System

Abstract

A system is provided.SOLUTION: The system includes a means for analyzing the read contents, a means for summarizing the analyzed contents, a means for extracting key points from the summary, a means for specifying the range read by the user, a means for preventing spoiler by using a means for filtering the range read by the user, and a means for providing the filtered summary and key points to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional e-book systems make it difficult for users to efficiently recall previously read content during fragmented reading sessions. Manually creating summaries and key points after each page turn is inefficient and can detract from the reading experience. Furthermore, summaries may contain spoilers, which can disrupt the user's reading experience. To address these issues, a method is needed to automatically summarize the content being read, extract key points, and prevent spoilers. [Means for solving the problem]

[0005] The present invention provides a system including a means for analyzing the content being read, a means for summarizing the analyzed content, a means for extracting key points from the summary, a means for identifying the range of content that the user has read, a means for filtering the range of content that the user has read to prevent spoilers, and a means for providing the filtered summary and key points to the user. Furthermore, by including a means for transmitting information about the pages being read to a server and a means for displaying the summary and key points received from the server, efficient reading support is realized. Furthermore, by including a means for analyzing text using natural language processing technology and identifying important sentences and key phrases, highly accurate summaries and key points can be extracted, and information can be provided in a format that is intuitively easy for the user to understand. This allows users to efficiently recall past content even during fragmented reading time, preventing spoilers and providing a rich reading experience.

[0006] "Progressed content" refers to the range of text or pages a user has already read within an e-book.

[0007] "Means of analysis" refers to the ability to analyze text using natural language processing techniques to provide a clear understanding of what has been read.

[0008] "Means of summarizing" refers to the function of extracting important information based on the analyzed content and reconstructing it into concise, key points.

[0009] "Key points" refer to particularly important terms, phrases, or sentences in the content you've read.

[0010] "Read range" refers to the range of consecutive pages that a user has read in an e-book.

[0011] "Filtering measures" refers to functions that allow users to filter out information about parts they have not yet read.

[0012] "Spoiler prevention" refers to features that control the display of summaries and key points to prevent users from learning about content they have not yet read.

[0013] "Means provided to the user" refers to the ability to display the filtered summary and key points on the user's device.

[0014] "Means for sending page information" refers to the function that communicates to the server the page number and range that the user has read.

[0015] "Natural language processing technology" refers to algorithms and technologies for analyzing text data and understanding and extracting meaning.

[0016] "Means for identifying important sentences and key phrases" refers to the ability to automatically identify particularly meaningful parts of text. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention relates to a system that automatically summarizes and extracts key points from the content of an electronic book as the user reads it. The program processing of this system is explained below in natural language.

[0039] server

[0040] Initialization

[0041] The server loads the e-book data and user information from the database, which allows it to know which e-book the user is currently reading and up to which page, for further analysis.

[0042] Receiving reading data

[0043] The page information that the user has read on the device is sent to the server, which receives this data and checks which pages the user has read.

[0044] Natural Language Processing (NLP) Analysis

[0045] The server analyzes the text data of the received page range using natural language processing technology. Specifically, it divides the text into sentences and analyzes the meaning of each sentence. For analysis, it uses libraries such as spaCy, NLTK, and Transformers.

[0046] Summary Generation

[0047] The server extracts key sentences and phrases from the analysis and generates a summary, which is a concise summary of what the user has read.

[0048] Spoiler Filtering

[0049] The server filters information about parts of the book that the user has not yet read, ensuring that summaries and key points do not contain spoilers.

[0050] Response generation and sending

[0051] The server packages the filtered summary and keypoints into JSON format and sends it to the device.

[0052] Terminal (user device)

[0053] User Interface Display

[0054] When users open an e-book, the device displays a dedicated reading assistance interface, allowing users to check the summary and key points at any time.

[0055] Data transmission

[0056] Each time the user turns a page, the device sends the new page information to the server, including the user ID and the page range that has been read.

[0057] Response reception and display

[0058] The device analyzes the summary and key points received from the server and displays them on the user interface, allowing users to efficiently understand the content they have read.

[0059] user

[0060] reading

[0061] The user continues reading the e-book, and the page information is sent from the device to the server.

[0062] Summary and key points review

[0063] Users can recall important content by reviewing summaries and key points displayed on the screen.

[0064] Continue reading

[0065] When users start their next reading, they can smoothly resume reading by checking the summary of their previous reading.

[0066] Specific examples

[0067] When a user begins reading an e-book, the device sends the user ID and current page range to the server. For example, if the user has read pages 1 to 10, that information is sent to the server. The server analyzes the content of pages 1 to 10 and generates a summary and key points. The generated summary and key points are filtered to ensure they do not contain spoilers and are sent to the device. The device displays them to the user. The next time the user starts reading from page 11, they can smoothly continue reading by checking the summary from the previous page.

[0068] In this way, the present invention is a system that allows you to enjoy reading efficiently even with fragmented reading time, and provides a rich reading experience while preventing spoilers.

[0069] The processing flow will be explained below.

[0070] Step 1:

[0071] The server loads the e-book data and user information from the database, so that the server can know which e-book the user is reading and up to which page they have read.

[0072] Step 2:

[0073] When a user opens an e-book and starts reading from page 1, the device sends the user ID and page number (pages 1 to 10) to the server.

[0074] Step 3:

[0075] The server analyzes the page information received from the device, checks which pages the user has read, and identifies the text data of the page range that the user has read based on the received information.

[0076] Step 4:

[0077] The server analyzes the text data of the received page range using natural language processing (NLP) techniques, such as splitting the text into sentences and analyzing the meaning of each sentence, using libraries such as spaCy, NLTK, and Transformers.

[0078] Step 5:

[0079] The server extracts important sentences and key phrases and uses them to generate a summary, which is a concise summary of what the user has read and includes the main points of information.

[0080] Step 6:

[0081] The server uses filtering measures to filter out information about parts of the book that the user has not yet read, an important step in preventing spoilers.

[0082] Step 7:

[0083] The server packages the filtered summary and keypoints into JSON format and sends it to the device.

[0084] Step 8:

[0085] The device receives the response from the server, parses the JSON data, extracts summaries and key points, and displays them on the user interface.

[0086] Step 9:

[0087] Users see a summary and key points displayed on the screen, which helps users remember important content.

[0088] Step 10:

[0089] The user starts reading from page 11 during the next reading time. The device sends the information "User ID" and "Pages 11 to 20" to the server.

[0090] Step 11:

[0091] The server analyzes the content of pages 11 to 20, generates a summary and key points, and sends them to the terminal, which displays them to the user, allowing the user to efficiently understand the content they are reading.

[0092] By repeating this series of processes, users can enjoy e-books while avoiding spoilers by checking summaries and key points of the content they have read.

[0093] Example 1

[0094] 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."

[0095] The present invention aims to enable users to efficiently grasp the content of e-books and quickly review important points when rereading them later. However, conventional e-book systems lack the functionality to automatically summarize the content they have read and extract key points, or to prevent spoilers from being revealed for content they have not yet read. This requires users to recall the content each time they read, which reduces the efficiency of their reading experience.

[0096] 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.

[0097] In this invention, the server includes means for loading e-book data and user information, means for receiving page information of the page that has been read, means for analyzing the text of the received page range using natural language processing technology, means for extracting important sentences and key phrases from the analysis result and generating a summary, means for filtering information of the part that the user has not yet read, and means for providing the filtered summary and key points to the user. This allows the user to efficiently grasp the content that they have read and easily confirm important points while avoiding spoilers.

[0098] "E-Book Data" refers to the text data of a book stored in digital form, often formatted for reading on an e-reader or other device.

[0099] "User information" refers to data about users who use the system, including user IDs, reading history, and settings information.

[0100] "Read page information" is data indicating a specific page range when a user reads an electronic book.

[0101] "Natural language processing technology" is a technology for analyzing text data and understanding and generating human language. Specifically, it includes processes such as text segmentation, semantic analysis, and summary generation.

[0102] The "analysis results" are analytical information on text data obtained using natural language processing technology, and include important sentences and key phrases.

[0103] A "summary" is a shortened version of important information or key points extracted from a long text.

[0104] "Keyphrases" are important words or phrases in a text that play an important role in summarizing and searching.

[0105] "Filtering" is the process of selecting data based on specific conditions and removing unnecessary parts. In this system, it refers to removing unread information to prevent spoilers.

[0106] A "user interface" is a collection of screens and operating methods that allow a user to operate a system and obtain information.

[0107] The "JSON format" is a lightweight data exchange format for structuring, storing, and transferring data, and is an abbreviation for JavaScript Object Notation.

[0108] This invention relates to a system that automatically summarizes and extracts key points from the content of an e-book as the user reads it. This system realizes efficient reading support in real time through the cooperation of a server and a terminal.

[0109] server

[0110] First, the server loads the e-book data and user information from a database, typically an SQL database, and executes SQL queries to retrieve the necessary data. This allows the server to determine which e-book the user has read and up to which page, and prepares for subsequent analysis.

[0111] Next, the server receives the page information sent from the device. This data is sent in JSON format and includes the user ID and the page range that has been read. The received data is temporarily stored in the server's memory and passed on to the next analysis process.

[0112] The text data of the received page range is analyzed using natural language processing techniques. Specifically, libraries such as "spaCy," "NLTK," and "Transformers" are used. For example, "spaCy" is used to divide the text into sentences and analyze the meaning of each sentence. In addition, the BERT model in "Transformers" is used to generate sentence embeddings and extract important sentences.

[0113] Extract important sentences and key phrases from the analysis results and generate summaries. The "transformers" library is used to generate summaries, and a summary model (e.g., a BERT-based summary model) is used to shorten and summarize important information. Furthermore, key phrases are extracted using TF-IDF and the Rake algorithm.

[0114] To prevent spoilers from being included in the generated summary and key points, information about parts that the user has not yet read is filtered out. This is done by the server retrieving the user's reading completion information from the database and detecting the unread parts. The spoiler parts are then removed using filtering logic.

[0115] Finally, the filtered summary and keypoints are packaged in JSON format and sent to the terminal.

[0116] Terminal (user device)

[0117] When a user opens an e-book, the device displays a dedicated reading support interface, which is built using JavaScript and HTML and is designed to allow users to easily check summaries and key points.

[0118] Every time the user turns a page, the device sends the new page information to the server, including the user ID and the page range that has been read. The data is sent using the "fetch" function, and is packaged in JSON format.

[0119] Analyze the summary and key points received from the server and display them on the user interface. Analyze the received JSON data and dynamically insert the summary and key points into HTML elements.

[0120] user

[0121] As users read e-books, information about the pages they have read is sent from their device to the server. By checking the automatic summary and key points of the content they have read, users can read more efficiently.

[0122] When you start reading again, you can smoothly resume where you left off by checking the summary from the previous time, providing a consistent reading experience even when reading in fragments.

[0123] Specific examples

[0124] For example, when a user starts reading an e-book, the device sends the user ID and the current page range to the server. If the user has read pages 1 to 10, that information is sent to the server. The server analyzes the content of pages 1 to 10 and generates a summary and key points. The generated summary and key points are filtered to avoid spoilers and sent to the device, which then displays them to the user.

[0125] The next time a user starts reading from page 11, they can smoothly continue reading while checking the summary from the previous page.

[0126] Prompt Sentence Examples

[0127] "Please summarize the contents of pages 1-10 and extract the main key points. Please be careful not to include spoilers."

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Step 1:

[0130] Initialization

[0131] The server loads e-book data and user information from the database. Specifically, it executes an SQL query to get the required data. For example, it executes a query like "SELECT FROM ebooks WHERE user_id = 'user ID'". The input is the response from the database, and the output is the loaded e-book data and user information.

[0132] Step 2:

[0133] Receiving reading data

[0134] The server receives the page information sent from the device. This data is sent in JSON format and includes the user ID and the page range that has been read. Specifically, it receives JSON data from the device such as "{"user_id": "User ID", "start_page": 1, "end_page": 10}". The input is the JSON data from the device, and the output is the saved page information that has been read.

[0135] Step 3:

[0136] Natural Language Processing (NLP) Analysis

[0137] The server analyzes the text data of the received page range using natural language processing techniques. Specifically, it uses the "spaCy" and "Transformers" libraries to split the text into sentences and perform semantic analysis. For example, it uses "spaCy" to initialize "nlp = spacy.load('en_core_web_sm')" to analyze the received text. It also uses the "Transformers" BERT model to generate sentence embeddings. The input is the text data to be analyzed, and the output is the analysis results: important sentences and key phrases.

[0138] Step 4:

[0139] Summary Generation

[0140] The server extracts important sentences and key phrases from the analysis results and generates summaries. Specifically, it uses a summarization model from the "transformers" library to shorten and summarize important information. It also extracts key phrases using TF-IDF and Rake algorithms. The input is the analysis results, and the output is the generated summary and key phrases.

[0141] Step 5:

[0142] Spoiler Filtering

[0143] The server filters information about parts that the user has not yet read to ensure that the generated summary and key points do not contain spoilers. This is done by retrieving the user's reading completion information from the database and detecting unread parts. For example, it applies filtering logic such as "delete if the if statement comes from an unread page." The input is the summary, key phrases, and the user's reading completion information, and the output is the filtered summary and key phrases.

[0144] Step 6:

[0145] Response generation and sending

[0146] The server packages the filtered summary and key phrases in JSON format and sends them to the device. Specifically, it packages and sends them as follows: "response = json.dumps({'summary': 'Summary content', 'key_points': 'Key points'})" and "send_response_to_device(response)". The input is the filtered summary and key phrases, and the output is the JSON data sent to the device.

[0147] Step 7:

[0148] User Interface Display

[0149] When a user opens an e-book, the device displays a dedicated reading support interface. This is done by building the interface using JavaScript and HTML and displaying information in "document.getElementById('reading-interface')". The input is the JSON data sent from the server, and the output is the displayed reading support interface.

[0150] Step 8:

[0151] Data transmission

[0152] Every time the user turns a page, the device sends new page information to the server. For example, use "fetch('server URL', { method: 'POST', body: JSON.stringify({ user_id: 'user ID', start_page: 11, end_page: 20})})" to send JSON data to the server. The input is the user's operation (turning a page), and the output is the new page information sent to the server.

[0153] Step 9:

[0154] Response reception and display

[0155] The terminal parses the summary and key points received from the server and displays them on the user interface. For example, "response = await fetch('server URL')" and "document.getElementById('summary').textContent = response.summary". The input is the JSON response from the server, and the output is the summary and key phrases displayed on the user interface.

[0156] Step 10:

[0157] Summary and key points review

[0158] Users can review the summary and key points displayed on the screen and recall important content. The input is the summary and key phrases displayed on the device, and the output is to improve the user's comprehension and reading efficiency.

[0159] Step 11:

[0160] Continue reading

[0161] When users start their next reading session, they can smoothly resume reading by checking the summary from the previous session. The input is the summary from the previous session, and the output is a smooth resumption and continuation of reading.

[0162] (Application example 1)

[0163] 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."

[0164] Traditional e-book systems lack the functionality to efficiently summarize what users have read and make it available for the next reading. This means that users have to spend a lot of time recalling what they read last time when rereading a book, especially when their reading time is fragmented. Furthermore, the summary system does not take into account spoilers, so there is a risk that users will learn about content they have not yet read. Furthermore, the lack of a summary or key point saving or reminder function results in an inconsistent reading experience.

[0165] 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.

[0166] In this invention, the server includes means for analyzing the content that has been read, means for summarizing the analyzed content, means for extracting key points from the summary, means for identifying the range that the user has read, means for filtering the range that the user has read to prevent spoilers, means for providing the filtered summary and key points to the user, means for saving the summary and key points to a server on the cloud, and means for notifying the user so that the user can continue reading while checking the previous summary. This makes it possible to efficiently summarize the content that has been read, prevent spoilers, and provide a consistent and comfortable reading experience even when reading in fragments.

[0167] "Means for analyzing what you read" refers to technology that uses natural language processing to analyze the text a user reads and understand the meaning of sentences and phrases.

[0168] "Means for summarizing the analyzed content" refers to the technique of concisely summarizing the main points of the entire text using important sentences and information obtained through the analysis.

[0169] "Methods for extracting key points from summaries" refers to techniques for selecting particularly important sentences or phrases from a summarized text.

[0170] "Means for determining user progress" refers to technology that tracks which pages or chapters a user has read.

[0171] "Means for preventing spoilers by filtering the range of content that the user has read" refers to a technology that automatically filters out information about parts that the user has not yet read.

[0172] The "means for providing filtered summaries and key points to users" refers to a technology for transmitting spoiler-free summaries and key points to users' terminals and displaying them.

[0173] "Means for storing the summary and key points on a server on the cloud" refers to a technology for storing the generated summary and key points in a cloud environment.

[0174] "Means for notifying users so that they can continue reading while checking the summary of the previous reading" refers to a technology that makes it easy for users to check the summary of what they read last time and notifies them so that they can smoothly resume reading the next time.

[0175] This invention is a system that automatically generates summaries and key points as a user reads an e-book, providing an efficient reading experience. Below, we will explain the mode for implementing the invention based on the program of this system.

[0176] server

[0177] Initialization

[0178] The server stores all e-book content and user information. It loads which books users have read and to what extent from the database for further analysis. This process uses a cloud-based database service.

[0179] Receiving reading data

[0180] Receives user reading progress information sent from the device. This information includes the user ID and the page range read. The received information is recorded in the database.

[0181] Natural Language Processing (NLP) Analysis

[0182] The server analyzes the text data of the read section using natural language processing technology. Specifically, it uses NLP libraries such as spaCy and Transformers to divide the text into sentences and analyze the meaning of each sentence. The important sentences and phrases obtained from this analysis are then used to generate a summary and extract key points.

[0183] Summary Generation

[0184] The server generates a summary based on the information obtained from the NLP analysis. It uses the Transformers summarization function to compile a summary of the entire text into a short document that concisely summarizes what the user has read.

[0185] Spoiler Filtering

[0186] The server automatically filters out information about unread content, ensuring that spoilers are not included when summaries and key points are provided to users.

[0187] Save and Notify

[0188] The generated summaries and key points are stored on a cloud server, and this information is notified to the user the next time they resume reading. This feature is important for supporting a continuous reading experience.

[0189] Terminal (user device)

[0190] User Interface Display

[0191] The device will display a dedicated reading assistance interface when users open an e-book, allowing them to check the summary and key points at any time.

[0192] Data transmission

[0193] Each time the user turns a page, the device sends the new page information to the server, including the user ID and the page range that has been read.

[0194] Response reception and display

[0195] The device displays the summary and key points received from the server on the user interface, allowing the user to efficiently understand the content they have read.

[0196] user

[0197] reading

[0198] The user continues reading the e-book, and the page information is sent from the device to the server.

[0199] Summary and key points review

[0200] Users can instantly recall important content by viewing summaries and key points displayed on the screen.

[0201] Continue reading

[0202] When users start their next reading, they can smoothly resume reading by checking the summary of their previous reading.

[0203] Specific examples

[0204] When a user reads pages 1 to 10 of an e-book, that information is sent from the device to the server. The server analyzes the content of pages 1 to 10 and generates a summary and key points. The generated summary and key points are applied with a spoiler prevention function before being sent to the device, which then displays them to the user. When the user starts reading from page 11 next time, they can smoothly continue reading by checking the previous summary.

[0205] Example prompts for generative AI models

[0206] Summarize what the user reads from page 1 to 10 of your eBook and list the main points.

[0207] This is an example of how to use this prompt to ask a generative AI model to generate a summary and key points.

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

[0209] Step 1:

[0210] The server loads e-book data and user information from the database. The input is the user ID and e-book identification information. The output is information about which e-book the user is currently reading and how many pages they have read. This information is necessary for analysis.

[0211] Step 2:

[0212] The page information that the user has read using the device is sent from the device to the server. The input is the user ID and the page range that has been read (e.g., page 1 to page 10). The output is the page range information received by the server. Based on this information, the next analysis step is performed.

[0213] Step 3:

[0214] The server analyzes the text data of the received page range using natural language processing (NLP) technology. Specifically, it divides the text data of pages 1 to 10 into sentences and uses libraries such as spaCy and Transformers to analyze the meaning of each sentence. The input is the text data of the received page range, and the output is a list of analyzed sentences and key phrases.

[0215] Step 4:

[0216] The server generates a summary based on the analyzed key sentences and key phrases. For this purpose, it uses the summarization function of Transformers. The input is a list of analyzed sentences and key phrases, and the output is the summarized text. The generated summary is a concise summary of what the user has read.

[0217] Step 5:

[0218] The server filters the generated summary to remove unread content and prevent spoilers. The input is the summarized text and the user's reading progress, and the output is the summary text with spoilers removed. This process is necessary to ensure a good user reading experience.

[0219] Step 6:

[0220] The server stores the filtered summaries and keypoints in a cloud database. The input is the filtered summaries and keypoints, and the output is confirmation of the stored data. This storage operation allows the summaries and keypoints to be reused the next time they are accessed.

[0221] Step 7:

[0222] The terminal displays the summaries and key points received from the server on the user interface. The input is the data received from the server, and the output is the summaries and key points on the screen that the user views, allowing the user to check the latest reading information in real time.

[0223] Step 8:

[0224] When the user starts reading again, the device will send a notification to the user to check the summary of the previous reading. The input is the summary and key points stored in the cloud, and the output is a notification to the user's device. This notification allows the user to resume reading smoothly.

[0225] 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.

[0226] This invention relates to a system that automatically summarizes and extracts key points from the content of an e-book as the user reads it. It also incorporates an emotion engine that recognizes the user's emotions to improve the reading experience. Below, we will explain the program processing of this system in natural language and provide detailed examples.

