the emotion engine" refers to a method or system for presenting optimal book recommendations and advertisements to users based on the results of analysis by the emotion engine.

[0919] This invention is a system that uses an emotion engine to generate book summaries, recommendations, and optimize advertisement display based on book information entered by the user. This system receives book titles or cover images as input from the user, processes them with a generative AI model to generate summaries, and also uses the emotion engine to make recommendations and display advertisements that match the user's emotions.

[0920] First, a user enters information about the book they want to read, such as the title, cover image, keywords, author, and genre, through a smartphone application. This information is sent from the device to a server. The server then sends a summary creation request to the generative AI model based on the received book information. For example, a prompt sentence such as "Please provide a summary of Harry Potter" is entered into the generative AI model. The generative AI model then generates a readable summary in a few minutes based on a large database and returns the summary to the server.

[0921] The generated summary is then sent from the server to the terminal and displayed to the user. By looking at this summary, the user can quickly understand the contents of the book. The server also searches a database for related books based on the keywords, author, genre, etc. entered by the user, and generates a list of recommended books. Here, an emotion engine is utilized to recognize emotions from the user's input and behavior. For example, if a user enters a keyword such as "moving story," books that match that emotion will be recommended.

[0922] Furthermore, after users finish reading a book, they can enter their evaluation comments and ratings. For example, they can comment, "This is an engrossing story!" and rate it five stars. This evaluation data is sent to the server and stored in a database. The server then generates a popularity ranking based on this evaluation data and displays it to the user.

[0923] Furthermore, the server selects relevant advertising data based on the user's interests and preferences. At this time, an emotion engine analyzes the user's emotions in real time and reflects them in the advertisement selection. For example, if the user is feeling "fun," an advertisement for a book that matches that emotion will be selected and displayed on the device. For example, an advertisement such as "Buy the Dragon Adventure series now" will be displayed.

[0924] In this invention, the smartphone functions as the user interface, and data input from the device is processed on a backend server. The server is responsible for data processing and storage, while the generative AI model and emotion engine perform advanced algorithmic processing. Specific software used includes React Native (mobile app development) on the front end, Node.js and Express (data processing) on ​​the back end, MongoDB for the database, Microsoft Azure Cognitive Services for emotion analysis, and OpenAI GPT-4 for the generative AI model.

[0925] Specific examples of prompt sentences are as follows:

[0926] "Please provide a summary of Harry Potter. Also, please provide related books that contain inspirational elements."

[0927] In this way, the present invention enables optimal book recommendations and advertisement display based on the user's emotions, providing the user with a highly satisfying reading experience.

[0928] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0929] Step 1:

[0930] Users use the smartphone interface to input information about the book they want to read, such as the title, cover image, keywords, author, and genre. This input data is then sent from the device to the server.

[0931] Step 2:

[0932] The server analyzes the book input data received from the device, formats it as needed, creates a prompt for the generative AI model based on the formatted data, and sends a summary generation request.

[0933] Input: Book title, cover image, keywords, author, genre, and other information

[0934] Data processing: Prompt sentence creation for generative AI models

[0935] Output: Prompt: "Please provide a summary of the book title."

[0936] Step 3:

[0937] The generative AI model generates a summary of the book based on the prompt received from the server, and the generated summary is sent back to the server.

[0938] Input: prompt statement

[0939] Data computation: Applying summary generation algorithms

[0940] Output: Generated book summary

[0941] Step 4:

[0942] The server sends the generated summary to the terminal and displays it to the user.

[0943] Input: Generated book summary

[0944] Data processing: Formatting and sending summary data

[0945] Output: Book summary displayed on terminal

[0946] Step 5:

[0947] The server searches the database for related books based on the keywords, authors, and genres entered by the user, and generates a list of recommended books. It uses an emotion engine to recognize emotions from the user's input and behavior, and optimizes the ranking of recommendations based on these emotions.

[0948] Input: User's keywords, author, and genre information

[0949] Data Computing: Database Search and Emotion Recognition

[0950] Output: Recommended book list

[0951] Step 6:

[0952] The server sends the recommended book list to the terminal and displays it to the user, who can then view the recommended books.

[0953] Input: Recommended book list

[0954] Data processing: Formatting and sending the recommendation list

[0955] Output: Recommended book list displayed on the device

[0956] Step 7:

[0957] Users input evaluation comments and ratings for the books they have read, and the evaluation comments and rating data are sent from the terminal to the server.

[0958] Input: User's evaluation comments, ratings

[0959] Data transmission: Sending evaluation data to the server

[0960] Output: Evaluation comments and scoring data saved on the server

[0961] Step 8:

[0962] The server stores the received evaluation comments and scoring data in a database and generates popularity rankings based on this data.

[0963] Input: Evaluation comments, scoring data

[0964] Data arithmetic: Analysis of evaluation data and generation of rankings

[0965] Output: Popularity ranking

[0966] Step 9:

[0967] The server transmits the generated popularity ranking to the terminal and displays it to the user.

[0968] Input: Popularity Ranking

[0969] Data processing: Formatting and sending ranking data

[0970] Output: Popular rankings displayed on the device

[0971] Step 10:

[0972] The server selects relevant advertising data based on the user's interests and emotions, and uses an emotion engine to analyze the user's real-time emotions and reflect them in the selection of advertisements.

[0973] Input: User sentiment analysis results

[0974] Data calculation: Selection of advertising data

[0975] Output: Highly relevant advertising data

[0976] Step 11:

[0977] The server sends the selected advertisement data to the terminal and displays it to the user. For example, an advertisement saying "Buy the Dragon Adventure series now" is displayed.

[0978] Input: Selected advertising data

[0979] Data processing: Formatting and sending advertising data

[0980] Output: Ad data displayed on the device

[0981] This program is a system that generates book summaries based on book information entered by the user, and uses an emotion engine to optimize recommended books and advertisements according to the user's emotions, thereby providing users with an effective and satisfying service.

[0982] 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.

[0983] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0984] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0985] [Third embodiment]

[0986] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0987] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0988] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0989] 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.

[0990] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0991] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0992] 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.

[0993] 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.

[0994] 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 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.

[0995] 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.

[0996] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0997] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0998] ---

[0999] The system of the present invention allows users to input information such as the book title, cover image, keywords, author, genre, etc., and provides book summaries and recommendations based on that information. The processing of the system's program is explained below in natural language.

[1000] First, the user uses the terminal to input the title and cover image of the book they want to read into the system. For example, the user may input the title "Harry Potter." The terminal then sends this input data to the server.

[1001] Based on the received data, the server sends a request to the generative AI model to generate a summary of the book. The generative AI model uses advanced algorithms based on large amounts of data to generate a summary that can be read in a few minutes. For example, a summary might be generated that reads, "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[1002] The server then sends the generated summary to the terminal, which then displays it to the user, allowing the user to quickly understand the contents of the book by reading the summary.

[1003] Furthermore, the server searches the database for related books based on the keywords, author, genre, etc. entered by the user, and generates a list of recommended books. The recommended book list is sent to the terminal and displayed to the user. For example, related books such as "The Lord of the Rings" and "The Chronicles of Narnia" are recommended.

[1004] Next, the user enters a rating and comment for the book they have just read. For example, they might comment, "This is an engrossing story!" and rate it five stars. The device then sends this rating data to the server. The server then stores the received rating data in a database and generates a popularity ranking based on this data. The generated popularity ranking is then sent back to the device and displayed to the user.

[1005] The server also selects relevant advertisements based on the user's interests and preferences and sends them to the device. For example, an advertisement for a book related to the "Fantasy" genre may be selected and displayed on the device. For example, an advertisement such as "Buy the Dragonlance series now" may be displayed.

[1006] In this way, the system of the present invention enriches the user's reading experience and enables them to efficiently find books by generating book summaries, recommending related books, storing evaluation data and generating popularity rankings, and displaying advertisements.

[1007] ---

[1008] The processing flow will be explained below.

[1009] ---

[1010] Step 1:

[1011] User: Enter the title or keywords of the book you want to read into the input form on your device. For example, enter "Harry Potter."

[1012] Step 2:

[1013] Terminal: Receives the entered book title and keywords and executes an API request to send them to the server.

[1014] Step 3:

[1015] Server: Based on the received book title and keywords, sends a summary generation request to the generative AI model.

[1016] Step 4:

[1017] Generative AI model: Generates a book summary based on the given book title and keywords. For example, it generates a summary such as "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[1018] Step 5:

[1019] Server: Sends the generated summary to the terminal.

[1020] Step 6:

[1021] Terminal: The generated summary is displayed to the user, who can then check the overview of the book.

[1022] Step 7:

[1023] Server: Searches the database for relevant books based on the keywords, author, and genre entered by the user.

[1024] Step 8:

[1025] Server: Generates a recommended book list from the search results by selecting highly relevant books.

[1026] Step 9:

[1027] Server: Sends the generated recommended book list to the terminal.

[1028] Step 10:

[1029] Terminal: Display a recommended book list to the user. For example, "The Lord of the Rings" and "The Chronicles of Narnia" are displayed as recommended books.

[1030] Step 11:

[1031] User: Enters a rating and comment for the book after reading it. For example, a user might comment, "This is an engrossing story!" and rate it five stars.

[1032] Step 12:

[1033] Device: Executes an API request to send the entered evaluation comments and scores to the server.

[1034] Step 13:

[1035] Server: Stores the received evaluation data in a database.

[1036] Step 14:

[1037] Server: Generates popularity rankings based on the saved rating data.

[1038] Step 15:

[1039] Server: Sends the generated popularity ranking to the device.

[1040] Step 16:

[1041] Device: Shows users a popularity ranking. For example, "Harry Potter" is displayed at the top of the popularity ranking.

[1042] Step 17:

[1043] Server: Selects relevant advertising data based on the user's interests and preferences.

[1044] Step 18:

[1045] Server: Sends the selected advertising data to the terminal.

[1046] Step 19:

[1047] Device: Shows relevant ads and book purchase links to users. For example, ads like "Buy the Dragonlance series now" may be displayed.

[1048] ---

[1049] Example 1

[1050] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1051] Many readers today want to efficiently find books that match their interests and understand summaries within the constraints of time and information. However, conventional search and recommendation systems have had difficulty efficiently retrieving the necessary information from the vast amount of book information available. It has also been difficult to reflect reader ratings and ranking information in real time and present popular books appropriately.

[1052] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1053] In this invention, the server includes means for receiving book information as input from a user, means for generating a summary based on the book information using a generative AI model, and means for displaying the summary to the user, thereby enabling users to efficiently obtain book summaries and find books that match their interests.

[1054] A "user" is a person who uses the system and inputs book information.

[1055] "Book information" refers to data such as the book title, cover image, keywords, author, genre, etc.

[1056] A "generative AI model" is an artificial intelligence model that uses advanced algorithms to generate summaries and recommendations based on input data.

[1057] A "summary" is a short sentence that abridges the contents of a book.

[1058] "Keywords" are words or phrases that indicate the content or characteristics of a book.

[1059] An "author" is a person who writes a book.

[1060] "Type" refers to the genre or category of the book.

[1061] "Recommendation" refers to providing related books that are suggested based on the user's interests.

[1062] An "evaluation comment" is a sentence that describes a user's impressions or opinions about a book.

[1063] A "rating" is a numerical value that a user gives to a book.

[1064] "Storage device" refers to a database or storage for storing evaluation data and book information.

[1065] "Popularity ranking" is a ranking calculated based on evaluation data and the popularity of books.

[1066] "Advertising information" is advertising data for products and services related to books.

