System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to upload an e-book file; means for a server to convert the e-book file into textual data; means for the server to extract scenes and keywords from the textual data; means for the server to send an illustration generation request to a generation AI; means for the generation AI to generate an illustration image and return it to the server; means for the server to integrate the illustration image into the textual data; and means for the server to provide the user with the generated e-book file with illustration.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] In modern foreign language learning, when learners attempt to "read a book," especially text-only formats such as novels and essays, they tend to become discouraged by the large number of unknown words, even with translation functions. This problem is particularly pronounced in the early stages of learning and can lead to a loss of motivation. Furthermore, e-book users are often dissatisfied with the convenience and functionality of e-books, and existing e-book services are not fully satisfying their needs. This currently hinders effective foreign language learning through reading. Therefore, the objective of the present invention is to make text-only books easier to understand and improve the reading experience, especially for foreign language learners. [Means for solving the problem]

[0005] The present invention consists of a system in which a user uploads an e-book file and a server converts the e-book file into text data. The server then uses a natural language processing algorithm to extract scenes and keywords from the text data and sends an illustration generation request to a generation AI. The generation AI generates illustration images based on the received scenes and keywords and returns them to the server. The server then integrates the generated illustration images into the text data and generates an e-book file in picture book format. Finally, the generated illustrated e-book file is provided to the user, who can download the e-book and begin reading. This series of steps makes it possible to provide a system that makes traditional text-only books easier to understand, which is particularly useful for foreign language learners.

[0006] A "user" is an entity that uses the system to upload an e-book file and download the generated illustrated e-book.

[0007] The "server" is a computer device that converts the e-book file received from the user into text data, extracts scenes and keywords, and sends an illustration generation request to the generation AI.

[0008] An "e-book file" is digital book data uploaded by a user, and includes formats such as PDF and EPUB.

[0009] "Text data" is character information extracted from an e-book file and is used to extract scenes and keywords.

[0010] A "scene" is a sentence or paragraph that indicates a specific situation or situation within text data.

[0011] "Keywords" are important words or short phrases in the text data that contain information necessary for understanding a scene and generating an illustration.

[0012] "Generation AI" is an artificial intelligence system that receives illustration generation requests from the server and generates illustration images based on the corresponding scenes and keywords.

[0013] "Illustration images" are visual images generated by the generative AI based on scenes and keywords, and are inserted into text data.

[0014] A "natural language processing algorithm" is a computer program used to analyze and extract scenes and keywords from text data.

[0015] The "picture book format" is an e-book file format in which illustrated images are inserted into the text data, providing a visually easy-to-understand reading experience. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram 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

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention is a system in which a user uploads an e-book file, a server converts the e-book file into text data, extracts scenes and keywords, and generates illustrations based on the data. This system aims to provide an easy-to-understand reading experience, especially for foreign language learners.

[0038] System Overview

[0039] 1. User Action:

[0040] The user accesses the system's web interface through a terminal, selects the e-book file (e.g., PDF or EPUB) they want to read, and uploads it.

[0041] 2. Server Processing:

[0042] The server receives the electronic book file uploaded by the user, and then converts the electronic book file into text data.

[0043] 3. Text Analysis:

[0044] The server uses natural language processing algorithms to analyze the text data and extract scenes and keywords, providing the basis for generating illustrations.

[0045] 4. Illustration generation request:

[0046] The server sends an illustration generation request to the AI ​​based on the extracted scenes and keywords. The request includes specific scenes and keywords.

[0047] 5. Generative AI behavior:

[0048] The AI ​​generates illustration images based on requests received from the server, and the generated illustration images are sent back to the server.

[0049] 6. Illustration Integration:

[0050] The server receives the illustration images returned by the generation AI and inserts them into the original text data in the appropriate places, thereby generating an e-book file in picture book format.

[0051] 7. Provision to Users:

[0052] The server generates a link to provide the generated e-book file in picture book format to the user, who can then download the e-book via the link and begin reading.

