system

Abstract

Provide a system. [Solution] A means for the user to select a genre of interest; means for collecting book information based on the user's interests; A means for summarizing the collected book information using a natural language processing algorithm; means for providing the summary results to a user; means for improving a recommendation algorithm based on feedback from said users; 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] Modern people often find it difficult to find time to read in their busy daily lives. Furthermore, the sheer number of reading options makes it difficult to find books that fit their interests. As a result, an increasing number of people are giving up on reading due to the lack of time and options. Furthermore, there is a lack of systems that can provide quick and accurate summaries, creating a need for more efficient reading methods. [Means for solving the problem]

[0005] The present invention provides a system including: a means for allowing a user to select a genre of interest; a means for collecting book information based on the user's interests; a means for summarizing the collected book information using a natural language processing algorithm; a means for providing the summary results to the user; and a means for improving a recommendation algorithm based on feedback from the user. Book information is obtained from external sources, and user feedback is stored in a database, which can be used to analyze and improve the performance of the algorithm. This system allows users to efficiently read summaries of a variety of books and develop a broad perspective.

[0006] "User" refers to a person who uses the system.

[0007] "Genre" refers to a category or classification of books or content.

[0008] "Book Information" refers to information related to a particular book, such as title, author, publication year, and abstract.

[0009] A "natural language processing algorithm" refers to a computational method for analyzing text data and understanding its meaning and structure.

[0010] A "summary" is a short text summarizing the contents of the original book.

[0011] "System" refers to a combination of a set of computer programs and hardware for collecting book information and providing summaries according to a user's interests.

[0012] "Feedback" refers to ratings and comments provided by users.

[0013] A "recommendation algorithm" refers to a computational method that suggests relevant books based on a user's interests and past data.

[0014] "Database" refers to a software system for efficiently storing, retrieving, and managing structured data.

[0015] "Performance" refers to how effectively and efficiently a system or algorithm performs a particular task. [Brief explanation of the drawings]

[0016] [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 illustrating 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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention provides a system for enabling users to efficiently obtain summaries of a wide variety of books and cultivate a broad perspective. The system includes a process for recommending book information based on the user's interests and providing summaries of the books.

[0038] User registration and interest collection

[0039] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. After registering, they select the genre of interest (e.g., history, science, fiction, etc.).

[0040] Server: Stores registration information and user interests in a database.

[0041] Book recommendation and summary generation

[0042] Server: Retrieves user interests from the database and collects corresponding book information using external APIs, such as public book information sources like Google® Books API.

[0043] Server: Generates summaries of collected book information using natural language processing algorithms. Specific examples include models such as BERT and GPT-3 (registered trademark).

[0044] Providing a summary

[0045] Terminal: When the user logs back into the system, they will see a customized list of recommended books and their summaries. They can click on summaries that interest them to find out more information.

[0046] Server: Optionally, provide further details about the book the user clicked on (reviews, related books, etc.).

[0047] Feedback and System Improvement

[0048] On the device: Users can provide feedback on summaries and books they have read, rating them on a scale of 1 to 5 stars, and adding comments.

[0049] Server: Stores the feedback in a database and uses the data to analyze and improve the performance of the recommendation algorithm.

[0050] Specific examples

[0051] User A creates a new account and selects "Science" and "History" as his / her genre of interest. This information is stored in the database, and the server then searches for corresponding books using the Google Books API. For example, two books, "The Origin of the Universe" and "The Rise of the Roman Empire," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two books. Based on these summaries, a recommended book list is generated and provided to User A.

[0052] User A clicks on the summary of "The Rise of the Roman Empire" to learn more and gives it a 5-star rating, which is saved in the database and used to refine the recommendation algorithm next time.

[0053] In this way, the system of the present invention can efficiently provide summaries of various books and improve the user's reading experience, allowing the user to use their time effectively and develop a broad perspective.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. They then click the "Register" button to submit this information.

[0057] Step 2:

[0058] Server: Receives and validates the registration information, ensuring that the information entered is in the correct format and, if there are no problems, saves it to the database.

[0059] Step 3:

[0060] On the device: After completing registration, the user will be taken to a screen where they can select the genre they are interested in (e.g., history, science fiction, etc.). After selecting the genre they are interested in, they click the "Save" button.

[0061] Step 4:

[0062] Server: Receives the user's selected genre information and stores it in a database.

[0063] Step 5:

[0064] Server: Every time a user logs in, retrieve the user's interest genres from the database.

[0065] Step 6:

[0066] Server: Collects book information using an external API (e.g., Google Books API) based on the requested genre.

[0067] Step 7:

[0068] Server: Processes the collected book information and obtains detailed information for each book (title, author, publication year, abstract).

[0069] Step 8:

[0070] Server: Summarizes the book details using natural language processing algorithms (e.g., BERT or GPT-3).

[0071] Step 9:

[0072] Server: Based on the summary results, a customized book recommendation list is created for the user.

[0073] Step 10:

[0074] Terminal: When a user logs in, the server generates a list of recommended books and their summaries, and the user selects and clicks on a summary of interest.

[0075] Step 11:

[0076] Server: Provides more information based on the abstract the user clicks on, making additional API calls as needed to gather reviews and related books.

[0077] Step 12:

[0078] On the device: The user enters a rating (e.g., a rating from 1 to 5 stars) and comments for the summary they have read, and clicks the "Submit" button.

[0079] Step 13:

[0080] Server: Receives user feedback and stores it in a database.

[0081] Step 14:

[0082] Server: Analyzes the stored feedback and improves the performance of the recommendation algorithm, specifically incorporating the characteristics of highly rated books into future recommendations.

[0083] The above are the specific processing steps for carrying out the invention. This system allows users to efficiently read summaries of various books and develop a broad perspective.

[0084] Example 1

[0085] 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."

[0086] In today's society, a wide variety of books are published, making it difficult for users to efficiently find books that match their interests. Furthermore, users need to digest a huge amount of information quickly, and book summaries are a useful way to do this. However, manually creating summaries requires a great deal of time and effort, and without an appropriate recommendation system, it is impossible to provide books that match the user's interests. Therefore, a system is needed that allows users to quickly obtain book summaries customized based on their interests and efficiently improve their reading experience.

[0087] 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.

[0088] In this invention, the server includes means for selecting a genre of interest to a user, means for collecting book information based on the user's interests, means for generating summaries of the collected book information using a natural language processing algorithm, means for providing the user with the summary results and the book information, means for improving the recommendation algorithm based on feedback from the user, and means for storing the user's feedback in a database and analyzing and improving the performance of the algorithm using the database. This allows users to efficiently obtain book summaries based on their interests, and the recommendation accuracy is improved by the feedback, thereby providing an effective reading experience.

[0089] "User" refers to a person who uses this system.

[0090] A "genre" is a classification based on the content or category of a book, such as history, science, or fiction.

[0091] "Book information" refers to basic data such as the book's title, author, publication date, and summary.

[0092] "Natural language processing algorithms" refer to programs and methods for analyzing, understanding, and processing text data.

[0093] A "summary" is a short summary that extracts only the important parts from the original book information.

[0094] "Feedback" refers to ratings and comments provided by users to the system, providing data for evaluating and improving system performance.

[0095] A "recommendation algorithm" refers to a computational method for suggesting appropriate books and summaries based on a user's interests and past behavior.

[0096] A "database" refers to a system for systematically managing and storing various types of information.

[0097] "Server" refers to a central computing unit that stores, processes, and serves data.

[0098] "Generative AI models" refer to machine learning models trained to perform text generation and natural language processing, such as GPT-3.

[0099] "External data sources" refer to information sources outside the system that are used to obtain book information. For example, a public API.

[0100] The present invention provides a system for enabling users to efficiently obtain summaries of a wide variety of books and cultivate a broad perspective. This system includes a process for recommending book information based on the user's interests and providing summaries of the books. Specific embodiments of the system are described below.

[0101] User registration and interest collection

[0102] Terminal: The user accesses the system and creates an account by entering their name, email address, and password. After registering, they select the genre of interest (e.g., history, science, fiction, etc.).

[0103] Server: Stores registration information and user interests in a database, which is used later in the book recommendation process.

[0104] Book recommendation and summary generation

[0105] Server: Gets user interests from a database and collects corresponding book information using external data sources (e.g., public API).

[0106] As a concrete example, the Google Books API is used.

[0107] Server: Generate a summary of the collected book information using a generative AI model (e.g., GPT-3). Specific prompt examples are as follows:

[0108] Please provide a book summary: Title: "The Origin of the Universe", Author: "John Smith", Publication Date: "2022", Summary: "Introduces the latest research on the origin of the universe"

[0109] The generated summaries are stored in a database.

[0110] Providing a summary

[0111] Terminal: When the user logs back into the system, they will see a customized list of recommended books and their summaries. They can click on summaries that interest them to find out more information.

[0112] Server: Optionally, provide further details about the book the user clicked on (reviews, related books, etc.).

[0113] Feedback and System Improvement

[0114] On the device: Users can give feedback on summaries and books they have read. Ratings are expressed on a scale of 1 to 5 stars, and comments can be added.

[0115] Server: Stores the feedback in a database and uses the data to analyze and improve the performance of the recommendation algorithm.

[0116] Specific examples

[0117] User A creates a new account and selects "Science" and "History" as his / her genre of interest. This information is stored in the database, and the server then searches for corresponding books using the Google Books API. For example, two books, "The Origin of the Universe" and "The Rise of the Roman Empire," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two books. Based on these summaries, a recommended book list is generated and provided to User A.

[0118] User A clicks on the summary of "The Rise of the Roman Empire" to learn more and gives it a 5-star rating, which is saved in the database and used to refine the recommendation algorithm next time.

[0119] In this way, the system of the present invention can efficiently provide summaries of various books and improve the user's reading experience, allowing the user to use their time effectively and develop a broad perspective.

[0120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0121] Step 1:

[0122] User Registration

[0123] User: A user visits the system's web page or app, enters their name, email address, and password, and clicks "Create Account."

[0124] Input: Name, Email Address, Password

[0125] Output: Registration request

[0126] Server: The server receives the entered user information and stores it in the database. If the save is successful, it sends the user a confirmation message saying "Account creation successful."

[0127] Input: Registration Request

[0128] Output: A confirmation message that the user information will be saved to the database.

[0129] What happens: The server establishes a database connection and saves the user information using an INSERT query.

[0130] Step 2:

[0131] Collecting user interests

[0132] User: After logging in, the user selects their genre of interest (e.g., history, science, fiction, etc.) and clicks "Update Interests."

[0133] Input: Selected genre information

[0134] Output: Interest update request

[0135] Server: The server saves the selected genre information to the database. If the save is successful, the updated information is reflected in the user's profile.

[0136] Input: Interest Update Request

[0137] Output: Updated interest information

[0138] What happens: The server establishes a database connection and stores the user's interests using an UPDATE query.

[0139] Step 3:

[0140] Collecting book information

[0141] Server: Periodically (e.g., once a day), retrieve each user's interest information from the database. Use external data sources (e.g., public APIs) to gather interest-based book information.

[0142] Input: Interest information for each user

[0143] Output: Book information collection request

[0144] What it does: The server sends an HTTP request to an external API, receives book information in JSON format, and parses the data to extract information such as the book title, author, publication date, and summary.

[0145] Step 4:

[0146] Generate a summary

[0147] Server: Generates summaries based on collected book information using natural language processing algorithms (e.g., generative AI models).

[0148] Input: Book information

[0149] Output: Book summary

[0150] What it does: The server inputs the book details into a generative AI model, creates a prompt to generate a summary, sends the prompt to the generative model (e.g., GPT-3), and stores the generated summary in a database.

[0151] Step 5:

[0152] Providing summaries and book information

[0153] Terminal: When the user logs back into the system, they will see their customized list of recommended books and their summaries.

[0154] Input: Login request

[0155] Output: Recommended reading list and summary

[0156] Server: The server processes the login request, retrieves recommended books and summaries based on the user's interests from the database, and sends them to the terminal.

[0157] Input: Request for recommended reading list and summary

[0158] Output: Recommended reading list and summary data

[0159] Specific operation: The server searches the database for recommended books based on the user's interest information and returns them to the terminal in JSON format along with their summaries.

[0160] Step 6:

[0161] Gathering feedback and improving the system

[0162] On the device: Users can provide feedback on summaries and books they have read by rating them on a scale of 1 to 5 stars and adding comments.

[0163] Input: Rating and Comments

[0164] Output: Feedback submission request

[0165] Server: The server stores the feedback in a database and analyzes and improves the performance of the recommendation algorithm based on the collected feedback.

[0166] Input: Rating and comment data

[0167] Output: Saving feedback and improving the algorithm

[0168] What it does: The server stores user feedback in a database using INSERT queries and performs statistical analysis to provide data for tuning the algorithm.

[0169] (Application example 1)

[0170] 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."

[0171] Conventional book recommendation systems can only provide users with one-dimensional information, making it difficult to interactively review summaries in real time. Furthermore, the means for improving system performance through feedback are limited, leaving further improvements needed to improve the accuracy of book recommendations to users. The present invention aims to solve these problems by providing a system that allows users to visually review book summaries in a virtual environment and efficiently provide feedback.

[0172] 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.

[0173] In this invention, the server includes a means for selecting a genre of interest to a user, a means for collecting book information based on the user's interests, a means for summarizing the collected book information using a natural language processing algorithm, a means for providing the summary result to the user, a means for improving the recommendation algorithm based on feedback from the user, a means for visually displaying the book information in a virtual environment, and a means for the user to visually check the summary result using smart glasses, thereby enabling the user to more intuitively grasp book information and provide feedback in real time.

[0174] The "means for selecting a genre of interest to the user" is an interface that allows the user to select a genre category based on their predetermined interests and preferences, and is a procedure by which the system grasps the user's area of ​​interest.

[0175] The "means for collecting book information based on the user's interests" refers to an algorithm and interface for obtaining related book data from the Internet or a database based on the genre selected by the user.

[0176] "Means for summarizing collected book information using natural language processing algorithms" refers to a program that applies natural language processing technology to analyze collected book data, extract key points, and summarize them concisely.

[0177] "Means for providing the summary result to the user" refers to a display screen or notification system for providing the generated book summary to the user in an accessible form.

[0178] The "means for improving the recommendation algorithm based on feedback from the user" is a system for collecting user ratings and comments, analyzing the data, and improving the accuracy of the recommendation algorithm.

[0179] "Means for visually displaying book information in a virtual environment" refers to technology that visually represents collected and summarized book information in a virtual reality or augmented reality environment, allowing users to view it in a virtual space.

[0180] "Means for the user to visually confirm the summary results using smart glasses" refers to a system in which the user wears smart glasses and the summarized book information is displayed superimposed on their field of vision, allowing them to intuitively confirm the information.

[0181] The present invention provides book summaries based on the genres of interest to users, improves system performance through feedback, and provides a system that allows users to intuitively view book information in a virtual environment using smart glasses.

[0182] System Configuration

[0183] 1. User registration and interest collection

[0184] Terminal: Users access the system through a terminal and create an account by entering their name, email address, and password. They then select the genre of interest (e.g., science, history, fiction, etc.).

[0185] Server: Stores registration information and user interests in a database.

[0186] 2. Book recommendation and summary generation

[0187] Server: Retrieves user interests from the database and collects corresponding book information using external sources (e.g., Google Books API).

[0188] Server: Generates summaries of collected book information using natural language processing algorithms (e.g., BERT, GPT-3).

[0189] 3. Virtual display of book information

[0190] Server: Generates data for visually displaying the generated book summary information in a virtual environment.

[0191] 4. Providing a Summary

[0192] Device: The user wears smart glasses and visually checks book information in the virtual bookstore. When the user focuses their gaze on a specific book, a summary is displayed.

[0193] 5. Feedback and System Improvement

[0194] Terminal: Users provide feedback (ratings, comments) on summaries and books they have read.

[0195] Server: Stores the feedback in a database and uses the data to improve the performance of the recommendation algorithm.

[0196] Hardware and software used

[0197] Hardware: Smart glasses, devices (smartphones, tablets, etc.)

[0198] Software: Databases (e.g., SQLite), external APIs (e.g., Google Books API), natural language processing algorithms (BERT, GPT-3)

[0199] Specific examples

[0200] User A creates a new account and selects "Science" and "History" as his or her genre of interest. The server uses the Google Books API to search for corresponding books. For example, two books, "Origin of the Universe" and "Historical Empires," are found, and detailed information about each is collected. BERT and GPT-3 are used to generate summaries of these two books. Based on these summaries, a virtual bookstore is created and provided to User A through smart glasses. User A clicks on the summary for "Historical Empires" to check the details, and provides a five-star rating and comment. This feedback is used to adjust the recommendation algorithm next time.

[0201] Prompt Sentence Examples

[0202] Generate a book summary using a prompt like this:

[0203] Generate a summary based on the given book details, which are:

[0204] Title: "Historical Empires"

[0205] Author:John Doe

[0206] Description: This book details the development and influence of historical empires, covering their political, cultural, and economic impacts, and includes key events and their context.

[0207] This invention is a system that improves the reading experience by enabling users to efficiently obtain book information of interest and intuitively check it in a virtual environment.

[0208] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0209] Specific processing steps of the program

[0210] Step 1: User registration and interest gathering

[0211] Input: The user enters their name, email address, password, and genre of interest.

[0212] Data processing: The terminal sends the entered information to the server, which stores it in a database.

[0213] Output: A user ID is generated and the user's information is registered in the database.

[0214] Specific operation: The terminal displays an input form, the user enters the required information, and the server records the received information in a database.

[0215] Step 2: Gathering book information

[0216] Input: The server retrieves the user's interest data in a database.

[0217] Data processing: The server calls an external API (e.g., Google Books API) to collect book information that matches the user's interests.

[0218] Output: The collected book list is returned to the server.

[0219] Specific operation: The server makes an API call and obtains related book data.

[0220] Step 3: Generate a summary of the book information

[0221] Input: Collected book information is provided to the server.

[0222] Data processing: The server uses a natural language processing algorithm (e.g., GPT-3) to generate a summary. It generates a book summary based on the input.

[0223] Output: The generated summaries are stored in a database.

[0224] Specific operation: The server inputs detailed information about the book into the summary generation model and saves the output summary.

[0225] Step 4: Generate virtual display data for book information

[0226] Input: Abstracted book information is kept on the server.

[0227] Data processing: The server generates display data for the virtual environment, including book cover images and summary text.

[0228] Output: The generated display data is stored and later provided to the smart glasses.

[0229] Specific operation: The server performs image processing and text formatting to prepare the display data.

[0230] Step 5: Provide a summary

[0231] Input: A user puts on smart glasses and accesses a virtual bookstore.