[1067] "Control means" refers to a device or function that links each means and manages the operation of the entire system.

[1068] MODE FOR CARRYING OUT THE INVENTION

[1069] The system of the present invention uses a generative AI model to recommend summaries and related books, save evaluation comments, generate popularity rankings, and display advertisements based on user input of book information. The specific operation of the system is shown below.

[1070] User enters book information

[1071] A user uses a device (such as a smartphone or PC) to input information about the book they want to read, such as the title, cover image, keywords, author, and genre. For example, a user might input the title "Adventures at Magic School" into the device as book information. This information is then sent from the device to the server.

[1072] Generate a book summary

[1073] The server generates a summary by sending a prompt to the generative AI model based on the received book information. Specifically, the server passes the following prompt to the generative AI model:

[1074] Generate a summary of the book 'Adventures at Magic School'.

[1075] The generative AI model generates a summary based on this prompt, such as "Adventures at Wizarding School tells the story of a young wizard uncovering a secret," and returns it to the server, which then sends the summary to the device and displays it to the user.

[1076] Recommend a book

[1077] The server searches for and recommends related books based on the keywords, author, genre, etc. entered by the user. For example, the following prompt sentence is input to the generative AI model:

[1078] List five books related to 'wizard school adventures' in the fantasy genre.

[1079] The generative AI model returns a list of related books, such as "The Magic Star" and "The Tale of the Dragon," to the server, which then sends this list to the device and displays it to the user.

[1080] User ratings and popularity ranking generation

[1081] After reading a book, the user inputs a rating and comment. For example, they might comment, "What a wonderful adventure story!" and rate it five stars. The device sends this rating data to the server, which stores it in a database. The server generates a popularity ranking based on the accumulated rating data, and sends information to the device, such as, "Magic School Adventure is currently ranked number one in popularity," to display to the user.

[1082] Displaying relevant ads

[1083] The server selects relevant advertisements based on the user's interests and preferences. For example, it uses a prompt such as "Show me advertisements for new books related to fantasy novels." The selected advertisement information is sent to the terminal and displayed to the user. For example, an advertisement such as "Buy the new book 'Wizard Training' now" is displayed.

[1084] In this way, the system performs various data processing and calculations based on the information entered by the user, efficiently providing book summaries and recommendations, and enriching the user's reading experience.

[1085] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1086] Processing Steps

[1087] Step 1:

[1088] The user enters book information into the device.

[1089] Input: Book title, cover image, keywords, author, genre, etc.

[1090] Specific behavior: The user uses the terminal to enter the title "Adventures at Magic School."

[1091] Output: The terminal sends the input data to the server in JSON format.

[1092] Step 2:

[1093] The server receives and analyzes the book information.

[1094] Input: Book information data in JSON format

[1095] Specific operation: The server parses the received JSON data and obtains the book title, cover image, keywords, author, genre, etc.

[1096] Output: Summary generation prompt "Generate a summary for the book 'Adventures at Wizarding School'."

[1097] Step 3:

[1098] The server sends a summary generation request to the generative AI model

[1099] Input: Summary generation prompt statement

[1100] Specific operation: The server sends a summary generation prompt to the generative AI model.

[1101] Output: The generative AI model generates a summary and sends it back to the server (e.g., "Adventures at Wizarding School tells the story of a young wizard who uncovers a secret.").

[1102] Step 4:

[1103] The server sends the generated summary to the device.

[1104] Input: Generated summary text

[1105] Specific operation: The server converts the summary text into JSON format and sends it to the terminal as an HTTP response.

[1106] Output: A summary is displayed on the user's terminal.

[1107] Step 5:

[1108] The server sends a request for related book recommendations to the generative AI model.

[1109] Input: Keywords, author, genre of book information

[1110] Specific behavior: The server sends the prompt "List five books in the fantasy genre that relate to 'wizard school adventures'" to the generative AI model.

[1111] Output: The generative AI model generates a list of related books and sends it back to the server (e.g., "Magic Star," "Tales of Dragons," etc.).

[1112] Step 6:

[1113] The server sends the recommended book list to the device.

[1114] Input: Related Book List

[1115] Specific operation: The server converts the book list into JSON format and sends it to the terminal as an HTTP response.

[1116] Output: A list of related books is displayed on the user's device.

[1117] Step 7:

[1118] Users enter evaluation comments and ratings for books

[1119] Input: Evaluation comments, score

[1120] What happens: A user comments, "A great adventure story!" and leaves a 5-star rating.

[1121] Output: The device sends the rating data in JSON format to the server.

[1122] Step 8:

[1123] The server stores the rating data in a database

[1124] Input: JSON data of evaluation comments and scores

[1125] Specific behavior: The server executes an SQL query to store the received rating data in a database.

[1126] Output: Evaluation data stored in a database

[1127] Step 9:

[1128] The server generates a popularity ranking and sends it to the device.

[1129] Input: Evaluation data from the database

[1130] Specific operation: The server aggregates the rating data from the database, generates a popularity ranking, converts the ranking data into JSON format, and sends it to the device as an HTTP response.

[1131] Output: The popularity ranking is displayed on the user's device.

[1132] Step 10:

[1133] The server selects relevant advertisements and sends them to the device.

[1134] Input: Prompt text based on user interests and preferences

[1135] Specific operation: The server sends a prompt such as "Show me advertisements for new books related to fantasy novels" to the generative AI model. The generated advertisement information is converted into JSON format and sent to the device as an HTTP response.

[1136] Output: The ad is displayed on the user's device.

[1137] The above are the specific processing steps of the system.

[1138] (Application example 1)

[1139] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1140] In the past, users had to manually input information to obtain book summaries and recommendations, which was inefficient. Furthermore, the collection of book review data and the display of advertisements were done separately, making it difficult to provide a comprehensive reading experience. The present invention aims to solve these problems by providing a system that allows users to efficiently obtain book information and provides related books and advertisements all at once.

[1141] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1142] In this invention, the server includes: means for receiving a book title or cover image as input from a user; means for generating a summary based on the book input data using a generative AI model; means for displaying the summary to the user; means for recommending related books based on keywords, authors, and genres; means for receiving evaluation comments and ratings as input from the user; means for storing the evaluation comments and ratings in a database and generating a popularity ranking; means for displaying the popularity ranking to the user; means for selecting advertising data and displaying it to the user; means for identifying the user's gaze point using eye-tracking technology and acquiring book information; and means for generating a book summary based on the book information acquired through the eye-tracking using a generative AI model and displaying it on a smart device. This allows users to intuitively acquire book information using their gaze and receive summaries, related book recommendations, evaluation data, and advertisements in a comprehensive manner.

[1143] "Book title" refers to the name of a book, which is usually printed on the cover or spine.

[1144] "Cover Image" refers to an image file containing visual data of the cover of a book.

[1145] A "generative AI model" is a model that uses artificial intelligence algorithms to generate text from input data.

[1146] A "summary" is a written summary of the contents of a book that can be understood concisely and quickly.

[1147] "Means for displaying to the user" refers to a method for displaying the generated information on a terminal screen or the like so that the user can visually confirm it.

[1148] "Keywords" are words or phrases that characterize the content of a book.

[1149] An "author" is a person who writes a book.

[1150] A "genre" is a classification that indicates what theme or category a book belongs to.

[1151] "Recommendation methods" refer to methods that recommend related books based on a user's interests and past behavior.

[1152] "Evaluation comments" are impressions and opinions entered by users about books they have read.

[1153] "Rating" refers to the user's evaluation of a book they have read, expressed as a number or number of stars.

[1154] A "database" is a system that efficiently stores and manages data and allows you to quickly search for the information you need.

[1155] "Popularity ranking" is a list of books ranked by popularity based on user ratings and comments.

[1156] "Advertising data" refers to information created for the purpose of advertising a specific product.

[1157] "Eye tracking technology" refers to technology that tracks the movement of a user's eyes and identifies their point of gaze.

[1158] "Smart devices" refers to electronic devices with internet connectivity and high-performance computing capabilities, including smart glasses and smartphones.

[1159] The system of the present invention comprises multiple means for providing book summaries and related book recommendations. Each component of the system and its operation are described in detail below.

[1160] Corresponding measures and technologies

[1161] 1. Input Method

[1162] A user inputs the book title or cover image using smart glasses or other smart devices (e.g., smartphones). The smart glasses are equipped with a camera and eye-tracking technology, which are used to identify the gaze point and obtain book information.

[1163] 2. Summary generation method using generative AI models

[1164] Input data (book title and cover image) is sent to the server, and a generative AI model (e.g., OpenAI's GPT-4) receives it and generates a summary. An example prompt might be, "Write a brief summary of the following book: Harry Potter." The generated summary is sent from the server to the device and displayed to the user.

[1165] 3. Recommendation methods

[1166] The server searches the database for related books based on the input keywords, author, and genre, and obtains recommendation information. This information is also sent to the terminal and displayed to the user. For example, "The Lord of the Rings" and "The Chronicles of Narnia" are recommended.

[1167] 4. Evaluation Receipt Method

[1168] It also includes a means for receiving user input such as a rating and comment on the book after reading it. For example, a user might comment, "An engrossing story!" and give it a five-star rating.

[1169] 5. Means for generating and displaying popularity rankings

[1170] The server stores the received rating data in a database and generates a popularity ranking based on this data. The generated popularity ranking is then sent back to the terminal and displayed to the user.

[1171] 6. Advertisement Selection Method

[1172] The system also includes a means for selecting and displaying relevant advertising data to users based on their interests and preferences. For example, an advertisement such as "Buy the Dragonlance series now" may be displayed.

[1173] Hardware and Software

[1174] Smart devices: Smart glasses and smartphones with eye tracking

[1175] Server: Uses a Python-based framework (e.g., Flask)

[1176] Generative AI model: OpenAI's GPT-4

[1177] Database: MongoDB, etc.

[1178] Specific examples

[1179] The user puts on the smart glasses and directs their gaze at the book cover. Eye-tracking technology identifies the point of gaze and sends that information to the server. The server then sends prompts to the generative AI model based on the book cover image, generating a summary. Related books are then recommended, and all information is displayed on the smart glasses' display. This allows the user to easily obtain summaries and recommended information with just eye-gaze interaction.

[1180] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1181] Step 1:

[1182] A user puts on the smart glasses and looks at the cover of a book.

[1183] Specific operation: The smart glasses' built-in camera tracks the user's gaze direction and captures an image of the book cover.

[1184] Input: User gaze and book cover image.

[1185] Output: Send the gaze information and cover image data to the server.

[1186] Step 2:

[1187] The terminal transmits the cover image data to the server.

[1188] Specific operation: The smart glasses upload the processed cover image data to the server.

[1189] Input: Cover image data.

[1190] Output: The cover image data is saved on the server.

[1191] Step 3:

[1192] The server analyzes the cover image data and extracts the book title.

[1193] Specific operation: The server uses a machine learning algorithm to perform character recognition on the cover image and identify the title.

[1194] Input: Cover image data.

[1195] Output: The book title.

[1196] Step 4:

[1197] The server sends prompts to the generative AI model to generate summaries.

[1198] How it works: The server generates a prompt containing the book title and sends it to a generative AI model (e.g., GPT-4), which then generates a summary based on the prompt.

[1199] Input: Book title and prompt (e.g., "Write a brief summary of the following book: Harry Potter").

[1200] Output: The generated summary text.

[1201] Step 5:

[1202] The server sends the generated summary to the terminal for display to the user.