[0053] Specific examples

[0054] For example, let's say a user wants to read "The Great Gatsby." Here's the specific flow:

[0055] 1. The user selects the e-book file (EPUB format) of "The Great Gatsby" and uploads it through the system's web interface.

[0056] 2. The server receives the file and converts the EPUB file into text data.

[0057] 3. The server uses a natural language processing algorithm to extract scenes and keywords such as "the scene where Gatsby looks at Daisy" and "description of the party" from the text data.

[0058] 4. The server sends an illustration generation request to the generation AI based on the extracted scenes and keywords.

[0059] 5. The AI ​​generates an illustration based on the scene of Gatsby gazing at Daisy and sends the image back to the server.

[0060] 6. The server inserts the received illustration images into the original text data in the appropriate locations to generate a new picture book format e-book file of "The Great Gatsby."

[0061] 7. The user can click on the link provided by the system to download the generated picture book version of "The Great Gatsby" and begin reading.

[0062] The above is a specific embodiment for carrying out the present invention. This system allows users to easily understand the content visually through illustrations, providing an effective reading experience, especially for foreign language learning.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] A user accesses the system's web interface using a terminal, selects the e-book file (e.g., PDF or EPUB) they want to read, and clicks the upload button.

[0066] Step 2:

[0067] The server receives the e-book file sent by the user and stores the received e-book file in temporary storage.

[0068] Step 3:

[0069] The server analyzes the e-book file and converts it into text data in an appropriate way. For example, if it is an EPUB file, it analyzes the XML data inside it and extracts the text data.

[0070] Step 4:

[0071] The server applies a natural language processing algorithm to the converted text data, allowing the server to extract important scenes and keywords from the text data.

[0072] Step 5:

[0073] The server generates an illustration generation request based on the extracted scenes and keywords. The request includes specific descriptions of the scenes and keywords.

[0074] Step 6:

[0075] The server sends an illustration generation request to the generation AI. The request is sent via the generation AI's API.

[0076] Step 7:

[0077] The AI ​​generates an illustration image based on the request it receives, taking into account the scene and keywords, and creating the most suitable illustration.

[0078] Step 8:

[0079] The generated illustration image is sent back to the server by the AI ​​generator, and is then temporarily stored in the server.

[0080] Step 9:

[0081] The server integrates the received illustration images into the original text data, inserts the illustrations into specific positions in the text data, and generates an e-book file in picture book format.

[0082] Step 10:

[0083] The server generates a download link for providing the generated e-book file in picture book format to the user, and the link is displayed on the web interface so that the user can access it.

[0084] Step 11:

[0085] The user uses the device to click the provided download link to download the generated e-book file in picture book format. The user can then open the downloaded e-book and begin reading it with the illustrations.

[0086] Example 1

[0087] 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."

[0088] Conventional e-books lack visual information, and lack illustrations to aid comprehension, especially in foreign language learning, limiting their effectiveness. Furthermore, manually inserting illustrations takes time and effort, making it difficult to provide efficient learning support.

[0089] 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.

[0090] In this invention, the server includes means for a user to upload a book file, means for the server to convert the book file into document data, means for the server to extract scenes and key phrases from the document data, means for the server to send an illustration generation request to a generation algorithm, means for the generation algorithm to generate illustration images and return them to the server, means for the server to integrate the illustration images into the document data, and means for the server to provide the user with the generated illustrated book file. This allows users to efficiently use books with added visual information, which can aid understanding, particularly in foreign language learning.

[0091] "User" refers to an individual who uses the System to upload Book Files and download Illustrated Book Files.

[0092] "Server" refers to a computer system that receives book files, converts them into document data, extracts scenes and key phrases, sends illustration generation requests, and integrates illustration images.

[0093] "Book File" refers to a digital file of an electronically recorded book, including formats such as PDF and EPUB.

[0094] "Document data" refers to text data extracted from a book file.