[0232] Data processing: The terminal sends the user's gaze detection data to the server, and the server obtains the corresponding book summary.

[0233] Output: A summary of the selected book is displayed on the user's smart glasses.

[0234] Specific operation: The device detects the user's gaze using an eye-tracking sensor, and the server provides summary data.

[0235] Step 6: Gather feedback and improve the system

[0236] Input: User provides feedback and enters star ratings and comments.

[0237] Data processing: The device sends the feedback information to the server, which stores it in a database. The server then analyzes the feedback data and adjusts the recommendation algorithm.

[0238] Output: New book recommendation results based on the improved algorithm are generated.

[0239] How it works: The device provides a rating form and records user input. The server analyzes the feedback data and retunes the recommendation algorithm.

[0240] Through this series of processing steps, users can efficiently obtain book information of their interest and improve the system's performance through feedback.

[0241] 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.

[0242] The present invention is a system that allows users to efficiently obtain summaries of a wide variety of books and optimizes recommendations through emotion recognition. This system includes a process for recommending book information based on the user's interests and providing summaries of those books. Furthermore, by combining this process with an emotion engine, optimal recommendations for each user are realized.

[0243] User registration and interest collection

[0244] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. After registering, they select the genre of interest (e.g., history, science, fiction, etc.).

[0245] Server: Stores registration information and user interests in a database.

[0246] Book recommendation and summary generation

[0247] Server: Retrieves user interests from the database and collects corresponding book information using external APIs, which use public book information sources.

[0248] Server: Generates summaries of collected book information using natural language processing algorithms.

[0249] Emotion recognition by emotion engine

[0250] On the device: While the user is using the system, the emotion recognition module reads emotions from the user's facial expressions and text input, and this information is sent to the server in real time.

[0251] Server: Stores the emotion data analyzed by the emotion engine.

[0252] Providing summaries and optimizing sentiment-based recommendations

[0253] Terminal: When a user logs in, the server generates a list of recommended books and their summaries, and the user selects and clicks on a summary of interest.

[0254] Server: Analyzes user sentiment data and adjusts recommendation algorithms based on which summaries and books users respond most positively to.

[0255] Server: Recommends books that are more relevant to the user based on their sentiment.

[0256] Feedback and System Improvement

[0257] On the device: The user enters a rating (e.g., a rating from 1 to 5 stars) and comments for the summary or book they have read, and clicks the "Submit" button.

[0258] Server: Stores user feedback and sentiment data in a database to continuously analyze and improve the performance of the recommendation algorithm.

[0259] Specific examples

[0260] User A creates a new account and selects "Science" and "History" as his / her genre of interest. This information is stored in the database, and the server then searches for corresponding books using public book information sources. For example, two books, "The Origin of the Universe" and "The Rise of the Roman Empire," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two books. Based on these summaries, a recommended book list is generated and provided to User A.

[0261] User A clicks on the summary of "The Rise of the Roman Empire" to check the details, and the emotion engine reads User A's facial expressions and reactions to detect positive emotions. This information is stored in the database and used to adjust the recommendation algorithm next time.

[0262] In this way, the system of the present invention can efficiently provide summaries of various books while taking into account the user's emotions, improving the user's reading experience, allowing the user to use their time more efficiently and receive more tailored reading recommendations.

[0263] The processing flow will be explained below.

[0264] Step 1:

[0265] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. They then click the "Register" button to submit this information.

[0266] Step 2:

[0267] Server: Receives and validates the registration information, ensuring that the information entered is in the correct format and, if there are no problems, saves it to the database.

[0268] Step 3:

[0269] On the device: After completing registration, the user will be taken to a screen where they can select the genre they are interested in (e.g., history, science fiction, etc.). After selecting the genre they are interested in, they click the "Save" button.

[0270] Step 4:

[0271] Server: Receives the user's selected genre information and stores it in a database.

[0272] Step 5:

[0273] Server: Every time a user logs in, retrieve the user's interest genres from the database.

[0274] Step 6:

[0275] Server: Collects book information using an external API (e.g., Google Books API) based on the requested genre.

[0276] Step 7:

[0277] Server: Processes the collected book information and obtains detailed information for each book (title, author, publication year, abstract).

[0278] Step 8:

[0279] Server: Summarizes the book details using natural language processing algorithms (e.g., BERT or GPT-3).

[0280] Step 9:

[0281] Server: Based on the summary results, a customized book recommendation list is created for the user.

[0282] Step 10:

[0283] Terminal: When a user logs in, the server generates a list of recommended books and their summaries, and the user selects and clicks on a summary of interest.

[0284] Step 11:

[0285] Server: Provides more information based on the abstract the user clicks on, making additional API calls as needed to gather reviews and related books.

[0286] Step 12:

[0287] Device: While the user is using the system, the emotion recognition module collects the user's facial expressions and tone of voice using a camera and microphone.

[0288] Step 13:

[0289] Server: The emotion recognition module analyzes the user's facial expressions and speech in real time to generate emotion data, which is then stored in a database in real time.

[0290] Step 14:

[0291] Device: The user enters a rating (e.g., a rating from 1 to 5 stars) and comments for the summary or book they have read, and clicks the "Submit" button.

[0292] Step 15:

[0293] Server: Receives user feedback and emotion data collected in real time and stores it in a database.

[0294] Step 16:

[0295] Server: Analyzes the stored emotion data and feedback data to improve the performance of the recommendation algorithm. Extracts the characteristics of books that users have expressed positive emotions about and reflects them in the next recommendation.

[0296] The above are the specific processing steps for implementing the invention. This system allows users to efficiently read summaries of various books and receive optimal recommendations based on real-time emotional data. This system allows users to use their time efficiently and enjoy a more personalized reading experience.

[0297] Example 2

[0298] 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."

[0299] Currently, a large amount of information exists on the Internet, making it difficult for users to efficiently obtain the information they need. In particular, when it comes to book summaries and recommendations, personalized recommendations that reflect the user's interests and emotions are difficult to make. To solve this problem, a system is needed that accurately captures the user's interests and provides appropriate book information.

[0300] 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 selecting a category in which a user is interested, a means for collecting data based on the user's interests, a means for summarizing the collected data using a natural language processing algorithm, a means for analyzing emotion data and adjusting a recommendation algorithm, a means for providing the summary result to the user, and a means for improving the recommendation algorithm based on feedback from the user. This enables the user to efficiently obtain optimal book information based on their own interests and emotions.

[0301] "User Interest Categories" refers to specific themes or genres that interest a user.

[0302] "Data" refers to units of information such as book information, including text, metadata, and summaries.

[0303] "Natural language processing algorithm" refers to a computer program for processing and understanding human language, including specifically algorithms for summarizing and analyzing text.

[0304] "Emotional data" refers to information that represents the user's emotional state obtained from facial expressions and text input.

[0305] A "recommendation algorithm" refers to a calculation method for selecting and providing optimal book information based on a user's interests and emotions.

[0306] "External source" refers to an external resource for obtaining information, such as a database or API that exists outside the system.

[0307] An "emotion recognition module" refers to a component that has the function of reading emotions from a user's facial expressions, voice, text, etc.

[0308] "Database" means a system for storing and managing information in an organized form.

[0309] "Feedback" refers to information provided by users to evaluate the performance of a system, such as ratings and comments.

[0310] The present invention is a system that allows users to efficiently obtain summaries of a wide variety of books and optimizes recommendations through emotion recognition. This system includes a process for recommending book information based on the user's interests and providing summaries of those books. Furthermore, by combining this process with an emotion engine, optimal recommendations for each user are realized.

[0311] User registration and interest collection

[0312] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. After registering, they select the categories of interest (e.g., history, science, fiction, etc.).

[0313] Server: The server stores registration information and user interests in a database (e.g., MySQL or PostgreSQL).

[0314] Collecting book recommendations

[0315] Server: The server obtains the user's interests from the database and collects the corresponding book information using external APIs (e.g., Google Books API or Open Library API).

[0316] Server: Stores collected book information in a database.

[0317] Book summary generation

[0318] Server: The server generates a summary of the collected book information using a natural language processing algorithm (e.g., a generative AI model such as GPT-3).

[0319] Server: Organizes the generated summaries for each user and stores them in a database.

[0320] Emotion recognition by emotion engine

[0321] Device: While the user is using the system, the emotion recognition module reads emotions from the user's facial expressions and text input. For example, it analyzes the user's facial expressions and voice in real time using a camera and microphone.

[0322] Server: Analyzes the data sent from the emotion recognition module in real time and stores the user's emotions in a database.

[0323] Providing summaries and optimizing sentiment-based recommendations

[0324] Terminal: When the user logs in, the server displays a list of recommended books and summaries. The user selects a summary of interest and checks the details.

[0325] Server: While the user is reviewing the summaries, the server analyzes the user's emotions based on emotion recognition data and records which summaries the user responded positively to.

[0326] Server: Based on this data, the next recommendation algorithm is adjusted for each user to recommend more suitable books.

[0327] Feedback and System Improvement

[0328] Terminal: The user enters a rating or comment on the summary or book they have read and clicks the "Submit" button.

[0329] Server: Stores user feedback and sentiment data in a database and uses that information to update algorithms, for example by using mining techniques to reinforce categories with more positive feedback.

[0330] Specific examples

[0331] User A creates a new account and selects "Science" and "History" as his / her interest categories. This information is stored in the database, and the server then searches for corresponding books using an external API. For example, two books, "The Origin of the Universe" and "The Rise of Civilization," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two books. Based on these summaries, a recommended book list is generated and provided to User A.

[0332] User A clicks on the summary of "The Rise of Civilization" to check the details, and the emotion engine reads User A's facial expressions and reactions to detect positive emotions. This information is stored in the database and used to adjust the recommendation algorithm next time.

[0333] Example prompts to input to the generative AI model

[0334] "Develop an outline of a system that generates book summaries based on interest categories and optimizes recommendations according to user sentiment. As a concrete example, consider a case where a user is interested in science and history and is provided with book summaries in those categories."

[0335] This allows users to efficiently obtain optimal book information based on their interests and feelings, enabling them to have a richer reading experience.

[0336] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0337] Step 1:

[0338] User registration and interest collection

[0339] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. After completing registration, the user selects the category of interest (e.g., history, science, fiction, etc.).

[0340] Input: User-entered information (name, email address, password, interest categories)

[0341] Server: Receives the information entered by the user, performs validation, and if validation is successful, stores it in a database (MySQL or PostgreSQL). Stored data includes the user ID, name, email address, password hash, and interest categories.

[0342] Output: The user information is saved in the database and the registration process is complete.

[0343] Step 2:

[0344] Collecting book recommendations

[0345] Server: The server retrieves registered user interest data from the database. Based on the retrieved interest data, it collects book information using external APIs such as the Google Books API and the Open Library API.

[0346] Input: User interest data (category)

[0347] Server: Sends a request to the external API endpoint and receives the relevant book information in JSON format, including the title, author, publication date, summary, etc.

[0348] Output: Book information is collected on the server and stored in a database.

[0349] Step 3:

[0350] Book summary generation

[0351] Server: Processes the collected book information using a natural language processing algorithm and generates a summary. Specifically, it uses a generative AI model such as GPT-3.

[0352] Input: Book information (title, author, publication date, summary, etc.)

[0353] Server: Book information is input as text data into the generative AI model, and a summary text is generated. The generated summary text is then saved back into the database.

[0354] Output: The generated book summaries are stored in a database.

[0355] Step 4:

[0356] Emotion recognition by emotion engine

[0357] Device: While the user is using the system, the camera and microphone capture the user's facial expressions and voice in real time. The emotion recognition module uses this data to analyze the user's emotions.

[0358] Input: User's facial expression data, voice data

[0359] Server: Analyzes the received emotion data in real time and identifies the emotional state. For example, using the Emotion API. The analysis results are stored in a database.

[0360] Output: The user's emotion data is stored in the database.

[0361] Step 5:

[0362] Providing summaries and optimizing sentiment-based recommendations

[0363] Terminal: When a user logs in, the server retrieves the recommended book list and summaries from the database and displays them on the terminal. The user selects the summary of interest and checks the details.

[0364] Input: User login information, emotion data, summary list

[0365] Server: While reviewing the summaries, analyzes the emotion recognition data in real time and records which summaries elicited positive reactions, optimizing the recommendation algorithm for future use.

[0366] Output: Recommendations optimized for the user are made, and the recommendation list is adjusted for future use.

[0367] Step 6:

[0368] Feedback and System Improvement

[0369] Terminal: The user enters a rating or comment on the summary or book they have read and clicks the "Submit" button.

[0370] Input: User ratings, comments, and sentiment data

[0371] Server: Receives user feedback and stores it in a database. Updates the algorithm based on the stored data and performs data mining to improve recommendation accuracy.

[0372] Output: The feedback is stored in a database and the algorithm is optimized.

[0373] (Application example 2)

[0374] 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."

[0375] Conventional book recommendation systems have difficulty fully reflecting users' interests and preferences, preventing them from making effective use of their time. Furthermore, they are unable to fully utilize feedback from user interactions, making it difficult to improve the accuracy of recommendation algorithms. Furthermore, they are unable to take into account the user's emotional state, resulting in incomplete personalization. A system that can solve these problems and improve users' reading experience is needed.

[0376] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0377] In this invention, the server includes a means for collecting information based on the user's interests, a means for summarizing the collected information using a natural language processing algorithm, and a means for reading the user's emotional data and optimizing the recommendation algorithm, thereby making it possible to provide information based on the user's interests and to optimize the recommendation algorithm in a way that reflects the user's emotions.

[0378] "User" refers to an individual or organization that uses the system.

[0379] "Genre" refers to a theme or category classified based on the user's interests and concerns.

[0380] "Information" refers to data and metadata about content such as books and documents.

[0381] "Natural language processing algorithms" refer to technical methods used to summarize and analyze collected information.

[0382] A "summary" is a concise representation of the main points or content of the collected information.

[0383] "Providing" refers to displaying the generated summary and recommendation results to the user.

[0384] "Emotion data" refers to data related to emotions obtained by analyzing a user's facial expressions and text input.

[0385] A "recommendation algorithm" refers to a program that selects and recommends the most appropriate information based on the user's interests and emotions.

[0386] "Optimization" refers to using user feedback and sentiment data to adjust recommendation algorithms to improve their performance.

[0387] "Feedback" refers to reactions such as ratings and comments provided by users.

[0388] A "database" refers to a system for storing collected information, user feedback, emotional data, etc.

[0389] MODE FOR CARRYING OUT THE INVENTION

[0390] This invention is a system that allows users to efficiently obtain summaries of a wide variety of information and optimizes recommendations through emotion recognition. This system includes a process for recommending information based on the user's interests and providing summaries of that information. Furthermore, by combining this process with an emotion engine, it is possible to realize optimal recommendations for each user.

[0391] User registration and interest collection

[0392] The server provides a means for users to access the system and create an account by entering their name, email address, and password. After registering, users also have the ability to select their genre of interest (e.g., history, science fiction, etc.). This information is stored in a database.

[0393] Information recommendation and summary generation

[0394] The server has means for obtaining user interests from the database and collecting corresponding information using external APIs, which use public information sources, and means for generating summaries of the collected information using natural language processing algorithms.

[0395] Emotion recognition by emotion engine

[0396] The terminal includes a means for an emotion recognition module to read emotions from the user's facial expressions and text inputs while the user is using the system, and this information is transmitted in real time to a server, which then stores the analyzed emotion data.

[0397] Providing summaries and optimizing sentiment-based recommendations

[0398] The terminal provides a means to display the recommended information list and summaries generated by the server when the user logs in. The user can view the summaries of interest by selecting them and clicking on them. The server has a means to analyze the user's emotional data and adjust the recommendation algorithm based on which summaries and information the user responded positively to.

[0399] Feedback and System Improvement

[0400] The terminal is equipped with a means for the user to input a rating (e.g., a rating from 1 to 5 stars) or comment on the summary or information they have read and click the "Submit" button. The server then stores the user's feedback and emotional data in a database and continuously analyzes and improves the performance of the recommendation algorithm.

[0401] Hardware and software used

[0402] The hardware used to implement this invention is primarily a smartphone (with iOS or ANDROID (registered trademark)), and the main software used is Python, the emotion_recognition library, the spacy library, and a custom recommender system (RecommenderSystem).

[0403] Specific examples

[0404] For example, if a user creates a new account and selects "science" and "history" as their interest areas, this information is stored in the database. The server then searches for corresponding information using public information sources. For example, two pieces of information, "The Origin of the Universe" and "The Rise of the Roman Empire," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two pieces of information, and a list of recommended information is generated and served to the user based on the summaries.

[0405] When a user clicks on the summary of "The Rise of the Roman Empire" to learn more, the emotion engine reads the user's facial expressions and reactions to detect positive emotions. This information is stored in the database and used to adjust the recommendation algorithm for the next time.

[0406] Prompt Sentence Examples

[0407] If a user is interested in science or history and reads the summary of the information "The Rise of the Roman Empire" and responds with "This is fascinating!", please tell me how to build a system that recognizes this response as positive and reflects it in future recommendations.

[0408] In this way, the system of the present invention can efficiently provide a summary of various information while taking into account the user's emotions, improving the user experience, allowing the user to use their time more efficiently and receive more tailored information recommendations.

[0409] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0410] Step 1:

[0411] User registration and interest collection

[0412] Users create an account by entering their name, email address, and password on their device. Then, they select the genre they are interested in (e.g., history, science fiction, etc.). The input data (name, email address, password, genre) is sent to the server and stored in a database.

[0413] Step 2:

[0414] Information recommendation and summary generation

[0415] The server retrieves the user's interests from a database and collects corresponding information using an external API. Specifically, it retrieves information related to the user's interests (input data: genre of interest) from a public information source (e.g., a book database), and then generates a summary of the information using a natural language processing algorithm. The generated summary is stored as temporary data and later provided to the user.

[0416] Step 3:

[0417] Emotion recognition by emotion engine

[0418] The user views the summary of information on the device. At this time, the device uses an emotion recognition module to acquire emotional data in real time from the user's facial expressions and text input (input data: facial expressions, comments). The acquired emotional data is sent to the server and analyzed by the emotion analysis engine. The analysis results are stored in a database.

[0419] Step 4:

[0420] Providing summaries and optimizing sentiment-based recommendations

[0421] When a user logs in, the server displays the generated recommendation information list and summaries on the terminal. When the user selects a summary of interest, they can check its details. Emotional data (input data: browsing history, emotional data) obtained while the user is viewing the summary is sent to the server, and the recommendation algorithm analyzes the data and optimizes future recommendations.

[0422] Step 5:

[0423] Feedback and System Improvement

[0424] Users input ratings (e.g., ratings from 1 to 5 stars) and comments (input data: ratings, comments) on the summaries and information they have read from their devices. This feedback is sent to the server and stored in a database. The server continuously analyzes and improves the performance of the recommendation algorithm based on the feedback data and sentiment data.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] [Second embodiment]

[0429] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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."