[0227] server

[0228] Initialization

[0229] The server loads the e-book data, user information, and emotion engine from the database, allowing the server to know which e-book the user is reading and up to which page, for further analysis and emotion recognition.

[0230] Receiving reading data

[0231] The page information that the user has read on the device is sent to the server, which receives this data and checks which pages the user has read.

[0232] Natural Language Processing (NLP) Analysis

[0233] The server analyzes the text data of the received page range using natural language processing technology. Specifically, it divides the text into sentences and analyzes the meaning of each sentence. For analysis, it uses libraries such as spaCy, NLTK, and Transformers.

[0234] Summary Generation

[0235] The server extracts key sentences and phrases from the analysis and generates a summary, which is a concise summary of what the user has read.

[0236] Spoiler Filtering

[0237] The server filters information about parts of the book that the user has not yet read, ensuring that summaries and key points do not contain spoilers.

[0238] emotion recognition

[0239] The emotion engine analyzes the emotion data received from the device to recognize the user's emotions, including facial expression recognition, voice analysis, and biometric data analysis.

[0240] Response generation and sending

[0241] The server packages the filtered summary and key points into a JSON format and dynamically adjusts it based on the user's emotional state. The content and display format of the summary can be changed based on the analysis results of the emotion engine. The summary is then sent to the device.

[0242] Terminal (user device)

[0243] User Interface Display

[0244] When a user opens an e-book, the device displays a dedicated reading support interface, which also includes emotion recognition functionality, collecting emotional data using the camera and microphone.

[0245] Data transmission

[0246] Each time the user turns a page, the device sends the new page information and collected emotion data to the server, including the user ID, the page range read, and the emotion data.

[0247] Response reception and display

[0248] The device analyzes the summary, key points, and emotion-based advice received from the server and displays it on the user interface. For example, if the user is feeling nervous, it displays advice on how to continue reading to relax.

[0249] user

[0250] reading

[0251] As the user reads the e-book, the page information and emotional data of the page being read are sent from the device to the server.

[0252] Summary and key points review

[0253] Users can view summaries, key points, and emotion-based advice displayed on the screen, helping them easily recall important content and receiving advice tailored to their emotions.

[0254] Continue reading

[0255] When users start their next reading, they can smoothly resume reading by checking the summary of their previous reading, and enjoy reading while receiving emotional advice.

[0256] Specific examples

[0257] When a user begins reading an e-book, the device sends the user ID, current page range, and emotional data to the server. For example, if the user reads from page 1 to page 10, emotional data collected during that time (e.g., whether the user is concentrating) is also sent. The server analyzes the content of pages 1 to 10, generates a summary and key points, and adjusts the summary content based on the emotional data. The filtered summary and key points are then sent to the device, allowing the user to review them as they continue reading.

[0258] This series of processes allows users to efficiently check summaries and key points of what they have read, receive advice based on their own emotions, and enjoy reading efficiently and enjoyably.

[0259] The processing flow will be explained below.

[0260] Step 1:

[0261] The server loads the e-book data, user information, and emotion engine from the database, allowing the server to know which e-book the user is reading and up to which page, for further analysis and emotion recognition.

[0262] Step 2:

[0263] When a user opens an e-book, the device sends the "user ID," "current page range," and "emotion data" to the server. Emotion data is collected using the camera and microphone.

[0264] Step 3:

[0265] The server analyzes the page information and emotional data received from the device to determine which page the user has read and their current emotional state.

[0266] Step 4:

[0267] The server analyzes the text data of the received page range using natural language processing (NLP) techniques, using libraries such as spaCy, NLTK, and Transformers to divide the text into sentences and analyze the meaning of each sentence.

[0268] Step 5:

[0269] The server extracts important sentences and key phrases and uses them to generate a summary, a concise summary of what the user has read, containing the gist of the information.

[0270] Step 6:

[0271] The server filters information about parts of the book that the user has not yet read, ensuring that summaries and key points do not contain spoilers.

[0272] Step 7:

[0273] The emotion engine analyzes the user's emotional state and dynamically adjusts the content of the summary and key points based on the emotional data, for example, if the user is feeling stressed, it will provide a concise and positive summary.

[0274] Step 8:

[0275] The server packages the filtered summary and keypoints into JSON format and sends it to the device along with a summary adjusted based on the user's sentiment data.

[0276] Step 9:

[0277] The device analyzes the summary, key points, and emotion-based advice received from the server, thereby optimizing the display content to the user's emotional state.

[0278] Step 10:

[0279] The device displays summaries, key points, and emotion-based advice on the user interface. For example, if the user is feeling nervous, the device displays advice on how to continue reading to relax.

[0280] Step 11:

[0281] Users see summaries and key points displayed on the screen, along with advice based on their emotions, helping them remember important content and receiving advice tailored to their emotions.

[0282] Step 12:

[0283] When the user starts reading from page 11 in the next reading session, the device will send the information "User ID," "Pages 11 to 20," and "Emotion Data" to the server.

[0284] Step 13:

[0285] The server analyzes the content of pages 11 to 20 and generates a summary and key points. The emotion engine then analyzes the emotion data and adjusts the summary content, so that the next reading session will also receive an emotion-based summary.

[0286] Step 14:

[0287] The device displays the new summary and key points received from the server in the user interface, allowing the user to review the content they have read and receive emotional advice as they continue reading.

[0288] This series of processes allows users to efficiently check summaries and key points of what they have read, receive advice based on their own emotions, and enjoy reading efficiently and enjoyably.

[0289] Example 2

[0290] 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."

[0291] Conventional e-book reading systems have limited functionality for providing summaries and key points of the content the user has read, making it difficult to prevent spoilers or provide appropriate feedback based on the user's emotions. Furthermore, they lack a mechanism for communicating to the system in real time how far the user has read and the emotions they felt at the time, which results in a lack of a way to enhance the user's reading experience. Our goal is to solve these issues and improve the user's reading experience.

[0292] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0293] In this invention, the server includes means for analyzing the content that the user has read, means for summarizing the analyzed content, means for extracting key points from the summary, means for identifying the range that the user has read, means for filtering the range that the user has read to prevent spoilers, means for providing the filtered summary and key points to the user, means for recognizing the user's emotional state, means for dynamically adjusting the summary and key points based on the user's emotional state, and means for transmitting and receiving data between the server and the terminal. This makes it possible to efficiently summarize the content that the user has read and provide feedback according to their emotions, thereby improving the user's reading experience while preventing spoilers.

[0294] "Means for analyzing the content being read" refers to a method or device used to analyze the text data of the e-book that the user has read.

[0295] "Means for summarizing the analyzed content" refers to a method or device for extracting important information from the analyzed text data and summarizing it concisely.

[0296] A "means for extracting key points from summaries" refers to a method or apparatus for identifying and extracting particularly important sentences or phrases from summarized information.

[0297] "Means for determining the extent to which a user has read" refers to a method or device for determining how many pages a user has read in an e-book.

[0298] A "spoiler prevention means using filtering means" refers to a method or device for removing information about parts of a book that the user has not yet read from the summary or key points.

[0299] "Means for providing a filtered summary and key points to a user" refers to a method or device for presenting a spoiler-free summary and key points to a user.

[0300] "Means for recognizing the user's emotional state" refers to a method or device for analyzing the user's facial expressions, voice, biometric data, etc. to determine their emotions.

[0301] "Means for dynamically adjusting summaries and key points based on a user's emotional state" refers to a method or apparatus for changing the presentation or content of summaries and key points in response to a recognized emotional state of a user.

[0302] "Means for sending and receiving data between the server and the terminal" refers to a communication method or device for exchanging necessary information between the server and the user's device.

[0303] "Filtered summaries and keypoints" refers to summaries and keypoints that have been processed to remove spoilers.

[0304] "Emotion-based advice" refers to suggestions for things like how to read or relax that take into account the user's emotional state.

[0305] "Natural language processing technology" refers to the general technology that enables computers to understand and analyze human language.

[0306] The "means for identifying important sentences and key phrases" refers to a method or device for identifying important sentences and key phrases within text data using natural language processing techniques.

[0307] MODE FOR CARRYING OUT THE INVENTION

[0308] This invention relates to a system that automatically summarizes and extracts key points from the content of an e-book as the user reads it. It also incorporates an emotion engine that recognizes the user's emotions to improve the reading experience. Below, we will explain the program processing of this system in natural language and provide detailed examples.

[0309] server

[0310] The server loads e-book data, user information, and the emotion engine from a database. This uses a database management system such as MySQL or PostgreSQL. It also uses a cache system (e.g., Redis) to ensure fast data access. The server uses NLP (natural language processing) libraries such as spaCy, NLTK, and Transformers to analyze the text data as it is read. The server also uses TF-IDF and BERT models to generate summaries and extract key points.

[0311] When the page information and emotion data sent by the user on their device is received, the server begins analysis. First, the received data is verified, and then the text data for the relevant page is extracted from the e-book database. NLP analysis is performed on the extracted text data to identify important sentences and key phrases and generate a summary. The generated summary is then filtered to remove unnecessary information. The summary and key points are also adjusted based on the user's emotion data.

[0312] Finally, the server sends the summary and key points packaged in JSON format to the device, along with sentiment-based advice. For example, if the user is concentrating, it can provide a more detailed summary, or if they need to relax, it can provide a concise, easy-to-read summary.

[0313] Terminal (user device)

[0314] When a user opens an e-book, the device displays a reading assistance interface. This interface includes a function to collect the user's emotional data using a camera and microphone. Each time a page is turned, the device sends the new page information and emotional data to the server. This data transmission is performed using an HTTP POST request. The device then analyzes the filtered summary, key points, and emotional advice received from the server and displays them on the user interface.

[0315] user

[0316] As users read e-books, page information and emotional data collected during the process are sent to the server via their device. A summary of the content they have read and key points can be viewed on the device's interface. Based on this, users can recall important content and receive advice based on their emotions. Furthermore, when they resume reading, they can check the summary from the previous page, allowing them to continue reading smoothly.

[0317] Specific examples

[0318] When a user begins reading an e-book, the device sends the user ID, current page range, and emotional data to the server. For example, if the user reads from page 1 to page 10, emotional data collected during that time (e.g., whether the user is concentrating) is also sent. The server analyzes the content of pages 1 to 10, generates a summary and key points, and adjusts the summary content based on the emotional data. The filtered summary and key points are then sent to the device, allowing the user to review them as they continue reading.

[0319] This series of processes allows users to efficiently check summaries and key points of what they have read, receive advice based on their own emotions, and enjoy reading efficiently and enjoyably.

[0320] Specific examples of prompts to input to generative AI models

[0321] "Generate a summary of what the user reads from pages 1 to 10, with key points tailored based on their emotional focus."

[0322] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0323] Step 1:

[0324] The server performs initialization. It loads e-book data, user information, and the emotion engine from the database. Specifically, it uses a database management system such as MySQL or PostgreSQL to retrieve the necessary data using SQL queries, and stores it in a cache such as Redis for fast data access. This prepares the e-book text data, the user's reading history, and the emotion engine configuration information.

[0325] Input: Database

[0326] Output: Cached data

[0327] Specific behavior: Executes SQL queries and caches data

[0328] Step 2:

[0329] The device displays a reading assistance interface when a user opens an e-book, and also includes a function to collect user emotional data using the camera and microphone.

[0330] Input: User actions

[0331] Output: Display of reading assistance interface

[0332] Specific behavior: Display a dialog box requesting permission to access the camera and microphone

[0333] Step 3:

[0334] As the user progresses through the e-book, the device sends new page information and emotion data to the server using an HTTP POST request, packaging the user ID, the page range that has been read, and the emotion data in JSON format.

[0335] Input: page information, emotional data

[0336] Output: Send data to the server

[0337] Specific behavior: Serializes data in JSON format and sends an HTTP POST request

[0338] Step 4:

[0339] The server validates the received page information and emotion data. It checks whether the data format of the user ID and page range is correct. If invalid data is sent, it returns an error message.

[0340] Input: Received data

[0341] Output: Validation result (error message or progress to the next processing step)

[0342] Specific behavior: Data format check and error handling

[0343] Step 5:

[0344] The server extracts the text data of the corresponding page from the e-book database, and efficiently extracts the data corresponding to the received page range using the Python Pandas library.

[0345] Input: Page range read

[0346] Output: Extracted text data

[0347] Specific behavior: Executes a database query and retrieves text data

[0348] Step 6:

[0349] The server performs natural language processing (NLP) analysis on the extracted text data, using NLP libraries (e.g., spaCy, NLTK, Transformers) to split the text data into sentences, identify important sentences and key phrases, and generate summaries.

[0350] Input: Text data

[0351] Output: Summary and key points

[0352] Specific operations: Text tokenization, part-of-speech tagging, dependency analysis

[0353] Step 7:

[0354] The server filters information about parts of the text that the user has not yet read from the summary and key points, using regular expressions and filtering algorithms to remove those parts.

[0355] Input: Summary, key points, and unread information

[0356] Output: filtered summary and keypoints

[0357] Specific behavior: Applying regular expression processing and filtering algorithms

[0358] Step 8:

[0359] The server analyzes the user's emotional data, and the emotion engine performs facial expression recognition, voice analysis, and biometric data analysis to identify the user's emotional state.

[0360] Input: Emotion data

[0361] Output: Emotional state

[0362] Specific operations: facial expression recognition, voice analysis, biometric data analysis

[0363] Step 9:

[0364] The server dynamically adjusts the filtered summaries and key points based on the user's emotional state: if the user is focused, it provides a detailed summary; if the user is tense, it provides a concise summary for relaxation.

[0365] Input: Emotional state, filtered summary and keypoints

[0366] Output: Tailored summary and key points

[0367] Specific actions: summarizing and adjusting key points

[0368] Step 10:

[0369] The server packages the adjusted summary and key points in JSON format and sends it to the device, returning the data as an HTTP response through a REST API.

[0370] Input: Adjusted summary and key points

[0371] Output: Sending data to the terminal

[0372] Specific operation: Serializing in JSON format and sending HTTP response

[0373] Step 11:

[0374] The device analyzes the summary, key points, and emotion-based advice received from the server and displays it on the user interface. For example, if the user is feeling nervous, it displays advice on how to continue reading to relax.

[0375] Input: Received data

[0376] Output: Displayed in the user interface

[0377] Specific operation: Analyzing data and displaying it in the interface

[0378] The above is the flow of processing of the program of this system.

[0379] (Application example 2)

[0380] 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."

[0381] Currently, there are systems that automatically generate summaries and key points when reading e-books, but there are no systems that can prevent spoilers and improve the reading experience by taking user emotions into account. As a result, the reading experience is not optimized to suit the emotions of each individual user, resulting in a decrease in user satisfaction.

[0382] 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.

[0383] In this invention, the server includes means for analyzing the content that has been read, means for summarizing the analyzed content, means for extracting key points from the summary, means for recognizing and analyzing the user's emotions, and means for adjusting and providing the summary and key points based on the analyzed emotion data. This makes it possible to adjust the summary and key points of the content that has been read based on the emotions, and provide an optimal reading experience that is adapted to the emotions of each individual user.

[0384] A "means for analyzing what has been read" is a means for a user to analyze the content of the text they have read and understand the meaning of each sentence.

[0385] "Means for summarizing the analyzed content" refers to means for extracting important information from the analyzed text and summarizing it concisely.

[0386] The "means for extracting key points from summaries" refers to a means for identifying and extracting more important phrases and sentences from the generated summaries.

[0387] "Means for identifying how far the user has read" is a means for identifying how much text the user has read.

[0388] "Means for preventing spoilers by filtering the portion of the content that the user has read" refers to filtering information about portions that the user has not yet read, and eliminating spoilers from summaries and key points.

[0389] The "means for providing a filtered summary and key points to a user" is a means for presenting a spoiler-free summary and key points in a form accessible to a user.

[0390] "Means for recognizing and analyzing user emotions" refers to a means for identifying and analyzing emotions from the user's facial expressions, voice, etc.

[0391] "Means for adjusting and providing summaries and key points based on analyzed emotional data" refers to means for dynamically changing the content of summaries and key points based on the user's emotional data and providing them in the most optimal form.

[0392] The "means for transmitting information about the page that has been read and emotional data to the server" refers to a means for transmitting the page that the user has read and emotional data to the server.

[0393] The "means for displaying the summary and adjustment data based on key points and emotion received from the server" refers to means for displaying the summary sent from the server and the adjustment results based on key points and emotion data.

[0394] "A method of analyzing text using natural language processing technology to identify important sentences and key phrases, and inputting prompts into a generative AI model based on emotional data to adjust the summary and key points" refers to a method of analyzing text using natural language processing technology to extract important information, and then inputting prompts into a generative AI model based on emotional data to adjust the summary and key points in an optimal form.

[0395] The present invention provides a system that automatically summarizes and extracts key points from e-books and other text content as a user reads them, and also recognizes the user's emotions to improve the reading experience. This system functions by combining a server and a terminal.

[0396] server

[0397] Initialization

[0398] The server loads the e-book data, user information, and emotion engine from the database, allowing the server to know which e-book the user is reading and up to which page, for further analysis and emotion recognition.

[0399] Receiving reading data

[0400] The page information and emotion data of the user that has been read using the device are sent to the server, which receives this data and checks which pages the user has read.

[0401] Natural Language Processing (NLP) Analysis

[0402] The server analyzes the text data of the received page range using natural language processing technology (NLTK, spaCy, Hugging Face Transformers, etc.). Specifically, it divides the text into sentences and analyzes the meaning of each sentence.

[0403] Summary Generation

[0404] The server extracts key sentences and phrases from the analysis and generates a summary, which is a concise summary of what the user has read.

[0405] Spoiler Filtering

[0406] The server filters information about parts of the book that the user has not yet read, ensuring that summaries and key points do not contain spoilers.

[0407] emotion recognition

[0408] In order for the emotion engine to recognize the user's emotions, it analyzes the emotion data (facial expression recognition, voice analysis, biometric data) received from the device.

[0409] Response generation and sending

[0410] The server packages the filtered summary and key points in JSON format and dynamically adjusts it based on the user's emotional state. Based on the analysis results of the emotion engine, the content and display format of the summary are changed and sent to the device.

[0411] Terminal (user device)

[0412] User Interface Display

[0413] When a user opens an e-book, the device displays a dedicated reading support interface, which also includes emotion recognition functionality, collecting emotional data using the camera and microphone.

[0414] Data transmission

[0415] Each time the user turns a page, the device sends the new page information and collected emotion data to the server, including the user ID, the page range read, and the emotion data.

[0416] Response reception and display

[0417] The device analyzes the summary, key points, and emotion-based advice received from the server and displays it on the user interface. For example, if the user is feeling nervous, it displays advice on how to continue reading to relax.

[0418] User examples

[0419] When a user begins reading an e-book, the device sends the user ID, current page range, and emotional data to the server. For example, if the user reads from page 1 to page 10, emotional data collected during that time (e.g., whether the user is concentrating) is also sent. The server analyzes the content of pages 1 to 10, generates a summary and key points, and adjusts the summary content based on the emotional data. The filtered summary and key points are then sent to the device, allowing the user to review them as they continue reading.

[0420] Prompt Sentence Examples

[0421] The following format is input to the generative AI model to generate and refine summaries and key points:

[0422] Instructions: Extract automatic summaries and key points from the following text, and adjust the summaries and key points to take into account user focus.

[0423] <<Text on pages 1-10>>

[0424] In this way, users can continue reading efficiently while enjoying an optimal reading experience tailored to their emotions.

[0425] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0426] Program processing steps

[0427] Step 1: Initialization

[0428] The server loads e-book data, user information, and emotion engine from the database. This prepares the server to know which e-book the user is reading and up to which page. The input is data from the e-book database and user database, and the output is the initial setting for analysis and emotion recognition.

[0429] Step 2: Receiving reading data

[0430] The device sends page information and emotional data about the page the user has progressed to to the server. The server receives this data and records which pages have been progressed and the user's emotional state. The input is the page information and emotional data sent from the device, and the output is prepared data for analysis.

[0431] Step 3: Natural Language Processing (NLP) Analysis

[0432] The server analyzes the text of the submitted page range using natural language processing technology. Specifically, it divides the text into sentences and identifies important sentences and key phrases. Software used includes NLTK, spaCy, and Hugging Face Transformers. The input is text data, and the output is the analysis results, which are important sentences and key phrases.

[0433] Step 4: Summary generation

[0434] The server generates a summary based on the analyzed important sentences and key phrases. This involves extracting the most important sentences and key phrases and connecting them to form a summary. The input is the analysis result, and the output is the generated summary.

[0435] Step 5: Spoiler filtering

[0436] The server filters information about parts that the user has not yet read, eliminating spoilers from summaries and keypoints. The input is the summary and keypoints, and the output is the spoiler-free filtered result.

[0437] Step 6: Emotion Recognition

[0438] The server uses an emotion engine to analyze the user's emotional data. Based on data obtained from the camera and microphone, it identifies the user's emotional state, such as whether they are focused or relaxed. The input is biometric data and voice data, and the output is analyzed emotional data.

[0439] Step 7: Generate and send a response

[0440] The server packages the filtered summary and key points into JSON format and dynamically adjusts the content based on the user's emotional state. For example, if the user is concentrating, it displays a detailed summary. The final package is sent to the device. The input is the filtering results and emotional data, and the output is the response data in JSON format.

[0441] Step 8: Displaying the User Interface

[0442] The terminal receives summaries, key points, and sentiment-based advice from the server and displays them on the user interface. The user can check these as they read. The input is the response data from the server, and the output is the content displayed to the user.