[0095] "Scene" refers to a description within document data that requires a specific scene or illustration.

[0096] "Key words" refer to notable keywords or phrases within document data.

[0097] "Generation algorithm" refers to an algorithm for generating an illustration image based on an illustration generation request.

[0098] An "illustration generation request" refers to a request to create an illustration image sent to the generation algorithm based on a scene or key phrases.

[0099] "Illustrated Image" refers to a visual image generated by a generative algorithm.

[0100] "Integration into book file" refers to the process of placing the generated illustration images in the appropriate positions in the original document data.

[0101] "Illustrated book file" refers to a book file in which the generated illustrated images have been integrated.

[0102] This system allows users to upload book files, converts them into document data, extracts scenes and key phrases, and generates illustrations based on them. This system aims to provide an easy-to-understand reading experience, especially for foreign language learners.

[0103] First, the user accesses the system's web interface using a terminal. Through this interface, the user selects and uploads the book file (e.g., PDF or EPUB format) they want to read. The upload operation can be performed in the same way as selecting a file.

[0104] The server receives the book file uploaded by the user. The server uses software libraries such as "Apache PDFBox" and "EPUB.js" to convert the book file into document data. For example, in the case of an EPUB format file, EPUB.js is used to extract the text data.

[0105] Next, the server analyzes the document data using natural language processing technology. Examples of software used for this analysis include "spaCy" and "NLTK." The server uses these libraries to extract scenes and key phrases from the document data. For example, "the scene where Gatsby looks at Daisy" or "description of the party" may be extracted.

[0106] The server sends an illustration generation request to the generation algorithm based on the extracted scenes and key phrases. This prompt includes a specific scene and key phrases. Examples of prompts include "The Great Gatsby: Generate an illustration of the scene where Gatsby looks at Daisy" and "The Great Gatsby: Generate an illustration based on the description of the party."

[0107] The generation algorithms used are "Stable Diffusion" and "DALL-E." The generation algorithm generates an illustration image based on the received prompt text and sends the illustration image back to the server.

[0108] The server then inserts the received illustration into the document at the appropriate location, possibly using the Pillow library. For example, the server could place the illustration immediately after the text "Gatsby looking at Daisy."

[0109] Finally, the server generates a link to provide the generated illustrated book file to the user, who can then download the book file and begin reading.

[0110] This allows users to efficiently use books with added visual information, which can aid comprehension, especially in foreign language learning. The system of the present invention provides a visual interface for users, supporting a more effective learning experience.

[0111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0112] Step 1:

[0113] A user accesses the system's web interface through a terminal. The user clicks the "Upload" button, selects the book file (e.g., PDF or EPUB format) they want to read, and uploads it. The input is the book file selected by the user, and the output is the book file sent to the server.

[0114] Step 2:

[0115] The server receives the book file uploaded by the user. Based on the received file, it converts it into document data using "Apache PDFBox" or "EPUB.js". This conversion process involves reading the file and converting it into text data. The input is the book file, and the output is the converted document data.

[0116] Step 3:

[0117] The server analyzes the converted document data using natural language processing technology (such as "spaCy" or "NLTK"). This analysis identifies and extracts scenes and key phrases. Specific processing involves morphological analysis of the text and scene identification. The input is the document data, and the output is the extracted scenes and key phrases.

[0118] Step 4:

[0119] The server sends an illustration generation request to a generation algorithm (such as "Stable Diffusion" or "DALL-E") based on the extracted scenes and key phrases. The prompt sentence includes a specific example, "Please generate an illustration of the scene in The Great Gatsby where Gatsby is gazing at Daisy." The input is the scene and key phrases, and the output is the prompt sentence sent to the generation AI.

[0120] Step 5:

[0121] The generation AI receives the prompt text and generates an illustration image based on it. The generated illustration image is then sent back to the server. In this process, the AI ​​analyzes the received prompt text and generates a visual image. The input is the prompt text and the output is the generated illustration image.