[1203] Specific operation: The generated summary text is sent to the terminal for display on the smart glasses display.

[1204] Input: The generated summary text.

[1205] Output: A summary is displayed on the smart glasses display.

[1206] Step 6:

[1207] The server recommends related books based on keywords, authors, and genres.

[1208] What happens: The server searches its database to identify books related to the entered title.

[1209] Input: Book title, keywords, author, genre.

[1210] Output: A list of related books.

[1211] Step 7:

[1212] The server sends a list of related books to the terminal and displays it to the user.

[1213] Specific operation: A list of related books is sent to the terminal for display on the smart glasses display.

[1214] Input: A list of related books.

[1215] Output: A list of related books is displayed on the smart glasses display.

[1216] Step 8:

[1217] The user inputs evaluation comments and ratings for the books he or she has read.

[1218] Specific operation: The user inputs evaluation comments and scores using smart glasses or a smartphone.

[1219] Input: Rating information such as rating comments and star ratings.

[1220] Output: Evaluation comments and score information are sent to the server.

[1221] Step 9:

[1222] The server stores the evaluation comments and scores in a database and generates a popularity ranking.

[1223] Specific operation: The server records evaluation comments and scoring data in a database and generates popularity rankings based on that.

[1224] Input: Evaluation comments and scoring data.

[1225] Output: Popularity ranking.

[1226] Step 10:

[1227] The server transmits the popularity ranking to the terminal and displays it to the user.

[1228] Specific operation: The generated popularity ranking is sent to the terminal for display on the smart glasses display.

[1229] Input: Popularity Rankings.

[1230] Output: The popularity ranking is displayed on the smart glasses display.

[1231] Step 11:

[1232] The server selects the advertisement data and displays it to the user.

[1233] Specific operation: The server identifies relevant advertisements based on the user's interests and past behavior data and sends them to the device.

[1234] Input: User interest and behavior data, advertising data.

[1235] Output: The advertisement is displayed on the smart glasses display.

[1236] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1237] ---

[1238] This invention combines an emotion engine with a system that provides book summaries and recommendations based on input information such as book titles, cover images, keywords, authors, and genres, recognizing user emotions and optimizing recommendations and advertisement display. The system's program processing is explained below in natural language.

[1239] First, the user uses the terminal to input the title and cover image of the book they want to read into the system. For example, the user inputs the title "Harry Potter." The terminal then sends this input data to the server.

[1240] Based on the received data, the server sends a request to the generative AI model to generate a summary of the book. The generative AI model uses advanced algorithms based on large amounts of data to generate a summary that can be read in a few minutes. For example, a summary might be generated that reads, "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[1241] The server then sends the generated summary to the terminal, which then displays it to the user, allowing the user to quickly understand the contents of the book by reading the summary.

[1242] Furthermore, the server searches the database for related books based on the keywords, authors, genres, etc. entered by the user, and generates a list of recommended books. At this time, an emotion engine can be used to recognize emotions from the user's input and behavior. For example, if a user enters keywords such as "moving" or "fun," the server will select recommended books that match that emotion. The display order of recommended books may also be adjusted based on the analysis results of the emotion engine.

[1243] The recommended books are then sent to the device and displayed to the user. For example, books related to "The Lord of the Rings" and "The Chronicles of Narnia" are recommended.

[1244] Users input their evaluation comments and ratings for books they have read. For example, they might comment, "This is an engrossing story!" and rate it five stars. The device then sends this evaluation data to the server. The server then stores the received evaluation data in a database and generates a popularity ranking based on this data. The stored evaluation data can also be used for sentiment analysis and may be incorporated as emotional feedback for the user.

[1245] The generated popularity ranking is sent back to the device and displayed to the user. For example, "Harry Potter" appears at the top of the popularity ranking.

[1246] The server then selects relevant advertising data based on the user's interests and preferences. The emotion engine analyzes the user's emotions in real time and reflects them in the advertisement selection. For example, if the user is feeling "fun," an advertisement for a book that matches that emotion may be selected.

[1247] The selected advertising data is sent to the device and displayed to the user. For example, an advertisement such as "Purchase the Dragonlance series now" is displayed.

[1248] In this way, the system of the present invention combines an emotion engine with book summary generation, related book recommendation, evaluation data storage and popularity ranking generation, and advertisement display to realize optimal recommendations and advertisement display based on the user's emotions, enriching the reading experience and enabling efficient book discovery.

[1249] ---

[1250] The processing flow will be explained below.

[1251] ---

[1252] Step 1:

[1253] User: Enter the title or keywords of the book you want to read into the input form on your device. For example, enter "Harry Potter."

[1254] Step 2:

[1255] Terminal: Receives the entered book title and keywords and executes an API request to send them to the server.

[1256] Step 3:

[1257] Server: Based on the received book title and keywords, sends a summary generation request to the generative AI model.

[1258] Step 4:

[1259] Generative AI model: Generates a book summary based on the given book title and keywords. For example, it generates a summary such as "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[1260] Step 5:

[1261] Server: Sends the generated summary to the terminal.

[1262] Step 6:

[1263] Terminal: The generated summary is displayed to the user, allowing the user to see an overview of the book.

[1264] Step 7:

[1265] Server: Searches the database for relevant books based on the keywords, author, and genre entered by the user.

[1266] Step 8:

[1267] Server: Generates a list of recommended books from the search results that are highly relevant. In this process, an emotion engine is used to recognize emotions from the user's input and behavior, and books that fit those emotions are selected.

[1268] Step 9:

[1269] Server: Analyzes user sentiment and adjusts the order in which recommended books are displayed.

[1270] Step 10:

[1271] Server: Sends the generated recommended book list to the terminal.

[1272] Step 11:

[1273] Terminal: Display a recommended book list to the user. For example, "The Lord of the Rings" and "The Chronicles of Narnia" are displayed as recommended books.

[1274] Step 12:

[1275] User: Enters a rating and comment for the book after reading it. For example, a user might comment, "This is an engrossing story!" and rate it five stars.

[1276] Step 13:

[1277] Device: Executes an API request to send the entered evaluation comments and scores to the server.

[1278] Step 14:

[1279] Server: Stores the received evaluation data in a database.

[1280] Step 15:

[1281] Server: Generates popularity rankings based on the saved rating data. Sentiment analysis is also used to capture user sentiment feedback.

[1282] Step 16:

[1283] Server: Sends the generated popularity ranking to the device.

[1284] Step 17:

[1285] Device: Shows users a popularity ranking. For example, "Harry Potter" is displayed at the top of the popularity ranking.

[1286] Step 18:

[1287] Server: Selects relevant advertising data based on the user's interests and preferences. In this process, it uses an emotion engine to analyze user emotions in real time and reflects this information in the selection of ads.

[1288] Step 19:

[1289] Server: Sends the selected advertising data to the terminal.

[1290] Step 20:

[1291] Device: Shows relevant ads and book purchase links to users. For example, ads like "Buy the Dragonlance series now" may be displayed.

[1292] ---

[1293] The above is the specific processing flow for carrying out the invention.

[1294] Example 2

[1295] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1296] Current book summarization and recommendation systems provide uniform results without considering user emotions, making it difficult to improve user experience. Furthermore, there is no way to efficiently display advertisements that match the user's interests and emotions, limiting the effectiveness of advertising. Furthermore, despite using a large amount of data, there is a lack of technology that utilizes user emotions to optimize recommendations and advertisements.

[1297] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving a book name or image as input from a user, a means for generating a summary based on the input data of the book using a generative AI model, a means for recommending related books based on keywords, authors, and genres, and a means for analyzing user emotions by combining an emotion recognition engine and optimizing recommendations and advertisement display. This makes it possible to optimize recommendations and advertisement display based on user emotions.

[1298] "User" refers to the person who operates the system to input information and receive results.

[1299] "Book" refers to any medium provided as reading material, including e-books and printed books.

[1300] "Name" refers to the title or title used to identify a book.

[1301] "Image" refers to the book cover and related visual data.

[1302] A "generative AI model" refers to artificial intelligence technology that uses machine learning to summarize and recommend books.

[1303] A "summary" is a brief summary of the book's contents.

[1304] "Keywords" refer to important words and phrases that indicate the content and characteristics of a book.

[1305] "Author" refers to the person who wrote the book.

[1306] "Genre" refers to a classification category based on the content or theme of a book.

[1307] "Recommendation" refers to the act of suggesting related books, items, etc. to a user.

[1308] An "emotion recognition engine" refers to a system that analyzes a user's emotions and reflects the results in other processes.

[1309] "Advertising" refers to information that encourages users to purchase products or use services.

[1310] "Database" refers to a recording device that systematically stores information and makes it possible to search and use it as needed.

[1311] "Evaluation comments" refer to the impressions and opinions that users give about a book.

[1312] "Scoring" refers to the score or rating a user gives to a book.

[1313] This invention combines an emotion recognition engine with a system that inputs book information (title, cover image, author name, genre, etc.) and provides book summaries and recommendations based on that information, thereby analyzing and reflecting the user's emotions and optimizing recommendations and advertisement display.

[1314] The system operates as follows.

[1315] First, the user uses the device to enter information about the book they want to read. Specifically, they enter the book title "Harry Potter" into the input form on the device, and also enter a cover image, author name, genre, etc. as needed. The entered data is sent to the server as an HTTP request.

[1316] The server analyzes the received data and confirms that the book title is "Harry Potter." It then sends a summary generation request to the generative AI model. Specifically, it sends the generative AI model a prompt message saying, "Please generate a summary for Harry Potter." The generative AI model uses its internal database and algorithms to generate a summary, returning, for example, a summary such as, "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[1317] The generated summary is sent to the terminal via the server, where it is analyzed and displayed to the user, allowing the user to quickly grasp the contents of the book.

[1318] Furthermore, the server searches a database based on information such as keywords, authors, and genres entered by the user, and recommends related books. At this time, an emotion recognition engine is used to recognize emotions from the user's input data and past behavior, and the order in which recommended books are displayed is adjusted based on this. For example, if a user enters the keyword "moving," books that match that emotion will be recommended first. The recommendation results are sent to the device in JSON format, and the device displays a list of recommended books to the user. Specifically, "The Lord of the Rings" and "The Chronicles of Narnia" are recommended.

[1319] Users input their evaluation comments and ratings for books they have read. For example, they can enter a comment like "What an engrossing story!" and a five-star rating. This evaluation data is sent from the device to the server. The server stores the received evaluation data in a database and generates a popularity ranking based on this. The evaluation data is also used as emotional feedback by an emotion recognition engine.

[1320] The generated popularity ranking is sent back to the device and displayed to the user. For example, "Harry Potter" appears at the top of the popularity ranking.

[1321] Finally, the server selects advertising data based on the user's interests and preferences, and uses an emotion recognition engine to select advertisements that match the user's emotions. For example, if the user feels "fun," an advertisement for a book that matches that emotion will be selected. The selected advertising data is sent to the device and displayed to the user. For example, an advertisement such as "Buy the Dragonlance series now" will be displayed.

[1322] In this way, the system generates book summaries, recommends related books, stores rating data and generates popularity rankings, and combines an emotion recognition engine with ad display to achieve optimal recommendations and ad display based on the user's emotions. The hardware required is a user device (smartphone, tablet, PC, etc.) and a server, while the software used is a generative AI model and an emotion recognition engine. Examples of prompts for the generative AI model include "Generate a summary of the following book: 'Harry Potter'" and "Recommend books that the user will find inspiring."

[1323] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1324] Step 1:

[1325] The user enters information about the book they want to read.