[0443] Step 9: User Examples

[0444] If a user reads from page 1 to page 10, emotional data collected during that time (for example, whether they are concentrating) is also sent. The server analyzes the content of pages 1 to 10 and inputs prompts into the generative AI model to generate and refine summaries and key points. The filtered summary and key points are then sent to the device, allowing the user to review them as they continue reading.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] [Second embodiment]

[0449] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0450] 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.

[0451] 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).

[0452] 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.

[0453] 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.

[0454] 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).

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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."

[0461] The present invention relates to a system that automatically summarizes and extracts key points from the content of an electronic book as the user reads it. The program processing of this system is explained below in natural language.

[0462] server

[0463] Initialization

[0464] The server loads the e-book data and user information from the database, which allows it to know which e-book the user is currently reading and up to which page, for further analysis.

[0465] Receiving reading data

[0466] The page information that the user has read on the device is sent to the server, which receives this data and checks which pages the user has read.

[0467] Natural Language Processing (NLP) Analysis

[0468] The server analyzes the text data of the received page range using natural language processing technology. Specifically, it divides the text into sentences and analyzes the meaning of each sentence. For analysis, it uses libraries such as spaCy, NLTK, and Transformers.

[0469] Summary Generation

[0470] The server extracts key sentences and phrases from the analysis and generates a summary, which is a concise summary of what the user has read.

[0471] Spoiler Filtering

[0472] The server filters information about parts of the book that the user has not yet read, ensuring that summaries and key points do not contain spoilers.

[0473] Response generation and sending

[0474] The server packages the filtered summary and keypoints into JSON format and sends it to the device.

[0475] Terminal (user device)

[0476] User Interface Display

[0477] When users open an e-book, the device displays a dedicated reading assistance interface, allowing users to check the summary and key points at any time.

[0478] Data transmission

[0479] Each time the user turns a page, the device sends the new page information to the server, including the user ID and the page range that has been read.

[0480] Response reception and display

[0481] The device analyzes the summary and key points received from the server and displays them on the user interface, allowing users to efficiently understand the content they have read.

[0482] user

[0483] reading

[0484] The user continues reading the e-book, and the page information is sent from the device to the server.

[0485] Summary and key points review

[0486] Users can recall important content by reviewing summaries and key points displayed on the screen.

[0487] Continue reading

[0488] When users start their next reading, they can smoothly resume reading by checking the summary of their previous reading.

[0489] Specific examples

[0490] When a user begins reading an e-book, the device sends the user ID and current page range to the server. For example, if the user has read pages 1 to 10, that information is sent to the server. The server analyzes the content of pages 1 to 10 and generates a summary and key points. The generated summary and key points are filtered to ensure they do not contain spoilers and are sent to the device. The device displays them to the user. The next time the user starts reading from page 11, they can smoothly continue reading by checking the summary from the previous page.

[0491] In this way, the present invention is a system that allows you to enjoy reading efficiently even with fragmented reading time, and provides a rich reading experience while preventing spoilers.

[0492] The processing flow will be explained below.

[0493] Step 1:

[0494] The server loads the e-book data and user information from the database, so that the server can know which e-book the user is reading and up to which page they have read.

[0495] Step 2:

[0496] When a user opens an e-book and starts reading from page 1, the device sends the user ID and page number (pages 1 to 10) to the server.

[0497] Step 3:

[0498] The server analyzes the page information received from the device, checks which pages the user has read, and identifies the text data of the page range that the user has read based on the received information.

[0499] Step 4:

[0500] The server analyzes the text data of the received page range using natural language processing (NLP) techniques, such as splitting the text into sentences and analyzing the meaning of each sentence, using libraries such as spaCy, NLTK, and Transformers.

[0501] Step 5:

[0502] The server extracts important sentences and key phrases and uses them to generate a summary, which is a concise summary of what the user has read and includes the main points of information.

[0503] Step 6:

[0504] The server uses filtering measures to filter out information about parts of the book that the user has not yet read, an important step in preventing spoilers.

[0505] Step 7:

[0506] The server packages the filtered summary and keypoints into JSON format and sends it to the device.

[0507] Step 8:

[0508] The device receives the response from the server, parses the JSON data, extracts summaries and key points, and displays them on the user interface.

[0509] Step 9:

[0510] Users see a summary and key points displayed on the screen, which helps users remember important content.

[0511] Step 10:

[0512] The user starts reading from page 11 during the next reading time. The device sends the information "User ID" and "Pages 11 to 20" to the server.

[0513] Step 11:

[0514] The server analyzes the content of pages 11 to 20, generates a summary and key points, and sends them to the terminal, which displays them to the user, allowing the user to efficiently understand the content they are reading.

[0515] By repeating this series of processes, users can enjoy e-books while avoiding spoilers by checking summaries and key points of the content they have read.

[0516] Example 1

[0517] 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."

[0518] The present invention aims to enable users to efficiently grasp the content of e-books and quickly review important points when rereading them later. However, conventional e-book systems lack the functionality to automatically summarize the content they have read and extract key points, or to prevent spoilers from being revealed for content they have not yet read. This requires users to recall the content each time they read, which reduces the efficiency of their reading experience.

[0519] 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.

[0520] In this invention, the server includes means for loading e-book data and user information, means for receiving page information of the page that has been read, means for analyzing the text of the received page range using natural language processing technology, means for extracting important sentences and key phrases from the analysis result and generating a summary, means for filtering information of the part that the user has not yet read, and means for providing the filtered summary and key points to the user. This allows the user to efficiently grasp the content that they have read and easily confirm important points while avoiding spoilers.

[0521] "E-Book Data" refers to the text data of a book stored in digital form, often formatted for reading on an e-reader or other device.

[0522] "User information" refers to data about users who use the system, including user IDs, reading history, and settings information.

[0523] "Read page information" is data indicating a specific page range when a user reads an electronic book.

[0524] "Natural language processing technology" is a technology for analyzing text data and understanding and generating human language. Specifically, it includes processes such as text segmentation, semantic analysis, and summary generation.

[0525] The "analysis results" are analytical information on text data obtained using natural language processing technology, and include important sentences and key phrases.

[0526] A "summary" is a shortened version of important information or key points extracted from a long text.

[0527] "Keyphrases" are important words or phrases in a text that play an important role in summarizing and searching.

[0528] "Filtering" is the process of selecting data based on specific conditions and removing unnecessary parts. In this system, it refers to removing unread information to prevent spoilers.

[0529] A "user interface" is a collection of screens and operating methods that allow a user to operate a system and obtain information.

[0530] The "JSON format" is a lightweight data exchange format for structuring, storing, and transferring data, and is an abbreviation for JavaScript Object Notation.

[0531] This invention relates to a system that automatically summarizes and extracts key points from the content of an e-book as the user reads it. This system realizes efficient reading support in real time through the cooperation of a server and a terminal.

[0532] server

[0533] First, the server loads the e-book data and user information from a database, typically an SQL database, and executes SQL queries to retrieve the necessary data. This allows the server to determine which e-book the user has read and up to which page, and prepares for subsequent analysis.

[0534] Next, the server receives the page information sent from the device. This data is sent in JSON format and includes the user ID and the page range that has been read. The received data is temporarily stored in the server's memory and passed on to the next analysis process.

[0535] The text data of the received page range is analyzed using natural language processing techniques. Specifically, libraries such as "spaCy," "NLTK," and "Transformers" are used. For example, "spaCy" is used to divide the text into sentences and analyze the meaning of each sentence. In addition, the BERT model in "Transformers" is used to generate sentence embeddings and extract important sentences.

[0536] Extract important sentences and key phrases from the analysis results and generate summaries. The "transformers" library is used to generate summaries, and a summary model (e.g., a BERT-based summary model) is used to shorten and summarize important information. Furthermore, key phrases are extracted using TF-IDF and the Rake algorithm.

[0537] To prevent spoilers from being included in the generated summary and key points, information about parts that the user has not yet read is filtered out. This is done by the server retrieving the user's reading completion information from the database and detecting the unread parts. The spoiler parts are then removed using filtering logic.

[0538] Finally, the filtered summary and keypoints are packaged in JSON format and sent to the terminal.

[0539] Terminal (user device)

[0540] When a user opens an e-book, the device displays a dedicated reading support interface, which is built using JavaScript and HTML and is designed to allow users to easily check summaries and key points.

[0541] Every time the user turns a page, the device sends the new page information to the server, including the user ID and the page range that has been read. The data is sent using the "fetch" function, and is packaged in JSON format.

[0542] Analyze the summary and key points received from the server and display them on the user interface. Analyze the received JSON data and dynamically insert the summary and key points into HTML elements.

[0543] user

[0544] As users read e-books, information about the pages they have read is sent from their device to the server. By checking the automatic summary and key points of the content they have read, users can read more efficiently.

[0545] When you start reading again, you can smoothly resume where you left off by checking the summary from the previous time, providing a consistent reading experience even when reading in fragments.

[0546] Specific examples

[0547] For example, when a user starts reading an e-book, the device sends the user ID and the current page range to the server. If the user has read pages 1 to 10, that information is sent to the server. The server analyzes the content of pages 1 to 10 and generates a summary and key points. The generated summary and key points are filtered to avoid spoilers and sent to the device, which then displays them to the user.

[0548] The next time a user starts reading from page 11, they can smoothly continue reading while checking the summary from the previous page.

[0549] Prompt Sentence Examples

[0550] "Please summarize the contents of pages 1-10 and extract the main key points. Please be careful not to include spoilers."

[0551] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0552] Step 1:

[0553] Initialization

[0554] The server loads e-book data and user information from the database. Specifically, it executes an SQL query to get the required data. For example, it executes a query like "SELECT FROM ebooks WHERE user_id = 'user ID'". The input is the response from the database, and the output is the loaded e-book data and user information.

[0555] Step 2:

[0556] Receiving reading data

[0557] The server receives the page information sent from the device. This data is sent in JSON format and includes the user ID and the page range that has been read. Specifically, it receives JSON data from the device such as "{"user_id": "User ID", "start_page": 1, "end_page": 10}". The input is the JSON data from the device, and the output is the saved page information that has been read.

[0558] Step 3:

[0559] Natural Language Processing (NLP) Analysis

[0560] The server analyzes the text data of the received page range using natural language processing techniques. Specifically, it uses the "spaCy" and "Transformers" libraries to split the text into sentences and perform semantic analysis. For example, it uses "spaCy" to initialize "nlp = spacy.load('en_core_web_sm')" to analyze the received text. It also uses the "Transformers" BERT model to generate sentence embeddings. The input is the text data to be analyzed, and the output is the analysis results: important sentences and key phrases.

[0561] Step 4:

[0562] Summary Generation

[0563] The server extracts important sentences and key phrases from the analysis results and generates summaries. Specifically, it uses a summarization model from the "transformers" library to shorten and summarize important information. It also extracts key phrases using TF-IDF and Rake algorithms. The input is the analysis results, and the output is the generated summary and key phrases.

[0564] Step 5:

[0565] Spoiler Filtering

[0566] The server filters information about parts that the user has not yet read to ensure that the generated summary and key points do not contain spoilers. This is done by retrieving the user's reading completion information from the database and detecting unread parts. For example, it applies filtering logic such as "delete if the if statement comes from an unread page." The input is the summary, key phrases, and the user's reading completion information, and the output is the filtered summary and key phrases.

[0567] Step 6:

[0568] Response generation and sending

[0569] The server packages the filtered summary and key phrases in JSON format and sends them to the device. Specifically, it packages and sends them as follows: "response = json.dumps({'summary': 'Summary content', 'key_points': 'Key points'})" and "send_response_to_device(response)". The input is the filtered summary and key phrases, and the output is the JSON data sent to the device.

[0570] Step 7:

[0571] User Interface Display

[0572] When a user opens an e-book, the device displays a dedicated reading support interface. This is done by building the interface using JavaScript and HTML and displaying information in "document.getElementById('reading-interface')". The input is the JSON data sent from the server, and the output is the displayed reading support interface.

[0573] Step 8:

[0574] Data transmission

[0575] Every time the user turns a page, the device sends new page information to the server. For example, use "fetch('server URL', { method: 'POST', body: JSON.stringify({ user_id: 'user ID', start_page: 11, end_page: 20})})" to send JSON data to the server. The input is the user's operation (turning a page), and the output is the new page information sent to the server.

[0576] Step 9:

[0577] Response reception and display

[0578] The terminal parses the summary and key points received from the server and displays them on the user interface. For example, "response = await fetch('server URL')" and "document.getElementById('summary').textContent = response.summary". The input is the JSON response from the server, and the output is the summary and key phrases displayed on the user interface.

[0579] Step 10:

[0580] Summary and key points review

[0581] Users can review the summary and key points displayed on the screen and recall important content. The input is the summary and key phrases displayed on the device, and the output is to improve the user's comprehension and reading efficiency.

[0582] Step 11:

[0583] Continue reading

[0584] When users start their next reading session, they can smoothly resume reading by checking the summary from the previous session. The input is the summary from the previous session, and the output is a smooth resumption and continuation of reading.

[0585] (Application example 1)

[0586] 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."

[0587] Traditional e-book systems lack the functionality to efficiently summarize what users have read and make it available for the next reading. This means that users have to spend a lot of time recalling what they read last time when rereading a book, especially when their reading time is fragmented. Furthermore, the summary system does not take into account spoilers, so there is a risk that users will learn about content they have not yet read. Furthermore, the lack of a summary or key point saving or reminder function results in an inconsistent reading experience.

[0588] 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.

[0589] In this invention, the server includes means for analyzing the content that has been read, means for summarizing the analyzed content, means for extracting key points from the summary, means for identifying the range that the user has read, means for filtering the range that the user has read to prevent spoilers, means for providing the filtered summary and key points to the user, means for saving the summary and key points to a server on the cloud, and means for notifying the user so that the user can continue reading while checking the previous summary. This makes it possible to efficiently summarize the content that has been read, prevent spoilers, and provide a consistent and comfortable reading experience even when reading in fragments.

[0590] "Means for analyzing what you read" refers to technology that uses natural language processing to analyze the text a user reads and understand the meaning of sentences and phrases.

[0591] "Means for summarizing the analyzed content" refers to the technique of concisely summarizing the main points of the entire text using important sentences and information obtained through the analysis.

[0592] "Methods for extracting key points from summaries" refers to techniques for selecting particularly important sentences or phrases from a summarized text.

[0593] "Means for determining user progress" refers to technology that tracks which pages or chapters a user has read.

[0594] "Means for preventing spoilers by filtering the range of content that the user has read" refers to a technology that automatically filters out information about parts that the user has not yet read.

[0595] The "means for providing filtered summaries and key points to users" refers to a technology for transmitting spoiler-free summaries and key points to users' terminals and displaying them.

[0596] "Means for storing the summary and key points on a server on the cloud" refers to a technology for storing the generated summary and key points in a cloud environment.

[0597] "Means for notifying users so that they can continue reading while checking the summary of the previous reading" refers to a technology that makes it easy for users to check the summary of what they read last time and notifies them so that they can smoothly resume reading the next time.

[0598] This invention is a system that automatically generates summaries and key points as a user reads an e-book, providing an efficient reading experience. Below, we will explain the mode for implementing the invention based on the program of this system.

[0599] server

[0600] Initialization

[0601] The server stores all e-book content and user information. It loads which books users have read and to what extent from the database for further analysis. This process uses a cloud-based database service.

[0602] Receiving reading data

[0603] Receives user reading progress information sent from the device. This information includes the user ID and the page range read. The received information is recorded in the database.

[0604] Natural Language Processing (NLP) Analysis

[0605] The server analyzes the text data of the read section using natural language processing technology. Specifically, it uses NLP libraries such as spaCy and Transformers to divide the text into sentences and analyze the meaning of each sentence. The important sentences and phrases obtained from this analysis are then used to generate a summary and extract key points.

[0606] Summary Generation

[0607] The server generates a summary based on the information obtained from the NLP analysis. It uses the Transformers summarization function to compile a summary of the entire text into a short document that concisely summarizes what the user has read.

[0608] Spoiler Filtering

[0609] The server automatically filters out information about unread content, ensuring that spoilers are not included when summaries and key points are provided to users.

[0610] Save and Notify

[0611] The generated summaries and key points are stored on a cloud server, and this information is notified to the user the next time they resume reading. This feature is important for supporting a continuous reading experience.

[0612] Terminal (user device)

[0613] User Interface Display

[0614] The device will display a dedicated reading assistance interface when users open an e-book, allowing them to check the summary and key points at any time.

[0615] Data transmission

[0616] Each time the user turns a page, the device sends the new page information to the server, including the user ID and the page range that has been read.

[0617] Response reception and display

[0618] The device displays the summary and key points received from the server on the user interface, allowing the user to efficiently understand the content they have read.

[0619] user

[0620] reading

[0621] The user continues reading the e-book, and the page information is sent from the device to the server.

[0622] Summary and key points review

[0623] Users can instantly recall important content by viewing summaries and key points displayed on the screen.

[0624] Continue reading

[0625] When users start their next reading, they can smoothly resume reading by checking the summary of their previous reading.

[0626] Specific examples

[0627] When a user reads pages 1 to 10 of an e-book, that information is sent from the device to the server. The server analyzes the content of pages 1 to 10 and generates a summary and key points. The generated summary and key points are applied with a spoiler prevention function before being sent to the device, which then displays them to the user. When the user starts reading from page 11 next time, they can smoothly continue reading by checking the previous summary.

[0628] Example prompts for generative AI models

[0629] Summarize what the user reads from page 1 to 10 of your eBook and list the main points.

[0630] This is an example of how to use this prompt to ask a generative AI model to generate a summary and key points.

[0631] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0632] Step 1:

[0633] The server loads e-book data and user information from the database. The input is the user ID and e-book identification information. The output is information about which e-book the user is currently reading and how many pages they have read. This information is necessary for analysis.

[0634] Step 2:

[0635] The page information that the user has read using the device is sent from the device to the server. The input is the user ID and the page range that has been read (e.g., page 1 to page 10). The output is the page range information received by the server. Based on this information, the next analysis step is performed.

[0636] Step 3:

[0637] The server analyzes the text data of the received page range using natural language processing (NLP) technology. Specifically, it divides the text data of pages 1 to 10 into sentences and uses libraries such as spaCy and Transformers to analyze the meaning of each sentence. The input is the text data of the received page range, and the output is a list of analyzed sentences and key phrases.

[0638] Step 4:

[0639] The server generates a summary based on the analyzed key sentences and key phrases. For this purpose, it uses the summarization function of Transformers. The input is a list of analyzed sentences and key phrases, and the output is the summarized text. The generated summary is a concise summary of what the user has read.

[0640] Step 5:

[0641] The server filters the generated summary to remove unread content and prevent spoilers. The input is the summarized text and the user's reading progress, and the output is the summary text with spoilers removed. This process is necessary to ensure a good user reading experience.

[0642] Step 6:

[0643] The server stores the filtered summaries and keypoints in a cloud database. The input is the filtered summaries and keypoints, and the output is confirmation of the stored data. This storage operation allows the summaries and keypoints to be reused the next time they are accessed.

[0644] Step 7:

[0645] The terminal displays the summaries and key points received from the server on the user interface. The input is the data received from the server, and the output is the summaries and key points on the screen that the user views, allowing the user to check the latest reading information in real time.

[0646] Step 8:

[0647] When the user starts reading again, the device will send a notification to the user to check the summary of the previous reading. The input is the summary and key points stored in the cloud, and the output is a notification to the user's device. This notification allows the user to resume reading smoothly.

[0648] 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.

[0649] This invention relates to a system that automatically summarizes and extracts key points from the content of an e-book as the user reads it. It also incorporates an emotion engine that recognizes the user's emotions to improve the reading experience. Below, we will explain the program processing of this system in natural language and provide detailed examples.

[0650] server

[0651] Initialization

[0652] The server loads the e-book data, user information, and emotion engine from the database, allowing the server to know which e-book the user is reading and up to which page, for further analysis and emotion recognition.

[0653] Receiving reading data

[0654] The page information that the user has read on the device is sent to the server, which receives this data and checks which pages the user has read.

[0655] Natural Language Processing (NLP) Analysis

[0656] The server analyzes the text data of the received page range using natural language processing technology. Specifically, it divides the text into sentences and analyzes the meaning of each sentence. For analysis, it uses libraries such as spaCy, NLTK, and Transformers.

[0657] Summary Generation

[0658] The server extracts key sentences and phrases from the analysis and generates a summary, which is a concise summary of what the user has read.

[0659] Spoiler Filtering

[0660] The server filters information about parts of the book that the user has not yet read, ensuring that summaries and key points do not contain spoilers.

[0661] emotion recognition

[0662] The emotion engine analyzes the emotion data received from the device to recognize the user's emotions, including facial expression recognition, voice analysis, and biometric data analysis.

[0663] Response generation and sending

[0664] The server packages the filtered summary and key points into a JSON format and dynamically adjusts it based on the user's emotional state. The content and display format of the summary can be changed based on the analysis results of the emotion engine. The summary is then sent to the device.

[0665] Terminal (user device)

[0666] User Interface Display

[0667] When a user opens an e-book, the device displays a dedicated reading support interface, which also includes emotion recognition functionality, collecting emotional data using the camera and microphone.

[0668] Data transmission

[0669] Each time the user turns a page, the device sends the new page information and collected emotion data to the server, including the user ID, the page range read, and the emotion data.

[0670] Response reception and display

[0671] The device analyzes the summary, key points, and emotion-based advice received from the server and displays it on the user interface. For example, if the user is feeling nervous, it displays advice on how to continue reading to relax.

[0672] user

[0673] reading

[0674] As the user reads the e-book, the page information and emotional data of the page being read are sent from the device to the server.