[0122] Step 6:

[0123] The server inserts the received illustration images into the original document data at the appropriate position. Here, the "Pillow" library is used to integrate the illustration images at the appropriate position and generate a book file in picture book format. The input is the illustration images and document data, and the output is a book file with the illustrations integrated.

[0124] Step 7:

[0125] The server generates a link to provide the generated illustrated book file to the user, who can use this link to download and view the new book file. The input is the final book file, and the output is the download link.

[0126] (Application example 1)

[0127] 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."

[0128] Conventional e-books have been difficult to understand, especially for foreign language learners. Visual aids are especially needed for situations and keywords that are difficult to understand with text alone. However, no system currently available can efficiently accomplish this. Furthermore, there is a need for a system that allows users to upload digital document files, generate visual aids, and easily download and view them.

[0129] 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.

[0130] In this invention, the server includes means for a user to upload a digital document file, means for the server to convert the digital document file into text data, means for the server to extract scenes and keywords from the text data, means for the server to send an illustration generation request to a generative AI model, means for the generative AI model to generate an illustration image and return it to the server, means for the server to integrate the illustration image into the text data, means for the server to provide the generated illustrated digital document file to the user, and means for the user to download and view the digital document file from a smartphone application. This allows users to view digital documents in a more understandable format, enabling effective learning, particularly for foreign language learners.

[0131] "User" refers to any individual or entity that uses the System.

[0132] "Digital document file" refers to a document that is stored and displayed electronically, including, for example, PDF and EPUB.

[0133] "Server" refers to a computer system that processes and stores data.

[0134] "Text data" refers to textual information extracted from a digital document file.

[0135] A "scene" refers to a specific event or situation in text data.

[0136] A "keyword" refers to a word or short phrase that has an important meaning within text data.

[0137] A "generative artificial intelligence model" refers to a machine learning algorithm for generating images or text based on specified input data.

[0138] "Illustration images" refer to illustrations and figures created to visually complement the content of text data.

[0139] "Smartphone application" refers to a program that runs on a smartphone.

[0140] A "natural language processing algorithm" refers to a computational method for analyzing text data and extracting meaning.

[0141] "Visual aid format" refers to a format that combines visual information (such as illustrations) with text.

[0142] The present invention is a system in which a user uploads a digital document file, a server converts the digital document file into text data, extracts scenes and keywords, and generates illustrations based on the text data.The system also integrates the generated illustrations into the text data, and provides a function that allows users to download and view the digital document file in visual aid format from a smartphone application.

[0143] System Overview

[0144] 1. User Action:

[0145] A user accesses the system interface using a smartphone, selects the digital document file (e.g., PDF or EPUB) they want to read, and uploads it.

[0146] 2. Server Processing:

[0147] The server receives the digital document file uploaded by the user, and then converts the digital document file into text data.

[0148] 3. Text Analysis:

[0149] The server analyzes the text data using natural language processing algorithms (e.g., spaCy or NLTK) to extract scenes and keywords, thereby obtaining the information needed to generate illustrations.

[0150] 4. Illustration generation request:

[0151] The server sends an illustration generation request to a generative AI model (e.g., OpenAI API) based on the extracted scenes and keywords. The request includes specific scenes and keywords.

[0152] 5. Generative AI behavior:

[0153] The generative AI model generates illustration images based on requests received from the server, and the generated illustration images are sent back to the server.

[0154] 6. Illustration Integration:

[0155] The server receives the illustration images returned by the generative AI model and inserts them into the original text data at the appropriate locations, generating a digital document file in the form of a visual aid.

[0156] 7. Provision to Users:

[0157] The server provides the generated illustrated digital document file to the user, who can then download the visually-aided digital document file through a smartphone application and begin viewing it.

[0158] Specific examples

[0159] For example, suppose a user wants to read a digital document file of "The Great Gatsby." Here's the specific flow:

[0160] 1. The user selects a digital document file (EPUB format) for "The Great Gatsby" and uploads it from the smartphone application.