[1326] Input: Book title, cover image, author name, genre.

[1327] Operation: The user enters book information into the input form on the device and presses the submit button.

[1328] Output: The input data is sent to the server as an HTTP request.

[1329] Step 2:

[1330] The server analyzes the received data and sends a summary generation request to the generative AI model.

[1331] Input: Data entered, such as the title of a book, "Harry Potter."

[1332] How it works: The server parses the incoming data, checks the book title, and then sends a request to the generative AI model with the prompt "Generate a summary of Harry Potter."

[1333] Output: The prompt is sent to the generative AI model.

[1334] Step 3:

[1335] A generative AI model generates a book summary.

[1336] Input: The prompt sent by the server: "Generate a summary of Harry Potter."

[1337] How it works: The generative AI model uses its internal database and algorithms to generate summaries.

[1338] Output: The generated summary (e.g. "Harry Potter is a fantasy novel about adventures at the magical school Hogwarts.") is sent back to the server.

[1339] Step 4:

[1340] The server sends the generated summary to the terminal.

[1341] Input: Summary data returned from the generative AI model.

[1342] How it works: The server parses the abstract data and generates an HTTP response to send back to the user's device.

[1343] Output: Summary data is sent to the terminal.

[1344] Step 5:

[1345] The terminal displays the generated summary to the user.

[1346] Input: Abstract data received from the server.

[1347] Actions: The device parses the summary data and updates the HTML for display in the user interface.

[1348] Output: User can see the summary.

[1349] Step 6:

[1350] The server recommends related books based on the user's keywords, authors, genres, etc.

[1351] Input: Keywords, author, and genre entered by the user.

[1352] How it works: The server searches the database to retrieve relevant books. It then uses an emotion recognition engine to recognize emotions from the user's input data and past behavior, and adjusts the order of recommended books accordingly.

[1353] Output: The recommended book list is sent to the device in JSON format.

[1354] Step 7:

[1355] The device displays a list of recommended books to the user.

[1356] Input: A list of recommended books received from the server.

[1357] How it works: The device parses the recommended books and displays them in a list format in the user interface.

[1358] Output: The user can see the recommended books list.

[1359] Step 8:

[1360] The user enters evaluation comments and ratings.

[1361] Input: Evaluation comments and ratings for books you have read.

[1362] How it works: The user enters a rating (e.g., 5 stars) or a rating comment (e.g., "A captivating story!") into the input form on their device and presses the submit button.

[1363] Output: The rating data is sent to the server as an HTTP request.

[1364] Step 9:

[1365] A server stores the rating data and generates a popularity ranking.

[1366] Input: Rating data received from users.

[1367] How it works: The server analyzes the rating data and stores it in a database. It then performs statistical processing based on the stored rating data to generate popularity rankings. It also uses the data as emotional feedback through an emotion recognition engine.

[1368] Output: Popularity ranking data is generated and sent to the device.

[1369] Step 10:

[1370] The terminal displays the generated popularity ranking to the user.

[1371] Input: Popularity ranking data received from the server.

[1372] Operation: The device analyzes the popularity ranking data and displays it in the user interface.

[1373] Output: User can check the popularity ranking.

[1374] Step 11:

[1375] The server selects relevant advertising data and transmits it to the terminal.

[1376] Input: Data based on user interests and preferences, analysis results of emotion recognition engine.

[1377] How it works: The server selects relevant advertising data based on the user's interests and preferences. It uses an emotion recognition engine to select ads that match the user's emotions.

[1378] Output: The selected advertising data is sent to the device.

[1379] Step 12:

[1380] The terminal displays the advertisement to the user.

[1381] Input: Advertisement data received from the server.

[1382] Operation: The device parses the advertising data and displays the advertisement in the user interface.

[1383] Output: User can see the ad.

[1384] (Application example 2)

[1385] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1386] Conventional book recommendation systems only summarize and recommend books based on static data without considering user emotions, making it difficult to provide optimal recommendations and display advertisements based on individual users' emotions and interests. For this reason, a system that can effectively enhance users' reading experience and purchasing motivation has been sought.

[1387] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a book title or cover image as input from a user, means for generating a summary using a generative AI model based on the input data of the book, means for displaying the summary to the user, and means for optimizing a recommended book list and advertisement display based on the analysis results of the emotion engine. This makes it possible to recommend books and display advertisements that match the user's emotions and interests.

[1388] The "means for receiving a book title or cover image as input from a user" refers to a device or interface that allows a user to input the title or cover image of a book that the user wants to read into the system.

[1389] "Means for generating a summary using a generative AI model based on input data of a book" refers to a method or system that automatically generates a summary of a book using a generative AI model based on input information about the book.

[1390] The "means for displaying the summary to the user" refers to a device or interface for providing the generated book summary to the user.

[1391] "Means for recommending related books based on keywords, authors, and genres" refers to a method or system that suggests related books based on keywords, authors, and genres entered by a user.

[1392] The "means for receiving evaluation comments and ratings as input from the user" refers to a device or interface that allows the user to input comments and ratings for the book that they have read.

[1393] "Means for storing the evaluation comments and ratings in a database and generating a popularity ranking" refers to a method or system for storing evaluation comments and ratings from users in a database and creating a popularity ranking of books based on the stored evaluation comments and ratings.

[1394] The "means for displaying the popularity ranking to the user" refers to a device or interface for providing the generated popularity ranking to the user.

[1395] The "means for selecting advertising data and displaying it to the user" refers to a method or system for selecting appropriate advertising data and displaying it to the user.

[1396] The "means including an emotion engine that recognizes emotions from user input and behavior" refers to algorithms or software that analyzes a user's text input and behavior data and recognizes their emotional state.

[1397] "Means for optimizing the recommended book list and advertisement display based on the analysis results of the emotion engine" refers to a method or system for presenting optimal book recommendations and advertisements to users based on the results of analysis by the emotion engine.

[1398] This invention is a system that uses an emotion engine to generate book summaries, recommendations, and optimize advertisement display based on book information entered by the user. This system receives book titles or cover images as input from the user, processes them with a generative AI model to generate summaries, and also uses the emotion engine to make recommendations and display advertisements that match the user's emotions.

[1399] First, a user enters information about the book they want to read, such as the title, cover image, keywords, author, and genre, through a smartphone application. This information is sent from the device to a server. The server then sends a summary creation request to the generative AI model based on the received book information. For example, a prompt sentence such as "Please provide a summary of Harry Potter" is entered into the generative AI model. The generative AI model then generates a readable summary in a few minutes based on a large database and returns the summary to the server.

[1400] The generated summary is then sent from the server to the terminal and displayed to the user. By looking at this summary, the user can quickly understand the contents of the book. The server also searches a database for related books based on the keywords, author, genre, etc. entered by the user, and generates a list of recommended books. Here, an emotion engine is utilized to recognize emotions from the user's input and behavior. For example, if a user enters a keyword such as "moving story," books that match that emotion will be recommended.

[1401] Furthermore, after users finish reading a book, they can enter their evaluation comments and ratings. For example, they can comment, "This is an engrossing story!" and rate it five stars. This evaluation data is sent to the server and stored in a database. The server then generates a popularity ranking based on this evaluation data and displays it to the user.

[1402] Furthermore, the server selects relevant advertising data based on the user's interests and preferences. At this time, an emotion engine analyzes the user's emotions in real time and reflects them in the advertisement selection. For example, if the user is feeling "fun," an advertisement for a book that matches that emotion will be selected and displayed on the device. For example, an advertisement such as "Buy the Dragon Adventure series now" will be displayed.

[1403] In this invention, the smartphone functions as the user interface, and data input from the device is processed on a backend server. The server is responsible for data processing and storage, while the generative AI model and emotion engine perform advanced algorithmic processing. Specific software used includes React Native (mobile app development) on the front end, Node.js and Express (data processing) on ​​the back end, MongoDB for the database, Microsoft Azure Cognitive Services for emotion analysis, and OpenAI GPT-4 for the generative AI model.

[1404] Specific examples of prompt sentences are as follows:

[1405] "Please provide a summary of Harry Potter. Also, please provide related books that contain inspirational elements."

[1406] In this way, the present invention enables optimal book recommendations and advertisement display based on the user's emotions, providing the user with a highly satisfying reading experience.

[1407] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1408] Step 1:

[1409] Users use the smartphone interface to input information about the book they want to read, such as the title, cover image, keywords, author, and genre. This input data is then sent from the device to the server.

[1410] Step 2:

[1411] The server analyzes the book input data received from the device, formats it as needed, creates a prompt for the generative AI model based on the formatted data, and sends a summary generation request.

[1412] Input: Book title, cover image, keywords, author, genre, and other information

[1413] Data processing: Prompt sentence creation for generative AI models

[1414] Output: Prompt: "Please provide a summary of the book title."

[1415] Step 3:

[1416] The generative AI model generates a summary of the book based on the prompt received from the server, and the generated summary is sent back to the server.

[1417] Input: prompt statement

[1418] Data computation: Applying summary generation algorithms

[1419] Output: Generated book summary

[1420] Step 4:

[1421] The server sends the generated summary to the terminal and displays it to the user.

[1422] Input: Generated book summary

[1423] Data processing: Formatting and sending summary data

[1424] Output: Book summary displayed on terminal

[1425] Step 5:

[1426] The server searches the database for related books based on the keywords, authors, and genres entered by the user, and generates a list of recommended books. It uses an emotion engine to recognize emotions from the user's input and behavior, and optimizes the ranking of recommendations based on these emotions.

[1427] Input: User's keywords, author, and genre information

[1428] Data Computing: Database Search and Emotion Recognition

[1429] Output: Recommended book list

[1430] Step 6:

[1431] The server sends the recommended book list to the terminal and displays it to the user, who can then view the recommended books.

[1432] Input: Recommended book list

[1433] Data processing: Formatting and sending the recommendation list

[1434] Output: Recommended book list displayed on the device

[1435] Step 7:

[1436] Users input evaluation comments and ratings for the books they have read, and the evaluation comments and rating data are sent from the terminal to the server.

[1437] Input: User's evaluation comments, ratings

[1438] Data transmission: Sending evaluation data to the server

[1439] Output: Evaluation comments and scoring data saved on the server

[1440] Step 8:

[1441] The server stores the received evaluation comments and scoring data in a database and generates popularity rankings based on this data.

[1442] Input: Evaluation comments, scoring data

[1443] Data arithmetic: Analysis of evaluation data and generation of rankings

[1444] Output: Popularity ranking

[1445] Step 9:

[1446] The server transmits the generated popularity ranking to the terminal and displays it to the user.

[1447] Input: Popularity Ranking

[1448] Data processing: Formatting and sending ranking data

[1449] Output: Popular rankings displayed on the device

[1450] Step 10:

[1451] The server selects relevant advertising data based on the user's interests and emotions, and uses an emotion engine to analyze the user's real-time emotions and reflect them in the selection of advertisements.

[1452] Input: User sentiment analysis results

[1453] Data calculation: Selection of advertising data

[1454] Output: Highly relevant advertising data

[1455] Step 11:

[1456] The server sends the selected advertisement data to the terminal and displays it to the user. For example, an advertisement saying "Buy the Dragon Adventure series now" is displayed.

[1457] Input: Selected advertising data

[1458] Data processing: Formatting and sending advertising data

[1459] Output: Ad data displayed on the device

[1460] This program is a system that generates book summaries based on book information entered by the user, and uses an emotion engine to optimize recommended books and advertisements according to the user's emotions, thereby providing users with an effective and satisfying service.