[0675] Summary and key points review

[0676] Users can view summaries, key points, and emotion-based advice displayed on the screen, helping them easily recall important content and receiving advice tailored to their emotions.

[0677] Continue reading

[0678] When users start their next reading, they can smoothly resume reading by checking the summary of their previous reading, and enjoy reading while receiving emotional advice.

[0679] Specific examples

[0680] When a user begins reading an e-book, the device sends the user ID, current page range, and emotional data to the server. For example, if the user reads from page 1 to page 10, emotional data collected during that time (e.g., whether the user is concentrating) is also sent. The server analyzes the content of pages 1 to 10, generates a summary and key points, and adjusts the summary content based on the emotional data. The filtered summary and key points are then sent to the device, allowing the user to review them as they continue reading.

[0681] This series of processes allows users to efficiently check summaries and key points of what they have read, receive advice based on their own emotions, and enjoy reading efficiently and enjoyably.

[0682] The processing flow will be explained below.

[0683] Step 1:

[0684] The server loads the e-book data, user information, and emotion engine from the database, allowing the server to know which e-book the user is reading and up to which page, for further analysis and emotion recognition.

[0685] Step 2:

[0686] When a user opens an e-book, the device sends the "user ID," "current page range," and "emotion data" to the server. Emotion data is collected using the camera and microphone.

[0687] Step 3:

[0688] The server analyzes the page information and emotional data received from the device to determine which page the user has read and their current emotional state.

[0689] Step 4:

[0690] The server analyzes the text data of the received page range using natural language processing (NLP) techniques, using libraries such as spaCy, NLTK, and Transformers to divide the text into sentences and analyze the meaning of each sentence.

[0691] Step 5:

[0692] The server extracts important sentences and key phrases and uses them to generate a summary, a concise summary of what the user has read, containing the gist of the information.

[0693] Step 6:

[0694] The server filters information about parts of the book that the user has not yet read, ensuring that summaries and key points do not contain spoilers.

[0695] Step 7:

[0696] The emotion engine analyzes the user's emotional state and dynamically adjusts the content of the summary and key points based on the emotional data, for example, if the user is feeling stressed, it will provide a concise and positive summary.

[0697] Step 8:

[0698] The server packages the filtered summary and keypoints into JSON format and sends it to the device along with a summary adjusted based on the user's sentiment data.

[0699] Step 9:

[0700] The device analyzes the summary, key points, and emotion-based advice received from the server, thereby optimizing the display content to the user's emotional state.

[0701] Step 10:

[0702] The device displays summaries, key points, and emotion-based advice on the user interface. For example, if the user is feeling nervous, the device displays advice on how to continue reading to relax.

[0703] Step 11:

[0704] Users see summaries and key points displayed on the screen, along with advice based on their emotions, helping them remember important content and receiving advice tailored to their emotions.

[0705] Step 12:

[0706] When the user starts reading from page 11 in the next reading session, the device will send the information "User ID," "Pages 11 to 20," and "Emotion Data" to the server.

[0707] Step 13:

[0708] The server analyzes the content of pages 11 to 20 and generates a summary and key points. The emotion engine then analyzes the emotion data and adjusts the summary content, so that the next reading session will also receive an emotion-based summary.

[0709] Step 14:

[0710] The device displays the new summary and key points received from the server in the user interface, allowing the user to review the content they have read and receive emotional advice as they continue reading.

[0711] This series of processes allows users to efficiently check summaries and key points of what they have read, receive advice based on their own emotions, and enjoy reading efficiently and enjoyably.

[0712] Example 2

[0713] 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."

[0714] Conventional e-book reading systems have limited functionality for providing summaries and key points of the content the user has read, making it difficult to prevent spoilers or provide appropriate feedback based on the user's emotions. Furthermore, they lack a mechanism for communicating to the system in real time how far the user has read and the emotions they felt at the time, which results in a lack of a way to enhance the user's reading experience. Our goal is to solve these issues and improve the user's reading experience.

[0715] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0716] In this invention, the server includes means for analyzing the content that the user has read, means for summarizing the analyzed content, means for extracting key points from the summary, means for identifying the range that the user has read, means for filtering the range that the user has read to prevent spoilers, means for providing the filtered summary and key points to the user, means for recognizing the user's emotional state, means for dynamically adjusting the summary and key points based on the user's emotional state, and means for transmitting and receiving data between the server and the terminal. This makes it possible to efficiently summarize the content that the user has read and provide feedback according to their emotions, thereby improving the user's reading experience while preventing spoilers.

[0717] "Means for analyzing the content being read" refers to a method or device used to analyze the text data of the e-book that the user has read.

[0718] "Means for summarizing the analyzed content" refers to a method or device for extracting important information from the analyzed text data and summarizing it concisely.

[0719] A "means for extracting key points from summaries" refers to a method or apparatus for identifying and extracting particularly important sentences or phrases from summarized information.

[0720] "Means for determining the extent to which a user has read" refers to a method or device for determining how many pages a user has read in an e-book.

[0721] A "spoiler prevention means using filtering means" refers to a method or device for removing information about parts of a book that the user has not yet read from the summary or key points.

[0722] "Means for providing a filtered summary and key points to a user" refers to a method or device for presenting a spoiler-free summary and key points to a user.

[0723] "Means for recognizing the user's emotional state" refers to a method or device for analyzing the user's facial expressions, voice, biometric data, etc. to determine their emotions.

[0724] "Means for dynamically adjusting summaries and key points based on a user's emotional state" refers to a method or apparatus for changing the presentation or content of summaries and key points in response to a recognized emotional state of a user.

[0725] "Means for sending and receiving data between the server and the terminal" refers to a communication method or device for exchanging necessary information between the server and the user's device.

[0726] "Filtered summaries and keypoints" refers to summaries and keypoints that have been processed to remove spoilers.

[0727] "Emotion-based advice" refers to suggestions for things like how to read or relax that take into account the user's emotional state.

[0728] "Natural language processing technology" refers to the general technology that enables computers to understand and analyze human language.

[0729] The "means for identifying important sentences and key phrases" refers to a method or device for identifying important sentences and key phrases within text data using natural language processing techniques.

[0730] MODE FOR CARRYING OUT THE INVENTION

[0731] This invention relates to a system that automatically summarizes and extracts key points from the content of an e-book as the user reads it. It also incorporates an emotion engine that recognizes the user's emotions to improve the reading experience. Below, we will explain the program processing of this system in natural language and provide detailed examples.

[0732] server

[0733] The server loads e-book data, user information, and the emotion engine from a database. This uses a database management system such as MySQL or PostgreSQL. It also uses a cache system (e.g., Redis) to ensure fast data access. The server uses NLP (natural language processing) libraries such as spaCy, NLTK, and Transformers to analyze the text data as it is read. The server also uses TF-IDF and BERT models to generate summaries and extract key points.

[0734] When the page information and emotion data sent by the user on their device is received, the server begins analysis. First, the received data is verified, and then the text data for the relevant page is extracted from the e-book database. NLP analysis is performed on the extracted text data to identify important sentences and key phrases and generate a summary. The generated summary is then filtered to remove unnecessary information. The summary and key points are also adjusted based on the user's emotion data.

[0735] Finally, the server sends the summary and key points packaged in JSON format to the device, along with sentiment-based advice. For example, if the user is concentrating, it can provide a more detailed summary, or if they need to relax, it can provide a concise, easy-to-read summary.

[0736] Terminal (user device)

[0737] When a user opens an e-book, the device displays a reading assistance interface. This interface includes a function to collect the user's emotional data using a camera and microphone. Each time a page is turned, the device sends the new page information and emotional data to the server. This data transmission is performed using an HTTP POST request. The device then analyzes the filtered summary, key points, and emotional advice received from the server and displays them on the user interface.

[0738] user

[0739] As users read e-books, page information and emotional data collected during the process are sent to the server via their device. A summary of the content they have read and key points can be viewed on the device's interface. Based on this, users can recall important content and receive advice based on their emotions. Furthermore, when they resume reading, they can check the summary from the previous page, allowing them to continue reading smoothly.

[0740] Specific examples

[0741] When a user begins reading an e-book, the device sends the user ID, current page range, and emotional data to the server. For example, if the user reads from page 1 to page 10, emotional data collected during that time (e.g., whether the user is concentrating) is also sent. The server analyzes the content of pages 1 to 10, generates a summary and key points, and adjusts the summary content based on the emotional data. The filtered summary and key points are then sent to the device, allowing the user to review them as they continue reading.

[0742] This series of processes allows users to efficiently check summaries and key points of what they have read, receive advice based on their own emotions, and enjoy reading efficiently and enjoyably.

[0743] Specific examples of prompts to input to generative AI models

[0744] "Generate a summary of what the user reads from pages 1 to 10, with key points tailored based on their emotional focus."

[0745] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0746] Step 1:

[0747] The server performs initialization. It loads e-book data, user information, and the emotion engine from the database. Specifically, it uses a database management system such as MySQL or PostgreSQL to retrieve the necessary data using SQL queries, and stores it in a cache such as Redis for fast data access. This prepares the e-book text data, the user's reading history, and the emotion engine configuration information.

[0748] Input: Database

[0749] Output: Cached data

[0750] Specific behavior: Executes SQL queries and caches data

[0751] Step 2:

[0752] The device displays a reading assistance interface when a user opens an e-book, and also includes a function to collect user emotional data using the camera and microphone.

[0753] Input: User actions

[0754] Output: Display of reading assistance interface

[0755] Specific behavior: Display a dialog box requesting permission to access the camera and microphone

[0756] Step 3:

[0757] As the user progresses through the e-book, the device sends new page information and emotion data to the server using an HTTP POST request, packaging the user ID, the page range that has been read, and the emotion data in JSON format.

[0758] Input: page information, emotional data

[0759] Output: Send data to the server

[0760] Specific behavior: Serializes data in JSON format and sends an HTTP POST request

[0761] Step 4:

[0762] The server validates the received page information and emotion data. It checks whether the data format of the user ID and page range is correct. If invalid data is sent, it returns an error message.

[0763] Input: Received data

[0764] Output: Validation result (error message or progress to the next processing step)

[0765] Specific behavior: Data format check and error handling

[0766] Step 5:

[0767] The server extracts the text data of the corresponding page from the e-book database, and efficiently extracts the data corresponding to the received page range using the Python Pandas library.

[0768] Input: Page range read

[0769] Output: Extracted text data

[0770] Specific behavior: Executes a database query and retrieves text data

[0771] Step 6:

[0772] The server performs natural language processing (NLP) analysis on the extracted text data, using NLP libraries (e.g., spaCy, NLTK, Transformers) to split the text data into sentences, identify important sentences and key phrases, and generate summaries.

[0773] Input: Text data

[0774] Output: Summary and key points

[0775] Specific operations: Text tokenization, part-of-speech tagging, dependency analysis

[0776] Step 7:

[0777] The server filters information about parts of the text that the user has not yet read from the summary and key points, using regular expressions and filtering algorithms to remove those parts.

[0778] Input: Summary, key points, and unread information

[0779] Output: filtered summary and keypoints

[0780] Specific behavior: Applying regular expression processing and filtering algorithms

[0781] Step 8:

[0782] The server analyzes the user's emotional data, and the emotion engine performs facial expression recognition, voice analysis, and biometric data analysis to identify the user's emotional state.

[0783] Input: Emotion data

[0784] Output: Emotional state

[0785] Specific operations: facial expression recognition, voice analysis, biometric data analysis

[0786] Step 9:

[0787] The server dynamically adjusts the filtered summaries and key points based on the user's emotional state: if the user is focused, it provides a detailed summary; if the user is tense, it provides a concise summary for relaxation.

[0788] Input: Emotional state, filtered summary and keypoints

[0789] Output: Tailored summary and key points

[0790] Specific actions: summarizing and adjusting key points

[0791] Step 10:

[0792] The server packages the adjusted summary and key points in JSON format and sends it to the device, returning the data as an HTTP response through a REST API.

[0793] Input: Adjusted summary and key points

[0794] Output: Sending data to the terminal

[0795] Specific operation: Serializing in JSON format and sending HTTP response

[0796] Step 11:

[0797] The device analyzes the summary, key points, and emotion-based advice received from the server and displays it on the user interface. For example, if the user is feeling nervous, it displays advice on how to continue reading to relax.

[0798] Input: Received data

[0799] Output: Displayed in the user interface

[0800] Specific operation: Analyzing data and displaying it in the interface

[0801] The above is the flow of processing of the program of this system.

[0802] (Application example 2)

[0803] 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."

[0804] Currently, there are systems that automatically generate summaries and key points when reading e-books, but there are no systems that can prevent spoilers and improve the reading experience by taking user emotions into account. As a result, the reading experience is not optimized to suit the emotions of each individual user, resulting in a decrease in user satisfaction.

[0805] 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.

[0806] In this invention, the server includes means for analyzing the content that has been read, means for summarizing the analyzed content, means for extracting key points from the summary, means for recognizing and analyzing the user's emotions, and means for adjusting and providing the summary and key points based on the analyzed emotion data. This makes it possible to adjust the summary and key points of the content that has been read based on the emotions, and provide an optimal reading experience that is adapted to the emotions of each individual user.

[0807] A "means for analyzing what has been read" is a means for a user to analyze the content of the text they have read and understand the meaning of each sentence.

[0808] "Means for summarizing the analyzed content" refers to means for extracting important information from the analyzed text and summarizing it concisely.

[0809] The "means for extracting key points from summaries" refers to a means for identifying and extracting more important phrases and sentences from the generated summaries.

[0810] "Means for identifying how far the user has read" is a means for identifying how much text the user has read.

[0811] "Means for preventing spoilers by filtering the portion of the content that the user has read" refers to filtering information about portions that the user has not yet read, and eliminating spoilers from summaries and key points.

[0812] The "means for providing a filtered summary and key points to a user" is a means for presenting a spoiler-free summary and key points in a form accessible to a user.

[0813] "Means for recognizing and analyzing user emotions" refers to a means for identifying and analyzing emotions from the user's facial expressions, voice, etc.

[0814] "Means for adjusting and providing summaries and key points based on analyzed emotional data" refers to means for dynamically changing the content of summaries and key points based on the user's emotional data and providing them in the most optimal form.

[0815] The "means for transmitting information about the page that has been read and emotional data to the server" refers to a means for transmitting the page that the user has read and emotional data to the server.

[0816] The "means for displaying the summary and adjustment data based on key points and emotion received from the server" refers to means for displaying the summary sent from the server and the adjustment results based on key points and emotion data.

[0817] "A method of analyzing text using natural language processing technology to identify important sentences and key phrases, and inputting prompts into a generative AI model based on emotional data to adjust the summary and key points" refers to a method of analyzing text using natural language processing technology to extract important information, and then inputting prompts into a generative AI model based on emotional data to adjust the summary and key points in an optimal form.

[0818] The present invention provides a system that automatically summarizes and extracts key points from e-books and other text content as a user reads them, and also recognizes the user's emotions to improve the reading experience. This system functions by combining a server and a terminal.

[0819] server

[0820] Initialization

[0821] The server loads the e-book data, user information, and emotion engine from the database, allowing the server to know which e-book the user is reading and up to which page, for further analysis and emotion recognition.

[0822] Receiving reading data

[0823] The page information and emotion data of the user that has been read using the device are sent to the server, which receives this data and checks which pages the user has read.

[0824] Natural Language Processing (NLP) Analysis

[0825] The server analyzes the text data of the received page range using natural language processing technology (NLTK, spaCy, Hugging Face Transformers, etc.). Specifically, it divides the text into sentences and analyzes the meaning of each sentence.

[0826] Summary Generation

[0827] The server extracts key sentences and phrases from the analysis and generates a summary, which is a concise summary of what the user has read.

[0828] Spoiler Filtering

[0829] The server filters information about parts of the book that the user has not yet read, ensuring that summaries and key points do not contain spoilers.

[0830] emotion recognition

[0831] In order for the emotion engine to recognize the user's emotions, it analyzes the emotion data (facial expression recognition, voice analysis, biometric data) received from the device.

[0832] Response generation and sending

[0833] The server packages the filtered summary and key points in JSON format and dynamically adjusts it based on the user's emotional state. Based on the analysis results of the emotion engine, the content and display format of the summary are changed and sent to the device.

[0834] Terminal (user device)

[0835] User Interface Display

[0836] When a user opens an e-book, the device displays a dedicated reading support interface, which also includes emotion recognition functionality, collecting emotional data using the camera and microphone.

[0837] Data transmission

[0838] Each time the user turns a page, the device sends the new page information and collected emotion data to the server, including the user ID, the page range read, and the emotion data.

[0839] Response reception and display

[0840] The device analyzes the summary, key points, and emotion-based advice received from the server and displays it on the user interface. For example, if the user is feeling nervous, it displays advice on how to continue reading to relax.

[0841] User examples

[0842] When a user begins reading an e-book, the device sends the user ID, current page range, and emotional data to the server. For example, if the user reads from page 1 to page 10, emotional data collected during that time (e.g., whether the user is concentrating) is also sent. The server analyzes the content of pages 1 to 10, generates a summary and key points, and adjusts the summary content based on the emotional data. The filtered summary and key points are then sent to the device, allowing the user to review them as they continue reading.

[0843] Prompt Sentence Examples

[0844] The following format is input to the generative AI model to generate and refine summaries and key points:

[0845] Instructions: Extract automatic summaries and key points from the following text, and adjust the summaries and key points to take into account user focus.

[0846] <<Text on pages 1-10>>

[0847] In this way, users can continue reading efficiently while enjoying an optimal reading experience tailored to their emotions.

[0848] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0849] Program processing steps

[0850] Step 1: Initialization

[0851] The server loads e-book data, user information, and emotion engine from the database. This prepares the server to know which e-book the user is reading and up to which page. The input is data from the e-book database and user database, and the output is the initial setting for analysis and emotion recognition.

[0852] Step 2: Receiving reading data

[0853] The device sends page information and emotional data about the page the user has progressed to to the server. The server receives this data and records which pages have been progressed and the user's emotional state. The input is the page information and emotional data sent from the device, and the output is prepared data for analysis.

[0854] Step 3: Natural Language Processing (NLP) Analysis

[0855] The server analyzes the text of the submitted page range using natural language processing technology. Specifically, it divides the text into sentences and identifies important sentences and key phrases. Software used includes NLTK, spaCy, and Hugging Face Transformers. The input is text data, and the output is the analysis results, which are important sentences and key phrases.

[0856] Step 4: Summary generation

[0857] The server generates a summary based on the analyzed important sentences and key phrases. This involves extracting the most important sentences and key phrases and connecting them to form a summary. The input is the analysis result, and the output is the generated summary.

[0858] Step 5: Spoiler filtering

[0859] The server filters information about parts that the user has not yet read, eliminating spoilers from summaries and keypoints. The input is the summary and keypoints, and the output is the spoiler-free filtered result.

[0860] Step 6: Emotion Recognition

[0861] The server uses an emotion engine to analyze the user's emotional data. Based on data obtained from the camera and microphone, it identifies the user's emotional state, such as whether they are focused or relaxed. The input is biometric data and voice data, and the output is analyzed emotional data.

[0862] Step 7: Generate and send a response

[0863] The server packages the filtered summary and key points into JSON format and dynamically adjusts the content based on the user's emotional state. For example, if the user is concentrating, it displays a detailed summary. The final package is sent to the device. The input is the filtering results and emotional data, and the output is the response data in JSON format.

[0864] Step 8: Displaying the User Interface

[0865] The terminal receives summaries, key points, and sentiment-based advice from the server and displays them on the user interface. The user can check these as they read. The input is the response data from the server, and the output is the content displayed to the user.

[0866] Step 9: User Examples

[0867] If a user reads from page 1 to page 10, emotional data collected during that time (for example, whether they are concentrating) is also sent. The server analyzes the content of pages 1 to 10 and inputs prompts into the generative AI model to generate and refine summaries and key points. The filtered summary and key points are then sent to the device, allowing the user to review them as they continue reading.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] [Third embodiment]

[0872] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0873] 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.

[0874] 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).

[0875] 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.

[0876] 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.

[0877] 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).

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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."

[0884] The present invention relates to a system that automatically summarizes and extracts key points from the content of an electronic book as the user reads it. The program processing of this system is explained below in natural language.

[0885] server

[0886] Initialization

[0887] The server loads the e-book data and user information from the database, which allows it to know which e-book the user is currently reading and up to which page, for further analysis.

[0888] Receiving reading data

[0889] The page information that the user has read on the device is sent to the server, which receives this data and checks which pages the user has read.

[0890] Natural Language Processing (NLP) Analysis

[0891] The server analyzes the text data of the received page range using natural language processing technology. Specifically, it divides the text into sentences and analyzes the meaning of each sentence. For analysis, it uses libraries such as spaCy, NLTK, and Transformers.

[0892] Summary Generation

[0893] The server extracts key sentences and phrases from the analysis and generates a summary, which is a concise summary of what the user has read.

[0894] Spoiler Filtering

[0895] The server filters information about parts of the book that the user has not yet read, ensuring that summaries and key points do not contain spoilers.

[0896] Response generation and sending

[0897] The server packages the filtered summary and keypoints into JSON format and sends it to the device.

[0898] Terminal (user device)

[0899] User Interface Display

[0900] When users open an e-book, the device displays a dedicated reading assistance interface, allowing users to check the summary and key points at any time.

[0901] Data transmission

[0902] Each time the user turns a page, the device sends the new page information to the server, including the user ID and the page range that has been read.

[0903] Response reception and display

[0904] The device analyzes the summary and key points received from the server and displays them on the user interface, allowing users to efficiently understand the content they have read.