[0161] 2. The server receives the file and converts the EPUB file into text data.

[0162] 3. The server uses a natural language processing algorithm to extract scenes and keywords from the text data, such as "the scene where Gatsby looks at Daisy" and "description of the party."

[0163] 4. The server sends an illustration generation request to the generative AI model based on the extracted scene and keywords. For example, the prompt sentence may include the following:

[0164] "A scene where Gatsby gazes at Daisy."

[0165] 5. The generative AI model generates an illustration based on the prompt and sends it back to the server.

[0166] 6. The server inserts the received illustrations into the original text data in the appropriate locations, generating a new visually-aided "The Great Gatsby" digital document file.

[0167] 7. The user can click on the link provided by the smartphone application to download and begin reading "The Great Gatsby" with the generated illustrations.

[0168] The above system allows users to view digital documents with added illustrations in a visually easy-to-understand format, making learning particularly effective for foreign language learners.

[0169] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0170] Program processing steps

[0171] Step 1: Upload your eBook file

[0172] Specific behavior:

[0173] A user starts a smartphone application, selects the digital document file (PDF, EPUB, etc.) they want to read, and selects the file on the upload screen. The selected file is then sent to the server within the application.

[0174] input:

[0175] User-selected digital document file (PDF or EPUB)

[0176] output:

[0177] Digital document files sent to the server

[0178] Step 2: Convert eBook files to text

[0179] Specific behavior:

[0180] The server converts the received digital document file into text data using a dedicated library (e.g., PyMuPDF, EPUB-lib). For PDF files, the text is extracted page by page, and for EPUB files, the text is extracted chapter by chapter.

[0181] input:

[0182] Digital document files (PDF and EPUB)

[0183] output:

[0184] Text data

[0185] Step 3: Analyzing the text data

[0186] Specific behavior:

[0187] The server uses natural language processing algorithms (e.g., spaCy or NLTK) to analyze the text data and extract scenes and keywords. This involves performing semantic and syntactic analysis of the text to identify important scenes and keywords.

[0188] input:

[0189] Text data

[0190] output:

[0191] Extracted scenes and keywords

[0192] Step 4: Create and submit an illustration generation request

[0193] Specific behavior:

[0194] The server generates a prompt based on the extracted scene and keywords, and sends a request to generate an illustration to a generative artificial intelligence model (e.g., OpenAI API). The prompt is created by including a description of the scene and keywords.

[0195] input:

[0196] Extracted scenes and keywords

[0197] output:

[0198] An illustration generation request sent to the generative AI model

[0199] Step 5: Creating and receiving illustrations

[0200] Specific behavior:

[0201] The generative artificial intelligence model generates an illustration image based on the request received from the server. The generated illustration image is sent back to the server, which receives and stores this image.

[0202] input:

[0203] Illustration generation request

[0204] output:

[0205] Generated illustration image

[0206] Step 6: Integrating illustrations into text data

[0207] Specific behavior:

[0208] The server inserts the generated illustrations into the original text data at the appropriate locations. The insertion position of the illustrations is determined based on the extracted scenes and keywords. A text editing library (e.g., PyMuPDF, EPUB-lib) is used for the processing.

[0209] input:

[0210] Text data, generated illustration images

[0211] output:

[0212] Illustrated digital document file

[0213] Step 7: Provide illustrated digital document files

[0214] Specific behavior:

[0215] The server generates a link for providing the created digital document file with illustrations to the user, and provides the link to the user through a smartphone application. The user can click the link to download and view the digital document file.

[0216] input:

[0217] Illustrated digital document file

[0218] output:

[0219] Download link provided to users

[0220] Specific examples

[0221] For example, suppose a user wants to read a digital document file of "The Great Gatsby." Here's the specific flow:

[0222] 1. The user selects a digital document file (EPUB format) for "The Great Gatsby" and uploads it from the smartphone application.