[1461] 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.

[1462] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1463] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1464] [Fourth embodiment]

[1465] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1466] 7, a 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.

[1467] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1468] 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.

[1469] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1470] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1471] 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.

[1472] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1473] 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.

[1474] 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 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.

[1475] 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.

[1476] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1477] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1478] ---

[1479] The system of the present invention allows users to input information such as the book title, cover image, keywords, author, genre, etc., and provides book summaries and recommendations based on that information. The processing of the system's program is explained below in natural language.

[1480] First, the user uses the terminal to input the title and cover image of the book they want to read into the system. For example, the user may input the title "Harry Potter." The terminal then sends this input data to the server.

[1481] Based on the received data, the server sends a request to the generative AI model to generate a summary of the book. The generative AI model uses advanced algorithms based on large amounts of data to generate a summary that can be read in a few minutes. For example, a summary might be generated that reads, "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[1482] The server then sends the generated summary to the terminal, which then displays it to the user, allowing the user to quickly understand the contents of the book by reading the summary.

[1483] Furthermore, the server searches the database for related books based on the keywords, author, genre, etc. entered by the user, and generates a list of recommended books. The recommended book list is sent to the terminal and displayed to the user. For example, related books such as "The Lord of the Rings" and "The Chronicles of Narnia" are recommended.

[1484] Next, the user enters a rating and comment for the book they have just read. For example, they might comment, "This is an engrossing story!" and rate it five stars. The device then sends this rating data to the server. The server then stores the received rating data in a database and generates a popularity ranking based on this data. The generated popularity ranking is then sent back to the device and displayed to the user.

[1485] The server also selects relevant advertisements based on the user's interests and preferences and sends them to the device. For example, an advertisement for a book related to the "Fantasy" genre may be selected and displayed on the device. For example, an advertisement such as "Buy the Dragonlance series now" may be displayed.

[1486] In this way, the system of the present invention enriches the user's reading experience and enables them to efficiently find books by generating book summaries, recommending related books, storing evaluation data and generating popularity rankings, and displaying advertisements.

[1487] ---

[1488] The processing flow will be explained below.

[1489] ---

[1490] Step 1:

[1491] User: Enter the title or keywords of the book you want to read into the input form on your device. For example, enter "Harry Potter."

[1492] Step 2:

[1493] Terminal: Receives the entered book title and keywords and executes an API request to send them to the server.

[1494] Step 3:

[1495] Server: Based on the received book title and keywords, sends a summary generation request to the generative AI model.

[1496] Step 4:

[1497] Generative AI model: Generates a book summary based on the given book title and keywords. For example, it generates a summary such as "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[1498] Step 5:

[1499] Server: Sends the generated summary to the terminal.

[1500] Step 6:

[1501] Terminal: The generated summary is displayed to the user, who can then check the overview of the book.

[1502] Step 7:

[1503] Server: Searches the database for relevant books based on the keywords, author, and genre entered by the user.

[1504] Step 8:

[1505] Server: Generates a recommended book list from the search results by selecting highly relevant books.

[1506] Step 9:

[1507] Server: Sends the generated recommended book list to the terminal.

[1508] Step 10:

[1509] Terminal: Display a recommended book list to the user. For example, "The Lord of the Rings" and "The Chronicles of Narnia" are displayed as recommended books.

[1510] Step 11:

[1511] User: Enters a rating and comment for the book after reading it. For example, a user might comment, "This is an engrossing story!" and rate it five stars.

[1512] Step 12:

[1513] Device: Executes an API request to send the entered evaluation comments and scores to the server.

[1514] Step 13:

[1515] Server: Stores the received evaluation data in a database.

[1516] Step 14:

[1517] Server: Generates popularity rankings based on the saved rating data.

[1518] Step 15:

[1519] Server: Sends the generated popularity ranking to the device.

[1520] Step 16:

[1521] Device: Shows users a popularity ranking. For example, "Harry Potter" is displayed at the top of the popularity ranking.

[1522] Step 17:

[1523] Server: Selects relevant advertising data based on the user's interests and preferences.

[1524] Step 18:

[1525] Server: Sends the selected advertising data to the terminal.

[1526] Step 19:

[1527] Device: Shows relevant ads and book purchase links to users. For example, ads like "Buy the Dragonlance series now" may be displayed.

[1528] ---

[1529] Example 1

[1530] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1531] Many readers today want to efficiently find books that match their interests and understand summaries within the constraints of time and information. However, conventional search and recommendation systems have had difficulty efficiently retrieving the necessary information from the vast amount of book information available. It has also been difficult to reflect reader ratings and ranking information in real time and present popular books appropriately.

[1532] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1533] In this invention, the server includes means for receiving book information as input from a user, means for generating a summary based on the book information using a generative AI model, and means for displaying the summary to the user, thereby enabling users to efficiently obtain book summaries and find books that match their interests.

[1534] A "user" is a person who uses the system and inputs book information.

[1535] "Book information" refers to data such as the book title, cover image, keywords, author, genre, etc.

[1536] A "generative AI model" is an artificial intelligence model that uses advanced algorithms to generate summaries and recommendations based on input data.

[1537] A "summary" is a short sentence that abridges the contents of a book.

[1538] "Keywords" are words or phrases that indicate the content or characteristics of a book.

[1539] An "author" is a person who writes a book.

[1540] "Type" refers to the genre or category of the book.

[1541] "Recommendation" refers to providing related books that are suggested based on the user's interests.

[1542] An "evaluation comment" is a sentence that describes a user's impressions or opinions about a book.

[1543] A "rating" is a numerical value that a user gives to a book.

[1544] "Storage device" refers to a database or storage for storing evaluation data and book information.

[1545] "Popularity ranking" is a ranking calculated based on evaluation data and the popularity of books.

[1546] "Advertising information" is advertising data for products and services related to books.

[1547] "Control means" refers to a device or function that links each means and manages the operation of the entire system.

[1548] MODE FOR CARRYING OUT THE INVENTION

[1549] The system of the present invention uses a generative AI model to recommend summaries and related books, save evaluation comments, generate popularity rankings, and display advertisements based on user input of book information. The specific operation of the system is shown below.

[1550] User enters book information

[1551] A user uses a device (such as a smartphone or PC) to input information about the book they want to read, such as the title, cover image, keywords, author, and genre. For example, a user might input the title "Adventures at Magic School" into the device as book information. This information is then sent from the device to the server.

[1552] Generate a book summary

[1553] The server generates a summary by sending a prompt to the generative AI model based on the received book information. Specifically, the server passes the following prompt to the generative AI model:

[1554] Generate a summary of the book 'Adventures at Magic School'.

[1555] The generative AI model generates a summary based on this prompt, such as "Adventures at Wizarding School tells the story of a young wizard uncovering a secret," and returns it to the server, which then sends the summary to the device and displays it to the user.

[1556] Recommend a book

[1557] The server searches for and recommends related books based on the keywords, author, genre, etc. entered by the user. For example, the following prompt sentence is input to the generative AI model:

[1558] List five books related to 'wizard school adventures' in the fantasy genre.

[1559] The generative AI model returns a list of related books, such as "The Magic Star" and "The Tale of the Dragon," to the server, which then sends this list to the device and displays it to the user.

[1560] User ratings and popularity ranking generation

[1561] After reading a book, the user inputs a rating and comment. For example, they might comment, "What a wonderful adventure story!" and rate it five stars. The device sends this rating data to the server, which stores it in a database. The server generates a popularity ranking based on the accumulated rating data, and sends information to the device, such as, "Magic School Adventure is currently ranked number one in popularity," to display to the user.

[1562] Displaying relevant ads

[1563] The server selects relevant advertisements based on the user's interests and preferences. For example, it uses a prompt such as "Show me advertisements for new books related to fantasy novels." The selected advertisement information is sent to the terminal and displayed to the user. For example, an advertisement such as "Buy the new book 'Wizard Training' now" is displayed.

[1564] In this way, the system performs various data processing and calculations based on the information entered by the user, efficiently providing book summaries and recommendations, and enriching the user's reading experience.

[1565] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1566] Processing Steps

[1567] Step 1:

[1568] The user enters book information into the device.

[1569] Input: Book title, cover image, keywords, author, genre, etc.

[1570] Specific behavior: The user uses the terminal to enter the title "Adventures at Magic School."

[1571] Output: The terminal sends the input data to the server in JSON format.

[1572] Step 2:

[1573] The server receives and analyzes the book information.

[1574] Input: Book information data in JSON format

[1575] Specific operation: The server parses the received JSON data and obtains the book title, cover image, keywords, author, genre, etc.

[1576] Output: Summary generation prompt "Generate a summary for the book 'Adventures at Wizarding School'."

[1577] Step 3:

[1578] The server sends a summary generation request to the generative AI model

[1579] Input: Summary generation prompt statement

[1580] Specific operation: The server sends a summary generation prompt to the generative AI model.

[1581] Output: The generative AI model generates a summary and sends it back to the server (e.g., "Adventures at Wizarding School tells the story of a young wizard who uncovers a secret.").

[1582] Step 4:

[1583] The server sends the generated summary to the device.

[1584] Input: Generated summary text

[1585] Specific operation: The server converts the summary text into JSON format and sends it to the terminal as an HTTP response.

[1586] Output: A summary is displayed on the user's terminal.

[1587] Step 5:

[1588] The server sends a request for related book recommendations to the generative AI model.

[1589] Input: Keywords, author, genre of book information

[1590] Specific behavior: The server sends the prompt "List five books in the fantasy genre that relate to 'wizard school adventures'" to the generative AI model.

[1591] Output: The generative AI model generates a list of related books and sends it back to the server (e.g., "Magic Star," "Tales of Dragons," etc.).

[1592] Step 6:

[1593] The server sends the recommended book list to the device.

[1594] Input: Related Book List

[1595] Specific operation: The server converts the book list into JSON format and sends it to the terminal as an HTTP response.

[1596] Output: A list of related books is displayed on the user's device.

[1597] Step 7:

[1598] Users enter evaluation comments and ratings for books

[1599] Input: Evaluation comments, score

[1600] What happens: A user comments, "A great adventure story!" and leaves a 5-star rating.

[1601] Output: The device sends the rating data in JSON format to the server.

[1602] Step 8:

[1603] The server stores the rating data in a database

[1604] Input: JSON data of evaluation comments and scores

[1605] Specific behavior: The server executes an SQL query to store the received rating data in a database.

[1606] Output: Evaluation data stored in a database

[1607] Step 9:

[1608] The server generates a popularity ranking and sends it to the device.

[1609] Input: Evaluation data from the database

[1610] Specific operation: The server aggregates the rating data from the database, generates a popularity ranking, converts the ranking data into JSON format, and sends it to the device as an HTTP response.

[1611] Output: The popularity ranking is displayed on the user's device.

[1612] Step 10:

[1613] The server selects relevant advertisements and sends them to the device.

[1614] Input: Prompt text based on user interests and preferences

[1615] Specific operation: The server sends a prompt such as "Show me advertisements for new books related to fantasy novels" to the generative AI model. The generated advertisement information is converted into JSON format and sent to the device as an HTTP response.

[1616] Output: The ad is displayed on the user's device.

[1617] The above are the specific processing steps of the system.

[1618] (Application example 1)

[1619] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1620] In the past, users had to manually input information to obtain book summaries and recommendations, which was inefficient. Furthermore, the collection of book review data and the display of advertisements were done separately, making it difficult to provide a comprehensive reading experience. The present invention aims to solve these problems by providing a system that allows users to efficiently obtain book information and provides related books and advertisements all at once.