[0441] The present invention provides a system for enabling users to efficiently obtain summaries of a wide variety of books and cultivate a broad perspective. The system includes a process for recommending book information based on the user's interests and providing summaries of the books.

[0442] User registration and interest collection

[0443] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. After registering, they select the genre of interest (e.g., history, science, fiction, etc.).

[0444] Server: Stores registration information and user interests in a database.

[0445] Book recommendation and summary generation

[0446] Server: Retrieves user interests from the database and collects corresponding book information using external APIs, such as public book information sources like Google Books API.

[0447] Server: Generates summaries of collected book information using natural language processing algorithms, such as BERT and GPT-3.

[0448] Providing a summary

[0449] Terminal: When the user logs back into the system, they will see a customized list of recommended books and their summaries. They can click on summaries that interest them to find out more information.

[0450] Server: Optionally, provide further details about the book the user clicked on (reviews, related books, etc.).

[0451] Feedback and System Improvement

[0452] On the device: Users can provide feedback on summaries and books they have read, rating them on a scale of 1 to 5 stars, and adding comments.

[0453] Server: Stores the feedback in a database and uses the data to analyze and improve the performance of the recommendation algorithm.

[0454] Specific examples

[0455] User A creates a new account and selects "Science" and "History" as his / her genre of interest. This information is stored in the database, and the server then searches for corresponding books using the Google Books API. For example, two books, "The Origin of the Universe" and "The Rise of the Roman Empire," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two books. Based on these summaries, a recommended book list is generated and provided to User A.

[0456] User A clicks on the summary of "The Rise of the Roman Empire" to learn more and gives it a 5-star rating, which is saved in the database and used to refine the recommendation algorithm next time.

[0457] In this way, the system of the present invention can efficiently provide summaries of various books and improve the user's reading experience, allowing the user to use their time effectively and develop a broad perspective.

[0458] The processing flow will be explained below.

[0459] Step 1:

[0460] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. They then click the "Register" button to submit this information.

[0461] Step 2:

[0462] Server: Receives and validates the registration information, ensuring that the information entered is in the correct format and, if there are no problems, saves it to the database.

[0463] Step 3:

[0464] On the device: After completing registration, the user will be taken to a screen where they can select the genre they are interested in (e.g., history, science fiction, etc.). After selecting the genre they are interested in, they click the "Save" button.

[0465] Step 4:

[0466] Server: Receives the user's selected genre information and stores it in a database.

[0467] Step 5:

[0468] Server: Every time a user logs in, retrieve the user's interest genres from the database.

[0469] Step 6:

[0470] Server: Collects book information using an external API (e.g., Google Books API) based on the requested genre.

[0471] Step 7:

[0472] Server: Processes the collected book information and obtains detailed information for each book (title, author, publication year, abstract).

[0473] Step 8:

[0474] Server: Summarizes the book details using natural language processing algorithms (e.g., BERT or GPT-3).

[0475] Step 9:

[0476] Server: Based on the summary results, a customized book recommendation list is created for the user.

[0477] Step 10:

[0478] Terminal: When a user logs in, the server generates a list of recommended books and their summaries, and the user selects and clicks on a summary of interest.

[0479] Step 11:

[0480] Server: Provides more information based on the abstract the user clicks on, making additional API calls as needed to gather reviews and related books.

[0481] Step 12:

[0482] On the device: The user enters a rating (e.g., a rating from 1 to 5 stars) and comments for the summary they have read, and clicks the "Submit" button.

[0483] Step 13:

[0484] Server: Receives user feedback and stores it in a database.

[0485] Step 14:

[0486] Server: Analyzes the stored feedback and improves the performance of the recommendation algorithm, specifically incorporating the characteristics of highly rated books into future recommendations.

[0487] The above are the specific processing steps for carrying out the invention. This system allows users to efficiently read summaries of various books and develop a broad perspective.

[0488] Example 1

[0489] 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."

[0490] In today's society, a wide variety of books are published, making it difficult for users to efficiently find books that match their interests. Furthermore, users need to digest a huge amount of information quickly, and book summaries are a useful way to do this. However, manually creating summaries requires a great deal of time and effort, and without an appropriate recommendation system, it is impossible to provide books that match the user's interests. Therefore, a system is needed that allows users to quickly obtain book summaries customized based on their interests and efficiently improve their reading experience.

[0491] 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.

[0492] In this invention, the server includes means for selecting a genre of interest to a user, means for collecting book information based on the user's interests, means for generating summaries of the collected book information using a natural language processing algorithm, means for providing the user with the summary results and the book information, means for improving the recommendation algorithm based on feedback from the user, and means for storing the user's feedback in a database and analyzing and improving the performance of the algorithm using the database. This allows users to efficiently obtain book summaries based on their interests, and the recommendation accuracy is improved by the feedback, thereby providing an effective reading experience.

[0493] "User" refers to a person who uses this system.

[0494] A "genre" is a classification based on the content or category of a book, such as history, science, or fiction.

[0495] "Book information" refers to basic data such as the book's title, author, publication date, and summary.

[0496] "Natural language processing algorithms" refer to programs and methods for analyzing, understanding, and processing text data.

[0497] A "summary" is a short summary that extracts only the important parts from the original book information.

[0498] "Feedback" refers to ratings and comments provided by users to the system, providing data for evaluating and improving system performance.

[0499] A "recommendation algorithm" refers to a computational method for suggesting appropriate books and summaries based on a user's interests and past behavior.

[0500] A "database" refers to a system for systematically managing and storing various types of information.

[0501] "Server" refers to a central computing unit that stores, processes, and serves data.

[0502] "Generative AI models" refer to machine learning models trained to perform text generation and natural language processing, such as GPT-3.

[0503] "External data sources" refer to information sources outside the system that are used to obtain book information. For example, a public API.

[0504] The present invention provides a system for enabling users to efficiently obtain summaries of a wide variety of books and cultivate a broad perspective. This system includes a process for recommending book information based on the user's interests and providing summaries of the books. Specific embodiments of the system are described below.

[0505] User registration and interest collection

[0506] Terminal: The user accesses the system and creates an account by entering their name, email address, and password. After registering, they select the genre of interest (e.g., history, science, fiction, etc.).

[0507] Server: Stores registration information and user interests in a database, which is used later in the book recommendation process.

[0508] Book recommendation and summary generation

[0509] Server: Gets user interests from a database and collects corresponding book information using external data sources (e.g., public API).

[0510] As a concrete example, the Google Books API is used.

[0511] Server: Generate a summary of the collected book information using a generative AI model (e.g., GPT-3). Specific prompt examples are as follows:

[0512] Please provide a book summary: Title: "The Origin of the Universe", Author: "John Smith", Publication Date: "2022", Summary: "Introduces the latest research on the origin of the universe"

[0513] The generated summaries are stored in a database.

[0514] Providing a summary

[0515] Terminal: When the user logs back into the system, they will see a customized list of recommended books and their summaries. They can click on summaries that interest them to find out more information.

[0516] Server: Optionally, provide further details about the book the user clicked on (reviews, related books, etc.).

[0517] Feedback and System Improvement

[0518] On the device: Users can give feedback on summaries and books they have read. Ratings are expressed on a scale of 1 to 5 stars, and comments can be added.

[0519] Server: Stores the feedback in a database and uses the data to analyze and improve the performance of the recommendation algorithm.

[0520] Specific examples

[0521] User A creates a new account and selects "Science" and "History" as his / her genre of interest. This information is stored in the database, and the server then searches for corresponding books using the Google Books API. For example, two books, "The Origin of the Universe" and "The Rise of the Roman Empire," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two books. Based on these summaries, a recommended book list is generated and provided to User A.

[0522] User A clicks on the summary of "The Rise of the Roman Empire" to learn more and gives it a 5-star rating, which is saved in the database and used to refine the recommendation algorithm next time.

[0523] In this way, the system of the present invention can efficiently provide summaries of various books and improve the user's reading experience, allowing the user to use their time effectively and develop a broad perspective.

[0524] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0525] Step 1:

[0526] User Registration

[0527] User: A user visits the system's web page or app, enters their name, email address, and password, and clicks "Create Account."

[0528] Input: Name, Email Address, Password

[0529] Output: Registration request

[0530] Server: The server receives the entered user information and stores it in the database. If the save is successful, it sends the user a confirmation message saying "Account creation successful."

[0531] Input: Registration Request

[0532] Output: A confirmation message that the user information will be saved to the database.

[0533] What happens: The server establishes a database connection and saves the user information using an INSERT query.

[0534] Step 2:

[0535] Collecting user interests

[0536] User: After logging in, the user selects their genre of interest (e.g., history, science, fiction, etc.) and clicks "Update Interests."

[0537] Input: Selected genre information

[0538] Output: Interest update request

[0539] Server: The server saves the selected genre information to the database. If the save is successful, the updated information is reflected in the user's profile.

[0540] Input: Interest Update Request

[0541] Output: Updated interest information

[0542] What happens: The server establishes a database connection and stores the user's interests using an UPDATE query.

[0543] Step 3:

[0544] Collecting book information

[0545] Server: Periodically (e.g., once a day), retrieve each user's interest information from the database. Use external data sources (e.g., public APIs) to gather interest-based book information.

[0546] Input: Interest information for each user

[0547] Output: Book information collection request

[0548] What it does: The server sends an HTTP request to an external API, receives book information in JSON format, and parses the data to extract information such as the book title, author, publication date, and summary.

[0549] Step 4:

[0550] Generate a summary

[0551] Server: Generates summaries based on collected book information using natural language processing algorithms (e.g., generative AI models).

[0552] Input: Book information

[0553] Output: Book summary

[0554] What it does: The server inputs the book details into a generative AI model, creates a prompt to generate a summary, sends the prompt to the generative model (e.g., GPT-3), and stores the generated summary in a database.

[0555] Step 5:

[0556] Providing summaries and book information

[0557] Terminal: When the user logs back into the system, they will see their customized list of recommended books and their summaries.

[0558] Input: Login request

[0559] Output: Recommended reading list and summary

[0560] Server: The server processes the login request, retrieves recommended books and summaries based on the user's interests from the database, and sends them to the terminal.

[0561] Input: Request for recommended reading list and summary

[0562] Output: Recommended reading list and summary data

[0563] Specific operation: The server searches the database for recommended books based on the user's interest information and returns them to the terminal in JSON format along with their summaries.

[0564] Step 6:

[0565] Gathering feedback and improving the system

[0566] On the device: Users can provide feedback on summaries and books they have read by rating them on a scale of 1 to 5 stars and adding comments.

[0567] Input: Rating and Comments

[0568] Output: Feedback submission request

[0569] Server: The server stores the feedback in a database and analyzes and improves the performance of the recommendation algorithm based on the collected feedback.

[0570] Input: Rating and comment data

[0571] Output: Saving feedback and improving the algorithm

[0572] What it does: The server stores user feedback in a database using INSERT queries and performs statistical analysis to provide data for tuning the algorithm.

[0573] (Application example 1)

[0574] 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."

[0575] Conventional book recommendation systems can only provide users with one-dimensional information, making it difficult to interactively review summaries in real time. Furthermore, the means for improving system performance through feedback are limited, leaving further improvements needed to improve the accuracy of book recommendations to users. The present invention aims to solve these problems by providing a system that allows users to visually review book summaries in a virtual environment and efficiently provide feedback.

[0576] 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.

[0577] In this invention, the server includes a means for selecting a genre of interest to a user, a means for collecting book information based on the user's interests, a means for summarizing the collected book information using a natural language processing algorithm, a means for providing the summary result to the user, a means for improving the recommendation algorithm based on feedback from the user, a means for visually displaying the book information in a virtual environment, and a means for the user to visually check the summary result using smart glasses, thereby enabling the user to more intuitively grasp book information and provide feedback in real time.

[0578] The "means for selecting a genre of interest to the user" is an interface that allows the user to select a genre category based on their predetermined interests and preferences, and is a procedure by which the system grasps the user's area of ​​interest.

[0579] The "means for collecting book information based on the user's interests" refers to an algorithm and interface for obtaining related book data from the Internet or a database based on the genre selected by the user.

[0580] "Means for summarizing collected book information using natural language processing algorithms" refers to a program that applies natural language processing technology to analyze collected book data, extract key points, and summarize them concisely.

[0581] "Means for providing the summary result to the user" refers to a display screen or notification system for providing the generated book summary to the user in an accessible form.

[0582] The "means for improving the recommendation algorithm based on feedback from the user" is a system for collecting user ratings and comments, analyzing the data, and improving the accuracy of the recommendation algorithm.

[0583] "Means for visually displaying book information in a virtual environment" refers to technology that visually represents collected and summarized book information in a virtual reality or augmented reality environment, allowing users to view it in a virtual space.

[0584] "Means for the user to visually confirm the summary results using smart glasses" refers to a system in which the user wears smart glasses and the summarized book information is displayed superimposed on their field of vision, allowing them to intuitively confirm the information.

[0585] The present invention provides book summaries based on the genres of interest to users, improves system performance through feedback, and provides a system that allows users to intuitively view book information in a virtual environment using smart glasses.

[0586] System Configuration

[0587] 1. User registration and interest collection

[0588] Terminal: Users access the system through a terminal and create an account by entering their name, email address, and password. They then select the genre of interest (e.g., science, history, fiction, etc.).

[0589] Server: Stores registration information and user interests in a database.

[0590] 2. Book recommendation and summary generation

[0591] Server: Retrieves user interests from the database and collects corresponding book information using external sources (e.g., Google Books API).

[0592] Server: Generates summaries of collected book information using natural language processing algorithms (e.g., BERT, GPT-3).

[0593] 3. Virtual display of book information

[0594] Server: Generates data for visually displaying the generated book summary information in a virtual environment.

[0595] 4. Providing a Summary

[0596] Device: The user wears smart glasses and visually checks book information in the virtual bookstore. When the user focuses their gaze on a specific book, a summary is displayed.

[0597] 5. Feedback and System Improvement

[0598] Terminal: Users provide feedback (ratings, comments) on summaries and books they have read.

[0599] Server: Stores the feedback in a database and uses the data to improve the performance of the recommendation algorithm.

[0600] Hardware and software used

[0601] Hardware: Smart glasses, devices (smartphones, tablets, etc.)

[0602] Software: Databases (e.g., SQLite), external APIs (e.g., Google Books API), natural language processing algorithms (BERT, GPT-3)

[0603] Specific examples

[0604] User A creates a new account and selects "Science" and "History" as his or her genre of interest. The server uses the Google Books API to search for corresponding books. For example, two books, "Origin of the Universe" and "Historical Empires," are found, and detailed information about each is collected. BERT and GPT-3 are used to generate summaries of these two books. Based on these summaries, a virtual bookstore is created and provided to User A through smart glasses. User A clicks on the summary for "Historical Empires" to check the details, and provides a five-star rating and comment. This feedback is used to adjust the recommendation algorithm next time.

[0605] Prompt Sentence Examples

[0606] Generate a book summary using a prompt like this:

[0607] Generate a summary based on the given book details, which are:

[0608] Title: "Historical Empires"

[0609] Author:John Doe

[0610] Description: This book details the development and influence of historical empires, covering their political, cultural, and economic impacts, and includes key events and their context.

[0611] This invention is a system that improves the reading experience by enabling users to efficiently obtain book information of interest and intuitively check it in a virtual environment.

[0612] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0613] Specific processing steps of the program

[0614] Step 1: User registration and interest gathering

[0615] Input: The user enters their name, email address, password, and genre of interest.

[0616] Data processing: The terminal sends the entered information to the server, which stores it in a database.

[0617] Output: A user ID is generated and the user's information is registered in the database.

[0618] Specific operation: The terminal displays an input form, the user enters the required information, and the server records the received information in a database.

[0619] Step 2: Gathering book information

[0620] Input: The server retrieves the user's interest data in a database.

[0621] Data processing: The server calls an external API (e.g., Google Books API) to collect book information that matches the user's interests.

[0622] Output: The collected book list is returned to the server.

[0623] Specific operation: The server makes an API call and obtains related book data.

[0624] Step 3: Generate a summary of the book information

[0625] Input: Collected book information is provided to the server.

[0626] Data processing: The server uses a natural language processing algorithm (e.g., GPT-3) to generate a summary. It generates a book summary based on the input.

[0627] Output: The generated summaries are stored in a database.

[0628] Specific operation: The server inputs detailed information about the book into the summary generation model and saves the output summary.

[0629] Step 4: Generate virtual display data for book information

[0630] Input: Abstracted book information is kept on the server.

[0631] Data processing: The server generates display data for the virtual environment, including book cover images and summary text.

[0632] Output: The generated display data is stored and later provided to the smart glasses.

[0633] Specific operation: The server performs image processing and text formatting to prepare the display data.

[0634] Step 5: Provide a summary

[0635] Input: A user puts on smart glasses and accesses a virtual bookstore.

[0636] Data processing: The terminal sends the user's gaze detection data to the server, and the server obtains the corresponding book summary.

[0637] Output: A summary of the selected book is displayed on the user's smart glasses.

[0638] Specific operation: The device detects the user's gaze using an eye-tracking sensor, and the server provides summary data.

[0639] Step 6: Gather feedback and improve the system

[0640] Input: User provides feedback and enters star ratings and comments.

[0641] Data processing: The device sends the feedback information to the server, which stores it in a database. The server then analyzes the feedback data and adjusts the recommendation algorithm.

[0642] Output: New book recommendation results based on the improved algorithm are generated.

[0643] How it works: The device provides a rating form and records user input. The server analyzes the feedback data and retunes the recommendation algorithm.

[0644] Through this series of processing steps, users can efficiently obtain book information of their interest and improve the system's performance through feedback.

[0645] 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.

[0646] The present invention is a system that allows users to efficiently obtain summaries of a wide variety of books and optimizes recommendations through emotion recognition. This system includes a process for recommending book information based on the user's interests and providing summaries of those books. Furthermore, by combining this process with an emotion engine, optimal recommendations for each user are realized.

[0647] User registration and interest collection

[0648] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. After registering, they select the genre of interest (e.g., history, science, fiction, etc.).

[0649] Server: Stores registration information and user interests in a database.

[0650] Book recommendation and summary generation

[0651] Server: Retrieves user interests from the database and collects corresponding book information using external APIs, which use public book information sources.

[0652] Server: Generates summaries of collected book information using natural language processing algorithms.

[0653] Emotion recognition by emotion engine

[0654] On the device: While the user is using the system, the emotion recognition module reads emotions from the user's facial expressions and text input, and this information is sent to the server in real time.

[0655] Server: Stores the emotion data analyzed by the emotion engine.