[0223] 2. The server receives the file and converts the EPUB file into text data.

[0224] 3. The server uses a natural language processing algorithm to extract scenes and keywords from the text data, such as "the scene where Gatsby looks at Daisy" and "description of the party."

[0225] 4. The server sends an illustration generation request to the generative AI model based on the extracted scene and keywords. For example, the prompt sentence may include the following:

[0226] "A scene where Gatsby gazes at Daisy."

[0227] 5. The generative AI model generates an illustration based on the prompt and sends it back to the server.

[0228] 6. The server inserts the received illustrations into the original text data in the appropriate locations, generating a new visually-aided "The Great Gatsby" digital document file.

[0229] 7. The user can click on the link provided by the smartphone application to download and begin reading "The Great Gatsby" with the generated illustrations.

[0230] 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.

[0231] This system allows users to upload e-book files, converts them into text data, extracts scenes and keywords, and generates appropriate illustrations using a generative AI and emotion engine. This system aims to provide an easy-to-understand reading experience, especially for foreign language learners.

[0232] System Overview

[0233] 1. User Action:

[0234] The user accesses the system's web interface through a terminal, selects the e-book file (e.g., PDF or EPUB) they want to read, and clicks the upload button.

[0235] 2. Server Processing:

[0236] The server receives the electronic book file uploaded by the user, and then converts the electronic book file into text data.

[0237] 3. Text Analysis:

[0238] The server uses natural language processing algorithms to analyze the text data and extract scenes and keywords, providing the basis for generating illustrations.

[0239] 4. Emotion Recognition with Emotion Engine:

[0240] While the user goes through the above process through the device, the emotion engine analyzes data collected from the user's camera and microphone to recognize the user's emotions in real time.

[0241] 5. Illustration generation request:

[0242] The server generates an illustration generation request based on the extracted scenes and keywords and the user's emotions detected by the emotion engine. The request includes specific descriptions of the scenes and keywords and emotion information.

[0243] 6. Generative AI behavior:

[0244] The AI ​​generates illustration images based on requests received from the server, and the generated illustration images are sent back to the server.

[0245] 7. Illustration Integration:

[0246] The server receives the illustration images returned by the generation AI and inserts them into the original text data in the appropriate places, thereby generating an e-book file in picture book format.

[0247] 8. User Offerings:

[0248] The server generates a link to provide the generated e-book file in picture book format to the user, who can then download the e-book via the link and begin reading.

[0249] Specific examples

[0250] For example, let's say a user wants to read "The Great Gatsby." Here's the specific flow:

[0251] 1. The user selects the e-book file (EPUB format) of "The Great Gatsby" and uploads it through the system's web interface.

[0252] 2. The server receives the file and converts the EPUB file into text data.

[0253] 3. The server uses a natural language processing algorithm to extract scenes and keywords such as "the scene where Gatsby looks at Daisy" and "description of the party" from the text data.

[0254] 4. The emotion engine recognizes emotions from the user's facial expressions and voice, detecting when the user is expressing emotions such as excitement or sadness in a particular scene.

[0255] 5. The server sends an illustration generation request to the generation AI based on the extracted scenes and keywords, and the user's emotions detected by the emotion engine.

[0256] 6. The AI ​​generates an illustration based on the scene of Gatsby gazing at Daisy and sends the image back to the server. It may also include elements corresponding to emotions.

[0257] 7. The server inserts the received illustration images into the original text data in the appropriate locations to generate a new picture book format e-book file of "The Great Gatsby."

[0258] 8. The user can click on the link provided by the system to download and start reading the generated picture book version of "The Great Gatsby."

[0259] The above is a concrete example of how to implement the present invention. This system not only allows users to visually understand the content through illustrations, but also allows them to enjoy a reading experience that is customized to their emotions. This provides an effective reading experience, especially for foreign language learning.

[0260] The processing flow will be explained below.