[1621] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1622] In this invention, the server includes: means for receiving a book title or cover image as input from a user; means for generating a summary based on the book input data using a generative AI model; means for displaying the summary to the user; means for recommending related books based on keywords, authors, and genres; means for receiving evaluation comments and ratings as input from the user; means for storing the evaluation comments and ratings in a database and generating a popularity ranking; means for displaying the popularity ranking to the user; means for selecting advertising data and displaying it to the user; means for identifying the user's gaze point using eye-tracking technology and acquiring book information; and means for generating a book summary based on the book information acquired through the eye-tracking using a generative AI model and displaying it on a smart device. This allows users to intuitively acquire book information using their gaze and receive summaries, related book recommendations, evaluation data, and advertisements in a comprehensive manner.

[1623] "Book title" refers to the name of a book, which is usually printed on the cover or spine.

[1624] "Cover Image" refers to an image file containing visual data of the cover of a book.

[1625] A "generative AI model" is a model that uses artificial intelligence algorithms to generate text from input data.

[1626] A "summary" is a written summary of the contents of a book that can be understood concisely and quickly.

[1627] "Means for displaying to the user" refers to a method for displaying the generated information on a terminal screen or the like so that the user can visually confirm it.

[1628] "Keywords" are words or phrases that characterize the content of a book.

[1629] An "author" is a person who writes a book.

[1630] A "genre" is a classification that indicates what theme or category a book belongs to.

[1631] "Recommendation methods" refer to methods that recommend related books based on a user's interests and past behavior.

[1632] "Evaluation comments" are impressions and opinions entered by users about books they have read.

[1633] "Rating" refers to the user's evaluation of a book they have read, expressed as a number or number of stars.

[1634] A "database" is a system that efficiently stores and manages data and allows you to quickly search for the information you need.

[1635] "Popularity ranking" is a list of books ranked by popularity based on user ratings and comments.

[1636] "Advertising data" refers to information created for the purpose of advertising a specific product.

[1637] "Eye tracking technology" refers to technology that tracks the movement of a user's eyes and identifies their point of gaze.

[1638] "Smart devices" refers to electronic devices with internet connectivity and high-performance computing capabilities, including smart glasses and smartphones.

[1639] The system of the present invention comprises multiple means for providing book summaries and related book recommendations. Each component of the system and its operation are described in detail below.

[1640] Corresponding measures and technologies

[1641] 1. Input Method

[1642] A user inputs the book title or cover image using smart glasses or other smart devices (e.g., smartphones). The smart glasses are equipped with a camera and eye-tracking technology, which are used to identify the gaze point and obtain book information.

[1643] 2. Summary generation method using generative AI models

[1644] Input data (book title and cover image) is sent to the server, and a generative AI model (e.g., OpenAI's GPT-4) receives it and generates a summary. An example prompt might be, "Write a brief summary of the following book: Harry Potter." The generated summary is sent from the server to the device and displayed to the user.

[1645] 3. Recommendation methods

[1646] The server searches the database for related books based on the input keywords, author, and genre, and obtains recommendation information. This information is also sent to the terminal and displayed to the user. For example, "The Lord of the Rings" and "The Chronicles of Narnia" are recommended.

[1647] 4. Evaluation Receipt Method

[1648] It also includes a means for receiving user input such as a rating and comment on the book after reading it. For example, a user might comment, "An engrossing story!" and give it a five-star rating.

[1649] 5. Means for generating and displaying popularity rankings

[1650] The server stores the received rating data in a database and generates a popularity ranking based on this data. The generated popularity ranking is then sent back to the terminal and displayed to the user.

[1651] 6. Advertisement Selection Method

[1652] The system also includes a means for selecting and displaying relevant advertising data to users based on their interests and preferences. For example, an advertisement such as "Buy the Dragonlance series now" may be displayed.

[1653] Hardware and Software

[1654] Smart devices: Smart glasses and smartphones with eye tracking

[1655] Server: Uses a Python-based framework (e.g., Flask)

[1656] Generative AI model: OpenAI's GPT-4

[1657] Database: MongoDB, etc.

[1658] Specific examples

[1659] The user puts on the smart glasses and directs their gaze at the book cover. Eye-tracking technology identifies the point of gaze and sends that information to the server. The server then sends prompts to the generative AI model based on the book cover image, generating a summary. Related books are then recommended, and all information is displayed on the smart glasses' display. This allows the user to easily obtain summaries and recommended information with just eye-gaze interaction.

[1660] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1661] Step 1:

[1662] A user puts on the smart glasses and looks at the cover of a book.

[1663] Specific operation: The smart glasses' built-in camera tracks the user's gaze direction and captures an image of the book cover.

[1664] Input: User gaze and book cover image.

[1665] Output: Send the gaze information and cover image data to the server.

[1666] Step 2:

[1667] The terminal transmits the cover image data to the server.

[1668] Specific operation: The smart glasses upload the processed cover image data to the server.

[1669] Input: Cover image data.

[1670] Output: The cover image data is saved on the server.

[1671] Step 3:

[1672] The server analyzes the cover image data and extracts the book title.

[1673] Specific operation: The server uses a machine learning algorithm to perform character recognition on the cover image and identify the title.

[1674] Input: Cover image data.

[1675] Output: The book title.

[1676] Step 4:

[1677] The server sends prompts to the generative AI model to generate summaries.

[1678] How it works: The server generates a prompt containing the book title and sends it to a generative AI model (e.g., GPT-4), which then generates a summary based on the prompt.

[1679] Input: Book title and prompt (e.g., "Write a brief summary of the following book: Harry Potter").

[1680] Output: The generated summary text.

[1681] Step 5:

[1682] The server sends the generated summary to the terminal for display to the user.

[1683] Specific operation: The generated summary text is sent to the terminal for display on the smart glasses display.

[1684] Input: The generated summary text.

[1685] Output: A summary is displayed on the smart glasses display.

[1686] Step 6:

[1687] The server recommends related books based on keywords, authors, and genres.

[1688] What happens: The server searches its database to identify books related to the entered title.

[1689] Input: Book title, keywords, author, genre.

[1690] Output: A list of related books.

[1691] Step 7:

[1692] The server sends a list of related books to the terminal and displays it to the user.

[1693] Specific operation: A list of related books is sent to the terminal for display on the smart glasses display.

[1694] Input: A list of related books.

[1695] Output: A list of related books is displayed on the smart glasses display.

[1696] Step 8:

[1697] The user inputs evaluation comments and ratings for the books he or she has read.

[1698] Specific operation: The user inputs evaluation comments and scores using smart glasses or a smartphone.

[1699] Input: Rating information such as rating comments and star ratings.

[1700] Output: Evaluation comments and score information are sent to the server.

[1701] Step 9:

[1702] The server stores the evaluation comments and scores in a database and generates a popularity ranking.

[1703] Specific operation: The server records evaluation comments and scoring data in a database and generates popularity rankings based on that.

[1704] Input: Evaluation comments and scoring data.

[1705] Output: Popularity ranking.

[1706] Step 10:

[1707] The server transmits the popularity ranking to the terminal and displays it to the user.

[1708] Specific operation: The generated popularity ranking is sent to the terminal for display on the smart glasses display.

[1709] Input: Popularity Rankings.

[1710] Output: The popularity ranking is displayed on the smart glasses display.

[1711] Step 11:

[1712] The server selects the advertisement data and displays it to the user.

[1713] Specific operation: The server identifies relevant advertisements based on the user's interests and past behavior data and sends them to the device.

[1714] Input: User interest and behavior data, advertising data.

[1715] Output: The advertisement is displayed on the smart glasses display.

[1716] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1717] ---

[1718] This invention combines an emotion engine with a system that provides book summaries and recommendations based on input information such as book titles, cover images, keywords, authors, and genres, recognizing user emotions and optimizing recommendations and advertisement display. The system's program processing is explained below in natural language.

[1719] First, the user uses the terminal to input the title and cover image of the book they want to read into the system. For example, the user inputs the title "Harry Potter." The terminal then sends this input data to the server.

[1720] Based on the received data, the server sends a request to the generative AI model to generate a summary of the book. The generative AI model uses advanced algorithms based on large amounts of data to generate a summary that can be read in a few minutes. For example, a summary might be generated that reads, "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[1721] The server then sends the generated summary to the terminal, which then displays it to the user, allowing the user to quickly understand the contents of the book by reading the summary.

[1722] Furthermore, the server searches the database for related books based on the keywords, authors, genres, etc. entered by the user, and generates a list of recommended books. At this time, an emotion engine can be used to recognize emotions from the user's input and behavior. For example, if a user enters keywords such as "moving" or "fun," the server will select recommended books that match that emotion. The display order of recommended books may also be adjusted based on the analysis results of the emotion engine.

[1723] The recommended books are then sent to the device and displayed to the user. For example, books related to "The Lord of the Rings" and "The Chronicles of Narnia" are recommended.

[1724] Users input their evaluation comments and ratings for books they have read. For example, they might comment, "This is an engrossing story!" and rate it five stars. The device then sends this evaluation data to the server. The server then stores the received evaluation data in a database and generates a popularity ranking based on this data. The stored evaluation data can also be used for sentiment analysis and may be incorporated as emotional feedback for the user.

[1725] The generated popularity ranking is sent back to the device and displayed to the user. For example, "Harry Potter" appears at the top of the popularity ranking.

[1726] The server then selects relevant advertising data based on the user's interests and preferences. The emotion engine analyzes the user's emotions in real time and reflects them in the advertisement selection. For example, if the user is feeling "fun," an advertisement for a book that matches that emotion may be selected.

[1727] The selected advertising data is sent to the device and displayed to the user. For example, an advertisement such as "Purchase the Dragonlance series now" is displayed.

[1728] In this way, the system of the present invention combines an emotion engine with book summary generation, related book recommendation, evaluation data storage and popularity ranking generation, and advertisement display to realize optimal recommendations and advertisement display based on the user's emotions, enriching the reading experience and enabling efficient book discovery.

[1729] ---

[1730] The processing flow will be explained below.

[1731] ---

[1732] Step 1:

[1733] User: Enter the title or keywords of the book you want to read into the input form on your device. For example, enter "Harry Potter."

[1734] Step 2:

[1735] Terminal: Receives the entered book title and keywords and executes an API request to send them to the server.

[1736] Step 3:

[1737] Server: Based on the received book title and keywords, sends a summary generation request to the generative AI model.

[1738] Step 4:

[1739] Generative AI model: Generates a book summary based on the given book title and keywords. For example, it generates a summary such as "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[1740] Step 5:

[1741] Server: Sends the generated summary to the terminal.

[1742] Step 6:

[1743] Terminal: The generated summary is displayed to the user, allowing the user to see an overview of the book.

[1744] Step 7:

[1745] Server: Searches the database for relevant books based on the keywords, author, and genre entered by the user.

[1746] Step 8:

[1747] Server: Generates a list of recommended books from the search results that are highly relevant. In this process, an emotion engine is used to recognize emotions from the user's input and behavior, and books that fit those emotions are selected.

[1748] Step 9:

[1749] Server: Analyzes user sentiment and adjusts the order in which recommended books are displayed.

[1750] Step 10:

[1751] Server: Sends the generated recommended book list to the terminal.

[1752] Step 11:

[1753] Terminal: Display a recommended book list to the user. For example, "The Lord of the Rings" and "The Chronicles of Narnia" are displayed as recommended books.