[0656] Providing summaries and optimizing sentiment-based recommendations

[0657] Terminal: When a user logs in, the server generates a list of recommended books and their summaries, and the user selects and clicks on a summary of interest.

[0658] Server: Analyzes user sentiment data and adjusts recommendation algorithms based on which summaries and books users respond most positively to.

[0659] Server: Recommends books that are more relevant to the user based on their sentiment.

[0660] Feedback and System Improvement

[0661] On the device: The user enters a rating (e.g., a rating from 1 to 5 stars) and comments for the summary or book they have read, and clicks the "Submit" button.

[0662] Server: Stores user feedback and sentiment data in a database to continuously analyze and improve the performance of the recommendation algorithm.

[0663] Specific examples

[0664] User A creates a new account and selects "Science" and "History" as his / her genre of interest. This information is stored in the database, and the server then searches for corresponding books using public book information sources. For example, two books, "The Origin of the Universe" and "The Rise of the Roman Empire," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two books. Based on these summaries, a recommended book list is generated and provided to User A.

[0665] User A clicks on the summary of "The Rise of the Roman Empire" to check the details, and the emotion engine reads User A's facial expressions and reactions to detect positive emotions. This information is stored in the database and used to adjust the recommendation algorithm next time.

[0666] In this way, the system of the present invention can efficiently provide summaries of various books while taking into account the user's emotions, improving the user's reading experience, allowing the user to use their time more efficiently and receive more tailored reading recommendations.

[0667] The processing flow will be explained below.

[0668] Step 1:

[0669] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. They then click the "Register" button to submit this information.

[0670] Step 2:

[0671] Server: Receives and validates the registration information, ensuring that the information entered is in the correct format and, if there are no problems, saves it to the database.

[0672] Step 3:

[0673] On the device: After completing registration, the user will be taken to a screen where they can select the genre they are interested in (e.g., history, science fiction, etc.). After selecting the genre they are interested in, they click the "Save" button.

[0674] Step 4:

[0675] Server: Receives the user's selected genre information and stores it in a database.

[0676] Step 5:

[0677] Server: Every time a user logs in, retrieve the user's interest genres from the database.

[0678] Step 6:

[0679] Server: Collects book information using an external API (e.g., Google Books API) based on the requested genre.

[0680] Step 7:

[0681] Server: Processes the collected book information and obtains detailed information for each book (title, author, publication year, abstract).

[0682] Step 8:

[0683] Server: Summarizes the book details using natural language processing algorithms (e.g., BERT or GPT-3).

[0684] Step 9:

[0685] Server: Based on the summary results, a customized book recommendation list is created for the user.

[0686] Step 10:

[0687] Terminal: When a user logs in, the server generates a list of recommended books and their summaries, and the user selects and clicks on a summary of interest.

[0688] Step 11:

[0689] Server: Provides more information based on the abstract the user clicks on, making additional API calls as needed to gather reviews and related books.

[0690] Step 12:

[0691] Device: While the user is using the system, the emotion recognition module collects the user's facial expressions and tone of voice using a camera and microphone.

[0692] Step 13:

[0693] Server: The emotion recognition module analyzes the user's facial expressions and speech in real time to generate emotion data, which is then stored in a database in real time.

[0694] Step 14:

[0695] Device: The user enters a rating (e.g., a rating from 1 to 5 stars) and comments for the summary or book they have read, and clicks the "Submit" button.

[0696] Step 15:

[0697] Server: Receives user feedback and emotion data collected in real time and stores it in a database.

[0698] Step 16:

[0699] Server: Analyzes the stored emotion data and feedback data to improve the performance of the recommendation algorithm. Extracts the characteristics of books that users have expressed positive emotions about and reflects them in the next recommendation.

[0700] The above are the specific processing steps for implementing the invention. This system allows users to efficiently read summaries of various books and receive optimal recommendations based on real-time emotional data. This system allows users to use their time efficiently and enjoy a more personalized reading experience.

[0701] Example 2

[0702] 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."

[0703] Currently, a large amount of information exists on the Internet, making it difficult for users to efficiently obtain the information they need. In particular, when it comes to book summaries and recommendations, personalized recommendations that reflect the user's interests and emotions are difficult to make. To solve this problem, a system is needed that accurately captures the user's interests and provides appropriate book information.

[0704] 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 selecting a category in which a user is interested, a means for collecting data based on the user's interests, a means for summarizing the collected data using a natural language processing algorithm, a means for analyzing emotion data and adjusting a recommendation algorithm, a means for providing the summary result to the user, and a means for improving the recommendation algorithm based on feedback from the user. This enables the user to efficiently obtain optimal book information based on their own interests and emotions.

[0705] "User Interest Categories" refers to specific themes or genres that interest a user.

[0706] "Data" refers to units of information such as book information, including text, metadata, and summaries.

[0707] "Natural language processing algorithm" refers to a computer program for processing and understanding human language, including specifically algorithms for summarizing and analyzing text.

[0708] "Emotional data" refers to information that represents the user's emotional state obtained from facial expressions and text input.

[0709] A "recommendation algorithm" refers to a calculation method for selecting and providing optimal book information based on a user's interests and emotions.

[0710] "External source" refers to an external resource for obtaining information, such as a database or API that exists outside the system.

[0711] An "emotion recognition module" refers to a component that has the function of reading emotions from a user's facial expressions, voice, text, etc.

[0712] "Database" means a system for storing and managing information in an organized form.

[0713] "Feedback" refers to information provided by users to evaluate the performance of a system, such as ratings and comments.

[0714] The present invention is a system that allows users to efficiently obtain summaries of a wide variety of books and optimizes recommendations through emotion recognition. This system includes a process for recommending book information based on the user's interests and providing summaries of those books. Furthermore, by combining this process with an emotion engine, optimal recommendations for each user are realized.

[0715] User registration and interest collection

[0716] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. After registering, they select the categories of interest (e.g., history, science, fiction, etc.).

[0717] Server: The server stores registration information and user interests in a database (e.g., MySQL or PostgreSQL).

[0718] Collecting book recommendations

[0719] Server: The server obtains the user's interests from the database and collects the corresponding book information using external APIs (e.g., Google Books API or Open Library API).

[0720] Server: Stores collected book information in a database.

[0721] Book summary generation

[0722] Server: The server generates a summary of the collected book information using a natural language processing algorithm (e.g., a generative AI model such as GPT-3).

[0723] Server: Organizes the generated summaries for each user and stores them in a database.

[0724] Emotion recognition by emotion engine

[0725] Device: While the user is using the system, the emotion recognition module reads emotions from the user's facial expressions and text input. For example, it analyzes the user's facial expressions and voice in real time using a camera and microphone.

[0726] Server: Analyzes the data sent from the emotion recognition module in real time and stores the user's emotions in a database.

[0727] Providing summaries and optimizing sentiment-based recommendations

[0728] Terminal: When the user logs in, the server displays a list of recommended books and summaries. The user selects a summary of interest and checks the details.

[0729] Server: While the user is reviewing the summaries, the server analyzes the user's emotions based on emotion recognition data and records which summaries the user responded positively to.

[0730] Server: Based on this data, the next recommendation algorithm is adjusted for each user to recommend more suitable books.

[0731] Feedback and System Improvement

[0732] Terminal: The user enters a rating or comment on the summary or book they have read and clicks the "Submit" button.

[0733] Server: Stores user feedback and sentiment data in a database and uses that information to update algorithms, for example by using mining techniques to reinforce categories with more positive feedback.

[0734] Specific examples

[0735] User A creates a new account and selects "Science" and "History" as his / her interest categories. This information is stored in the database, and the server then searches for corresponding books using an external API. For example, two books, "The Origin of the Universe" and "The Rise of Civilization," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two books. Based on these summaries, a recommended book list is generated and provided to User A.

[0736] User A clicks on the summary of "The Rise of Civilization" to check the details, and the emotion engine reads User A's facial expressions and reactions to detect positive emotions. This information is stored in the database and used to adjust the recommendation algorithm next time.

[0737] Example prompts to input to the generative AI model

[0738] "Develop an outline of a system that generates book summaries based on interest categories and optimizes recommendations according to user sentiment. As a concrete example, consider a case where a user is interested in science and history and is provided with book summaries in those categories."

[0739] This allows users to efficiently obtain optimal book information based on their interests and feelings, enabling them to have a richer reading experience.

[0740] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0741] Step 1:

[0742] User registration and interest collection

[0743] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. After completing registration, the user selects the category of interest (e.g., history, science, fiction, etc.).

[0744] Input: User-entered information (name, email address, password, interest categories)

[0745] Server: Receives the information entered by the user, performs validation, and if validation is successful, stores it in a database (MySQL or PostgreSQL). Stored data includes the user ID, name, email address, password hash, and interest categories.

[0746] Output: The user information is saved in the database and the registration process is complete.

[0747] Step 2:

[0748] Collecting book recommendations

[0749] Server: The server retrieves registered user interest data from the database. Based on the retrieved interest data, it collects book information using external APIs such as the Google Books API and the Open Library API.

[0750] Input: User interest data (category)

[0751] Server: Sends a request to the external API endpoint and receives the relevant book information in JSON format, including the title, author, publication date, summary, etc.

[0752] Output: Book information is collected on the server and stored in a database.

[0753] Step 3:

[0754] Book summary generation

[0755] Server: Processes the collected book information using a natural language processing algorithm and generates a summary. Specifically, it uses a generative AI model such as GPT-3.

[0756] Input: Book information (title, author, publication date, summary, etc.)

[0757] Server: Book information is input as text data into the generative AI model, and a summary text is generated. The generated summary text is then saved back into the database.

[0758] Output: The generated book summaries are stored in a database.

[0759] Step 4:

[0760] Emotion recognition by emotion engine

[0761] Device: While the user is using the system, the camera and microphone capture the user's facial expressions and voice in real time. The emotion recognition module uses this data to analyze the user's emotions.

[0762] Input: User's facial expression data, voice data

[0763] Server: Analyzes the received emotion data in real time and identifies the emotional state. For example, using the Emotion API. The analysis results are stored in a database.

[0764] Output: The user's emotion data is stored in the database.

[0765] Step 5:

[0766] Providing summaries and optimizing sentiment-based recommendations

[0767] Terminal: When a user logs in, the server retrieves the recommended book list and summaries from the database and displays them on the terminal. The user selects the summary of interest and checks the details.

[0768] Input: User login information, emotion data, summary list

[0769] Server: While reviewing the summaries, analyzes the emotion recognition data in real time and records which summaries elicited positive reactions, optimizing the recommendation algorithm for future use.

[0770] Output: Recommendations optimized for the user are made, and the recommendation list is adjusted for future use.

[0771] Step 6:

[0772] Feedback and System Improvement

[0773] Terminal: The user enters a rating or comment on the summary or book they have read and clicks the "Submit" button.

[0774] Input: User ratings, comments, and sentiment data

[0775] Server: Receives user feedback and stores it in a database. Updates the algorithm based on the stored data and performs data mining to improve recommendation accuracy.

[0776] Output: The feedback is stored in a database and the algorithm is optimized.

[0777] (Application example 2)

[0778] 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."

[0779] Conventional book recommendation systems have difficulty fully reflecting users' interests and preferences, preventing them from making effective use of their time. Furthermore, they are unable to fully utilize feedback from user interactions, making it difficult to improve the accuracy of recommendation algorithms. Furthermore, they are unable to take into account the user's emotional state, resulting in incomplete personalization. A system that can solve these problems and improve users' reading experience is needed.

[0780] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0781] In this invention, the server includes a means for collecting information based on the user's interests, a means for summarizing the collected information using a natural language processing algorithm, and a means for reading the user's emotional data and optimizing the recommendation algorithm, thereby making it possible to provide information based on the user's interests and to optimize the recommendation algorithm in a way that reflects the user's emotions.

[0782] "User" refers to an individual or organization that uses the system.

[0783] "Genre" refers to a theme or category classified based on the user's interests and concerns.

[0784] "Information" refers to data and metadata about content such as books and documents.

[0785] "Natural language processing algorithms" refer to technical methods used to summarize and analyze collected information.

[0786] A "summary" is a concise representation of the main points or content of the collected information.

[0787] "Providing" refers to displaying the generated summary and recommendation results to the user.

[0788] "Emotion data" refers to data related to emotions obtained by analyzing a user's facial expressions and text input.

[0789] A "recommendation algorithm" refers to a program that selects and recommends the most appropriate information based on the user's interests and emotions.

[0790] "Optimization" refers to using user feedback and sentiment data to adjust recommendation algorithms to improve their performance.

[0791] "Feedback" refers to reactions such as ratings and comments provided by users.

[0792] A "database" refers to a system for storing collected information, user feedback, emotional data, etc.

[0793] MODE FOR CARRYING OUT THE INVENTION

[0794] This invention is a system that allows users to efficiently obtain summaries of a wide variety of information and optimizes recommendations through emotion recognition. This system includes a process for recommending information based on the user's interests and providing summaries of that information. Furthermore, by combining this process with an emotion engine, it is possible to realize optimal recommendations for each user.

[0795] User registration and interest collection

[0796] The server provides a means for users to access the system and create an account by entering their name, email address, and password. After registering, users also have the ability to select their genre of interest (e.g., history, science fiction, etc.). This information is stored in a database.

[0797] Information recommendation and summary generation

[0798] The server has means for obtaining user interests from the database and collecting corresponding information using external APIs, which use public information sources, and means for generating summaries of the collected information using natural language processing algorithms.

[0799] Emotion recognition by emotion engine

[0800] The terminal includes a means for an emotion recognition module to read emotions from the user's facial expressions and text inputs while the user is using the system, and this information is transmitted in real time to a server, which then stores the analyzed emotion data.

[0801] Providing summaries and optimizing sentiment-based recommendations

[0802] The terminal provides a means to display the recommended information list and summaries generated by the server when the user logs in. The user can view the summaries of interest by selecting them and clicking on them. The server has a means to analyze the user's emotional data and adjust the recommendation algorithm based on which summaries and information the user responded positively to.

[0803] Feedback and System Improvement

[0804] The terminal is equipped with a means for the user to input a rating (e.g., a rating from 1 to 5 stars) or comment on the summary or information they have read and click the "Submit" button. The server then stores the user's feedback and emotional data in a database and continuously analyzes and improves the performance of the recommendation algorithm.

[0805] Hardware and software used

[0806] The hardware used to implement this invention is primarily a smartphone (with iOS or Android), and the main software used is Python, the emotion_recognition library, the spacy library, and a custom recommender system (RecommenderSystem).

[0807] Specific examples

[0808] For example, if a user creates a new account and selects "science" and "history" as their interest areas, this information is stored in the database. The server then searches for corresponding information using public information sources. For example, two pieces of information, "The Origin of the Universe" and "The Rise of the Roman Empire," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two pieces of information, and a list of recommended information is generated and served to the user based on the summaries.

[0809] When a user clicks on the summary of "The Rise of the Roman Empire" to learn more, the emotion engine reads the user's facial expressions and reactions to detect positive emotions. This information is stored in the database and used to adjust the recommendation algorithm for the next time.

[0810] Prompt Sentence Examples

[0811] If a user is interested in science or history and reads the summary of the information "The Rise of the Roman Empire" and responds with "This is fascinating!", please tell me how to build a system that recognizes this response as positive and reflects it in future recommendations.

[0812] In this way, the system of the present invention can efficiently provide a summary of various information while taking into account the user's emotions, improving the user experience, allowing the user to use their time more efficiently and receive more tailored information recommendations.

[0813] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0814] Step 1:

[0815] User registration and interest collection

[0816] Users create an account by entering their name, email address, and password on their device. Then, they select the genre they are interested in (e.g., history, science fiction, etc.). The input data (name, email address, password, genre) is sent to the server and stored in a database.

[0817] Step 2:

[0818] Information recommendation and summary generation

[0819] The server retrieves the user's interests from a database and collects corresponding information using an external API. Specifically, it retrieves information related to the user's interests (input data: genre of interest) from a public information source (e.g., a book database), and then generates a summary of the information using a natural language processing algorithm. The generated summary is stored as temporary data and later provided to the user.

[0820] Step 3:

[0821] Emotion recognition by emotion engine

[0822] The user views the summary of information on the device. At this time, the device uses an emotion recognition module to acquire emotional data in real time from the user's facial expressions and text input (input data: facial expressions, comments). The acquired emotional data is sent to the server and analyzed by the emotion analysis engine. The analysis results are stored in a database.

[0823] Step 4:

[0824] Providing summaries and optimizing sentiment-based recommendations

[0825] When a user logs in, the server displays the generated recommendation information list and summaries on the terminal. When the user selects a summary of interest, they can check its details. Emotional data (input data: browsing history, emotional data) obtained while the user is viewing the summary is sent to the server, and the recommendation algorithm analyzes the data and optimizes future recommendations.

[0826] Step 5:

[0827] Feedback and System Improvement

[0828] Users input ratings (e.g., ratings from 1 to 5 stars) and comments (input data: ratings, comments) on the summaries and information they have read from their devices. This feedback is sent to the server and stored in a database. The server continuously analyzes and improves the performance of the recommendation algorithm based on the feedback data and sentiment data.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] [Third embodiment]

[0833] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0834] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0835] 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).

[0836] 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.

[0837] 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.

[0838] 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).

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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."

[0845] The present invention provides a system for enabling users to efficiently obtain summaries of a wide variety of books and cultivate a broad perspective. The system includes a process for recommending book information based on the user's interests and providing summaries of the books.

[0846] User registration and interest collection

[0847] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. After registering, they select the genre of interest (e.g., history, science, fiction, etc.).

[0848] Server: Stores registration information and user interests in a database.

[0849] Book recommendation and summary generation

[0850] Server: Retrieves user interests from the database and collects corresponding book information using external APIs, such as public book information sources like Google Books API.

[0851] Server: Generates summaries of collected book information using natural language processing algorithms, such as BERT and GPT-3.

[0852] Providing a summary

[0853] Terminal: When the user logs back into the system, they will see a customized list of recommended books and their summaries. They can click on summaries that interest them to find out more information.

[0854] Server: Optionally, provide further details about the book the user clicked on (reviews, related books, etc.).

[0855] Feedback and System Improvement

[0856] On the device: Users can provide feedback on summaries and books they have read, rating them on a scale of 1 to 5 stars, and adding comments.

[0857] Server: Stores the feedback in a database and uses the data to analyze and improve the performance of the recommendation algorithm.

[0858] Specific examples

[0859] User A creates a new account and selects "Science" and "History" as his / her genre of interest. This information is stored in the database, and the server then searches for corresponding books using the Google Books API. For example, two books, "The Origin of the Universe" and "The Rise of the Roman Empire," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two books. Based on these summaries, a recommended book list is generated and provided to User A.