[0905] user

[0906] reading

[0907] The user continues reading the e-book, and the page information is sent from the device to the server.

[0908] Summary and key points review

[0909] Users can recall important content by reviewing summaries and key points displayed on the screen.

[0910] Continue reading

[0911] When users start their next reading, they can smoothly resume reading by checking the summary of their previous reading.

[0912] Specific examples

[0913] When a user begins reading an e-book, the device sends the user ID and current page range to the server. For example, if the user has read pages 1 to 10, that information is sent to the server. The server analyzes the content of pages 1 to 10 and generates a summary and key points. The generated summary and key points are filtered to ensure they do not contain spoilers and are sent to the device. The device displays them to the user. The next time the user starts reading from page 11, they can smoothly continue reading by checking the summary from the previous page.

[0914] In this way, the present invention is a system that allows you to enjoy reading efficiently even with fragmented reading time, and provides a rich reading experience while preventing spoilers.

[0915] The processing flow will be explained below.

[0916] Step 1:

[0917] The server loads the e-book data and user information from the database, so that the server can know which e-book the user is reading and up to which page they have read.

[0918] Step 2:

[0919] When a user opens an e-book and starts reading from page 1, the device sends the user ID and page number (pages 1 to 10) to the server.

[0920] Step 3:

[0921] The server analyzes the page information received from the device, checks which pages the user has read, and identifies the text data of the page range that the user has read based on the received information.

[0922] Step 4:

[0923] The server analyzes the text data of the received page range using natural language processing (NLP) techniques, such as splitting the text into sentences and analyzing the meaning of each sentence, using libraries such as spaCy, NLTK, and Transformers.

[0924] Step 5:

[0925] The server extracts important sentences and key phrases and uses them to generate a summary, which is a concise summary of what the user has read and includes the main points of information.

[0926] Step 6:

[0927] The server uses filtering measures to filter out information about parts of the book that the user has not yet read, an important step in preventing spoilers.

[0928] Step 7:

[0929] The server packages the filtered summary and keypoints into JSON format and sends it to the device.

[0930] Step 8:

[0931] The device receives the response from the server, parses the JSON data, extracts summaries and key points, and displays them on the user interface.

[0932] Step 9:

[0933] Users see a summary and key points displayed on the screen, which helps users remember important content.

[0934] Step 10:

[0935] The user starts reading from page 11 during the next reading time. The device sends the information "User ID" and "Pages 11 to 20" to the server.

[0936] Step 11:

[0937] The server analyzes the content of pages 11 to 20, generates a summary and key points, and sends them to the terminal, which displays them to the user, allowing the user to efficiently understand the content they are reading.

[0938] By repeating this series of processes, users can enjoy e-books while avoiding spoilers by checking summaries and key points of the content they have read.

[0939] Example 1

[0940] 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."

[0941] The present invention aims to enable users to efficiently grasp the content of e-books and quickly review important points when rereading them later. However, conventional e-book systems lack the functionality to automatically summarize the content they have read and extract key points, or to prevent spoilers from being revealed for content they have not yet read. This requires users to recall the content each time they read, which reduces the efficiency of their reading experience.

[0942] 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.

[0943] In this invention, the server includes means for loading e-book data and user information, means for receiving page information of the page that has been read, means for analyzing the text of the received page range using natural language processing technology, means for extracting important sentences and key phrases from the analysis result and generating a summary, means for filtering information of the part that the user has not yet read, and means for providing the filtered summary and key points to the user. This allows the user to efficiently grasp the content that they have read and easily confirm important points while avoiding spoilers.

[0944] "E-Book Data" refers to the text data of a book stored in digital form, often formatted for reading on an e-reader or other device.

[0945] "User information" refers to data about users who use the system, including user IDs, reading history, and settings information.

[0946] "Read page information" is data indicating a specific page range when a user reads an electronic book.

[0947] "Natural language processing technology" is a technology for analyzing text data and understanding and generating human language. Specifically, it includes processes such as text segmentation, semantic analysis, and summary generation.

[0948] The "analysis results" are analytical information on text data obtained using natural language processing technology, and include important sentences and key phrases.

[0949] A "summary" is a shortened version of important information or key points extracted from a long text.

[0950] "Keyphrases" are important words or phrases in a text that play an important role in summarizing and searching.

[0951] "Filtering" is the process of selecting data based on specific conditions and removing unnecessary parts. In this system, it refers to removing unread information to prevent spoilers.

[0952] A "user interface" is a collection of screens and operating methods that allow a user to operate a system and obtain information.

[0953] The "JSON format" is a lightweight data exchange format for structuring, storing, and transferring data, and is an abbreviation for JavaScript Object Notation.

[0954] This invention relates to a system that automatically summarizes and extracts key points from the content of an e-book as the user reads it. This system realizes efficient reading support in real time through the cooperation of a server and a terminal.

[0955] server

[0956] First, the server loads the e-book data and user information from a database, typically an SQL database, and executes SQL queries to retrieve the necessary data. This allows the server to determine which e-book the user has read and up to which page, and prepares for subsequent analysis.

[0957] Next, the server receives the page information sent from the device. This data is sent in JSON format and includes the user ID and the page range that has been read. The received data is temporarily stored in the server's memory and passed on to the next analysis process.

[0958] The text data of the received page range is analyzed using natural language processing techniques. Specifically, libraries such as "spaCy," "NLTK," and "Transformers" are used. For example, "spaCy" is used to divide the text into sentences and analyze the meaning of each sentence. In addition, the BERT model in "Transformers" is used to generate sentence embeddings and extract important sentences.

[0959] Extract important sentences and key phrases from the analysis results and generate summaries. The "transformers" library is used to generate summaries, and a summary model (e.g., a BERT-based summary model) is used to shorten and summarize important information. Furthermore, key phrases are extracted using TF-IDF and the Rake algorithm.

[0960] To prevent spoilers from being included in the generated summary and key points, information about parts that the user has not yet read is filtered out. This is done by the server retrieving the user's reading completion information from the database and detecting the unread parts. The spoiler parts are then removed using filtering logic.

[0961] Finally, the filtered summary and keypoints are packaged in JSON format and sent to the terminal.

[0962] Terminal (user device)

[0963] When a user opens an e-book, the device displays a dedicated reading support interface, which is built using JavaScript and HTML and is designed to allow users to easily check summaries and key points.

[0964] Every time the user turns a page, the device sends the new page information to the server, including the user ID and the page range that has been read. The data is sent using the "fetch" function, and is packaged in JSON format.

[0965] Analyze the summary and key points received from the server and display them on the user interface. Analyze the received JSON data and dynamically insert the summary and key points into HTML elements.

[0966] user

[0967] As users read e-books, information about the pages they have read is sent from their device to the server. By checking the automatic summary and key points of the content they have read, users can read more efficiently.

[0968] When you start reading again, you can smoothly resume where you left off by checking the summary from the previous time, providing a consistent reading experience even when reading in fragments.

[0969] Specific examples

[0970] For example, when a user starts reading an e-book, the device sends the user ID and the current page range to the server. If the user has read pages 1 to 10, that information is sent to the server. The server analyzes the content of pages 1 to 10 and generates a summary and key points. The generated summary and key points are filtered to avoid spoilers and sent to the device, which then displays them to the user.

[0971] The next time a user starts reading from page 11, they can smoothly continue reading while checking the summary from the previous page.

[0972] Prompt Sentence Examples

[0973] "Please summarize the contents of pages 1-10 and extract the main key points. Please be careful not to include spoilers."

[0974] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0975] Step 1:

[0976] Initialization

[0977] The server loads e-book data and user information from the database. Specifically, it executes an SQL query to get the required data. For example, it executes a query like "SELECT FROM ebooks WHERE user_id = 'user ID'". The input is the response from the database, and the output is the loaded e-book data and user information.

[0978] Step 2:

[0979] Receiving reading data

[0980] The server receives the page information sent from the device. This data is sent in JSON format and includes the user ID and the page range that has been read. Specifically, it receives JSON data from the device such as "{"user_id": "User ID", "start_page": 1, "end_page": 10}". The input is the JSON data from the device, and the output is the saved page information that has been read.

[0981] Step 3:

[0982] Natural Language Processing (NLP) Analysis

[0983] The server analyzes the text data of the received page range using natural language processing techniques. Specifically, it uses the "spaCy" and "Transformers" libraries to split the text into sentences and perform semantic analysis. For example, it uses "spaCy" to initialize "nlp = spacy.load('en_core_web_sm')" to analyze the received text. It also uses the "Transformers" BERT model to generate sentence embeddings. The input is the text data to be analyzed, and the output is the analysis results: important sentences and key phrases.

[0984] Step 4:

[0985] Summary Generation

[0986] The server extracts important sentences and key phrases from the analysis results and generates summaries. Specifically, it uses a summarization model from the "transformers" library to shorten and summarize important information. It also extracts key phrases using TF-IDF and Rake algorithms. The input is the analysis results, and the output is the generated summary and key phrases.

[0987] Step 5:

[0988] Spoiler Filtering

[0989] The server filters information about parts that the user has not yet read to ensure that the generated summary and key points do not contain spoilers. This is done by retrieving the user's reading completion information from the database and detecting unread parts. For example, it applies filtering logic such as "delete if the if statement comes from an unread page." The input is the summary, key phrases, and the user's reading completion information, and the output is the filtered summary and key phrases.

[0990] Step 6:

[0991] Response generation and sending

[0992] The server packages the filtered summary and key phrases in JSON format and sends them to the device. Specifically, it packages and sends them as follows: "response = json.dumps({'summary': 'Summary content', 'key_points': 'Key points'})" and "send_response_to_device(response)". The input is the filtered summary and key phrases, and the output is the JSON data sent to the device.

[0993] Step 7:

[0994] User Interface Display

[0995] When a user opens an e-book, the device displays a dedicated reading support interface. This is done by building the interface using JavaScript and HTML and displaying information in "document.getElementById('reading-interface')". The input is the JSON data sent from the server, and the output is the displayed reading support interface.

[0996] Step 8:

[0997] Data transmission

[0998] Every time the user turns a page, the device sends new page information to the server. For example, use "fetch('server URL', { method: 'POST', body: JSON.stringify({ user_id: 'user ID', start_page: 11, end_page: 20})})" to send JSON data to the server. The input is the user's operation (turning a page), and the output is the new page information sent to the server.

[0999] Step 9:

[1000] Response reception and display

[1001] The terminal parses the summary and key points received from the server and displays them on the user interface. For example, "response = await fetch('server URL')" and "document.getElementById('summary').textContent = response.summary". The input is the JSON response from the server, and the output is the summary and key phrases displayed on the user interface.

[1002] Step 10:

[1003] Summary and key points review

[1004] Users can review the summary and key points displayed on the screen and recall important content. The input is the summary and key phrases displayed on the device, and the output is to improve the user's comprehension and reading efficiency.

[1005] Step 11:

[1006] Continue reading

[1007] When users start their next reading session, they can smoothly resume reading by checking the summary from the previous session. The input is the summary from the previous session, and the output is a smooth resumption and continuation of reading.

[1008] (Application example 1)

[1009] 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."

[1010] Traditional e-book systems lack the functionality to efficiently summarize what users have read and make it available for the next reading. This means that users have to spend a lot of time recalling what they read last time when rereading a book, especially when their reading time is fragmented. Furthermore, the summary system does not take into account spoilers, so there is a risk that users will learn about content they have not yet read. Furthermore, the lack of a summary or key point saving or reminder function results in an inconsistent reading experience.

[1011] 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.

[1012] In this invention, the server includes means for analyzing the content that has been read, means for summarizing the analyzed content, means for extracting key points from the summary, means for identifying the range that the user has read, means for filtering the range that the user has read to prevent spoilers, means for providing the filtered summary and key points to the user, means for saving the summary and key points to a server on the cloud, and means for notifying the user so that the user can continue reading while checking the previous summary. This makes it possible to efficiently summarize the content that has been read, prevent spoilers, and provide a consistent and comfortable reading experience even when reading in fragments.

[1013] "Means for analyzing what you read" refers to technology that uses natural language processing to analyze the text a user reads and understand the meaning of sentences and phrases.

[1014] "Means for summarizing the analyzed content" refers to the technique of concisely summarizing the main points of the entire text using important sentences and information obtained through the analysis.

[1015] "Methods for extracting key points from summaries" refers to techniques for selecting particularly important sentences or phrases from a summarized text.

[1016] "Means for determining user progress" refers to technology that tracks which pages or chapters a user has read.

[1017] "Means for preventing spoilers by filtering the range of content that the user has read" refers to a technology that automatically filters out information about parts that the user has not yet read.

[1018] The "means for providing filtered summaries and key points to users" refers to a technology for transmitting spoiler-free summaries and key points to users' terminals and displaying them.

[1019] "Means for storing the summary and key points on a server on the cloud" refers to a technology for storing the generated summary and key points in a cloud environment.

[1020] "Means for notifying users so that they can continue reading while checking the summary of the previous reading" refers to a technology that makes it easy for users to check the summary of what they read last time and notifies them so that they can smoothly resume reading the next time.

[1021] This invention is a system that automatically generates summaries and key points as a user reads an e-book, providing an efficient reading experience. Below, we will explain the mode for implementing the invention based on the program of this system.

[1022] server

[1023] Initialization

[1024] The server stores all e-book content and user information. It loads which books users have read and to what extent from the database for further analysis. This process uses a cloud-based database service.

[1025] Receiving reading data

[1026] Receives user reading progress information sent from the device. This information includes the user ID and the page range read. The received information is recorded in the database.

[1027] Natural Language Processing (NLP) Analysis

[1028] The server analyzes the text data of the read section using natural language processing technology. Specifically, it uses NLP libraries such as spaCy and Transformers to divide the text into sentences and analyze the meaning of each sentence. The important sentences and phrases obtained from this analysis are then used to generate a summary and extract key points.

[1029] Summary Generation

[1030] The server generates a summary based on the information obtained from the NLP analysis. It uses the Transformers summarization function to compile a summary of the entire text into a short document that concisely summarizes what the user has read.

[1031] Spoiler Filtering

[1032] The server automatically filters out information about unread content, ensuring that spoilers are not included when summaries and key points are provided to users.

[1033] Save and Notify

[1034] The generated summaries and key points are stored on a cloud server, and this information is notified to the user the next time they resume reading. This feature is important for supporting a continuous reading experience.

[1035] Terminal (user device)

[1036] User Interface Display

[1037] The device will display a dedicated reading assistance interface when users open an e-book, allowing them to check the summary and key points at any time.

[1038] Data transmission

[1039] Each time the user turns a page, the device sends the new page information to the server, including the user ID and the page range that has been read.

[1040] Response reception and display

[1041] The device displays the summary and key points received from the server on the user interface, allowing the user to efficiently understand the content they have read.

[1042] user

[1043] reading

[1044] The user continues reading the e-book, and the page information is sent from the device to the server.

[1045] Summary and key points review

[1046] Users can instantly recall important content by viewing summaries and key points displayed on the screen.

[1047] Continue reading

[1048] When users start their next reading, they can smoothly resume reading by checking the summary of their previous reading.

[1049] Specific examples

[1050] When a user reads pages 1 to 10 of an e-book, that information is sent from the device to the server. The server analyzes the content of pages 1 to 10 and generates a summary and key points. The generated summary and key points are applied with a spoiler prevention function before being sent to the device, which then displays them to the user. When the user starts reading from page 11 next time, they can smoothly continue reading by checking the previous summary.

[1051] Example prompts for generative AI models

[1052] Summarize what the user reads from page 1 to 10 of your eBook and list the main points.

[1053] This is an example of how to use this prompt to ask a generative AI model to generate a summary and key points.

[1054] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1055] Step 1:

[1056] The server loads e-book data and user information from the database. The input is the user ID and e-book identification information. The output is information about which e-book the user is currently reading and how many pages they have read. This information is necessary for analysis.

[1057] Step 2:

[1058] The page information that the user has read using the device is sent from the device to the server. The input is the user ID and the page range that has been read (e.g., page 1 to page 10). The output is the page range information received by the server. Based on this information, the next analysis step is performed.

[1059] Step 3:

[1060] The server analyzes the text data of the received page range using natural language processing (NLP) technology. Specifically, it divides the text data of pages 1 to 10 into sentences and uses libraries such as spaCy and Transformers to analyze the meaning of each sentence. The input is the text data of the received page range, and the output is a list of analyzed sentences and key phrases.

[1061] Step 4:

[1062] The server generates a summary based on the analyzed key sentences and key phrases. For this purpose, it uses the summarization function of Transformers. The input is a list of analyzed sentences and key phrases, and the output is the summarized text. The generated summary is a concise summary of what the user has read.

[1063] Step 5:

[1064] The server filters the generated summary to remove unread content and prevent spoilers. The input is the summarized text and the user's reading progress, and the output is the summary text with spoilers removed. This process is necessary to ensure a good user reading experience.

[1065] Step 6:

[1066] The server stores the filtered summaries and keypoints in a cloud database. The input is the filtered summaries and keypoints, and the output is confirmation of the stored data. This storage operation allows the summaries and keypoints to be reused the next time they are accessed.

[1067] Step 7:

[1068] The terminal displays the summaries and key points received from the server on the user interface. The input is the data received from the server, and the output is the summaries and key points on the screen that the user views, allowing the user to check the latest reading information in real time.

[1069] Step 8:

[1070] When the user starts reading again, the device will send a notification to the user to check the summary of the previous reading. The input is the summary and key points stored in the cloud, and the output is a notification to the user's device. This notification allows the user to resume reading smoothly.

[1071] 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.

[1072] This invention relates to a system that automatically summarizes and extracts key points from the content of an e-book as the user reads it. It also incorporates an emotion engine that recognizes the user's emotions to improve the reading experience. Below, we will explain the program processing of this system in natural language and provide detailed examples.

[1073] server

[1074] Initialization

[1075] The server loads the e-book data, user information, and emotion engine from the database, allowing the server to know which e-book the user is reading and up to which page, for further analysis and emotion recognition.

[1076] Receiving reading data

[1077] The page information that the user has read on the device is sent to the server, which receives this data and checks which pages the user has read.

[1078] Natural Language Processing (NLP) Analysis

[1079] The server analyzes the text data of the received page range using natural language processing technology. Specifically, it divides the text into sentences and analyzes the meaning of each sentence. For analysis, it uses libraries such as spaCy, NLTK, and Transformers.

[1080] Summary Generation

[1081] The server extracts key sentences and phrases from the analysis and generates a summary, which is a concise summary of what the user has read.

[1082] Spoiler Filtering

[1083] The server filters information about parts of the book that the user has not yet read, ensuring that summaries and key points do not contain spoilers.

[1084] emotion recognition

[1085] The emotion engine analyzes the emotion data received from the device to recognize the user's emotions, including facial expression recognition, voice analysis, and biometric data analysis.

[1086] Response generation and sending

[1087] The server packages the filtered summary and key points into a JSON format and dynamically adjusts it based on the user's emotional state. The content and display format of the summary can be changed based on the analysis results of the emotion engine. The summary is then sent to the device.

[1088] Terminal (user device)

[1089] User Interface Display

[1090] When a user opens an e-book, the device displays a dedicated reading support interface, which also includes emotion recognition functionality, collecting emotional data using the camera and microphone.

[1091] Data transmission

[1092] Each time the user turns a page, the device sends the new page information and collected emotion data to the server, including the user ID, the page range read, and the emotion data.

[1093] Response reception and display

[1094] The device analyzes the summary, key points, and emotion-based advice received from the server and displays it on the user interface. For example, if the user is feeling nervous, it displays advice on how to continue reading to relax.

[1095] user

[1096] reading

[1097] As the user reads the e-book, the page information and emotional data of the page being read are sent from the device to the server.

[1098] Summary and key points review

[1099] Users can view summaries, key points, and emotion-based advice displayed on the screen, helping them easily recall important content and receiving advice tailored to their emotions.

[1100] Continue reading

[1101] When users start their next reading, they can smoothly resume reading by checking the summary of their previous reading, and enjoy reading while receiving emotional advice.

[1102] Specific examples

[1103] When a user begins reading an e-book, the device sends the user ID, current page range, and emotional data to the server. For example, if the user reads from page 1 to page 10, emotional data collected during that time (e.g., whether the user is concentrating) is also sent. The server analyzes the content of pages 1 to 10, generates a summary and key points, and adjusts the summary content based on the emotional data. The filtered summary and key points are then sent to the device, allowing the user to review them as they continue reading.

[1104] This series of processes allows users to efficiently check summaries and key points of what they have read, receive advice based on their own emotions, and enjoy reading efficiently and enjoyably.

[1105] The processing flow will be explained below.

[1106] Step 1:

[1107] The server loads the e-book data, user information, and emotion engine from the database, allowing the server to know which e-book the user is reading and up to which page, for further analysis and emotion recognition.

[1108] Step 2:

[1109] When a user opens an e-book, the device sends the "user ID," "current page range," and "emotion data" to the server. Emotion data is collected using the camera and microphone.

[1110] Step 3:

[1111] The server analyzes the page information and emotional data received from the device to determine which page the user has read and their current emotional state.

[1112] Step 4:

[1113] The server analyzes the text data of the received page range using natural language processing (NLP) techniques, using libraries such as spaCy, NLTK, and Transformers to divide the text into sentences and analyze the meaning of each sentence.

[1114] Step 5:

[1115] The server extracts important sentences and key phrases and uses them to generate a summary, a concise summary of what the user has read, containing the gist of the information.

[1116] Step 6:

[1117] The server filters information about parts of the book that the user has not yet read, ensuring that summaries and key points do not contain spoilers.