[1754] Step 12:

[1755] User: Enters a rating and comment for the book after reading it. For example, a user might comment, "This is an engrossing story!" and rate it five stars.

[1756] Step 13:

[1757] Device: Executes an API request to send the entered evaluation comments and scores to the server.

[1758] Step 14:

[1759] Server: Stores the received evaluation data in a database.

[1760] Step 15:

[1761] Server: Generates popularity rankings based on the saved rating data. Sentiment analysis is also used to capture user sentiment feedback.

[1762] Step 16:

[1763] Server: Sends the generated popularity ranking to the device.

[1764] Step 17:

[1765] Device: Shows users a popularity ranking. For example, "Harry Potter" is displayed at the top of the popularity ranking.

[1766] Step 18:

[1767] Server: Selects relevant advertising data based on the user's interests and preferences. In this process, it uses an emotion engine to analyze user emotions in real time and reflects this information in the selection of ads.

[1768] Step 19:

[1769] Server: Sends the selected advertising data to the terminal.

[1770] Step 20:

[1771] Device: Shows relevant ads and book purchase links to users. For example, ads like "Buy the Dragonlance series now" may be displayed.

[1772] ---

[1773] The above is the specific processing flow for carrying out the invention.

[1774] Example 2

[1775] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1776] Current book summarization and recommendation systems provide uniform results without considering user emotions, making it difficult to improve user experience. Furthermore, there is no way to efficiently display advertisements that match the user's interests and emotions, limiting the effectiveness of advertising. Furthermore, despite using a large amount of data, there is a lack of technology that utilizes user emotions to optimize recommendations and advertisements.

[1777] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving a book name or image as input from a user, a means for generating a summary based on the input data of the book using a generative AI model, a means for recommending related books based on keywords, authors, and genres, and a means for analyzing user emotions by combining an emotion recognition engine and optimizing recommendations and advertisement display. This makes it possible to optimize recommendations and advertisement display based on user emotions.

[1778] "User" refers to the person who operates the system to input information and receive results.

[1779] "Book" refers to any medium provided as reading material, including e-books and printed books.

[1780] "Name" refers to the title or title used to identify a book.

[1781] "Image" refers to the book cover and related visual data.

[1782] A "generative AI model" refers to artificial intelligence technology that uses machine learning to summarize and recommend books.

[1783] A "summary" is a brief summary of the book's contents.

[1784] "Keywords" refer to important words and phrases that indicate the content and characteristics of a book.

[1785] "Author" refers to the person who wrote the book.

[1786] "Genre" refers to a classification category based on the content or theme of a book.

[1787] "Recommendation" refers to the act of suggesting related books, items, etc. to a user.

[1788] An "emotion recognition engine" refers to a system that analyzes a user's emotions and reflects the results in other processes.

[1789] "Advertising" refers to information that encourages users to purchase products or use services.

[1790] "Database" refers to a recording device that systematically stores information and makes it possible to search and use it as needed.

[1791] "Evaluation comments" refer to the impressions and opinions that users give about a book.

[1792] "Scoring" refers to the score or rating a user gives to a book.

[1793] This invention combines an emotion recognition engine with a system that inputs book information (title, cover image, author name, genre, etc.) and provides book summaries and recommendations based on that information, thereby analyzing and reflecting the user's emotions and optimizing recommendations and advertisement display.

[1794] The system operates as follows.

[1795] First, the user uses the device to enter information about the book they want to read. Specifically, they enter the book title "Harry Potter" into the input form on the device, and also enter a cover image, author name, genre, etc. as needed. The entered data is sent to the server as an HTTP request.

[1796] The server analyzes the received data and confirms that the book title is "Harry Potter." It then sends a summary generation request to the generative AI model. Specifically, it sends the generative AI model a prompt message saying, "Please generate a summary for Harry Potter." The generative AI model uses its internal database and algorithms to generate a summary, returning, for example, a summary such as, "Harry Potter is a fantasy novel depicting adventures at the magical school Hogwarts."

[1797] The generated summary is sent to the terminal via the server, where it is analyzed and displayed to the user, allowing the user to quickly grasp the contents of the book.

[1798] Furthermore, the server searches a database based on information such as keywords, authors, and genres entered by the user, and recommends related books. At this time, an emotion recognition engine is used to recognize emotions from the user's input data and past behavior, and the order in which recommended books are displayed is adjusted based on this. For example, if a user enters the keyword "moving," books that match that emotion will be recommended first. The recommendation results are sent to the device in JSON format, and the device displays a list of recommended books to the user. Specifically, "The Lord of the Rings" and "The Chronicles of Narnia" are recommended.

[1799] Users input their evaluation comments and ratings for books they have read. For example, they can enter a comment like "What an engrossing story!" and a five-star rating. This evaluation data is sent from the device to the server. The server stores the received evaluation data in a database and generates a popularity ranking based on this. The evaluation data is also used as emotional feedback by an emotion recognition engine.

[1800] The generated popularity ranking is sent back to the device and displayed to the user. For example, "Harry Potter" appears at the top of the popularity ranking.

[1801] Finally, the server selects advertising data based on the user's interests and preferences, and uses an emotion recognition engine to select advertisements that match the user's emotions. For example, if the user feels "fun," an advertisement for a book that matches that emotion will be selected. The selected advertising data is sent to the device and displayed to the user. For example, an advertisement such as "Buy the Dragonlance series now" will be displayed.

[1802] In this way, the system generates book summaries, recommends related books, stores rating data and generates popularity rankings, and combines an emotion recognition engine with ad display to achieve optimal recommendations and ad display based on the user's emotions. The hardware required is a user device (smartphone, tablet, PC, etc.) and a server, while the software used is a generative AI model and an emotion recognition engine. Examples of prompts for the generative AI model include "Generate a summary of the following book: 'Harry Potter'" and "Recommend books that the user will find inspiring."

[1803] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1804] Step 1:

[1805] The user enters information about the book they want to read.

[1806] Input: Book title, cover image, author name, genre.

[1807] Operation: The user enters book information into the input form on the device and presses the submit button.

[1808] Output: The input data is sent to the server as an HTTP request.

[1809] Step 2:

[1810] The server analyzes the received data and sends a summary generation request to the generative AI model.

[1811] Input: Data entered, such as the title of a book, "Harry Potter."

[1812] How it works: The server parses the incoming data, checks the book title, and then sends a request to the generative AI model with the prompt "Generate a summary of Harry Potter."

[1813] Output: The prompt is sent to the generative AI model.

[1814] Step 3:

[1815] A generative AI model generates a book summary.

[1816] Input: The prompt sent by the server: "Generate a summary of Harry Potter."

[1817] How it works: The generative AI model uses its internal database and algorithms to generate summaries.

[1818] Output: The generated summary (e.g. "Harry Potter is a fantasy novel about adventures at the magical school Hogwarts.") is sent back to the server.

[1819] Step 4:

[1820] The server sends the generated summary to the terminal.

[1821] Input: Summary data returned from the generative AI model.

[1822] How it works: The server parses the abstract data and generates an HTTP response to send back to the user's device.

[1823] Output: Summary data is sent to the terminal.

[1824] Step 5:

[1825] The terminal displays the generated summary to the user.

[1826] Input: Abstract data received from the server.

[1827] Actions: The device parses the summary data and updates the HTML for display in the user interface.

[1828] Output: User can see the summary.

[1829] Step 6:

[1830] The server recommends related books based on the user's keywords, authors, genres, etc.

[1831] Input: Keywords, author, and genre entered by the user.

[1832] How it works: The server searches the database to retrieve relevant books. It then uses an emotion recognition engine to recognize emotions from the user's input data and past behavior, and adjusts the order of recommended books accordingly.

[1833] Output: The recommended book list is sent to the device in JSON format.

[1834] Step 7:

[1835] The device displays a list of recommended books to the user.

[1836] Input: A list of recommended books received from the server.

[1837] How it works: The device parses the recommended books and displays them in a list format in the user interface.

[1838] Output: The user can see the recommended books list.

[1839] Step 8:

[1840] The user enters evaluation comments and ratings.

[1841] Input: Evaluation comments and ratings for books you have read.

[1842] How it works: The user enters a rating (e.g., 5 stars) or a rating comment (e.g., "A captivating story!") into the input form on their device and presses the submit button.

[1843] Output: The rating data is sent to the server as an HTTP request.

[1844] Step 9:

[1845] A server stores the rating data and generates a popularity ranking.

[1846] Input: Rating data received from users.

[1847] How it works: The server analyzes the rating data and stores it in a database. It then performs statistical processing based on the stored rating data to generate popularity rankings. It also uses the data as emotional feedback through an emotion recognition engine.

[1848] Output: Popularity ranking data is generated and sent to the device.

[1849] Step 10:

[1850] The terminal displays the generated popularity ranking to the user.

[1851] Input: Popularity ranking data received from the server.

[1852] Operation: The device analyzes the popularity ranking data and displays it in the user interface.

[1853] Output: User can check the popularity ranking.

[1854] Step 11:

[1855] The server selects relevant advertising data and transmits it to the terminal.

[1856] Input: Data based on user interests and preferences, analysis results of emotion recognition engine.

[1857] How it works: The server selects relevant advertising data based on the user's interests and preferences. It uses an emotion recognition engine to select ads that match the user's emotions.

[1858] Output: The selected advertising data is sent to the device.

[1859] Step 12:

[1860] The terminal displays the advertisement to the user.

[1861] Input: Advertisement data received from the server.

[1862] Operation: The device parses the advertising data and displays the advertisement in the user interface.

[1863] Output: User can see the ad.

[1864] (Application example 2)

[1865] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1866] Conventional book recommendation systems only summarize and recommend books based on static data without considering user emotions, making it difficult to provide optimal recommendations and display advertisements based on individual users' emotions and interests. For this reason, a system that can effectively enhance users' reading experience and purchasing motivation has been sought.

[1867] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a book title or cover image as input from a user, means for generating a summary using a generative AI model based on the input data of the book, means for displaying the summary to the user, and means for optimizing a recommended book list and advertisement display based on the analysis results of the emotion engine. This makes it possible to recommend books and display advertisements that match the user's emotions and interests.

[1868] The "means for receiving a book title or cover image as input from a user" refers to a device or interface that allows a user to input the title or cover image of a book that the user wants to read into the system.

[1869] "Means for generating a summary using a generative AI model based on input data of a book" refers to a method or system that automatically generates a summary of a book using a generative AI model based on input information about the book.

[1870] The "means for displaying the summary to the user" refers to a device or interface for providing the generated book summary to the user.

[1871] "Means for recommending related books based on keywords, authors, and genres" refers to a method or system that suggests related books based on keywords, authors, and genres entered by a user.

[1872] The "means for receiving evaluation comments and ratings as input from the user" refers to a device or interface that allows the user to input comments and ratings for the book that they have read.

[1873] "Means for storing the evaluation comments and ratings in a database and generating a popularity ranking" refers to a method or system for storing evaluation comments and ratings from users in a database and creating a popularity ranking of books based on the stored evaluation comments and ratings.

[1874] The "means for displaying the popularity ranking to the user" refers to a device or interface for providing the generated popularity ranking to the user.

[1875] The "means for selecting advertising data and displaying it to the user" refers to a method or system for selecting appropriate advertising data and displaying it to the user.

[1876] The "means including an emotion engine that recognizes emotions from user input and behavior" refers to algorithms or software that analyzes a user's text input and behavior data and recognizes their emotional state.

[1877] "Means for optimizing the recommended book list and advertisement display based on the analysis results of the emotion engine" refers to a method or system for presenting optimal book recommendations and advertisements to users based on the results of analysis by the emotion engine.