[0860] User A clicks on the summary of "The Rise of the Roman Empire" to learn more and gives it a 5-star rating, which is saved in the database and used to refine the recommendation algorithm next time.

[0861] In this way, the system of the present invention can efficiently provide summaries of various books and improve the user's reading experience, allowing the user to use their time effectively and develop a broad perspective.

[0862] The processing flow will be explained below.

[0863] Step 1:

[0864] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. They then click the "Register" button to submit this information.

[0865] Step 2:

[0866] Server: Receives and validates the registration information, ensuring that the information entered is in the correct format and, if there are no problems, saves it to the database.

[0867] Step 3:

[0868] On the device: After completing registration, the user will be taken to a screen where they can select the genre they are interested in (e.g., history, science fiction, etc.). After selecting the genre they are interested in, they click the "Save" button.

[0869] Step 4:

[0870] Server: Receives the user's selected genre information and stores it in a database.

[0871] Step 5:

[0872] Server: Every time a user logs in, retrieve the user's interest genres from the database.

[0873] Step 6:

[0874] Server: Collects book information using an external API (e.g., Google Books API) based on the requested genre.

[0875] Step 7:

[0876] Server: Processes the collected book information and obtains detailed information for each book (title, author, publication year, abstract).

[0877] Step 8:

[0878] Server: Summarizes the book details using natural language processing algorithms (e.g., BERT or GPT-3).

[0879] Step 9:

[0880] Server: Based on the summary results, a customized book recommendation list is created for the user.

[0881] Step 10:

[0882] Terminal: When a user logs in, the server generates a list of recommended books and their summaries, and the user selects and clicks on a summary of interest.

[0883] Step 11:

[0884] Server: Provides more information based on the abstract the user clicks on, making additional API calls as needed to gather reviews and related books.

[0885] Step 12:

[0886] On the device: The user enters a rating (e.g., a rating from 1 to 5 stars) and comments for the summary they have read, and clicks the "Submit" button.

[0887] Step 13:

[0888] Server: Receives user feedback and stores it in a database.

[0889] Step 14:

[0890] Server: Analyzes the stored feedback and improves the performance of the recommendation algorithm, specifically incorporating the characteristics of highly rated books into future recommendations.

[0891] The above are the specific processing steps for carrying out the invention. This system allows users to efficiently read summaries of various books and develop a broad perspective.

[0892] Example 1

[0893] 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."

[0894] In today's society, a wide variety of books are published, making it difficult for users to efficiently find books that match their interests. Furthermore, users need to digest a huge amount of information quickly, and book summaries are a useful way to do this. However, manually creating summaries requires a great deal of time and effort, and without an appropriate recommendation system, it is impossible to provide books that match the user's interests. Therefore, a system is needed that allows users to quickly obtain book summaries customized based on their interests and efficiently improve their reading experience.

[0895] 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.

[0896] In this invention, the server includes means for selecting a genre of interest to a user, means for collecting book information based on the user's interests, means for generating summaries of the collected book information using a natural language processing algorithm, means for providing the user with the summary results and the book information, means for improving the recommendation algorithm based on feedback from the user, and means for storing the user's feedback in a database and analyzing and improving the performance of the algorithm using the database. This allows users to efficiently obtain book summaries based on their interests, and the recommendation accuracy is improved by the feedback, thereby providing an effective reading experience.

[0897] "User" refers to a person who uses this system.

[0898] A "genre" is a classification based on the content or category of a book, such as history, science, or fiction.

[0899] "Book information" refers to basic data such as the book's title, author, publication date, and summary.

[0900] "Natural language processing algorithms" refer to programs and methods for analyzing, understanding, and processing text data.

[0901] A "summary" is a short summary that extracts only the important parts from the original book information.

[0902] "Feedback" refers to ratings and comments provided by users to the system, providing data for evaluating and improving system performance.

[0903] A "recommendation algorithm" refers to a computational method for suggesting appropriate books and summaries based on a user's interests and past behavior.

[0904] A "database" refers to a system for systematically managing and storing various types of information.

[0905] "Server" refers to a central computing unit that stores, processes, and serves data.

[0906] "Generative AI models" refer to machine learning models trained to perform text generation and natural language processing, such as GPT-3.

[0907] "External data sources" refer to information sources outside the system that are used to obtain book information. For example, a public API.

[0908] The present invention provides a system for enabling users to efficiently obtain summaries of a wide variety of books and cultivate a broad perspective. This system includes a process for recommending book information based on the user's interests and providing summaries of the books. Specific embodiments of the system are described below.

[0909] User registration and interest collection

[0910] Terminal: The user accesses the system and creates an account by entering their name, email address, and password. After registering, they select the genre of interest (e.g., history, science, fiction, etc.).

[0911] Server: Stores registration information and user interests in a database, which is used later in the book recommendation process.

[0912] Book recommendation and summary generation

[0913] Server: Gets user interests from a database and collects corresponding book information using external data sources (e.g., public API).

[0914] As a concrete example, the Google Books API is used.

[0915] Server: Generate a summary of the collected book information using a generative AI model (e.g., GPT-3). Specific prompt examples are as follows:

[0916] Please provide a book summary: Title: "The Origin of the Universe", Author: "John Smith", Publication Date: "2022", Summary: "Introduces the latest research on the origin of the universe"

[0917] The generated summaries are stored in a database.

[0918] Providing a summary

[0919] Terminal: When the user logs back into the system, they will see a customized list of recommended books and their summaries. They can click on summaries that interest them to find out more information.

[0920] Server: Optionally, provide further details about the book the user clicked on (reviews, related books, etc.).

[0921] Feedback and System Improvement

[0922] On the device: Users can give feedback on summaries and books they have read. Ratings are expressed on a scale of 1 to 5 stars, and comments can be added.

[0923] Server: Stores the feedback in a database and uses the data to analyze and improve the performance of the recommendation algorithm.

[0924] Specific examples

[0925] User A creates a new account and selects "Science" and "History" as his / her genre of interest. This information is stored in the database, and the server then searches for corresponding books using the Google Books API. For example, two books, "The Origin of the Universe" and "The Rise of the Roman Empire," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two books. Based on these summaries, a recommended book list is generated and provided to User A.

[0926] User A clicks on the summary of "The Rise of the Roman Empire" to learn more and gives it a 5-star rating, which is saved in the database and used to refine the recommendation algorithm next time.

[0927] In this way, the system of the present invention can efficiently provide summaries of various books and improve the user's reading experience, allowing the user to use their time effectively and develop a broad perspective.

[0928] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0929] Step 1:

[0930] User Registration

[0931] User: A user visits the system's web page or app, enters their name, email address, and password, and clicks "Create Account."

[0932] Input: Name, Email Address, Password

[0933] Output: Registration request

[0934] Server: The server receives the entered user information and stores it in the database. If the save is successful, it sends the user a confirmation message saying "Account creation successful."

[0935] Input: Registration Request

[0936] Output: A confirmation message that the user information will be saved to the database.

[0937] What happens: The server establishes a database connection and saves the user information using an INSERT query.

[0938] Step 2:

[0939] Collecting user interests

[0940] User: After logging in, the user selects their genre of interest (e.g., history, science, fiction, etc.) and clicks "Update Interests."

[0941] Input: Selected genre information

[0942] Output: Interest update request

[0943] Server: The server saves the selected genre information to the database. If the save is successful, the updated information is reflected in the user's profile.

[0944] Input: Interest Update Request

[0945] Output: Updated interest information

[0946] What happens: The server establishes a database connection and stores the user's interests using an UPDATE query.

[0947] Step 3:

[0948] Collecting book information

[0949] Server: Periodically (e.g., once a day), retrieve each user's interest information from the database. Use external data sources (e.g., public APIs) to gather interest-based book information.

[0950] Input: Interest information for each user

[0951] Output: Book information collection request

[0952] What it does: The server sends an HTTP request to an external API, receives book information in JSON format, and parses the data to extract information such as the book title, author, publication date, and summary.

[0953] Step 4:

[0954] Generate a summary

[0955] Server: Generates summaries based on collected book information using natural language processing algorithms (e.g., generative AI models).

[0956] Input: Book information

[0957] Output: Book summary

[0958] What it does: The server inputs the book details into a generative AI model, creates a prompt to generate a summary, sends the prompt to the generative model (e.g., GPT-3), and stores the generated summary in a database.

[0959] Step 5:

[0960] Providing summaries and book information

[0961] Terminal: When the user logs back into the system, they will see their customized list of recommended books and their summaries.

[0962] Input: Login request

[0963] Output: Recommended reading list and summary

[0964] Server: The server processes the login request, retrieves recommended books and summaries based on the user's interests from the database, and sends them to the terminal.

[0965] Input: Request for recommended reading list and summary

[0966] Output: Recommended reading list and summary data

[0967] Specific operation: The server searches the database for recommended books based on the user's interest information and returns them to the terminal in JSON format along with their summaries.

[0968] Step 6:

[0969] Gathering feedback and improving the system

[0970] On the device: Users can provide feedback on summaries and books they have read by rating them on a scale of 1 to 5 stars and adding comments.

[0971] Input: Rating and Comments

[0972] Output: Feedback submission request

[0973] Server: The server stores the feedback in a database and analyzes and improves the performance of the recommendation algorithm based on the collected feedback.

[0974] Input: Rating and comment data

[0975] Output: Saving feedback and improving the algorithm

[0976] What it does: The server stores user feedback in a database using INSERT queries and performs statistical analysis to provide data for tuning the algorithm.

[0977] (Application example 1)

[0978] 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."

[0979] Conventional book recommendation systems can only provide users with one-dimensional information, making it difficult to interactively review summaries in real time. Furthermore, the means for improving system performance through feedback are limited, leaving further improvements needed to improve the accuracy of book recommendations to users. The present invention aims to solve these problems by providing a system that allows users to visually review book summaries in a virtual environment and efficiently provide feedback.

[0980] 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.

[0981] In this invention, the server includes a means for selecting a genre of interest to a user, a means for collecting book information based on the user's interests, a means for summarizing the collected book information using a natural language processing algorithm, a means for providing the summary result to the user, a means for improving the recommendation algorithm based on feedback from the user, a means for visually displaying the book information in a virtual environment, and a means for the user to visually check the summary result using smart glasses, thereby enabling the user to more intuitively grasp book information and provide feedback in real time.

[0982] The "means for selecting a genre of interest to the user" is an interface that allows the user to select a genre category based on their predetermined interests and preferences, and is a procedure by which the system grasps the user's area of ​​interest.

[0983] The "means for collecting book information based on the user's interests" refers to an algorithm and interface for obtaining related book data from the Internet or a database based on the genre selected by the user.

[0984] "Means for summarizing collected book information using natural language processing algorithms" refers to a program that applies natural language processing technology to analyze collected book data, extract key points, and summarize them concisely.

[0985] "Means for providing the summary result to the user" refers to a display screen or notification system for providing the generated book summary to the user in an accessible form.

[0986] The "means for improving the recommendation algorithm based on feedback from the user" is a system for collecting user ratings and comments, analyzing the data, and improving the accuracy of the recommendation algorithm.

[0987] "Means for visually displaying book information in a virtual environment" refers to technology that visually represents collected and summarized book information in a virtual reality or augmented reality environment, allowing users to view it in a virtual space.

[0988] "Means for the user to visually confirm the summary results using smart glasses" refers to a system in which the user wears smart glasses and the summarized book information is displayed superimposed on their field of vision, allowing them to intuitively confirm the information.

[0989] The present invention provides book summaries based on the genres of interest to users, improves system performance through feedback, and provides a system that allows users to intuitively view book information in a virtual environment using smart glasses.

[0990] System Configuration

[0991] 1. User registration and interest collection

[0992] Terminal: Users access the system through a terminal and create an account by entering their name, email address, and password. They then select the genre of interest (e.g., science, history, fiction, etc.).

[0993] Server: Stores registration information and user interests in a database.

[0994] 2. Book recommendation and summary generation

[0995] Server: Retrieves user interests from the database and collects corresponding book information using external sources (e.g., Google Books API).

[0996] Server: Generates summaries of collected book information using natural language processing algorithms (e.g., BERT, GPT-3).

[0997] 3. Virtual display of book information

[0998] Server: Generates data for visually displaying the generated book summary information in a virtual environment.

[0999] 4. Providing a Summary

[1000] Device: The user wears smart glasses and visually checks book information in the virtual bookstore. When the user focuses their gaze on a specific book, a summary is displayed.

[1001] 5. Feedback and System Improvement

[1002] Terminal: Users provide feedback (ratings, comments) on summaries and books they have read.

[1003] Server: Stores the feedback in a database and uses the data to improve the performance of the recommendation algorithm.

[1004] Hardware and software used

[1005] Hardware: Smart glasses, devices (smartphones, tablets, etc.)

[1006] Software: Databases (e.g., SQLite), external APIs (e.g., Google Books API), natural language processing algorithms (BERT, GPT-3)

[1007] Specific examples

[1008] User A creates a new account and selects "Science" and "History" as his or her genre of interest. The server uses the Google Books API to search for corresponding books. For example, two books, "Origin of the Universe" and "Historical Empires," are found, and detailed information about each is collected. BERT and GPT-3 are used to generate summaries of these two books. Based on these summaries, a virtual bookstore is created and provided to User A through smart glasses. User A clicks on the summary for "Historical Empires" to check the details, and provides a five-star rating and comment. This feedback is used to adjust the recommendation algorithm next time.

[1009] Prompt Sentence Examples

[1010] Generate a book summary using a prompt like this:

[1011] Generate a summary based on the given book details, which are:

[1012] Title: "Historical Empires"

[1013] Author:John Doe

[1014] Description: This book details the development and influence of historical empires, covering their political, cultural, and economic impacts, and includes key events and their context.

[1015] This invention is a system that improves the reading experience by enabling users to efficiently obtain book information of interest and intuitively check it in a virtual environment.

[1016] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1017] Specific processing steps of the program

[1018] Step 1: User registration and interest gathering

[1019] Input: The user enters their name, email address, password, and genre of interest.

[1020] Data processing: The terminal sends the entered information to the server, which stores it in a database.

[1021] Output: A user ID is generated and the user's information is registered in the database.

[1022] Specific operation: The terminal displays an input form, the user enters the required information, and the server records the received information in a database.

[1023] Step 2: Gathering book information

[1024] Input: The server retrieves the user's interest data in a database.

[1025] Data processing: The server calls an external API (e.g., Google Books API) to collect book information that matches the user's interests.

[1026] Output: The collected book list is returned to the server.

[1027] Specific operation: The server makes an API call and obtains related book data.

[1028] Step 3: Generate a summary of the book information

[1029] Input: Collected book information is provided to the server.

[1030] Data processing: The server uses a natural language processing algorithm (e.g., GPT-3) to generate a summary. It generates a book summary based on the input.

[1031] Output: The generated summaries are stored in a database.

[1032] Specific operation: The server inputs detailed information about the book into the summary generation model and saves the output summary.

[1033] Step 4: Generate virtual display data for book information

[1034] Input: Abstracted book information is kept on the server.

[1035] Data processing: The server generates display data for the virtual environment, including book cover images and summary text.

[1036] Output: The generated display data is stored and later provided to the smart glasses.

[1037] Specific operation: The server performs image processing and text formatting to prepare the display data.

[1038] Step 5: Provide a summary

[1039] Input: A user puts on smart glasses and accesses a virtual bookstore.

[1040] Data processing: The terminal sends the user's gaze detection data to the server, and the server obtains the corresponding book summary.

[1041] Output: A summary of the selected book is displayed on the user's smart glasses.

[1042] Specific operation: The device detects the user's gaze using an eye-tracking sensor, and the server provides summary data.

[1043] Step 6: Gather feedback and improve the system

[1044] Input: User provides feedback and enters star ratings and comments.

[1045] Data processing: The device sends the feedback information to the server, which stores it in a database. The server then analyzes the feedback data and adjusts the recommendation algorithm.

[1046] Output: New book recommendation results based on the improved algorithm are generated.

[1047] How it works: The device provides a rating form and records user input. The server analyzes the feedback data and retunes the recommendation algorithm.

[1048] Through this series of processing steps, users can efficiently obtain book information of their interest and improve the system's performance through feedback.

[1049] 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.

[1050] The present invention is a system that allows users to efficiently obtain summaries of a wide variety of books and optimizes recommendations through emotion recognition. This system includes a process for recommending book information based on the user's interests and providing summaries of those books. Furthermore, by combining this process with an emotion engine, optimal recommendations for each user are realized.

[1051] User registration and interest collection

[1052] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. After registering, they select the genre of interest (e.g., history, science, fiction, etc.).

[1053] Server: Stores registration information and user interests in a database.

[1054] Book recommendation and summary generation

[1055] Server: Retrieves user interests from the database and collects corresponding book information using external APIs, which use public book information sources.

[1056] Server: Generates summaries of collected book information using natural language processing algorithms.

[1057] Emotion recognition by emotion engine

[1058] On the device: While the user is using the system, the emotion recognition module reads emotions from the user's facial expressions and text input, and this information is sent to the server in real time.

[1059] Server: Stores the emotion data analyzed by the emotion engine.

[1060] Providing summaries and optimizing sentiment-based recommendations

[1061] Terminal: When a user logs in, the server generates a list of recommended books and their summaries, and the user selects and clicks on a summary of interest.

[1062] Server: Analyzes user sentiment data and adjusts recommendation algorithms based on which summaries and books users respond most positively to.

[1063] Server: Recommends books that are more relevant to the user based on their sentiment.

[1064] Feedback and System Improvement

[1065] On the device: The user enters a rating (e.g., a rating from 1 to 5 stars) and comments for the summary or book they have read, and clicks the "Submit" button.

[1066] Server: Stores user feedback and sentiment data in a database to continuously analyze and improve the performance of the recommendation algorithm.

[1067] Specific examples

[1068] User A creates a new account and selects "Science" and "History" as his / her genre of interest. This information is stored in the database, and the server then searches for corresponding books using public book information sources. For example, two books, "The Origin of the Universe" and "The Rise of the Roman Empire," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two books. Based on these summaries, a recommended book list is generated and provided to User A.

[1069] User A clicks on the summary of "The Rise of the Roman Empire" to check the details, and the emotion engine reads User A's facial expressions and reactions to detect positive emotions. This information is stored in the database and used to adjust the recommendation algorithm next time.

[1070] In this way, the system of the present invention can efficiently provide summaries of various books while taking into account the user's emotions, improving the user's reading experience, allowing the user to use their time more efficiently and receive more tailored reading recommendations.

[1071] The processing flow will be explained below.

[1072] Step 1:

[1073] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. They then click the "Register" button to submit this information.