[1118] Step 7:

[1119] The emotion engine analyzes the user's emotional state and dynamically adjusts the content of the summary and key points based on the emotional data, for example, if the user is feeling stressed, it will provide a concise and positive summary.

[1120] Step 8:

[1121] The server packages the filtered summary and keypoints into JSON format and sends it to the device along with a summary adjusted based on the user's sentiment data.

[1122] Step 9:

[1123] The device analyzes the summary, key points, and emotion-based advice received from the server, thereby optimizing the display content to the user's emotional state.

[1124] Step 10:

[1125] The device displays summaries, key points, and emotion-based advice on the user interface. For example, if the user is feeling nervous, the device displays advice on how to continue reading to relax.

[1126] Step 11:

[1127] Users see summaries and key points displayed on the screen, along with advice based on their emotions, helping them remember important content and receiving advice tailored to their emotions.

[1128] Step 12:

[1129] When the user starts reading from page 11 in the next reading session, the device will send the information "User ID," "Pages 11 to 20," and "Emotion Data" to the server.

[1130] Step 13:

[1131] The server analyzes the content of pages 11 to 20 and generates a summary and key points. The emotion engine then analyzes the emotion data and adjusts the summary content, so that the next reading session will also receive an emotion-based summary.

[1132] Step 14:

[1133] The device displays the new summary and key points received from the server in the user interface, allowing the user to review the content they have read and receive emotional advice as they continue reading.

[1134] This series of processes allows users to efficiently check summaries and key points of what they have read, receive advice based on their own emotions, and enjoy reading efficiently and enjoyably.

[1135] Example 2

[1136] 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."

[1137] Conventional e-book reading systems have limited functionality for providing summaries and key points of the content the user has read, making it difficult to prevent spoilers or provide appropriate feedback based on the user's emotions. Furthermore, they lack a mechanism for communicating to the system in real time how far the user has read and the emotions they felt at the time, which results in a lack of a way to enhance the user's reading experience. Our goal is to solve these issues and improve the user's reading experience.

[1138] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1139] In this invention, the server includes means for analyzing the content that the user has read, means for summarizing the analyzed content, means for extracting key points from the summary, means for identifying the range that the user has read, means for filtering the range that the user has read to prevent spoilers, means for providing the filtered summary and key points to the user, means for recognizing the user's emotional state, means for dynamically adjusting the summary and key points based on the user's emotional state, and means for transmitting and receiving data between the server and the terminal. This makes it possible to efficiently summarize the content that the user has read and provide feedback according to their emotions, thereby improving the user's reading experience while preventing spoilers.

[1140] "Means for analyzing the content being read" refers to a method or device used to analyze the text data of the e-book that the user has read.

[1141] "Means for summarizing the analyzed content" refers to a method or device for extracting important information from the analyzed text data and summarizing it concisely.

[1142] A "means for extracting key points from summaries" refers to a method or apparatus for identifying and extracting particularly important sentences or phrases from summarized information.

[1143] "Means for determining the extent to which a user has read" refers to a method or device for determining how many pages a user has read in an e-book.

[1144] A "spoiler prevention means using filtering means" refers to a method or device for removing information about parts of a book that the user has not yet read from the summary or key points.

[1145] "Means for providing a filtered summary and key points to a user" refers to a method or device for presenting a spoiler-free summary and key points to a user.

[1146] "Means for recognizing the user's emotional state" refers to a method or device for analyzing the user's facial expressions, voice, biometric data, etc. to determine their emotions.

[1147] "Means for dynamically adjusting summaries and key points based on a user's emotional state" refers to a method or apparatus for changing the presentation or content of summaries and key points in response to a recognized emotional state of a user.

[1148] "Means for sending and receiving data between the server and the terminal" refers to a communication method or device for exchanging necessary information between the server and the user's device.

[1149] "Filtered summaries and keypoints" refers to summaries and keypoints that have been processed to remove spoilers.

[1150] "Emotion-based advice" refers to suggestions for things like how to read or relax that take into account the user's emotional state.

[1151] "Natural language processing technology" refers to the general technology that enables computers to understand and analyze human language.

[1152] The "means for identifying important sentences and key phrases" refers to a method or device for identifying important sentences and key phrases within text data using natural language processing techniques.

[1153] MODE FOR CARRYING OUT THE INVENTION

[1154] This invention relates to a system that automatically summarizes and extracts key points from the content of an e-book as the user reads it. It also incorporates an emotion engine that recognizes the user's emotions to improve the reading experience. Below, we will explain the program processing of this system in natural language and provide detailed examples.

[1155] server

[1156] The server loads e-book data, user information, and the emotion engine from a database. This uses a database management system such as MySQL or PostgreSQL. It also uses a cache system (e.g., Redis) to ensure fast data access. The server uses NLP (natural language processing) libraries such as spaCy, NLTK, and Transformers to analyze the text data as it is read. The server also uses TF-IDF and BERT models to generate summaries and extract key points.

[1157] When the page information and emotion data sent by the user on their device is received, the server begins analysis. First, the received data is verified, and then the text data for the relevant page is extracted from the e-book database. NLP analysis is performed on the extracted text data to identify important sentences and key phrases and generate a summary. The generated summary is then filtered to remove unnecessary information. The summary and key points are also adjusted based on the user's emotion data.

[1158] Finally, the server sends the summary and key points packaged in JSON format to the device, along with sentiment-based advice. For example, if the user is concentrating, it can provide a more detailed summary, or if they need to relax, it can provide a concise, easy-to-read summary.

[1159] Terminal (user device)

[1160] When a user opens an e-book, the device displays a reading assistance interface. This interface includes a function to collect the user's emotional data using a camera and microphone. Each time a page is turned, the device sends the new page information and emotional data to the server. This data transmission is performed using an HTTP POST request. The device then analyzes the filtered summary, key points, and emotional advice received from the server and displays them on the user interface.

[1161] user

[1162] As users read e-books, page information and emotional data collected during the process are sent to the server via their device. A summary of the content they have read and key points can be viewed on the device's interface. Based on this, users can recall important content and receive advice based on their emotions. Furthermore, when they resume reading, they can check the summary from the previous page, allowing them to continue reading smoothly.

[1163] Specific examples

[1164] When a user begins reading an e-book, the device sends the user ID, current page range, and emotional data to the server. For example, if the user reads from page 1 to page 10, emotional data collected during that time (e.g., whether the user is concentrating) is also sent. The server analyzes the content of pages 1 to 10, generates a summary and key points, and adjusts the summary content based on the emotional data. The filtered summary and key points are then sent to the device, allowing the user to review them as they continue reading.

[1165] This series of processes allows users to efficiently check summaries and key points of what they have read, receive advice based on their own emotions, and enjoy reading efficiently and enjoyably.

[1166] Specific examples of prompts to input to generative AI models

[1167] "Generate a summary of what the user reads from pages 1 to 10, with key points tailored based on their emotional focus."

[1168] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1169] Step 1:

[1170] The server performs initialization. It loads e-book data, user information, and the emotion engine from the database. Specifically, it uses a database management system such as MySQL or PostgreSQL to retrieve the necessary data using SQL queries, and stores it in a cache such as Redis for fast data access. This prepares the e-book text data, the user's reading history, and the emotion engine configuration information.

[1171] Input: Database

[1172] Output: Cached data

[1173] Specific behavior: Executes SQL queries and caches data

[1174] Step 2:

[1175] The device displays a reading assistance interface when a user opens an e-book, and also includes a function to collect user emotional data using the camera and microphone.

[1176] Input: User actions

[1177] Output: Display of reading assistance interface

[1178] Specific behavior: Display a dialog box requesting permission to access the camera and microphone

[1179] Step 3:

[1180] As the user progresses through the e-book, the device sends new page information and emotion data to the server using an HTTP POST request, packaging the user ID, the page range that has been read, and the emotion data in JSON format.

[1181] Input: page information, emotional data

[1182] Output: Send data to the server

[1183] Specific behavior: Serializes data in JSON format and sends an HTTP POST request

[1184] Step 4:

[1185] The server validates the received page information and emotion data. It checks whether the data format of the user ID and page range is correct. If invalid data is sent, it returns an error message.

[1186] Input: Received data

[1187] Output: Validation result (error message or progress to the next processing step)

[1188] Specific behavior: Data format check and error handling

[1189] Step 5:

[1190] The server extracts the text data of the corresponding page from the e-book database, and efficiently extracts the data corresponding to the received page range using the Python Pandas library.

[1191] Input: Page range read

[1192] Output: Extracted text data

[1193] Specific behavior: Executes a database query and retrieves text data

[1194] Step 6:

[1195] The server performs natural language processing (NLP) analysis on the extracted text data, using NLP libraries (e.g., spaCy, NLTK, Transformers) to split the text data into sentences, identify important sentences and key phrases, and generate summaries.

[1196] Input: Text data

[1197] Output: Summary and key points

[1198] Specific operations: Text tokenization, part-of-speech tagging, dependency analysis

[1199] Step 7:

[1200] The server filters information about parts of the text that the user has not yet read from the summary and key points, using regular expressions and filtering algorithms to remove those parts.

[1201] Input: Summary, key points, and unread information

[1202] Output: filtered summary and keypoints

[1203] Specific behavior: Applying regular expression processing and filtering algorithms

[1204] Step 8:

[1205] The server analyzes the user's emotional data, and the emotion engine performs facial expression recognition, voice analysis, and biometric data analysis to identify the user's emotional state.

[1206] Input: Emotion data

[1207] Output: Emotional state

[1208] Specific operations: facial expression recognition, voice analysis, biometric data analysis

[1209] Step 9:

[1210] The server dynamically adjusts the filtered summaries and key points based on the user's emotional state: if the user is focused, it provides a detailed summary; if the user is tense, it provides a concise summary for relaxation.

[1211] Input: Emotional state, filtered summary and keypoints

[1212] Output: Tailored summary and key points

[1213] Specific actions: summarizing and adjusting key points

[1214] Step 10:

[1215] The server packages the adjusted summary and key points in JSON format and sends it to the device, returning the data as an HTTP response through a REST API.

[1216] Input: Adjusted summary and key points

[1217] Output: Sending data to the terminal

[1218] Specific operation: Serializing in JSON format and sending HTTP response

[1219] Step 11:

[1220] The device analyzes the summary, key points, and emotion-based advice received from the server and displays it on the user interface. For example, if the user is feeling nervous, it displays advice on how to continue reading to relax.

[1221] Input: Received data

[1222] Output: Displayed in the user interface

[1223] Specific operation: Analyzing data and displaying it in the interface

[1224] The above is the flow of processing of the program of this system.

[1225] (Application example 2)

[1226] 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."

[1227] Currently, there are systems that automatically generate summaries and key points when reading e-books, but there are no systems that can prevent spoilers and improve the reading experience by taking user emotions into account. As a result, the reading experience is not optimized to suit the emotions of each individual user, resulting in a decrease in user satisfaction.

[1228] 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.

[1229] In this invention, the server includes means for analyzing the content that has been read, means for summarizing the analyzed content, means for extracting key points from the summary, means for recognizing and analyzing the user's emotions, and means for adjusting and providing the summary and key points based on the analyzed emotion data. This makes it possible to adjust the summary and key points of the content that has been read based on the emotions, and provide an optimal reading experience that is adapted to the emotions of each individual user.

[1230] A "means for analyzing what has been read" is a means for a user to analyze the content of the text they have read and understand the meaning of each sentence.

[1231] "Means for summarizing the analyzed content" refers to means for extracting important information from the analyzed text and summarizing it concisely.

[1232] The "means for extracting key points from summaries" refers to a means for identifying and extracting more important phrases and sentences from the generated summaries.

[1233] "Means for identifying how far the user has read" is a means for identifying how much text the user has read.

[1234] "Means for preventing spoilers by filtering the portion of the content that the user has read" refers to filtering information about portions that the user has not yet read, and eliminating spoilers from summaries and key points.

[1235] The "means for providing a filtered summary and key points to a user" is a means for presenting a spoiler-free summary and key points in a form accessible to a user.

[1236] "Means for recognizing and analyzing user emotions" refers to a means for identifying and analyzing emotions from the user's facial expressions, voice, etc.

[1237] "Means for adjusting and providing summaries and key points based on analyzed emotional data" refers to means for dynamically changing the content of summaries and key points based on the user's emotional data and providing them in the most optimal form.

[1238] The "means for transmitting information about the page that has been read and emotional data to the server" refers to a means for transmitting the page that the user has read and emotional data to the server.

[1239] The "means for displaying the summary and adjustment data based on key points and emotion received from the server" refers to means for displaying the summary sent from the server and the adjustment results based on key points and emotion data.

[1240] "A method of analyzing text using natural language processing technology to identify important sentences and key phrases, and inputting prompts into a generative AI model based on emotional data to adjust the summary and key points" refers to a method of analyzing text using natural language processing technology to extract important information, and then inputting prompts into a generative AI model based on emotional data to adjust the summary and key points in an optimal form.

[1241] The present invention provides a system that automatically summarizes and extracts key points from e-books and other text content as a user reads them, and also recognizes the user's emotions to improve the reading experience. This system functions by combining a server and a terminal.

[1242] server

[1243] Initialization

[1244] The server loads the e-book data, user information, and emotion engine from the database, allowing the server to know which e-book the user is reading and up to which page, for further analysis and emotion recognition.

[1245] Receiving reading data

[1246] The page information and emotion data of the user that has been read using the device are sent to the server, which receives this data and checks which pages the user has read.

[1247] Natural Language Processing (NLP) Analysis

[1248] The server analyzes the text data of the received page range using natural language processing technology (NLTK, spaCy, Hugging Face Transformers, etc.). Specifically, it divides the text into sentences and analyzes the meaning of each sentence.

[1249] Summary Generation

[1250] The server extracts key sentences and phrases from the analysis and generates a summary, which is a concise summary of what the user has read.

[1251] Spoiler Filtering

[1252] The server filters information about parts of the book that the user has not yet read, ensuring that summaries and key points do not contain spoilers.

[1253] emotion recognition

[1254] In order for the emotion engine to recognize the user's emotions, it analyzes the emotion data (facial expression recognition, voice analysis, biometric data) received from the device.

[1255] Response generation and sending

[1256] The server packages the filtered summary and key points in JSON format and dynamically adjusts it based on the user's emotional state. Based on the analysis results of the emotion engine, the content and display format of the summary are changed and sent to the device.

[1257] Terminal (user device)

[1258] User Interface Display

[1259] When a user opens an e-book, the device displays a dedicated reading support interface, which also includes emotion recognition functionality, collecting emotional data using the camera and microphone.

[1260] Data transmission

[1261] Each time the user turns a page, the device sends the new page information and collected emotion data to the server, including the user ID, the page range read, and the emotion data.

[1262] Response reception and display

[1263] The device analyzes the summary, key points, and emotion-based advice received from the server and displays it on the user interface. For example, if the user is feeling nervous, it displays advice on how to continue reading to relax.

[1264] User examples

[1265] When a user begins reading an e-book, the device sends the user ID, current page range, and emotional data to the server. For example, if the user reads from page 1 to page 10, emotional data collected during that time (e.g., whether the user is concentrating) is also sent. The server analyzes the content of pages 1 to 10, generates a summary and key points, and adjusts the summary content based on the emotional data. The filtered summary and key points are then sent to the device, allowing the user to review them as they continue reading.

[1266] Prompt Sentence Examples

[1267] The following format is input to the generative AI model to generate and refine summaries and key points:

[1268] Instructions: Extract automatic summaries and key points from the following text, and adjust the summaries and key points to take into account user focus.

[1269] <<Text on pages 1-10>>

[1270] In this way, users can continue reading efficiently while enjoying an optimal reading experience tailored to their emotions.

[1271] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1272] Program processing steps

[1273] Step 1: Initialization

[1274] The server loads e-book data, user information, and emotion engine from the database. This prepares the server to know which e-book the user is reading and up to which page. The input is data from the e-book database and user database, and the output is the initial setting for analysis and emotion recognition.

[1275] Step 2: Receiving reading data

[1276] The device sends page information and emotional data about the page the user has progressed to to the server. The server receives this data and records which pages have been progressed and the user's emotional state. The input is the page information and emotional data sent from the device, and the output is prepared data for analysis.

[1277] Step 3: Natural Language Processing (NLP) Analysis

[1278] The server analyzes the text of the submitted page range using natural language processing technology. Specifically, it divides the text into sentences and identifies important sentences and key phrases. Software used includes NLTK, spaCy, and Hugging Face Transformers. The input is text data, and the output is the analysis results, which are important sentences and key phrases.

[1279] Step 4: Summary generation

[1280] The server generates a summary based on the analyzed important sentences and key phrases. This involves extracting the most important sentences and key phrases and connecting them to form a summary. The input is the analysis result, and the output is the generated summary.

[1281] Step 5: Spoiler filtering

[1282] The server filters information about parts that the user has not yet read, eliminating spoilers from summaries and keypoints. The input is the summary and keypoints, and the output is the spoiler-free filtered result.

[1283] Step 6: Emotion Recognition

[1284] The server uses an emotion engine to analyze the user's emotional data. Based on data obtained from the camera and microphone, it identifies the user's emotional state, such as whether they are focused or relaxed. The input is biometric data and voice data, and the output is analyzed emotional data.

[1285] Step 7: Generate and send a response

[1286] The server packages the filtered summary and key points into JSON format and dynamically adjusts the content based on the user's emotional state. For example, if the user is concentrating, it displays a detailed summary. The final package is sent to the device. The input is the filtering results and emotional data, and the output is the response data in JSON format.

[1287] Step 8: Displaying the User Interface

[1288] The terminal receives summaries, key points, and sentiment-based advice from the server and displays them on the user interface. The user can check these as they read. The input is the response data from the server, and the output is the content displayed to the user.

[1289] Step 9: User Examples

[1290] If a user reads from page 1 to page 10, emotional data collected during that time (for example, whether they are concentrating) is also sent. The server analyzes the content of pages 1 to 10 and inputs prompts into the generative AI model to generate and refine summaries and key points. The filtered summary and key points are then sent to the device, allowing the user to review them as they continue reading.

[1291] 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.

[1292] 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.

[1293] 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.

[1294] [Fourth embodiment]

[1295] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1296] 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.

[1297] 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).

[1298] 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.

[1299] 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.

[1300] 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).

[1301] 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.

[1302] 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.

[1303] 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.

[1304] 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.

[1305] 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.

[1306] 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.

[1307] 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."

[1308] The present invention relates to a system that automatically summarizes and extracts key points from the content of an electronic book as the user reads it. The program processing of this system is explained below in natural language.

[1309] server

[1310] Initialization

[1311] The server loads the e-book data and user information from the database, which allows it to know which e-book the user is currently reading and up to which page, for further analysis.

[1312] Receiving reading data

[1313] The page information that the user has read on the device is sent to the server, which receives this data and checks which pages the user has read.

[1314] Natural Language Processing (NLP) Analysis

[1315] The server analyzes the text data of the received page range using natural language processing technology. Specifically, it divides the text into sentences and analyzes the meaning of each sentence. For analysis, it uses libraries such as spaCy, NLTK, and Transformers.

[1316] Summary Generation

[1317] The server extracts key sentences and phrases from the analysis and generates a summary, which is a concise summary of what the user has read.

[1318] Spoiler Filtering

[1319] The server filters information about parts of the book that the user has not yet read, ensuring that summaries and key points do not contain spoilers.

[1320] Response generation and sending

[1321] The server packages the filtered summary and keypoints into JSON format and sends it to the device.

[1322] Terminal (user device)

[1323] User Interface Display

[1324] When users open an e-book, the device displays a dedicated reading assistance interface, allowing users to check the summary and key points at any time.

[1325] Data transmission

[1326] Each time the user turns a page, the device sends the new page information to the server, including the user ID and the page range that has been read.

[1327] Response reception and display

[1328] The device analyzes the summary and key points received from the server and displays them on the user interface, allowing users to efficiently understand the content they have read.

[1329] user

[1330] reading

[1331] The user continues reading the e-book, and the page information is sent from the device to the server.

[1332] Summary and key points review

[1333] Users can recall important content by reviewing summaries and key points displayed on the screen.

[1334] Continue reading

[1335] When users start their next reading, they can smoothly resume reading by checking the summary of their previous reading.

[1336] Specific examples

[1337] When a user begins reading an e-book, the device sends the user ID and current page range to the server. For example, if the user has read pages 1 to 10, that information is sent to the server. The server analyzes the content of pages 1 to 10 and generates a summary and key points. The generated summary and key points are filtered to ensure they do not contain spoilers and are sent to the device. The device displays them to the user. The next time the user starts reading from page 11, they can smoothly continue reading by checking the summary from the previous page.

[1338] In this way, the present invention is a system that allows you to enjoy reading efficiently even with fragmented reading time, and provides a rich reading experience while preventing spoilers.

[1339] The processing flow will be explained below.

[1340] Step 1:

[1341] The server loads the e-book data and user information from the database, so that the server can know which e-book the user is reading and up to which page they have read.

[1342] Step 2:

[1343] When a user opens an e-book and starts reading from page 1, the device sends the user ID and page number (pages 1 to 10) to the server.

[1344] Step 3:

[1345] The server analyzes the page information received from the device, checks which pages the user has read, and identifies the text data of the page range that the user has read based on the received information.

[1346] Step 4:

[1347] The server analyzes the text data of the received page range using natural language processing (NLP) techniques, such as splitting the text into sentences and analyzing the meaning of each sentence, using libraries such as spaCy, NLTK, and Transformers.

[1348] Step 5:

[1349] The server extracts important sentences and key phrases and uses them to generate a summary, which is a concise summary of what the user has read and includes the main points of information.