[1878] This invention is a system that uses an emotion engine to generate book summaries, recommendations, and optimize advertisement display based on book information entered by the user. This system receives book titles or cover images as input from the user, processes them with a generative AI model to generate summaries, and also uses the emotion engine to make recommendations and display advertisements that match the user's emotions.

[1879] First, a user enters information about the book they want to read, such as the title, cover image, keywords, author, and genre, through a smartphone application. This information is sent from the device to a server. The server then sends a summary creation request to the generative AI model based on the received book information. For example, a prompt sentence such as "Please provide a summary of Harry Potter" is entered into the generative AI model. The generative AI model then generates a readable summary in a few minutes based on a large database and returns the summary to the server.

[1880] The generated summary is then sent from the server to the terminal and displayed to the user. By looking at this summary, the user can quickly understand the contents of the book. The server also searches a database for related books based on the keywords, author, genre, etc. entered by the user, and generates a list of recommended books. Here, an emotion engine is utilized to recognize emotions from the user's input and behavior. For example, if a user enters a keyword such as "moving story," books that match that emotion will be recommended.

[1881] Furthermore, after users finish reading a book, they can enter their evaluation comments and ratings. For example, they can comment, "This is an engrossing story!" and rate it five stars. This evaluation data is sent to the server and stored in a database. The server then generates a popularity ranking based on this evaluation data and displays it to the user.

[1882] Furthermore, the server selects relevant advertising data based on the user's interests and preferences. At this time, an emotion engine analyzes the user's emotions in real time and reflects them in the advertisement selection. For example, if the user is feeling "fun," an advertisement for a book that matches that emotion will be selected and displayed on the device. For example, an advertisement such as "Buy the Dragon Adventure series now" will be displayed.

[1883] In this invention, the smartphone functions as the user interface, and data input from the device is processed on a backend server. The server is responsible for data processing and storage, while the generative AI model and emotion engine perform advanced algorithmic processing. Specific software used includes React Native (mobile app development) on the front end, Node.js and Express (data processing) on ​​the back end, MongoDB for the database, Microsoft Azure Cognitive Services for emotion analysis, and OpenAI GPT-4 for the generative AI model.

[1884] Specific examples of prompt sentences are as follows:

[1885] "Please provide a summary of Harry Potter. Also, please provide related books that contain inspirational elements."

[1886] In this way, the present invention enables optimal book recommendations and advertisement display based on the user's emotions, providing the user with a highly satisfying reading experience.

[1887] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1888] Step 1:

[1889] Users use the smartphone interface to input information about the book they want to read, such as the title, cover image, keywords, author, and genre. This input data is then sent from the device to the server.

[1890] Step 2:

[1891] The server analyzes the book input data received from the device, formats it as needed, creates a prompt for the generative AI model based on the formatted data, and sends a summary generation request.

[1892] Input: Book title, cover image, keywords, author, genre, and other information

[1893] Data processing: Prompt sentence creation for generative AI models

[1894] Output: Prompt: "Please provide a summary of the book title."

[1895] Step 3:

[1896] The generative AI model generates a summary of the book based on the prompt received from the server, and the generated summary is sent back to the server.

[1897] Input: prompt statement

[1898] Data computation: Applying summary generation algorithms

[1899] Output: Generated book summary

[1900] Step 4:

[1901] The server sends the generated summary to the terminal and displays it to the user.

[1902] Input: Generated book summary

[1903] Data processing: Formatting and sending summary data

[1904] Output: Book summary displayed on terminal

[1905] Step 5:

[1906] The server searches the database for related books based on the keywords, authors, and genres entered by the user, and generates a list of recommended books. It uses an emotion engine to recognize emotions from the user's input and behavior, and optimizes the ranking of recommendations based on these emotions.

[1907] Input: User's keywords, author, and genre information

[1908] Data Computing: Database Search and Emotion Recognition

[1909] Output: Recommended book list

[1910] Step 6:

[1911] The server sends the recommended book list to the terminal and displays it to the user, who can then view the recommended books.

[1912] Input: Recommended book list

[1913] Data processing: Formatting and sending the recommendation list

[1914] Output: Recommended book list displayed on the device

[1915] Step 7:

[1916] Users input evaluation comments and ratings for the books they have read, and the evaluation comments and rating data are sent from the terminal to the server.

[1917] Input: User's evaluation comments, ratings

[1918] Data transmission: Sending evaluation data to the server

[1919] Output: Evaluation comments and scoring data saved on the server

[1920] Step 8:

[1921] The server stores the received evaluation comments and scoring data in a database and generates popularity rankings based on this data.

[1922] Input: Evaluation comments, scoring data

[1923] Data arithmetic: Analysis of evaluation data and generation of rankings

[1924] Output: Popularity ranking

[1925] Step 9:

[1926] The server transmits the generated popularity ranking to the terminal and displays it to the user.

[1927] Input: Popularity Ranking

[1928] Data processing: Formatting and sending ranking data

[1929] Output: Popular rankings displayed on the device

[1930] Step 10:

[1931] The server selects relevant advertising data based on the user's interests and emotions, and uses an emotion engine to analyze the user's real-time emotions and reflect them in the selection of advertisements.

[1932] Input: User sentiment analysis results

[1933] Data calculation: Selection of advertising data

[1934] Output: Highly relevant advertising data

[1935] Step 11:

[1936] The server sends the selected advertisement data to the terminal and displays it to the user. For example, an advertisement saying "Buy the Dragon Adventure series now" is displayed.

[1937] Input: Selected advertising data

[1938] Data processing: Formatting and sending advertising data

[1939] Output: Ad data displayed on the device

[1940] This program is a system that generates book summaries based on book information entered by the user, and uses an emotion engine to optimize recommended books and advertisements according to the user's emotions, thereby providing users with an effective and satisfying service.

[1941] 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.

[1942] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1943] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1944] 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.

[1945] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1946] 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.

[1947] 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).

[1948] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1949] 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."

[1950] 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.

[1951] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1952] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1953] 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.

[1954] 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.

[1955] 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.

[1956] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1957] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1958] 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.

[1959] 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.

[1960] 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.

[1961] 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.

[1962] The following is further disclosed regarding the above embodiment.

[1963] (Claim 1)

[1964] means for receiving a book title or cover image as input from a user;

[1965] A means for generating a summary using a generative AI model based on input data of the book;

[1966] means for displaying said summary to a user;

[1967] A way to recommend related books based on keywords, authors, and genres,

[1968] a means for receiving evaluation comments and ratings as input from a user;

[1969] a means for storing the evaluation comments and scores in a database and generating a popularity ranking;

[1970] means for displaying the popularity ranking to a user;

[1971] means for selecting and displaying advertising data to a user;

[1972] A system including:

[1973] (Claim 2)

[1974] The system according to claim 1, characterized in that the book summary generation means uses a generative AI model.

[1975] (Claim 3)

[1976] 2. The system according to claim 1, wherein the means for recommending related books based on keywords, authors, and genres includes searching a database.

[1977]

[1978] "Example 1"

[1979] (Claim 1)

[1980] means for receiving book information as input from a user;

[1981] A means for generating a summary using a generative AI model based on information about the book;

[1982] means for displaying said summary to a user;

[1983] A means of recommending related books based on keywords, authors, and types;

[1984] means for receiving evaluation comments and ratings as input from users;

[1985] a means for storing the evaluation comments and ratings in a storage device and generating a popularity ranking;

[1986] means for displaying the popularity ranking to a user;

[1987] means for selecting and displaying advertising information to a user;

[1988] a control means for coordinating the means;

[1989] A system including:

[1990] (Claim 2)

[1991] The system of claim 1, characterized in that it generates book summaries using a generative AI model.

[1992] (Claim 3)

[1993] 10. The system of claim 1, wherein the system searches a storage device to recommend related books based on keywords, authors, and genres.

[1994] "Application Example 1"

[1995] (Claim 1)

[1996] means for receiving a book title or cover image as input from a user;

[1997] A means for generating a summary using a generative AI model based on input data of the book;

[1998] means for displaying said summary to a user;

[1999] A way to recommend related books based on keywords, authors, and genres,

[2000] a means for receiving evaluation comments and ratings as input from a user;

[2001] a means for storing the evaluation comments and scores in a database and generating a popularity ranking;

[2002] means for displaying the popularity ranking to a user;

[2003] means for selecting and displaying advertising data to a user;

[2004] A means for identifying a user's gaze point using eye-tracking technology and acquiring book information;

[2005] a means for generating a summary of a book using a generation AI model based on the book information acquired through the gaze tracking and displaying the summary on a smart device;

[2006] A system including:

[2007] (Claim 2)

[2008] The system according to claim 1, characterized in that the book summary generation means uses a generative AI model.

[2009] (Claim 3)

[2010] 2. The system according to claim 1, wherein the means for recommending related books based on keywords, authors, and genres includes searching a database.

[2011] "Example 2: Combining Emotion Engines"

[2012] (Claim 1)

[2013] means for receiving an input from a user, the name or image of a book;

[2014] A means for generating a summary using a generative AI model based on input data of the book;

[2015] means for displaying said summary to a user;

[2016] A means of recommending related books based on keywords, authors, and genres;

[2017] a means for receiving evaluation comments and ratings as input from a user;

[2018] a means for storing the evaluation comments and scores in a database and generating a popularity ranking;

[2019] means for displaying the popularity ranking to a user;

[2020] means for selecting and displaying advertising data to a user;

[2021] By combining an emotion recognition engine, we can analyze user emotions and optimize recommendations and advertisement display.

[2022] A system including:

[2023] (Claim 2)

[2024] The system according to claim 1, characterized in that the book summary generation means uses a generative AI model.

[2025] (Claim 3)

[2026] 2. The system according to claim 1, wherein the means for recommending related books based on keywords, authors, and genres is characterized by searching a database.

[2027] "Application example 2 when combining emotion engines"

[2028] (Claim 1)

[2029] means for receiving a book title or cover image as input from a user;

[2030] A means for generating a summary using a generative AI model based on input data of the book;

[2031] means for displaying said summary to a user;

[2032] A way to recommend related books based on keywords, authors, and genres,

[2033] a means for receiving evaluation comments and ratings as input from a user;

[2034] a means for storing the evaluation comments and scores in a database and generating a popularity ranking;

[2035] means for displaying the popularity ranking to a user;

[2036] means for selecting and displaying advertising data to a user;

[2037] means including an emotion engine for recognizing emotions from user inputs and actions;

[2038] A means for optimizing a recommended book list and advertisement display based on the analysis results of the emotion engine;

[2039] A system including:

[2040] (Claim 2)

[2041] The system according to claim 1, characterized in that the book summary generation means uses a generative AI model.

[2042] (Claim 3)

[2043] 2. The system according to claim 1, wherein the means for recommending related books based on keywords, authors, and genres includes searching a database. [Explanation of symbols]

[2044] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving a book title or cover image as input from a user; A means for generating a summary using a generative AI model based on input data of the book; means for displaying said summary to a user; A way to recommend related books based on keywords, authors, and genres, a means for receiving evaluation comments and ratings as input from a user; a means for storing the evaluation comments and scores in a database and generating a popularity ranking; means for displaying the popularity ranking to a user; means for selecting and displaying advertising data to a user; A system including:

2. 2. The system according to claim 1, wherein the book summary generating means utilizes a generative AI model.

3. 2. The system according to claim 1, wherein the means for recommending related books based on keywords, authors, and genres performs a search from a database.

Images