[1074] Step 2:

[1075] Server: Receives and validates the registration information, ensuring that the information entered is in the correct format and, if there are no problems, saves it to the database.

[1076] Step 3:

[1077] On the device: After completing registration, the user will be taken to a screen where they can select the genre they are interested in (e.g., history, science fiction, etc.). After selecting the genre they are interested in, they click the "Save" button.

[1078] Step 4:

[1079] Server: Receives the user's selected genre information and stores it in a database.

[1080] Step 5:

[1081] Server: Every time a user logs in, retrieve the user's interest genres from the database.

[1082] Step 6:

[1083] Server: Collects book information using an external API (e.g., Google Books API) based on the requested genre.

[1084] Step 7:

[1085] Server: Processes the collected book information and obtains detailed information for each book (title, author, publication year, abstract).

[1086] Step 8:

[1087] Server: Summarizes the book details using natural language processing algorithms (e.g., BERT or GPT-3).

[1088] Step 9:

[1089] Server: Based on the summary results, a customized book recommendation list is created for the user.

[1090] Step 10:

[1091] Terminal: When a user logs in, the server generates a list of recommended books and their summaries, and the user selects and clicks on a summary of interest.

[1092] Step 11:

[1093] Server: Provides more information based on the abstract the user clicks on, making additional API calls as needed to gather reviews and related books.

[1094] Step 12:

[1095] Device: While the user is using the system, the emotion recognition module collects the user's facial expressions and tone of voice using a camera and microphone.

[1096] Step 13:

[1097] Server: The emotion recognition module analyzes the user's facial expressions and speech in real time to generate emotion data, which is then stored in a database in real time.

[1098] Step 14:

[1099] Device: The user enters a rating (e.g., a rating from 1 to 5 stars) and comments for the summary or book they have read, and clicks the "Submit" button.

[1100] Step 15:

[1101] Server: Receives user feedback and emotion data collected in real time and stores it in a database.

[1102] Step 16:

[1103] Server: Analyzes the stored emotion data and feedback data to improve the performance of the recommendation algorithm. Extracts the characteristics of books that users have expressed positive emotions about and reflects them in the next recommendation.

[1104] The above are the specific processing steps for implementing the invention. This system allows users to efficiently read summaries of various books and receive optimal recommendations based on real-time emotional data. This system allows users to use their time efficiently and enjoy a more personalized reading experience.

[1105] Example 2

[1106] 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."

[1107] Currently, a large amount of information exists on the Internet, making it difficult for users to efficiently obtain the information they need. In particular, when it comes to book summaries and recommendations, personalized recommendations that reflect the user's interests and emotions are difficult to make. To solve this problem, a system is needed that accurately captures the user's interests and provides appropriate book information.

[1108] 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 selecting a category in which a user is interested, a means for collecting data based on the user's interests, a means for summarizing the collected data using a natural language processing algorithm, a means for analyzing emotion data and adjusting a recommendation algorithm, a means for providing the summary result to the user, and a means for improving the recommendation algorithm based on feedback from the user. This enables the user to efficiently obtain optimal book information based on their own interests and emotions.

[1109] "User Interest Categories" refers to specific themes or genres that interest a user.

[1110] "Data" refers to units of information such as book information, including text, metadata, and summaries.

[1111] "Natural language processing algorithm" refers to a computer program for processing and understanding human language, including specifically algorithms for summarizing and analyzing text.

[1112] "Emotional data" refers to information that represents the user's emotional state obtained from facial expressions and text input.

[1113] A "recommendation algorithm" refers to a calculation method for selecting and providing optimal book information based on a user's interests and emotions.

[1114] "External source" refers to an external resource for obtaining information, such as a database or API that exists outside the system.

[1115] An "emotion recognition module" refers to a component that has the function of reading emotions from a user's facial expressions, voice, text, etc.

[1116] "Database" means a system for storing and managing information in an organized form.

[1117] "Feedback" refers to information provided by users to evaluate the performance of a system, such as ratings and comments.

[1118] The present invention is a system that allows users to efficiently obtain summaries of a wide variety of books and optimizes recommendations through emotion recognition. This system includes a process for recommending book information based on the user's interests and providing summaries of those books. Furthermore, by combining this process with an emotion engine, optimal recommendations for each user are realized.

[1119] User registration and interest collection

[1120] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. After registering, they select the categories of interest (e.g., history, science, fiction, etc.).

[1121] Server: The server stores registration information and user interests in a database (e.g., MySQL or PostgreSQL).

[1122] Collecting book recommendations

[1123] Server: The server obtains the user's interests from the database and collects the corresponding book information using external APIs (e.g., Google Books API or Open Library API).

[1124] Server: Stores collected book information in a database.

[1125] Book summary generation

[1126] Server: The server generates a summary of the collected book information using a natural language processing algorithm (e.g., a generative AI model such as GPT-3).

[1127] Server: Organizes the generated summaries for each user and stores them in a database.

[1128] Emotion recognition by emotion engine

[1129] Device: While the user is using the system, the emotion recognition module reads emotions from the user's facial expressions and text input. For example, it analyzes the user's facial expressions and voice in real time using a camera and microphone.

[1130] Server: Analyzes the data sent from the emotion recognition module in real time and stores the user's emotions in a database.

[1131] Providing summaries and optimizing sentiment-based recommendations

[1132] Terminal: When the user logs in, the server displays a list of recommended books and summaries. The user selects a summary of interest and checks the details.

[1133] Server: While the user is reviewing the summaries, the server analyzes the user's emotions based on emotion recognition data and records which summaries the user responded positively to.

[1134] Server: Based on this data, the next recommendation algorithm is adjusted for each user to recommend more suitable books.

[1135] Feedback and System Improvement

[1136] Terminal: The user enters a rating or comment on the summary or book they have read and clicks the "Submit" button.

[1137] Server: Stores user feedback and sentiment data in a database and uses that information to update algorithms, for example by using mining techniques to reinforce categories with more positive feedback.

[1138] Specific examples

[1139] User A creates a new account and selects "Science" and "History" as his / her interest categories. This information is stored in the database, and the server then searches for corresponding books using an external API. For example, two books, "The Origin of the Universe" and "The Rise of Civilization," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two books. Based on these summaries, a recommended book list is generated and provided to User A.

[1140] User A clicks on the summary of "The Rise of Civilization" to check the details, and the emotion engine reads User A's facial expressions and reactions to detect positive emotions. This information is stored in the database and used to adjust the recommendation algorithm next time.

[1141] Example prompts to input to the generative AI model

[1142] "Develop an outline of a system that generates book summaries based on interest categories and optimizes recommendations according to user sentiment. As a concrete example, consider a case where a user is interested in science and history and is provided with book summaries in those categories."

[1143] This allows users to efficiently obtain optimal book information based on their interests and feelings, enabling them to have a richer reading experience.

[1144] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1145] Step 1:

[1146] User registration and interest collection

[1147] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. After completing registration, the user selects the category of interest (e.g., history, science, fiction, etc.).

[1148] Input: User-entered information (name, email address, password, interest categories)

[1149] Server: Receives the information entered by the user, performs validation, and if validation is successful, stores it in a database (MySQL or PostgreSQL). Stored data includes the user ID, name, email address, password hash, and interest categories.

[1150] Output: The user information is saved in the database and the registration process is complete.

[1151] Step 2:

[1152] Collecting book recommendations

[1153] Server: The server retrieves registered user interest data from the database. Based on the retrieved interest data, it collects book information using external APIs such as the Google Books API and the Open Library API.

[1154] Input: User interest data (category)

[1155] Server: Sends a request to the external API endpoint and receives the relevant book information in JSON format, including the title, author, publication date, summary, etc.

[1156] Output: Book information is collected on the server and stored in a database.

[1157] Step 3:

[1158] Book summary generation

[1159] Server: Processes the collected book information using a natural language processing algorithm and generates a summary. Specifically, it uses a generative AI model such as GPT-3.

[1160] Input: Book information (title, author, publication date, summary, etc.)

[1161] Server: Book information is input as text data into the generative AI model, and a summary text is generated. The generated summary text is then saved back into the database.

[1162] Output: The generated book summaries are stored in a database.

[1163] Step 4:

[1164] Emotion recognition by emotion engine

[1165] Device: While the user is using the system, the camera and microphone capture the user's facial expressions and voice in real time. The emotion recognition module uses this data to analyze the user's emotions.

[1166] Input: User's facial expression data, voice data

[1167] Server: Analyzes the received emotion data in real time and identifies the emotional state. For example, using the Emotion API. The analysis results are stored in a database.

[1168] Output: The user's emotion data is stored in the database.

[1169] Step 5:

[1170] Providing summaries and optimizing sentiment-based recommendations

[1171] Terminal: When a user logs in, the server retrieves the recommended book list and summaries from the database and displays them on the terminal. The user selects the summary of interest and checks the details.

[1172] Input: User login information, emotion data, summary list

[1173] Server: While reviewing the summaries, analyzes the emotion recognition data in real time and records which summaries elicited positive reactions, optimizing the recommendation algorithm for future use.

[1174] Output: Recommendations optimized for the user are made, and the recommendation list is adjusted for future use.

[1175] Step 6:

[1176] Feedback and System Improvement

[1177] Terminal: The user enters a rating or comment on the summary or book they have read and clicks the "Submit" button.

[1178] Input: User ratings, comments, and sentiment data

[1179] Server: Receives user feedback and stores it in a database. Updates the algorithm based on the stored data and performs data mining to improve recommendation accuracy.

[1180] Output: The feedback is stored in a database and the algorithm is optimized.

[1181] (Application example 2)

[1182] 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."

[1183] Conventional book recommendation systems have difficulty fully reflecting users' interests and preferences, preventing them from making effective use of their time. Furthermore, they are unable to fully utilize feedback from user interactions, making it difficult to improve the accuracy of recommendation algorithms. Furthermore, they are unable to take into account the user's emotional state, resulting in incomplete personalization. A system that can solve these problems and improve users' reading experience is needed.

[1184] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1185] In this invention, the server includes a means for collecting information based on the user's interests, a means for summarizing the collected information using a natural language processing algorithm, and a means for reading the user's emotional data and optimizing the recommendation algorithm, thereby making it possible to provide information based on the user's interests and to optimize the recommendation algorithm in a way that reflects the user's emotions.

[1186] "User" refers to an individual or organization that uses the system.

[1187] "Genre" refers to a theme or category classified based on the user's interests and concerns.

[1188] "Information" refers to data and metadata about content such as books and documents.

[1189] "Natural language processing algorithms" refer to technical methods used to summarize and analyze collected information.

[1190] A "summary" is a concise representation of the main points or content of the collected information.

[1191] "Providing" refers to displaying the generated summary and recommendation results to the user.

[1192] "Emotion data" refers to data related to emotions obtained by analyzing a user's facial expressions and text input.

[1193] A "recommendation algorithm" refers to a program that selects and recommends the most appropriate information based on the user's interests and emotions.

[1194] "Optimization" refers to using user feedback and sentiment data to adjust recommendation algorithms to improve their performance.

[1195] "Feedback" refers to reactions such as ratings and comments provided by users.

[1196] A "database" refers to a system for storing collected information, user feedback, emotional data, etc.

[1197] MODE FOR CARRYING OUT THE INVENTION

[1198] This invention is a system that allows users to efficiently obtain summaries of a wide variety of information and optimizes recommendations through emotion recognition. This system includes a process for recommending information based on the user's interests and providing summaries of that information. Furthermore, by combining this process with an emotion engine, it is possible to realize optimal recommendations for each user.

[1199] User registration and interest collection

[1200] The server provides a means for users to access the system and create an account by entering their name, email address, and password. After registering, users also have the ability to select their genre of interest (e.g., history, science fiction, etc.). This information is stored in a database.

[1201] Information recommendation and summary generation

[1202] The server has means for obtaining user interests from the database and collecting corresponding information using external APIs, which use public information sources, and means for generating summaries of the collected information using natural language processing algorithms.

[1203] Emotion recognition by emotion engine

[1204] The terminal includes a means for an emotion recognition module to read emotions from the user's facial expressions and text inputs while the user is using the system, and this information is transmitted in real time to a server, which then stores the analyzed emotion data.

[1205] Providing summaries and optimizing sentiment-based recommendations

[1206] The terminal provides a means to display the recommended information list and summaries generated by the server when the user logs in. The user can view the summaries of interest by selecting them and clicking on them. The server has a means to analyze the user's emotional data and adjust the recommendation algorithm based on which summaries and information the user responded positively to.

[1207] Feedback and System Improvement

[1208] The terminal is equipped with a means for the user to input a rating (e.g., a rating from 1 to 5 stars) or comment on the summary or information they have read and click the "Submit" button. The server then stores the user's feedback and emotional data in a database and continuously analyzes and improves the performance of the recommendation algorithm.

[1209] Hardware and software used

[1210] The hardware used to implement this invention is primarily a smartphone (with iOS or Android), and the main software used is Python, the emotion_recognition library, the spacy library, and a custom recommender system (RecommenderSystem).

[1211] Specific examples

[1212] For example, if a user creates a new account and selects "science" and "history" as their interest areas, this information is stored in the database. The server then searches for corresponding information using public information sources. For example, two pieces of information, "The Origin of the Universe" and "The Rise of the Roman Empire," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two pieces of information, and a list of recommended information is generated and served to the user based on the summaries.

[1213] When a user clicks on the summary of "The Rise of the Roman Empire" to learn more, the emotion engine reads the user's facial expressions and reactions to detect positive emotions. This information is stored in the database and used to adjust the recommendation algorithm for the next time.

[1214] Prompt Sentence Examples

[1215] If a user is interested in science or history and reads the summary of the information "The Rise of the Roman Empire" and responds with "This is fascinating!", please tell me how to build a system that recognizes this response as positive and reflects it in future recommendations.

[1216] In this way, the system of the present invention can efficiently provide a summary of various information while taking into account the user's emotions, improving the user experience, allowing the user to use their time more efficiently and receive more tailored information recommendations.

[1217] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1218] Step 1:

[1219] User registration and interest collection

[1220] Users create an account by entering their name, email address, and password on their device. Then, they select the genre they are interested in (e.g., history, science fiction, etc.). The input data (name, email address, password, genre) is sent to the server and stored in a database.

[1221] Step 2:

[1222] Information recommendation and summary generation

[1223] The server retrieves the user's interests from a database and collects corresponding information using an external API. Specifically, it retrieves information related to the user's interests (input data: genre of interest) from a public information source (e.g., a book database), and then generates a summary of the information using a natural language processing algorithm. The generated summary is stored as temporary data and later provided to the user.

[1224] Step 3:

[1225] Emotion recognition by emotion engine

[1226] The user views the summary of information on the device. At this time, the device uses an emotion recognition module to acquire emotional data in real time from the user's facial expressions and text input (input data: facial expressions, comments). The acquired emotional data is sent to the server and analyzed by the emotion analysis engine. The analysis results are stored in a database.

[1227] Step 4:

[1228] Providing summaries and optimizing sentiment-based recommendations

[1229] When a user logs in, the server displays the generated recommendation information list and summaries on the terminal. When the user selects a summary of interest, they can check its details. Emotional data (input data: browsing history, emotional data) obtained while the user is viewing the summary is sent to the server, and the recommendation algorithm analyzes the data and optimizes future recommendations.

[1230] Step 5:

[1231] Feedback and System Improvement

[1232] Users input ratings (e.g., ratings from 1 to 5 stars) and comments (input data: ratings, comments) on the summaries and information they have read from their devices. This feedback is sent to the server and stored in a database. The server continuously analyzes and improves the performance of the recommendation algorithm based on the feedback data and sentiment data.

[1233] 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.

[1234] 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.

[1235] 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.

[1236] [Fourth embodiment]

[1237] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1238] 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.

[1239] 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).

[1240] 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.

[1241] 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.

[1242] 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).

[1243] 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.

[1244] 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.

[1245] 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.

[1246] 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.

[1247] 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.

[1248] 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.

[1249] 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."

[1250] The present invention provides a system for enabling users to efficiently obtain summaries of a wide variety of books and cultivate a broad perspective. The system includes a process for recommending book information based on the user's interests and providing summaries of the books.

[1251] User registration and interest collection

[1252] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. After registering, they select the genre of interest (e.g., history, science, fiction, etc.).

[1253] Server: Stores registration information and user interests in a database.

[1254] Book recommendation and summary generation

[1255] Server: Retrieves user interests from the database and collects corresponding book information using external APIs, such as public book information sources like Google Books API.

[1256] Server: Generates summaries of collected book information using natural language processing algorithms, such as BERT and GPT-3.

[1257] Providing a summary

[1258] Terminal: When the user logs back into the system, they will see a customized list of recommended books and their summaries. They can click on summaries that interest them to find out more information.

[1259] Server: Optionally, provide further details about the book the user clicked on (reviews, related books, etc.).

[1260] Feedback and System Improvement

[1261] On the device: Users can provide feedback on summaries and books they have read, rating them on a scale of 1 to 5 stars, and adding comments.

[1262] Server: Stores the feedback in a database and uses the data to analyze and improve the performance of the recommendation algorithm.

[1263] Specific examples

[1264] User A creates a new account and selects "Science" and "History" as his / her genre of interest. This information is stored in the database, and the server then searches for corresponding books using the Google Books API. For example, two books, "The Origin of the Universe" and "The Rise of the Roman Empire," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two books. Based on these summaries, a recommended book list is generated and provided to User A.

[1265] User A clicks on the summary of "The Rise of the Roman Empire" to learn more and gives it a 5-star rating, which is saved in the database and used to refine the recommendation algorithm next time.

[1266] In this way, the system of the present invention can efficiently provide summaries of various books and improve the user's reading experience, allowing the user to use their time effectively and develop a broad perspective.

[1267] The processing flow will be explained below.

[1268] Step 1:

[1269] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. They then click the "Register" button to submit this information.

[1270] Step 2:

[1271] Server: Receives and validates the registration information, ensuring that the information entered is in the correct format and, if there are no problems, saves it to the database.

[1272] Step 3:

[1273] On the device: After completing registration, the user will be taken to a screen where they can select the genre they are interested in (e.g., history, science fiction, etc.). After selecting the genre they are interested in, they click the "Save" button.

[1274] Step 4:

[1275] Server: Receives the user's selected genre information and stores it in a database.

[1276] Step 5:

[1277] Server: Every time a user logs in, retrieve the user's interest genres from the database.

[1278] Step 6:

[1279] Server: Collects book information using an external API (e.g., Google Books API) based on the requested genre.

[1280] Step 7:

[1281] Server: Processes the collected book information and obtains detailed information for each book (title, author, publication year, abstract).

[1282] Step 8:

[1283] Server: Summarizes the book details using natural language processing algorithms (e.g., BERT or GPT-3).