[1350] Step 6:

[1351] The server uses filtering measures to filter out information about parts of the book that the user has not yet read, an important step in preventing spoilers.

[1352] Step 7:

[1353] The server packages the filtered summary and keypoints into JSON format and sends it to the device.

[1354] Step 8:

[1355] The device receives the response from the server, parses the JSON data, extracts summaries and key points, and displays them on the user interface.

[1356] Step 9:

[1357] Users see a summary and key points displayed on the screen, which helps users remember important content.

[1358] Step 10:

[1359] The user starts reading from page 11 during the next reading time. The device sends the information "User ID" and "Pages 11 to 20" to the server.

[1360] Step 11:

[1361] The server analyzes the content of pages 11 to 20, generates a summary and key points, and sends them to the terminal, which displays them to the user, allowing the user to efficiently understand the content they are reading.

[1362] By repeating this series of processes, users can enjoy e-books while avoiding spoilers by checking summaries and key points of the content they have read.

[1363] Example 1

[1364] 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."

[1365] The present invention aims to enable users to efficiently grasp the content of e-books and quickly review important points when rereading them later. However, conventional e-book systems lack the functionality to automatically summarize the content they have read and extract key points, or to prevent spoilers from being revealed for content they have not yet read. This requires users to recall the content each time they read, which reduces the efficiency of their reading experience.

[1366] 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.

[1367] In this invention, the server includes means for loading e-book data and user information, means for receiving page information of the page that has been read, means for analyzing the text of the received page range using natural language processing technology, means for extracting important sentences and key phrases from the analysis result and generating a summary, means for filtering information of the part that the user has not yet read, and means for providing the filtered summary and key points to the user. This allows the user to efficiently grasp the content that they have read and easily confirm important points while avoiding spoilers.

[1368] "E-Book Data" refers to the text data of a book stored in digital form, often formatted for reading on an e-reader or other device.

[1369] "User information" refers to data about users who use the system, including user IDs, reading history, and settings information.

[1370] "Read page information" is data indicating a specific page range when a user reads an electronic book.

[1371] "Natural language processing technology" is a technology for analyzing text data and understanding and generating human language. Specifically, it includes processes such as text segmentation, semantic analysis, and summary generation.

[1372] The "analysis results" are analytical information on text data obtained using natural language processing technology, and include important sentences and key phrases.

[1373] A "summary" is a shortened version of important information or key points extracted from a long text.

[1374] "Keyphrases" are important words or phrases in a text that play an important role in summarizing and searching.

[1375] "Filtering" is the process of selecting data based on specific conditions and removing unnecessary parts. In this system, it refers to removing unread information to prevent spoilers.

[1376] A "user interface" is a collection of screens and operating methods that allow a user to operate a system and obtain information.

[1377] The "JSON format" is a lightweight data exchange format for structuring, storing, and transferring data, and is an abbreviation for JavaScript Object Notation.

[1378] This invention relates to a system that automatically summarizes and extracts key points from the content of an e-book as the user reads it. This system realizes efficient reading support in real time through the cooperation of a server and a terminal.

[1379] server

[1380] First, the server loads the e-book data and user information from a database, typically an SQL database, and executes SQL queries to retrieve the necessary data. This allows the server to determine which e-book the user has read and up to which page, and prepares for subsequent analysis.

[1381] Next, the server receives the page information sent from the device. This data is sent in JSON format and includes the user ID and the page range that has been read. The received data is temporarily stored in the server's memory and passed on to the next analysis process.

[1382] The text data of the received page range is analyzed using natural language processing techniques. Specifically, libraries such as "spaCy," "NLTK," and "Transformers" are used. For example, "spaCy" is used to divide the text into sentences and analyze the meaning of each sentence. In addition, the BERT model in "Transformers" is used to generate sentence embeddings and extract important sentences.

[1383] Extract important sentences and key phrases from the analysis results and generate summaries. The "transformers" library is used to generate summaries, and a summary model (e.g., a BERT-based summary model) is used to shorten and summarize important information. Furthermore, key phrases are extracted using TF-IDF and the Rake algorithm.

[1384] To prevent spoilers from being included in the generated summary and key points, information about parts that the user has not yet read is filtered out. This is done by the server retrieving the user's reading completion information from the database and detecting the unread parts. The spoiler parts are then removed using filtering logic.

[1385] Finally, the filtered summary and keypoints are packaged in JSON format and sent to the terminal.

[1386] Terminal (user device)

[1387] When a user opens an e-book, the device displays a dedicated reading support interface, which is built using JavaScript and HTML and is designed to allow users to easily check summaries and key points.

[1388] Every time the user turns a page, the device sends the new page information to the server, including the user ID and the page range that has been read. The data is sent using the "fetch" function, and is packaged in JSON format.

[1389] Analyze the summary and key points received from the server and display them on the user interface. Analyze the received JSON data and dynamically insert the summary and key points into HTML elements.

[1390] user

[1391] As users read e-books, information about the pages they have read is sent from their device to the server. By checking the automatic summary and key points of the content they have read, users can read more efficiently.

[1392] When you start reading again, you can smoothly resume where you left off by checking the summary from the previous time, providing a consistent reading experience even when reading in fragments.

[1393] Specific examples

[1394] For example, when a user starts reading an e-book, the device sends the user ID and the current page range to the server. If the user has read pages 1 to 10, that information is sent to the server. The server analyzes the content of pages 1 to 10 and generates a summary and key points. The generated summary and key points are filtered to avoid spoilers and sent to the device, which then displays them to the user.

[1395] The next time a user starts reading from page 11, they can smoothly continue reading while checking the summary from the previous page.

[1396] Prompt Sentence Examples

[1397] "Please summarize the contents of pages 1-10 and extract the main key points. Please be careful not to include spoilers."

[1398] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1399] Step 1:

[1400] Initialization

[1401] The server loads e-book data and user information from the database. Specifically, it executes an SQL query to get the required data. For example, it executes a query like "SELECT FROM ebooks WHERE user_id = 'user ID'". The input is the response from the database, and the output is the loaded e-book data and user information.

[1402] Step 2:

[1403] Receiving reading data

[1404] The server receives the page information sent from the device. This data is sent in JSON format and includes the user ID and the page range that has been read. Specifically, it receives JSON data from the device such as "{"user_id": "User ID", "start_page": 1, "end_page": 10}". The input is the JSON data from the device, and the output is the saved page information that has been read.

[1405] Step 3:

[1406] Natural Language Processing (NLP) Analysis

[1407] The server analyzes the text data of the received page range using natural language processing techniques. Specifically, it uses the "spaCy" and "Transformers" libraries to split the text into sentences and perform semantic analysis. For example, it uses "spaCy" to initialize "nlp = spacy.load('en_core_web_sm')" to analyze the received text. It also uses the "Transformers" BERT model to generate sentence embeddings. The input is the text data to be analyzed, and the output is the analysis results: important sentences and key phrases.

[1408] Step 4:

[1409] Summary Generation

[1410] The server extracts important sentences and key phrases from the analysis results and generates summaries. Specifically, it uses a summarization model from the "transformers" library to shorten and summarize important information. It also extracts key phrases using TF-IDF and Rake algorithms. The input is the analysis results, and the output is the generated summary and key phrases.

[1411] Step 5:

[1412] Spoiler Filtering

[1413] The server filters information about parts that the user has not yet read to ensure that the generated summary and key points do not contain spoilers. This is done by retrieving the user's reading completion information from the database and detecting unread parts. For example, it applies filtering logic such as "delete if the if statement comes from an unread page." The input is the summary, key phrases, and the user's reading completion information, and the output is the filtered summary and key phrases.

[1414] Step 6:

[1415] Response generation and sending

[1416] The server packages the filtered summary and key phrases in JSON format and sends them to the device. Specifically, it packages and sends them as follows: "response = json.dumps({'summary': 'Summary content', 'key_points': 'Key points'})" and "send_response_to_device(response)". The input is the filtered summary and key phrases, and the output is the JSON data sent to the device.

[1417] Step 7:

[1418] User Interface Display

[1419] When a user opens an e-book, the device displays a dedicated reading support interface. This is done by building the interface using JavaScript and HTML and displaying information in "document.getElementById('reading-interface')". The input is the JSON data sent from the server, and the output is the displayed reading support interface.

[1420] Step 8:

[1421] Data transmission

[1422] Every time the user turns a page, the device sends new page information to the server. For example, use "fetch('server URL', { method: 'POST', body: JSON.stringify({ user_id: 'user ID', start_page: 11, end_page: 20})})" to send JSON data to the server. The input is the user's operation (turning a page), and the output is the new page information sent to the server.

[1423] Step 9:

[1424] Response reception and display

[1425] The terminal parses the summary and key points received from the server and displays them on the user interface. For example, "response = await fetch('server URL')" and "document.getElementById('summary').textContent = response.summary". The input is the JSON response from the server, and the output is the summary and key phrases displayed on the user interface.

[1426] Step 10:

[1427] Summary and key points review

[1428] Users can review the summary and key points displayed on the screen and recall important content. The input is the summary and key phrases displayed on the device, and the output is to improve the user's comprehension and reading efficiency.

[1429] Step 11:

[1430] Continue reading

[1431] When users start their next reading session, they can smoothly resume reading by checking the summary from the previous session. The input is the summary from the previous session, and the output is a smooth resumption and continuation of reading.

[1432] (Application example 1)

[1433] 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."

[1434] Traditional e-book systems lack the functionality to efficiently summarize what users have read and make it available for the next reading. This means that users have to spend a lot of time recalling what they read last time when rereading a book, especially when their reading time is fragmented. Furthermore, the summary system does not take into account spoilers, so there is a risk that users will learn about content they have not yet read. Furthermore, the lack of a summary or key point saving or reminder function results in an inconsistent reading experience.

[1435] 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.

[1436] In this invention, the server includes means for analyzing the content that has been read, means for summarizing the analyzed content, means for extracting key points from the summary, means for identifying the range that the user has read, means for filtering the range that the user has read to prevent spoilers, means for providing the filtered summary and key points to the user, means for saving the summary and key points to a server on the cloud, and means for notifying the user so that the user can continue reading while checking the previous summary. This makes it possible to efficiently summarize the content that has been read, prevent spoilers, and provide a consistent and comfortable reading experience even when reading in fragments.

[1437] "Means for analyzing what you read" refers to technology that uses natural language processing to analyze the text a user reads and understand the meaning of sentences and phrases.

[1438] "Means for summarizing the analyzed content" refers to the technique of concisely summarizing the main points of the entire text using important sentences and information obtained through the analysis.

[1439] "Methods for extracting key points from summaries" refers to techniques for selecting particularly important sentences or phrases from a summarized text.

[1440] "Means for determining user progress" refers to technology that tracks which pages or chapters a user has read.

[1441] "Means for preventing spoilers by filtering the range of content that the user has read" refers to a technology that automatically filters out information about parts that the user has not yet read.

[1442] The "means for providing filtered summaries and key points to users" refers to a technology for transmitting spoiler-free summaries and key points to users' terminals and displaying them.

[1443] "Means for storing the summary and key points on a server on the cloud" refers to a technology for storing the generated summary and key points in a cloud environment.

[1444] "Means for notifying users so that they can continue reading while checking the summary of the previous reading" refers to a technology that makes it easy for users to check the summary of what they read last time and notifies them so that they can smoothly resume reading the next time.

[1445] This invention is a system that automatically generates summaries and key points as a user reads an e-book, providing an efficient reading experience. Below, we will explain the mode for implementing the invention based on the program of this system.

[1446] server

[1447] Initialization

[1448] The server stores all e-book content and user information. It loads which books users have read and to what extent from the database for further analysis. This process uses a cloud-based database service.

[1449] Receiving reading data

[1450] Receives user reading progress information sent from the device. This information includes the user ID and the page range read. The received information is recorded in the database.

[1451] Natural Language Processing (NLP) Analysis

[1452] The server analyzes the text data of the read section using natural language processing technology. Specifically, it uses NLP libraries such as spaCy and Transformers to divide the text into sentences and analyze the meaning of each sentence. The important sentences and phrases obtained from this analysis are then used to generate a summary and extract key points.

[1453] Summary Generation

[1454] The server generates a summary based on the information obtained from the NLP analysis. It uses the Transformers summarization function to compile a summary of the entire text into a short document that concisely summarizes what the user has read.

[1455] Spoiler Filtering

[1456] The server automatically filters out information about unread content, ensuring that spoilers are not included when summaries and key points are provided to users.

[1457] Save and Notify

[1458] The generated summaries and key points are stored on a cloud server, and this information is notified to the user the next time they resume reading. This feature is important for supporting a continuous reading experience.

[1459] Terminal (user device)

[1460] User Interface Display

[1461] The device will display a dedicated reading assistance interface when users open an e-book, allowing them to check the summary and key points at any time.

[1462] Data transmission

[1463] Each time the user turns a page, the device sends the new page information to the server, including the user ID and the page range that has been read.

[1464] Response reception and display

[1465] The device displays the summary and key points received from the server on the user interface, allowing the user to efficiently understand the content they have read.

[1466] user

[1467] reading

[1468] The user continues reading the e-book, and the page information is sent from the device to the server.

[1469] Summary and key points review

[1470] Users can instantly recall important content by viewing summaries and key points displayed on the screen.

[1471] Continue reading

[1472] When users start their next reading, they can smoothly resume reading by checking the summary of their previous reading.

[1473] Specific examples

[1474] When a user reads pages 1 to 10 of an e-book, that information is sent from the device to the server. The server analyzes the content of pages 1 to 10 and generates a summary and key points. The generated summary and key points are applied with a spoiler prevention function before being sent to the device, which then displays them to the user. When the user starts reading from page 11 next time, they can smoothly continue reading by checking the previous summary.

[1475] Example prompts for generative AI models

[1476] Summarize what the user reads from page 1 to 10 of your eBook and list the main points.

[1477] This is an example of how to use this prompt to ask a generative AI model to generate a summary and key points.

[1478] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1479] Step 1:

[1480] The server loads e-book data and user information from the database. The input is the user ID and e-book identification information. The output is information about which e-book the user is currently reading and how many pages they have read. This information is necessary for analysis.

[1481] Step 2:

[1482] The page information that the user has read using the device is sent from the device to the server. The input is the user ID and the page range that has been read (e.g., page 1 to page 10). The output is the page range information received by the server. Based on this information, the next analysis step is performed.

[1483] Step 3:

[1484] The server analyzes the text data of the received page range using natural language processing (NLP) technology. Specifically, it divides the text data of pages 1 to 10 into sentences and uses libraries such as spaCy and Transformers to analyze the meaning of each sentence. The input is the text data of the received page range, and the output is a list of analyzed sentences and key phrases.

[1485] Step 4:

[1486] The server generates a summary based on the analyzed key sentences and key phrases. For this purpose, it uses the summarization function of Transformers. The input is a list of analyzed sentences and key phrases, and the output is the summarized text. The generated summary is a concise summary of what the user has read.

[1487] Step 5:

[1488] The server filters the generated summary to remove unread content and prevent spoilers. The input is the summarized text and the user's reading progress, and the output is the summary text with spoilers removed. This process is necessary to ensure a good user reading experience.

[1489] Step 6:

[1490] The server stores the filtered summaries and keypoints in a cloud database. The input is the filtered summaries and keypoints, and the output is confirmation of the stored data. This storage operation allows the summaries and keypoints to be reused the next time they are accessed.

[1491] Step 7:

[1492] The terminal displays the summaries and key points received from the server on the user interface. The input is the data received from the server, and the output is the summaries and key points on the screen that the user views, allowing the user to check the latest reading information in real time.

[1493] Step 8:

[1494] When the user starts reading again, the device will send a notification to the user to check the summary of the previous reading. The input is the summary and key points stored in the cloud, and the output is a notification to the user's device. This notification allows the user to resume reading smoothly.

[1495] 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.

[1496] This invention relates to a system that automatically summarizes and extracts key points from the content of an e-book as the user reads it. It also incorporates an emotion engine that recognizes the user's emotions to improve the reading experience. Below, we will explain the program processing of this system in natural language and provide detailed examples.

[1497] server

[1498] Initialization

[1499] The server loads the e-book data, user information, and emotion engine from the database, allowing the server to know which e-book the user is reading and up to which page, for further analysis and emotion recognition.

[1500] Receiving reading data

[1501] The page information that the user has read on the device is sent to the server, which receives this data and checks which pages the user has read.

[1502] Natural Language Processing (NLP) Analysis

[1503] The server analyzes the text data of the received page range using natural language processing technology. Specifically, it divides the text into sentences and analyzes the meaning of each sentence. For analysis, it uses libraries such as spaCy, NLTK, and Transformers.

[1504] Summary Generation

[1505] The server extracts key sentences and phrases from the analysis and generates a summary, which is a concise summary of what the user has read.

[1506] Spoiler Filtering

[1507] The server filters information about parts of the book that the user has not yet read, ensuring that summaries and key points do not contain spoilers.

[1508] emotion recognition

[1509] The emotion engine analyzes the emotion data received from the device to recognize the user's emotions, including facial expression recognition, voice analysis, and biometric data analysis.

[1510] Response generation and sending

[1511] The server packages the filtered summary and key points into a JSON format and dynamically adjusts it based on the user's emotional state. The content and display format of the summary can be changed based on the analysis results of the emotion engine. The summary is then sent to the device.

[1512] Terminal (user device)

[1513] User Interface Display

[1514] When a user opens an e-book, the device displays a dedicated reading support interface, which also includes emotion recognition functionality, collecting emotional data using the camera and microphone.

[1515] Data transmission

[1516] Each time the user turns a page, the device sends the new page information and collected emotion data to the server, including the user ID, the page range read, and the emotion data.

[1517] Response reception and display

[1518] The device analyzes the summary, key points, and emotion-based advice received from the server and displays it on the user interface. For example, if the user is feeling nervous, it displays advice on how to continue reading to relax.

[1519] user

[1520] reading

[1521] As the user reads the e-book, the page information and emotional data of the page being read are sent from the device to the server.

[1522] Summary and key points review

[1523] Users can view summaries, key points, and emotion-based advice displayed on the screen, helping them easily recall important content and receiving advice tailored to their emotions.

[1524] Continue reading

[1525] When users start their next reading, they can smoothly resume reading by checking the summary of their previous reading, and enjoy reading while receiving emotional advice.

[1526] Specific examples

[1527] When a user begins reading an e-book, the device sends the user ID, current page range, and emotional data to the server. For example, if the user reads from page 1 to page 10, emotional data collected during that time (e.g., whether the user is concentrating) is also sent. The server analyzes the content of pages 1 to 10, generates a summary and key points, and adjusts the summary content based on the emotional data. The filtered summary and key points are then sent to the device, allowing the user to review them as they continue reading.

[1528] This series of processes allows users to efficiently check summaries and key points of what they have read, receive advice based on their own emotions, and enjoy reading efficiently and enjoyably.

[1529] The processing flow will be explained below.

[1530] Step 1:

[1531] The server loads the e-book data, user information, and emotion engine from the database, allowing the server to know which e-book the user is reading and up to which page, for further analysis and emotion recognition.

[1532] Step 2:

[1533] When a user opens an e-book, the device sends the "user ID," "current page range," and "emotion data" to the server. Emotion data is collected using the camera and microphone.

[1534] Step 3:

[1535] The server analyzes the page information and emotional data received from the device to determine which page the user has read and their current emotional state.

[1536] Step 4:

[1537] The server analyzes the text data of the received page range using natural language processing (NLP) techniques, using libraries such as spaCy, NLTK, and Transformers to divide the text into sentences and analyze the meaning of each sentence.

[1538] Step 5:

[1539] The server extracts important sentences and key phrases and uses them to generate a summary, a concise summary of what the user has read, containing the gist of the information.

[1540] Step 6:

[1541] The server filters information about parts of the book that the user has not yet read, ensuring that summaries and key points do not contain spoilers.

[1542] Step 7:

[1543] The emotion engine analyzes the user's emotional state and dynamically adjusts the content of the summary and key points based on the emotional data, for example, if the user is feeling stressed, it will provide a concise and positive summary.

[1544] Step 8:

[1545] The server packages the filtered summary and keypoints into JSON format and sends it to the device along with a summary adjusted based on the user's sentiment data.

[1546] Step 9:

[1547] The device analyzes the summary, key points, and emotion-based advice received from the server, thereby optimizing the display content to the user's emotional state.

[1548] Step 10:

[1549] The device displays summaries, key points, and emotion-based advice on the user interface. For example, if the user is feeling nervous, the device displays advice on how to continue reading to relax.

[1550] Step 11:

[1551] Users see summaries and key points displayed on the screen, along with advice based on their emotions, helping them remember important content and receiving advice tailored to their emotions.

[1552] Step 12:

[1553] When the user starts reading from page 11 in the next reading session, the device will send the information "User ID," "Pages 11 to 20," and "Emotion Data" to the server.

[1554] Step 13:

[1555] The server analyzes the content of pages 11 to 20 and generates a summary and key points. The emotion engine then analyzes the emotion data and adjusts the summary content, so that the next reading session will also receive an emotion-based summary.

[1556] Step 14:

[1557] The device displays the new summary and key points received from the server in the user interface, allowing the user to review the content they have read and receive emotional advice as they continue reading.

[1558] This series of processes allows users to efficiently check summaries and key points of what they have read, receive advice based on their own emotions, and enjoy reading efficiently and enjoyably.

[1559] Example 2

[1560] 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."

[1561] Conventional e-book reading systems have limited functionality for providing summaries and key points of the content the user has read, making it difficult to prevent spoilers or provide appropriate feedback based on the user's emotions. Furthermore, they lack a mechanism for communicating to the system in real time how far the user has read and the emotions they felt at the time, which results in a lack of a way to enhance the user's reading experience. Our goal is to solve these issues and improve the user's reading experience.

[1562] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1563] In this invention, the server includes means for analyzing the content that the user has read, means for summarizing the analyzed content, means for extracting key points from the summary, means for identifying the range that the user has read, means for filtering the range that the user has read to prevent spoilers, means for providing the filtered summary and key points to the user, means for recognizing the user's emotional state, means for dynamically adjusting the summary and key points based on the user's emotional state, and means for transmitting and receiving data between the server and the terminal. This makes it possible to efficiently summarize the content that the user has read and provide feedback according to their emotions, thereby improving the user's reading experience while preventing spoilers.

[1564] "Means for analyzing the content being read" refers to a method or device used to analyze the text data of the e-book that the user has read.

[1565] "Means for summarizing the analyzed content" refers to a method or device for extracting important information from the analyzed text data and summarizing it concisely.

[1566] A "means for extracting key points from summaries" refers to a method or apparatus for identifying and extracting particularly important sentences or phrases from summarized information.

[1567] "Means for determining the extent to which a user has read" refers to a method or device for determining how many pages a user has read in an e-book.

[1568] A "spoiler prevention means using filtering means" refers to a method or device for removing information about parts of a book that the user has not yet read from the summary or key points.

[1569] "Means for providing a filtered summary and key points to a user" refers to a method or device for presenting a spoiler-free summary and key points to a user.

[1570] "Means for recognizing the user's emotional state" refers to a method or device for analyzing the user's facial expressions, voice, biometric data, etc. to determine their emotions.

[1571] "Means for dynamically adjusting summaries and key points based on a user's emotional state" refers to a method or apparatus for changing the presentation or content of summaries and key points in response to a recognized emotional state of a user.

[1572] "Means for sending and receiving data between the server and the terminal" refers to a communication method or device for exchanging necessary information between the server and the user's device.

[1573] "Filtered summaries and keypoints" refers to summaries and keypoints that have been processed to remove spoilers.

[1574] "Emotion-based advice" refers to suggestions for things like how to read or relax that take into account the user's emotional state.

[1575] "Natural language processing technology" refers to the general technology that enables computers to understand and analyze human language.

[1576] The "means for identifying important sentences and key phrases" refers to a method or device for identifying important sentences and key phrases within text data using natural language processing techniques.

[1577] MODE FOR CARRYING OUT THE INVENTION

[1578] This invention relates to a system that automatically summarizes and extracts key points from the content of an e-book as the user reads it. It also incorporates an emotion engine that recognizes the user's emotions to improve the reading experience. Below, we will explain the program processing of this system in natural language and provide detailed examples.

[1579] server

[1580] The server loads e-book data, user information, and the emotion engine from a database. This uses a database management system such as MySQL or PostgreSQL. It also uses a cache system (e.g., Redis) to ensure fast data access. The server uses NLP (natural language processing) libraries such as spaCy, NLTK, and Transformers to analyze the text data as it is read. The server also uses TF-IDF and BERT models to generate summaries and extract key points.

[1581] When the page information and emotion data sent by the user on their device is received, the server begins analysis. First, the received data is verified, and then the text data for the relevant page is extracted from the e-book database. NLP analysis is performed on the extracted text data to identify important sentences and key phrases and generate a summary. The generated summary is then filtered to remove unnecessary information. The summary and key points are also adjusted based on the user's emotion data.

[1582] Finally, the server sends the summary and key points packaged in JSON format to the device, along with sentiment-based advice. For example, if the user is concentrating, it can provide a more detailed summary, or if they need to relax, it can provide a concise, easy-to-read summary.

[1583] Terminal (user device)

[1584] When a user opens an e-book, the device displays a reading assistance interface. This interface includes a function to collect the user's emotional data using a camera and microphone. Each time a page is turned, the device sends the new page information and emotional data to the server. This data transmission is performed using an HTTP POST request. The device then analyzes the filtered summary, key points, and emotional advice received from the server and displays them on the user interface.

[1585] user

[1586] As users read e-books, page information and emotional data collected during the process are sent to the server via their device. A summary of the content they have read and key points can be viewed on the device's interface. Based on this, users can recall important content and receive advice based on their emotions. Furthermore, when they resume reading, they can check the summary from the previous page, allowing them to continue reading smoothly.

[1587] Specific examples

[1588] When a user begins reading an e-book, the device sends the user ID, current page range, and emotional data to the server. For example, if the user reads from page 1 to page 10, emotional data collected during that time (e.g., whether the user is concentrating) is also sent. The server analyzes the content of pages 1 to 10, generates a summary and key points, and adjusts the summary content based on the emotional data. The filtered summary and key points are then sent to the device, allowing the user to review them as they continue reading.

[1589] This series of processes allows users to efficiently check summaries and key points of what they have read, receive advice based on their own emotions, and enjoy reading efficiently and enjoyably.

[1590] Specific examples of prompts to input to generative AI models

[1591] "Generate a summary of what the user reads from pages 1 to 10, with key points tailored based on their emotional focus."

[1592] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1593] Step 1:

[1594] The server performs initialization. It loads e-book data, user information, and the emotion engine from the database. Specifically, it uses a database management system such as MySQL or PostgreSQL to retrieve the necessary data using SQL queries, and stores it in a cache such as Redis for fast data access. This prepares the e-book text data, the user's reading history, and the emotion engine configuration information.

[1595] Input: Database

[1596] Output: Cached data

[1597] Specific behavior: Executes SQL queries and caches data

[1598] Step 2:

[1599] The device displays a reading assistance interface when a user opens an e-book, and also includes a function to collect user emotional data using the camera and microphone.

[1600] Input: User actions

[1601] Output: Display of reading assistance interface

[1602] Specific behavior: Display a dialog box requesting permission to access the camera and microphone

[1603] Step 3:

[1604] As the user progresses through the e-book, the device sends new page information and emotion data to the server using an HTTP POST request, packaging the user ID, the page range that has been read, and the emotion data in JSON format.

[1605] Input: page information, emotional data

[1606] Output: Send data to the server

[1607] Specific behavior: Serializes data in JSON format and sends an HTTP POST request

[1608] Step 4:

[1609] The server validates the received page information and emotion data. It checks whether the data format of the user ID and page range is correct. If invalid data is sent, it returns an error message.

[1610] Input: Received data

[1611] Output: Validation result (error message or progress to the next processing step)

[1612] Specific behavior: Data format check and error handling

[1613] Step 5:

[1614] The server extracts the text data of the corresponding page from the e-book database, and efficiently extracts the data corresponding to the received page range using the Python Pandas library.

[1615] Input: Page range read

[1616] Output: Extracted text data

[1617] Specific behavior: Executes a database query and retrieves text data

[1618] Step 6:

[1619] The server performs natural language processing (NLP) analysis on the extracted text data, using NLP libraries (e.g., spaCy, NLTK, Transformers) to split the text data into sentences, identify important sentences and key phrases, and generate summaries.

[1620] Input: Text data

[1621] Output: Summary and key points

[1622] Specific operations: Text tokenization, part-of-speech tagging, dependency analysis

[1623] Step 7:

[1624] The server filters information about parts of the text that the user has not yet read from the summary and key points, using regular expressions and filtering algorithms to remove those parts.

[1625] Input: Summary, key points, and unread information

[1626] Output: filtered summary and keypoints

[1627] Specific behavior: Applying regular expression processing and filtering algorithms

[1628] Step 8:

[1629] The server analyzes the user's emotional data, and the emotion engine performs facial expression recognition, voice analysis, and biometric data analysis to identify the user's emotional state.

[1630] Input: Emotion data

[1631] Output: Emotional state

[1632] Specific operations: facial expression recognition, voice analysis, biometric data analysis

[1633] Step 9:

[1634] The server dynamically adjusts the filtered summaries and key points based on the user's emotional state: if the user is focused, it provides a detailed summary; if the user is tense, it provides a concise summary for relaxation.

[1635] Input: Emotional state, filtered summary and keypoints

[1636] Output: Tailored summary and key points

[1637] Specific actions: summarizing and adjusting key points

[1638] Step 10:

[1639] The server packages the adjusted summary and key points in JSON format and sends it to the device, returning the data as an HTTP response through a REST API.

[1640] Input: Adjusted summary and key points

[1641] Output: Sending data to the terminal

[1642] Specific operation: Serializing in JSON format and sending HTTP response

[1643] Step 11:

[1644] The device analyzes the summary, key points, and emotion-based advice received from the server and displays it on the user interface. For example, if the user is feeling nervous, it displays advice on how to continue reading to relax.

[1645] Input: Received data

[1646] Output: Displayed in the user interface

[1647] Specific operation: Analyzing data and displaying it in the interface

[1648] The above is the flow of processing of the program of this system.

[1649] (Application example 2)

[1650] 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."

[1651] Currently, there are systems that automatically generate summaries and key points when reading e-books, but there are no systems that can prevent spoilers and improve the reading experience by taking user emotions into account. As a result, the reading experience is not optimized to suit the emotions of each individual user, resulting in a decrease in user satisfaction.

[1652] 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.

[1653] In this invention, the server includes means for analyzing the content that has been read, means for summarizing the analyzed content, means for extracting key points from the summary, means for recognizing and analyzing the user's emotions, and means for adjusting and providing the summary and key points based on the analyzed emotion data. This makes it possible to adjust the summary and key points of the content that has been read based on the emotions, and provide an optimal reading experience that is adapted to the emotions of each individual user.

[1654] A "means for analyzing what has been read" is a means for a user to analyze the content of the text they have read and understand the meaning of each sentence.

[1655] "Means for summarizing the analyzed content" refers to means for extracting important information from the analyzed text and summarizing it concisely.

[1656] The "means for extracting key points from summaries" refers to a means for identifying and extracting more important phrases and sentences from the generated summaries.

[1657] "Means for identifying how far the user has read" is a means for identifying how much text the user has read.

[1658] "Means for preventing spoilers by filtering the portion of the content that the user has read" refers to filtering information about portions that the user has not yet read, and eliminating spoilers from summaries and key points.

[1659] The "means for providing a filtered summary and key points to a user" is a means for presenting a spoiler-free summary and key points in a form accessible to a user.

[1660] "Means for recognizing and analyzing user emotions" refers to a means for identifying and analyzing emotions from the user's facial expressions, voice, etc.

[1661] "Means for adjusting and providing summaries and key points based on analyzed emotional data" refers to means for dynamically changing the content of summaries and key points based on the user's emotional data and providing them in the most optimal form.

[1662] The "means for transmitting information about the page that has been read and emotional data to the server" refers to a means for transmitting the page that the user has read and emotional data to the server.

[1663] The "means for displaying the summary and adjustment data based on key points and emotion received from the server" refers to means for displaying the summary sent from the server and the adjustment results based on key points and emotion data.

[1664] "A method of analyzing text using natural language processing technology to identify important sentences and key phrases, and inputting prompts into a generative AI model based on emotional data to adjust the summary and key points" refers to a method of analyzing text using natural language processing technology to extract important information, and then inputting prompts into a generative AI model based on emotional data to adjust the summary and key points in an optimal form.

[1665] The present invention provides a system that automatically summarizes and extracts key points from e-books and other text content as a user reads them, and also recognizes the user's emotions to improve the reading experience. This system functions by combining a server and a terminal.

[1666] server

[1667] Initialization

[1668] The server loads the e-book data, user information, and emotion engine from the database, allowing the server to know which e-book the user is reading and up to which page, for further analysis and emotion recognition.

[1669] Receiving reading data

[1670] The page information and emotion data of the user that has been read using the device are sent to the server, which receives this data and checks which pages the user has read.

[1671] Natural Language Processing (NLP) Analysis

[1672] The server analyzes the text data of the received page range using natural language processing technology (NLTK, spaCy, Hugging Face Transformers, etc.). Specifically, it divides the text into sentences and analyzes the meaning of each sentence.

[1673] Summary Generation

[1674] The server extracts key sentences and phrases from the analysis and generates a summary, which is a concise summary of what the user has read.

[1675] Spoiler Filtering

[1676] The server filters information about parts of the book that the user has not yet read, ensuring that summaries and key points do not contain spoilers.

[1677] emotion recognition

[1678] In order for the emotion engine to recognize the user's emotions, it analyzes the emotion data (facial expression recognition, voice analysis, biometric data) received from the device.

[1679] Response generation and sending

[1680] The server packages the filtered summary and key points in JSON format and dynamically adjusts it based on the user's emotional state. Based on the analysis results of the emotion engine, the content and display format of the summary are changed and sent to the device.

[1681] Terminal (user device)

[1682] User Interface Display

[1683] When a user opens an e-book, the device displays a dedicated reading support interface, which also includes emotion recognition functionality, collecting emotional data using the camera and microphone.

[1684] Data transmission

[1685] Each time the user turns a page, the device sends the new page information and collected emotion data to the server, including the user ID, the page range read, and the emotion data.

[1686] Response reception and display

[1687] The device analyzes the summary, key points, and emotion-based advice received from the server and displays it on the user interface. For example, if the user is feeling nervous, it displays advice on how to continue reading to relax.

[1688] User examples

[1689] When a user begins reading an e-book, the device sends the user ID, current page range, and emotional data to the server. For example, if the user reads from page 1 to page 10, emotional data collected during that time (e.g., whether the user is concentrating) is also sent. The server analyzes the content of pages 1 to 10, generates a summary and key points, and adjusts the summary content based on the emotional data. The filtered summary and key points are then sent to the device, allowing the user to review them as they continue reading.

[1690] Prompt Sentence Examples

[1691] The following format is input to the generative AI model to generate and refine summaries and key points:

[1692] Instructions: Extract automatic summaries and key points from the following text, and adjust the summaries and key points to take into account user focus.

[1693] <<Text on pages 1-10>>

[1694] In this way, users can continue reading efficiently while enjoying an optimal reading experience tailored to their emotions.

[1695] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1696] Program processing steps

[1697] Step 1: Initialization

[1698] The server loads e-book data, user information, and emotion engine from the database. This prepares the server to know which e-book the user is reading and up to which page. The input is data from the e-book database and user database, and the output is the initial setting for analysis and emotion recognition.

[1699] Step 2: Receiving reading data

[1700] The device sends page information and emotional data about the page the user has progressed to to the server. The server receives this data and records which pages have been progressed and the user's emotional state. The input is the page information and emotional data sent from the device, and the output is prepared data for analysis.

[1701] Step 3: Natural Language Processing (NLP) Analysis

[1702] The server analyzes the text of the submitted page range using natural language processing technology. Specifically, it divides the text into sentences and identifies important sentences and key phrases. Software used includes NLTK, spaCy, and Hugging Face Transformers. The input is text data, and the output is the analysis results, which are important sentences and key phrases.

[1703] Step 4: Summary generation

[1704] The server generates a summary based on the analyzed important sentences and key phrases. This involves extracting the most important sentences and key phrases and connecting them to form a summary. The input is the analysis result, and the output is the generated summary.

[1705] Step 5: Spoiler filtering

[1706] The server filters information about parts that the user has not yet read, eliminating spoilers from summaries and keypoints. The input is the summary and keypoints, and the output is the spoiler-free filtered result.

[1707] Step 6: Emotion Recognition

[1708] The server uses an emotion engine to analyze the user's emotional data. Based on data obtained from the camera and microphone, it identifies the user's emotional state, such as whether they are focused or relaxed. The input is biometric data and voice data, and the output is analyzed emotional data.

[1709] Step 7: Generate and send a response

[1710] The server packages the filtered summary and key points into JSON format and dynamically adjusts the content based on the user's emotional state. For example, if the user is concentrating, it displays a detailed summary. The final package is sent to the device. The input is the filtering results and emotional data, and the output is the response data in JSON format.

[1711] Step 8: Displaying the User Interface

[1712] The terminal receives summaries, key points, and sentiment-based advice from the server and displays them on the user interface. The user can check these as they read. The input is the response data from the server, and the output is the content displayed to the user.

[1713] Step 9: User Examples

[1714] If a user reads from page 1 to page 10, emotional data collected during that time (for example, whether they are concentrating) is also sent. The server analyzes the content of pages 1 to 10 and inputs prompts into the generative AI model to generate and refine summaries and key points. The filtered summary and key points are then sent to the device, allowing the user to review them as they continue reading.

[1715] 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.

[1716] 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.

[1717] 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.

[1718] 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.

[1719] 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.

[1720] 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.

[1721] 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).

[1722] 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.

[1723] 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."

[1724] 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.

[1725] 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).

[1726] 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.

[1727] 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.

[1728] 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.

[1729] 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.

[1730] 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.

[1731] 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.

[1732] 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.

[1733] 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.

[1734] 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.

[1735] 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.

[1736] The following is further disclosed regarding the above embodiment.

[1737] (Claim 1)

[1738] A means of analyzing what you have read,

[1739] A means of summarizing the analyzed content;

[1740] A means for extracting key points from the summary;

[1741] A way to determine how far a user has read; and

[1742] A means for preventing spoilers by filtering the range of content that a user has read;

[1743] The system includes a means for providing filtered summaries and key points to the user.

[1744] (Claim 2)

[1745] A means for transmitting page information to a server;

[1746] Further includes means for displaying the summary and key points received from the server.

[1747] 10. The system of claim 1.

[1748] (Claim 3)

[1749] Further includes means for analyzing text using natural language processing techniques to identify important sentences and key phrases.

[1750] 10. The system of claim 1.

[1751] "Example 1"

[1752] (Claim 1)

[1753] means for loading e-book data and user information;

[1754] A means for receiving page information that has been read;

[1755] means for analyzing the text of the received page range using natural language processing techniques;

[1756] A means of extracting important sentences and key phrases from the analysis results and generating summaries;

[1757] A way to filter information that users have not yet read, and

[1758] The system includes a means for providing filtered summaries and key points to the user.

[1759] (Claim 2)

[1760] A means for transmitting page information to a server;

[1761] Further includes means for displaying the summary and key points received from the server.

[1762] 10. The system of claim 1.

[1763] (Claim 3)

[1764] Further includes the use of natural language processing libraries to analyze text and identify important sentences and key phrases

[1765] 10. The system of claim 1.

[1766] "Application Example 1"

[1767] (Claim 1)

[1768] A means of analyzing what you have read,

[1769] A means of summarizing the analyzed content;

[1770] A means for extracting key points from the summary;

[1771] A way to determine how far a user has read; and

[1772] A means for preventing spoilers by filtering the range of content that a user has read;

[1773] a means of providing filtered summaries and key points to users;

[1774] a means for storing the summaries and key points on a cloud server;

[1775] The system includes a means of notifying the user so that they can continue while reviewing a summary of their previous experience.

[1776] (Claim 2)

[1777] means for transmitting information on the page that has been read to a central processing unit;

[1778] and means for displaying the summary and key points received from the central processing unit.

[1779] 10. The system of claim 1.

[1780] (Claim 3)

[1781] Further includes means for analyzing text using language information processing techniques to identify important sentences and key phrases.

[1782] 10. The system of claim 1.

[1783] "Example 2: Combining Emotion Engines"

[1784] (Claim 1)

[1785] A means of analyzing what you have read,

[1786] A means of summarizing the analyzed content;

[1787] A means for extracting key points from the summary;

[1788] A way to determine how far a user has read; and

[1789] A means for preventing spoilers by filtering the range of content that a user has read;

[1790] a means of providing filtered summaries and key points to users;

[1791] a means for recognizing the emotional state of a user;

[1792] A means for dynamically adjusting summaries and key points based on the user's emotional state;

[1793] A means for transmitting and receiving data between the server and the terminal;

[1794] A system including:

[1795] (Claim 2)

[1796] 2. The system according to claim 1, wherein the user ID, the page range read, and emotion data are transmitted from the terminal to the server.

[1797] (Claim 3)

[1798] 10. The system of claim 1, further comprising means for displaying on the terminal the filtered summary and key points received from the server, and the advice based on the sentiment.

[1799] (Claim 4)

[1800] 10. The system of claim 1, further comprising means for analyzing the text using natural language processing techniques to identify important sentences and key phrases.

[1801] "Application example 2 when combining emotion engines"

[1802] Claiming a new invention

[1803] (Claim 1)

[1804] A means of analyzing what you have read,

[1805] A means of summarizing the analyzed content;

[1806] A means for extracting key points from the summary;

[1807] A way to determine how far a user has read; and

[1808] A means for preventing spoilers by filtering the range of content that a user has read;

[1809] a means of providing filtered summaries and key points to users;

[1810] A means of recognizing and analyzing user emotions;

[1811] A means for tailoring and providing summaries and key points based on the analyzed sentiment data;

[1812] A system including:

[1813] (Claim 2)

[1814] A means for transmitting page information and emotional data to a server;

[1815] and means for displaying the summary and the key point and sentiment-based adjustment data received from the server.

[1816] 10. The system of claim 1.

[1817] (Claim 3)

[1818] Natural language processing technology is used to analyze text, identify important sentences and key phrases,

[1819] It also includes a means for inputting prompt sentences into the generative AI model based on emotion data to adjust the summary and key points.

[1820] 10. The system of claim 1. [Explanation of symbols]

[1821] 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 of analyzing what you have read, A means of summarizing the analyzed content; A means for extracting key points from the summary; A way to determine how far a user has read; and A means for preventing spoilers by filtering the range of content that a user has read; The system includes a means for providing filtered summaries and key points to the user.

2. A means for transmitting page information to a server; Further includes means for displaying the summary and key points received from the server. The system of claim 1 .

3. Further includes means for analyzing text using natural language processing techniques to identify important sentences and key phrases. The system of claim 1 .

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