[1284] Step 9:

[1285] Server: Based on the summary results, a customized book recommendation list is created for the user.

[1286] Step 10:

[1287] Terminal: When a user logs in, the server generates a list of recommended books and their summaries, and the user selects and clicks on a summary of interest.

[1288] Step 11:

[1289] Server: Provides more information based on the abstract the user clicks on, making additional API calls as needed to gather reviews and related books.

[1290] Step 12:

[1291] On the device: The user enters a rating (e.g., a rating from 1 to 5 stars) and comments for the summary they have read, and clicks the "Submit" button.

[1292] Step 13:

[1293] Server: Receives user feedback and stores it in a database.

[1294] Step 14:

[1295] Server: Analyzes the stored feedback and improves the performance of the recommendation algorithm, specifically incorporating the characteristics of highly rated books into future recommendations.

[1296] The above are the specific processing steps for carrying out the invention. This system allows users to efficiently read summaries of various books and develop a broad perspective.

[1297] Example 1

[1298] 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."

[1299] In today's society, a wide variety of books are published, making it difficult for users to efficiently find books that match their interests. Furthermore, users need to digest a huge amount of information quickly, and book summaries are a useful way to do this. However, manually creating summaries requires a great deal of time and effort, and without an appropriate recommendation system, it is impossible to provide books that match the user's interests. Therefore, a system is needed that allows users to quickly obtain book summaries customized based on their interests and efficiently improve their reading experience.

[1300] 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.

[1301] In this invention, the server includes means for selecting a genre of interest to a user, means for collecting book information based on the user's interests, means for generating summaries of the collected book information using a natural language processing algorithm, means for providing the user with the summary results and the book information, means for improving the recommendation algorithm based on feedback from the user, and means for storing the user's feedback in a database and analyzing and improving the performance of the algorithm using the database. This allows users to efficiently obtain book summaries based on their interests, and the recommendation accuracy is improved by the feedback, thereby providing an effective reading experience.

[1302] "User" refers to a person who uses this system.

[1303] A "genre" is a classification based on the content or category of a book, such as history, science, or fiction.

[1304] "Book information" refers to basic data such as the book's title, author, publication date, and summary.

[1305] "Natural language processing algorithms" refer to programs and methods for analyzing, understanding, and processing text data.

[1306] A "summary" is a short summary that extracts only the important parts from the original book information.

[1307] "Feedback" refers to ratings and comments provided by users to the system, providing data for evaluating and improving system performance.

[1308] A "recommendation algorithm" refers to a computational method for suggesting appropriate books and summaries based on a user's interests and past behavior.

[1309] A "database" refers to a system for systematically managing and storing various types of information.

[1310] "Server" refers to a central computing unit that stores, processes, and serves data.

[1311] "Generative AI models" refer to machine learning models trained to perform text generation and natural language processing, such as GPT-3.

[1312] "External data sources" refer to information sources outside the system that are used to obtain book information. For example, a public API.

[1313] The present invention provides a system for enabling users to efficiently obtain summaries of a wide variety of books and cultivate a broad perspective. This system includes a process for recommending book information based on the user's interests and providing summaries of the books. Specific embodiments of the system are described below.

[1314] User registration and interest collection

[1315] Terminal: The user accesses the system and creates an account by entering their name, email address, and password. After registering, they select the genre of interest (e.g., history, science, fiction, etc.).

[1316] Server: Stores registration information and user interests in a database, which is used later in the book recommendation process.

[1317] Book recommendation and summary generation

[1318] Server: Gets user interests from a database and collects corresponding book information using external data sources (e.g., public API).

[1319] As a concrete example, the Google Books API is used.

[1320] Server: Generate a summary of the collected book information using a generative AI model (e.g., GPT-3). Specific prompt examples are as follows:

[1321] Please provide a book summary: Title: "The Origin of the Universe", Author: "John Smith", Publication Date: "2022", Summary: "Introduces the latest research on the origin of the universe"

[1322] The generated summaries are stored in a database.

[1323] Providing a summary

[1324] Terminal: When the user logs back into the system, they will see a customized list of recommended books and their summaries. They can click on summaries that interest them to find out more information.

[1325] Server: Optionally, provide further details about the book the user clicked on (reviews, related books, etc.).

[1326] Feedback and System Improvement

[1327] On the device: Users can give feedback on summaries and books they have read. Ratings are expressed on a scale of 1 to 5 stars, and comments can be added.

[1328] Server: Stores the feedback in a database and uses the data to analyze and improve the performance of the recommendation algorithm.

[1329] Specific examples

[1330] User A creates a new account and selects "Science" and "History" as his / her genre of interest. This information is stored in the database, and the server then searches for corresponding books using the Google Books API. For example, two books, "The Origin of the Universe" and "The Rise of the Roman Empire," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two books. Based on these summaries, a recommended book list is generated and provided to User A.

[1331] User A clicks on the summary of "The Rise of the Roman Empire" to learn more and gives it a 5-star rating, which is saved in the database and used to refine the recommendation algorithm next time.

[1332] In this way, the system of the present invention can efficiently provide summaries of various books and improve the user's reading experience, allowing the user to use their time effectively and develop a broad perspective.

[1333] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1334] Step 1:

[1335] User Registration

[1336] User: A user visits the system's web page or app, enters their name, email address, and password, and clicks "Create Account."

[1337] Input: Name, Email Address, Password

[1338] Output: Registration request

[1339] Server: The server receives the entered user information and stores it in the database. If the save is successful, it sends the user a confirmation message saying "Account creation successful."

[1340] Input: Registration Request

[1341] Output: A confirmation message that the user information will be saved to the database.

[1342] What happens: The server establishes a database connection and saves the user information using an INSERT query.

[1343] Step 2:

[1344] Collecting user interests

[1345] User: After logging in, the user selects their genre of interest (e.g., history, science, fiction, etc.) and clicks "Update Interests."

[1346] Input: Selected genre information

[1347] Output: Interest update request

[1348] Server: The server saves the selected genre information to the database. If the save is successful, the updated information is reflected in the user's profile.

[1349] Input: Interest Update Request

[1350] Output: Updated interest information

[1351] What happens: The server establishes a database connection and stores the user's interests using an UPDATE query.

[1352] Step 3:

[1353] Collecting book information

[1354] Server: Periodically (e.g., once a day), retrieve each user's interest information from the database. Use external data sources (e.g., public APIs) to gather interest-based book information.

[1355] Input: Interest information for each user

[1356] Output: Book information collection request

[1357] What it does: The server sends an HTTP request to an external API, receives book information in JSON format, and parses the data to extract information such as the book title, author, publication date, and summary.

[1358] Step 4:

[1359] Generate a summary

[1360] Server: Generates summaries based on collected book information using natural language processing algorithms (e.g., generative AI models).

[1361] Input: Book information

[1362] Output: Book summary

[1363] What it does: The server inputs the book details into a generative AI model, creates a prompt to generate a summary, sends the prompt to the generative model (e.g., GPT-3), and stores the generated summary in a database.

[1364] Step 5:

[1365] Providing summaries and book information

[1366] Terminal: When the user logs back into the system, they will see their customized list of recommended books and their summaries.

[1367] Input: Login request

[1368] Output: Recommended reading list and summary

[1369] Server: The server processes the login request, retrieves recommended books and summaries based on the user's interests from the database, and sends them to the terminal.

[1370] Input: Request for recommended reading list and summary

[1371] Output: Recommended reading list and summary data

[1372] Specific operation: The server searches the database for recommended books based on the user's interest information and returns them to the terminal in JSON format along with their summaries.

[1373] Step 6:

[1374] Gathering feedback and improving the system

[1375] On the device: Users can provide feedback on summaries and books they have read by rating them on a scale of 1 to 5 stars and adding comments.

[1376] Input: Rating and Comments

[1377] Output: Feedback submission request

[1378] Server: The server stores the feedback in a database and analyzes and improves the performance of the recommendation algorithm based on the collected feedback.

[1379] Input: Rating and comment data

[1380] Output: Saving feedback and improving the algorithm

[1381] What it does: The server stores user feedback in a database using INSERT queries and performs statistical analysis to provide data for tuning the algorithm.

[1382] (Application example 1)

[1383] 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."

[1384] Conventional book recommendation systems can only provide users with one-dimensional information, making it difficult to interactively review summaries in real time. Furthermore, the means for improving system performance through feedback are limited, leaving further improvements needed to improve the accuracy of book recommendations to users. The present invention aims to solve these problems by providing a system that allows users to visually review book summaries in a virtual environment and efficiently provide feedback.

[1385] 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.

[1386] In this invention, the server includes a means for selecting a genre of interest to a user, a means for collecting book information based on the user's interests, a means for summarizing the collected book information using a natural language processing algorithm, a means for providing the summary result to the user, a means for improving the recommendation algorithm based on feedback from the user, a means for visually displaying the book information in a virtual environment, and a means for the user to visually check the summary result using smart glasses, thereby enabling the user to more intuitively grasp book information and provide feedback in real time.

[1387] The "means for selecting a genre of interest to the user" is an interface that allows the user to select a genre category based on their predetermined interests and preferences, and is a procedure by which the system grasps the user's area of ​​interest.

[1388] The "means for collecting book information based on the user's interests" refers to an algorithm and interface for obtaining related book data from the Internet or a database based on the genre selected by the user.

[1389] "Means for summarizing collected book information using natural language processing algorithms" refers to a program that applies natural language processing technology to analyze collected book data, extract key points, and summarize them concisely.

[1390] "Means for providing the summary result to the user" refers to a display screen or notification system for providing the generated book summary to the user in an accessible form.

[1391] The "means for improving the recommendation algorithm based on feedback from the user" is a system for collecting user ratings and comments, analyzing the data, and improving the accuracy of the recommendation algorithm.

[1392] "Means for visually displaying book information in a virtual environment" refers to technology that visually represents collected and summarized book information in a virtual reality or augmented reality environment, allowing users to view it in a virtual space.

[1393] "Means for the user to visually confirm the summary results using smart glasses" refers to a system in which the user wears smart glasses and the summarized book information is displayed superimposed on their field of vision, allowing them to intuitively confirm the information.

[1394] The present invention provides book summaries based on the genres of interest to users, improves system performance through feedback, and provides a system that allows users to intuitively view book information in a virtual environment using smart glasses.

[1395] System Configuration

[1396] 1. User registration and interest collection

[1397] Terminal: Users access the system through a terminal and create an account by entering their name, email address, and password. They then select the genre of interest (e.g., science, history, fiction, etc.).

[1398] Server: Stores registration information and user interests in a database.

[1399] 2. Book recommendation and summary generation

[1400] Server: Retrieves user interests from the database and collects corresponding book information using external sources (e.g., Google Books API).

[1401] Server: Generates summaries of collected book information using natural language processing algorithms (e.g., BERT, GPT-3).

[1402] 3. Virtual display of book information

[1403] Server: Generates data for visually displaying the generated book summary information in a virtual environment.

[1404] 4. Providing a Summary

[1405] Device: The user wears smart glasses and visually checks book information in the virtual bookstore. When the user focuses their gaze on a specific book, a summary is displayed.

[1406] 5. Feedback and System Improvement

[1407] Terminal: Users provide feedback (ratings, comments) on summaries and books they have read.

[1408] Server: Stores the feedback in a database and uses the data to improve the performance of the recommendation algorithm.

[1409] Hardware and software used

[1410] Hardware: Smart glasses, devices (smartphones, tablets, etc.)

[1411] Software: Databases (e.g., SQLite), external APIs (e.g., Google Books API), natural language processing algorithms (BERT, GPT-3)

[1412] Specific examples

[1413] User A creates a new account and selects "Science" and "History" as his or her genre of interest. The server uses the Google Books API to search for corresponding books. For example, two books, "Origin of the Universe" and "Historical Empires," are found, and detailed information about each is collected. BERT and GPT-3 are used to generate summaries of these two books. Based on these summaries, a virtual bookstore is created and provided to User A through smart glasses. User A clicks on the summary for "Historical Empires" to check the details, and provides a five-star rating and comment. This feedback is used to adjust the recommendation algorithm next time.

[1414] Prompt Sentence Examples

[1415] Generate a book summary using a prompt like this:

[1416] Generate a summary based on the given book details, which are:

[1417] Title: "Historical Empires"

[1418] Author:John Doe

[1419] Description: This book details the development and influence of historical empires, covering their political, cultural, and economic impacts, and includes key events and their context.

[1420] This invention is a system that improves the reading experience by enabling users to efficiently obtain book information of interest and intuitively check it in a virtual environment.

[1421] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1422] Specific processing steps of the program

[1423] Step 1: User registration and interest gathering

[1424] Input: The user enters their name, email address, password, and genre of interest.

[1425] Data processing: The terminal sends the entered information to the server, which stores it in a database.

[1426] Output: A user ID is generated and the user's information is registered in the database.

[1427] Specific operation: The terminal displays an input form, the user enters the required information, and the server records the received information in a database.

[1428] Step 2: Gathering book information

[1429] Input: The server retrieves the user's interest data in a database.

[1430] Data processing: The server calls an external API (e.g., Google Books API) to collect book information that matches the user's interests.

[1431] Output: The collected book list is returned to the server.

[1432] Specific operation: The server makes an API call and obtains related book data.

[1433] Step 3: Generate a summary of the book information

[1434] Input: Collected book information is provided to the server.

[1435] Data processing: The server uses a natural language processing algorithm (e.g., GPT-3) to generate a summary. It generates a book summary based on the input.

[1436] Output: The generated summaries are stored in a database.

[1437] Specific operation: The server inputs detailed information about the book into the summary generation model and saves the output summary.

[1438] Step 4: Generate virtual display data for book information

[1439] Input: Abstracted book information is kept on the server.

[1440] Data processing: The server generates display data for the virtual environment, including book cover images and summary text.

[1441] Output: The generated display data is stored and later provided to the smart glasses.

[1442] Specific operation: The server performs image processing and text formatting to prepare the display data.

[1443] Step 5: Provide a summary

[1444] Input: A user puts on smart glasses and accesses a virtual bookstore.

[1445] Data processing: The terminal sends the user's gaze detection data to the server, and the server obtains the corresponding book summary.

[1446] Output: A summary of the selected book is displayed on the user's smart glasses.

[1447] Specific operation: The device detects the user's gaze using an eye-tracking sensor, and the server provides summary data.

[1448] Step 6: Gather feedback and improve the system

[1449] Input: User provides feedback and enters star ratings and comments.

[1450] Data processing: The device sends the feedback information to the server, which stores it in a database. The server then analyzes the feedback data and adjusts the recommendation algorithm.

[1451] Output: New book recommendation results based on the improved algorithm are generated.

[1452] How it works: The device provides a rating form and records user input. The server analyzes the feedback data and retunes the recommendation algorithm.

[1453] Through this series of processing steps, users can efficiently obtain book information of their interest and improve the system's performance through feedback.

[1454] 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.

[1455] The present invention is a system that allows users to efficiently obtain summaries of a wide variety of books and optimizes recommendations through emotion recognition. This system includes a process for recommending book information based on the user's interests and providing summaries of those books. Furthermore, by combining this process with an emotion engine, optimal recommendations for each user are realized.

[1456] User registration and interest collection

[1457] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. After registering, they select the genre of interest (e.g., history, science, fiction, etc.).

[1458] Server: Stores registration information and user interests in a database.

[1459] Book recommendation and summary generation

[1460] Server: Retrieves user interests from the database and collects corresponding book information using external APIs, which use public book information sources.

[1461] Server: Generates summaries of collected book information using natural language processing algorithms.

[1462] Emotion recognition by emotion engine

[1463] On the device: While the user is using the system, the emotion recognition module reads emotions from the user's facial expressions and text input, and this information is sent to the server in real time.

[1464] Server: Stores the emotion data analyzed by the emotion engine.

[1465] Providing summaries and optimizing sentiment-based recommendations

[1466] Terminal: When a user logs in, the server generates a list of recommended books and their summaries, and the user selects and clicks on a summary of interest.

[1467] Server: Analyzes user sentiment data and adjusts recommendation algorithms based on which summaries and books users respond most positively to.

[1468] Server: Recommends books that are more relevant to the user based on their sentiment.

[1469] Feedback and System Improvement

[1470] On the device: The user enters a rating (e.g., a rating from 1 to 5 stars) and comments for the summary or book they have read, and clicks the "Submit" button.

[1471] Server: Stores user feedback and sentiment data in a database to continuously analyze and improve the performance of the recommendation algorithm.

[1472] Specific examples

[1473] User A creates a new account and selects "Science" and "History" as his / her genre of interest. This information is stored in the database, and the server then searches for corresponding books using public book information sources. For example, two books, "The Origin of the Universe" and "The Rise of the Roman Empire," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two books. Based on these summaries, a recommended book list is generated and provided to User A.

[1474] User A clicks on the summary of "The Rise of the Roman Empire" to check the details, and the emotion engine reads User A's facial expressions and reactions to detect positive emotions. This information is stored in the database and used to adjust the recommendation algorithm next time.

[1475] In this way, the system of the present invention can efficiently provide summaries of various books while taking into account the user's emotions, improving the user's reading experience, allowing the user to use their time more efficiently and receive more tailored reading recommendations.

[1476] The processing flow will be explained below.

[1477] Step 1:

[1478] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. They then click the "Register" button to submit this information.

[1479] Step 2:

[1480] Server: Receives and validates the registration information, ensuring that the information entered is in the correct format and, if there are no problems, saves it to the database.

[1481] Step 3:

[1482] On the device: After completing registration, the user will be taken to a screen where they can select the genre they are interested in (e.g., history, science fiction, etc.). After selecting the genre they are interested in, they click the "Save" button.

[1483] Step 4:

[1484] Server: Receives the user's selected genre information and stores it in a database.

[1485] Step 5:

[1486] Server: Every time a user logs in, retrieve the user's interest genres from the database.

[1487] Step 6:

[1488] Server: Collects book information using an external API (e.g., Google Books API) based on the requested genre.

[1489] Step 7:

[1490] Server: Processes the collected book information and obtains detailed information for each book (title, author, publication year, abstract).

[1491] Step 8:

[1492] Server: Summarizes the book details using natural language processing algorithms (e.g., BERT or GPT-3).

[1493] Step 9:

[1494] Server: Based on the summary results, a customized book recommendation list is created for the user.

[1495] Step 10:

[1496] Terminal: When a user logs in, the server generates a list of recommended books and their summaries, and the user selects and clicks on a summary of interest.

[1497] Step 11:

[1498] Server: Provides more information based on the abstract the user clicks on, making additional API calls as needed to gather reviews and related books.

[1499] Step 12:

[1500] Device: While the user is using the system, the emotion recognition module collects the user's facial expressions and tone of voice using a camera and microphone.

[1501] Step 13:

[1502] Server: The emotion recognition module analyzes the user's facial expressions and speech in real time to generate emotion data, which is then stored in a database in real time.

[1503] Step 14:

[1504] Device: The user enters a rating (e.g., a rating from 1 to 5 stars) and comments for the summary or book they have read, and clicks the "Submit" button.

[1505] Step 15:

[1506] Server: Receives user feedback and emotion data collected in real time and stores it in a database.

[1507] Step 16:

[1508] Server: Analyzes the stored emotion data and feedback data to improve the performance of the recommendation algorithm. Extracts the characteristics of books that users have expressed positive emotions about and reflects them in the next recommendation.

[1509] The above are the specific processing steps for implementing the invention. This system allows users to efficiently read summaries of various books and receive optimal recommendations based on real-time emotional data. This system allows users to use their time efficiently and enjoy a more personalized reading experience.

[1510] Example 2

[1511] 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."

[1512] Currently, a large amount of information exists on the Internet, making it difficult for users to efficiently obtain the information they need. In particular, when it comes to book summaries and recommendations, personalized recommendations that reflect the user's interests and emotions are difficult to make. To solve this problem, a system is needed that accurately captures the user's interests and provides appropriate book information.

[1513] 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 selecting a category in which a user is interested, a means for collecting data based on the user's interests, a means for summarizing the collected data using a natural language processing algorithm, a means for analyzing emotion data and adjusting a recommendation algorithm, a means for providing the summary result to the user, and a means for improving the recommendation algorithm based on feedback from the user. This enables the user to efficiently obtain optimal book information based on their own interests and emotions.

[1514] "User Interest Categories" refers to specific themes or genres that interest a user.

[1515] "Data" refers to units of information such as book information, including text, metadata, and summaries.

[1516] "Natural language processing algorithm" refers to a computer program for processing and understanding human language, including specifically algorithms for summarizing and analyzing text.

[1517] "Emotional data" refers to information that represents the user's emotional state obtained from facial expressions and text input.

[1518] A "recommendation algorithm" refers to a calculation method for selecting and providing optimal book information based on a user's interests and emotions.

[1519] "External source" refers to an external resource for obtaining information, such as a database or API that exists outside the system.

[1520] An "emotion recognition module" refers to a component that has the function of reading emotions from a user's facial expressions, voice, text, etc.

[1521] "Database" means a system for storing and managing information in an organized form.

[1522] "Feedback" refers to information provided by users to evaluate the performance of a system, such as ratings and comments.

[1523] The present invention is a system that allows users to efficiently obtain summaries of a wide variety of books and optimizes recommendations through emotion recognition. This system includes a process for recommending book information based on the user's interests and providing summaries of those books. Furthermore, by combining this process with an emotion engine, optimal recommendations for each user are realized.

[1524] User registration and interest collection

[1525] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. After registering, they select the categories of interest (e.g., history, science, fiction, etc.).

[1526] Server: The server stores registration information and user interests in a database (e.g., MySQL or PostgreSQL).

[1527] Collecting book recommendations

[1528] Server: The server obtains the user's interests from the database and collects the corresponding book information using external APIs (e.g., Google Books API or Open Library API).

[1529] Server: Stores collected book information in a database.

[1530] Book summary generation

[1531] Server: The server generates a summary of the collected book information using a natural language processing algorithm (e.g., a generative AI model such as GPT-3).

[1532] Server: Organizes the generated summaries for each user and stores them in a database.

[1533] Emotion recognition by emotion engine

[1534] Device: While the user is using the system, the emotion recognition module reads emotions from the user's facial expressions and text input. For example, it analyzes the user's facial expressions and voice in real time using a camera and microphone.

[1535] Server: Analyzes the data sent from the emotion recognition module in real time and stores the user's emotions in a database.

[1536] Providing summaries and optimizing sentiment-based recommendations

[1537] Terminal: When the user logs in, the server displays a list of recommended books and summaries. The user selects a summary of interest and checks the details.

[1538] Server: While the user is reviewing the summaries, the server analyzes the user's emotions based on emotion recognition data and records which summaries the user responded positively to.

[1539] Server: Based on this data, the next recommendation algorithm is adjusted for each user to recommend more suitable books.

[1540] Feedback and System Improvement

[1541] Terminal: The user enters a rating or comment on the summary or book they have read and clicks the "Submit" button.

[1542] Server: Stores user feedback and sentiment data in a database and uses that information to update algorithms, for example by using mining techniques to reinforce categories with more positive feedback.

[1543] Specific examples

[1544] User A creates a new account and selects "Science" and "History" as his / her interest categories. This information is stored in the database, and the server then searches for corresponding books using an external API. For example, two books, "The Origin of the Universe" and "The Rise of Civilization," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two books. Based on these summaries, a recommended book list is generated and provided to User A.

[1545] User A clicks on the summary of "The Rise of Civilization" to check the details, and the emotion engine reads User A's facial expressions and reactions to detect positive emotions. This information is stored in the database and used to adjust the recommendation algorithm next time.

[1546] Example prompts to input to the generative AI model

[1547] "Develop an outline of a system that generates book summaries based on interest categories and optimizes recommendations according to user sentiment. As a concrete example, consider a case where a user is interested in science and history and is provided with book summaries in those categories."

[1548] This allows users to efficiently obtain optimal book information based on their interests and feelings, enabling them to have a richer reading experience.

[1549] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1550] Step 1:

[1551] User registration and interest collection

[1552] Terminal: A user accesses the system and creates an account by entering their name, email address, and password. After completing registration, the user selects the category of interest (e.g., history, science, fiction, etc.).

[1553] Input: User-entered information (name, email address, password, interest categories)

[1554] Server: Receives the information entered by the user, performs validation, and if validation is successful, stores it in a database (MySQL or PostgreSQL). Stored data includes the user ID, name, email address, password hash, and interest categories.

[1555] Output: The user information is saved in the database and the registration process is complete.

[1556] Step 2:

[1557] Collecting book recommendations

[1558] Server: The server retrieves registered user interest data from the database. Based on the retrieved interest data, it collects book information using external APIs such as the Google Books API and the Open Library API.

[1559] Input: User interest data (category)

[1560] Server: Sends a request to the external API endpoint and receives the relevant book information in JSON format, including the title, author, publication date, summary, etc.

[1561] Output: Book information is collected on the server and stored in a database.

[1562] Step 3:

[1563] Book summary generation

[1564] Server: Processes the collected book information using a natural language processing algorithm and generates a summary. Specifically, it uses a generative AI model such as GPT-3.

[1565] Input: Book information (title, author, publication date, summary, etc.)

[1566] Server: Book information is input as text data into the generative AI model, and a summary text is generated. The generated summary text is then saved back into the database.

[1567] Output: The generated book summaries are stored in a database.

[1568] Step 4:

[1569] Emotion recognition by emotion engine

[1570] Device: While the user is using the system, the camera and microphone capture the user's facial expressions and voice in real time. The emotion recognition module uses this data to analyze the user's emotions.

[1571] Input: User's facial expression data, voice data

[1572] Server: Analyzes the received emotion data in real time and identifies the emotional state. For example, using the Emotion API. The analysis results are stored in a database.

[1573] Output: The user's emotion data is stored in the database.

[1574] Step 5:

[1575] Providing summaries and optimizing sentiment-based recommendations

[1576] Terminal: When a user logs in, the server retrieves the recommended book list and summaries from the database and displays them on the terminal. The user selects the summary of interest and checks the details.

[1577] Input: User login information, emotion data, summary list

[1578] Server: While reviewing the summaries, analyzes the emotion recognition data in real time and records which summaries elicited positive reactions, optimizing the recommendation algorithm for future use.

[1579] Output: Recommendations optimized for the user are made, and the recommendation list is adjusted for future use.

[1580] Step 6:

[1581] Feedback and System Improvement

[1582] Terminal: The user enters a rating or comment on the summary or book they have read and clicks the "Submit" button.

[1583] Input: User ratings, comments, and sentiment data

[1584] Server: Receives user feedback and stores it in a database. Updates the algorithm based on the stored data and performs data mining to improve recommendation accuracy.

[1585] Output: The feedback is stored in a database and the algorithm is optimized.

[1586] (Application example 2)

[1587] 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."

[1588] Conventional book recommendation systems have difficulty fully reflecting users' interests and preferences, preventing them from making effective use of their time. Furthermore, they are unable to fully utilize feedback from user interactions, making it difficult to improve the accuracy of recommendation algorithms. Furthermore, they are unable to take into account the user's emotional state, resulting in incomplete personalization. A system that can solve these problems and improve users' reading experience is needed.

[1589] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1590] In this invention, the server includes a means for collecting information based on the user's interests, a means for summarizing the collected information using a natural language processing algorithm, and a means for reading the user's emotional data and optimizing the recommendation algorithm, thereby making it possible to provide information based on the user's interests and to optimize the recommendation algorithm in a way that reflects the user's emotions.

[1591] "User" refers to an individual or organization that uses the system.

[1592] "Genre" refers to a theme or category classified based on the user's interests and concerns.

[1593] "Information" refers to data and metadata about content such as books and documents.

[1594] "Natural language processing algorithms" refer to technical methods used to summarize and analyze collected information.

[1595] A "summary" is a concise representation of the main points or content of the collected information.

[1596] "Providing" refers to displaying the generated summary and recommendation results to the user.

[1597] "Emotion data" refers to data related to emotions obtained by analyzing a user's facial expressions and text input.

[1598] A "recommendation algorithm" refers to a program that selects and recommends the most appropriate information based on the user's interests and emotions.

[1599] "Optimization" refers to using user feedback and sentiment data to adjust recommendation algorithms to improve their performance.

[1600] "Feedback" refers to reactions such as ratings and comments provided by users.

[1601] A "database" refers to a system for storing collected information, user feedback, emotional data, etc.

[1602] MODE FOR CARRYING OUT THE INVENTION

[1603] This invention is a system that allows users to efficiently obtain summaries of a wide variety of information and optimizes recommendations through emotion recognition. This system includes a process for recommending information based on the user's interests and providing summaries of that information. Furthermore, by combining this process with an emotion engine, it is possible to realize optimal recommendations for each user.

[1604] User registration and interest collection

[1605] The server provides a means for users to access the system and create an account by entering their name, email address, and password. After registering, users also have the ability to select their genre of interest (e.g., history, science fiction, etc.). This information is stored in a database.

[1606] Information recommendation and summary generation

[1607] The server has means for obtaining user interests from the database and collecting corresponding information using external APIs, which use public information sources, and means for generating summaries of the collected information using natural language processing algorithms.

[1608] Emotion recognition by emotion engine

[1609] The terminal includes a means for an emotion recognition module to read emotions from the user's facial expressions and text inputs while the user is using the system, and this information is transmitted in real time to a server, which then stores the analyzed emotion data.

[1610] Providing summaries and optimizing sentiment-based recommendations

[1611] The terminal provides a means to display the recommended information list and summaries generated by the server when the user logs in. The user can view the summaries of interest by selecting them and clicking on them. The server has a means to analyze the user's emotional data and adjust the recommendation algorithm based on which summaries and information the user responded positively to.

[1612] Feedback and System Improvement

[1613] The terminal is equipped with a means for the user to input a rating (e.g., a rating from 1 to 5 stars) or comment on the summary or information they have read and click the "Submit" button. The server then stores the user's feedback and emotional data in a database and continuously analyzes and improves the performance of the recommendation algorithm.

[1614] Hardware and software used

[1615] The hardware used to implement this invention is primarily a smartphone (with iOS or Android), and the main software used is Python, the emotion_recognition library, the spacy library, and a custom recommender system (RecommenderSystem).

[1616] Specific examples

[1617] For example, if a user creates a new account and selects "science" and "history" as their interest areas, this information is stored in the database. The server then searches for corresponding information using public information sources. For example, two pieces of information, "The Origin of the Universe" and "The Rise of the Roman Empire," are found, and detailed information about each is collected. A natural language processing algorithm is used to generate summaries of these two pieces of information, and a list of recommended information is generated and served to the user based on the summaries.

[1618] When a user clicks on the summary of "The Rise of the Roman Empire" to learn more, the emotion engine reads the user's facial expressions and reactions to detect positive emotions. This information is stored in the database and used to adjust the recommendation algorithm for the next time.

[1619] Prompt Sentence Examples

[1620] If a user is interested in science or history and reads the summary of the information "The Rise of the Roman Empire" and responds with "This is fascinating!", please tell me how to build a system that recognizes this response as positive and reflects it in future recommendations.

[1621] In this way, the system of the present invention can efficiently provide a summary of various information while taking into account the user's emotions, improving the user experience, allowing the user to use their time more efficiently and receive more tailored information recommendations.

[1622] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1623] Step 1:

[1624] User registration and interest collection

[1625] Users create an account by entering their name, email address, and password on their device. Then, they select the genre they are interested in (e.g., history, science fiction, etc.). The input data (name, email address, password, genre) is sent to the server and stored in a database.

[1626] Step 2:

[1627] Information recommendation and summary generation

[1628] The server retrieves the user's interests from a database and collects corresponding information using an external API. Specifically, it retrieves information related to the user's interests (input data: genre of interest) from a public information source (e.g., a book database), and then generates a summary of the information using a natural language processing algorithm. The generated summary is stored as temporary data and later provided to the user.

[1629] Step 3:

[1630] Emotion recognition by emotion engine

[1631] The user views the summary of information on the device. At this time, the device uses an emotion recognition module to acquire emotional data in real time from the user's facial expressions and text input (input data: facial expressions, comments). The acquired emotional data is sent to the server and analyzed by the emotion analysis engine. The analysis results are stored in a database.

[1632] Step 4:

[1633] Providing summaries and optimizing sentiment-based recommendations

[1634] When a user logs in, the server displays the generated recommendation information list and summaries on the terminal. When the user selects a summary of interest, they can check its details. Emotional data (input data: browsing history, emotional data) obtained while the user is viewing the summary is sent to the server, and the recommendation algorithm analyzes the data and optimizes future recommendations.

[1635] Step 5:

[1636] Feedback and System Improvement

[1637] Users input ratings (e.g., ratings from 1 to 5 stars) and comments (input data: ratings, comments) on the summaries and information they have read from their devices. This feedback is sent to the server and stored in a database. The server continuously analyzes and improves the performance of the recommendation algorithm based on the feedback data and sentiment data.

[1638] 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.

[1639] 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.

[1640] 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.

[1641] 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.

[1642] 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.

[1643] 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.

[1644] 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).

[1645] 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.

[1646] 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."

[1647] 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.

[1648] 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).

[1649] 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.

[1650] 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.

[1651] 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.

[1652] 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.

[1653] 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.

[1654] 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.

[1655] 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.

[1656] 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.

[1657] 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.

[1658] 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.

[1659] The following is further disclosed regarding the above embodiment.

[1660] (Claim 1)

[1661] A means for the user to select a genre of interest;

[1662] means for collecting book information based on the user's interests;

[1663] A means for summarizing the collected book information using a natural language processing algorithm;

[1664] means for providing the summary results to a user;

[1665] means for improving a recommendation algorithm based on feedback from said users;

[1666] A system including:

[1667] (Claim 2)

[1668] 10. The system of claim 1, further comprising means for obtaining book information from an external source.

[1669] (Claim 3)

[1670] 10. The system of claim 1, further comprising means for storing user feedback in a database and using said database to analyze and improve the performance of the algorithm.

[1671] "Example 1"

[1672] (Claim 1)

[1673] A means for the user to select a genre of interest;

[1674] means for collecting book information based on the user's interests;

[1675] A means for generating a summary of the collected book information using a natural language processing algorithm;

[1676] means for providing the summary results and book information to a user;

[1677] means for improving a recommendation algorithm based on feedback from said users;

[1678] means for storing user feedback in a database and using said database to analyze and improve the performance of the algorithm;

[1679] A system including:

[1680] (Claim 2)

[1681] 10. The system of claim 1, further comprising means for obtaining book information from an external data source.

[1682] (Claim 3)

[1683] 10. The system of claim 1, further comprising: means for using the generative AI model as a natural language processing algorithm.

[1684] "Application Example 1"

[1685] Claims based on new inventions

[1686] (Claim 1)

[1687] A means for the user to select a genre of interest;

[1688] means for collecting book information based on the user's interests;

[1689] A means for summarizing the collected book information using a natural language processing algorithm;

[1690] means for providing the summary results to a user;

[1691] means for improving a recommendation algorithm based on feedback from said users;

[1692] a means for visually displaying book information in a virtual environment;

[1693] A means for a user to visually confirm the summary result using smart glasses;

[1694] A system including:

[1695] (Claim 2)

[1696] 10. The system of claim 1, further comprising means for obtaining book information from an external source.

[1697] (Claim 3)

[1698] 10. The system of claim 1, further comprising means for storing user feedback in a database and using said database to analyze and improve the performance of the algorithm.

[1699] "Example 2: Combining Emotion Engines"

[1700] (Claim 1)

[1701] a means for the user to select categories of interest;

[1702] means for collecting data based on the user's interests;

[1703] A means for summarizing the collected data using natural language processing algorithms;

[1704] a means of analyzing the sentiment data to adjust the recommendation algorithm;

[1705] means for providing the summary results to a user;

[1706] means for improving a recommendation algorithm based on feedback from said users;

[1707] A system including:

[1708] (Claim 2)

[1709] 10. The system of claim 1, further comprising: means for obtaining data from an external source; and an emotion recognition module.

[1710] (Claim 3)

[1711] 10. The system of claim 1, further comprising means for storing user feedback and sentiment data in a database and using said database to analyze and improve algorithm performance.

[1712] "Application example 2 when combining emotion engines"

[1713] (Claim 1)

[1714] A means for the user to select a genre of interest;

[1715] means for collecting information based on the user's interests;

[1716] A means for summarizing the collected information using a natural language processing algorithm;

[1717] means for providing the summary results to a user;

[1718] A means for reading user emotional data and optimizing the recommendation algorithm;

[1719] means for improving a recommendation algorithm based on feedback from said users;

[1720] A system including:

[1721] (Claim 2)

[1722] 10. The system of claim 1, further comprising: means for obtaining information from an external source.

[1723] (Claim 3)

[1724] 10. The system of claim 1, further comprising means for storing user feedback and sentiment data in a database and using said database to analyze and improve algorithm performance. [Explanation of symbols]

[1725] 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. A means for the user to select a genre of interest; means for collecting book information based on the user's interests; A means for summarizing the collected book information using a natural language processing algorithm; means for providing the summary results to a user; means for improving a recommendation algorithm based on feedback from said users; A system including:

2. The system of claim 1 further comprising means for obtaining book information from an external source.

3. 10. The system of claim 1, further comprising means for storing user feedback in a database and using said database to analyze and improve the performance of the algorithm.

Images