[0261] Step 1:

[0262] A user accesses the system's web interface using a terminal, selects the e-book file (e.g., PDF or EPUB) they want to read, and clicks the upload button.

[0263] Step 2:

[0264] The server receives the e-book file sent by the user and stores the received e-book file in temporary storage.

[0265] Step 3:

[0266] The server analyzes the e-book file and converts it into text data in an appropriate way. For example, if it is an EPUB file, it analyzes the XML data inside it and extracts the text data.

[0267] Step 4:

[0268] The server applies a natural language processing algorithm to the converted text data, allowing the server to extract important scenes and keywords from the text data.

[0269] Step 5:

[0270] While the user is operating the system through the device, the emotion engine collects data from the device's camera and microphone, analyzes the user's facial expressions and voice, and recognizes emotions in real time.

[0271] Step 6:

[0272] The server generates an illustration generation request based on the extracted scenes and keywords, and the user's emotions recognized by the emotion engine. The request includes specific descriptions of the scenes and keywords, as well as emotional information.

[0273] Step 7:

[0274] The server sends an illustration generation request to the generation AI. The request is sent via the generation AI's API.

[0275] Step 8:

[0276] The AI ​​generates an illustration based on the request it receives, taking into account the scene, keywords, and the user's emotions to create the most appropriate illustration.

[0277] Step 9:

[0278] The generated illustration image is sent back to the server by the AI ​​generator, and is then temporarily stored in the server.

[0279] Step 10:

[0280] The server integrates the received illustration images into the original text data, inserts the illustrations into specific positions in the text data, and generates an e-book file in picture book format.

[0281] Step 11:

[0282] The server generates a download link for providing the generated e-book file in picture book format to the user, and the link is displayed on the web interface so that the user can access it.

[0283] Step 12:

[0284] The user uses the device to click the provided download link to download the generated e-book file in picture book format. The user can then open the downloaded e-book and begin reading it with the illustrations.

[0285] Example 2

[0286] 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."

[0287] Current electronic content systems lack the ability to create appropriate illustrations to visually support the text being read, and lack a means to provide a customized reading experience based on the user's emotions, making it difficult to provide effective reading support, especially for foreign language learners.

[0288] 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.

[0289] In this invention, the server includes: a means for a user to upload an electronic content file; a means for the server to convert the electronic content file into text data; a means for the server to extract scenes and keywords from the text data using a natural language processing algorithm; a means for the server to use an emotion engine that recognizes the user's emotions in real time; a means for the server to generate a prompt sentence for a generative AI model and send an illustration generation request; a means for the generative AI model to generate an illustration image and return it to the server; a means for the server to integrate the illustration image into the text data; and a means for the server to provide the user with the generated illustrated electronic content file, thereby enabling a reading experience with illustrations customized according to the user's emotions.

[0290] A "user" is an entity that uploads electronic content files to the system and receives the resulting illustrated electronic content files.

[0291] An "electronic content file" is a digital file containing text data, such as a PDF or EPUB file.

[0292] "Server" refers to the hardware and software that processes electronic content files uploaded by users and converts them into text data.

[0293] "Text data" is character string data extracted from an electronic content file, and is the subject of analysis and processing.

[0294] "Natural language processing algorithms" are technologies that include machine learning and statistical methods for extracting scenes and keywords from text data.

[0295] "Scenes and keywords" refer to specific scenes and important words within the text data, and are the information that forms the basis for generating illustrations.

[0296] The "emotion engine" is a technology that recognizes a user's emotions by analyzing data collected from a camera or microphone.

[0297] A "generative AI model" is an artificial intelligence algorithm that generates images based on a prompt, and examples include deep learning techniques.

[0298] A "prompt sentence" is a specific explanatory sentence used when sending an illustration generation request to the generative AI model.

[0299] An "illustrated image" is a visual picture generated by a generative AI model based on a prompt sentence.

[0300] "Integrating" means inserting the generated illustration image into the original text data at an appropriate location.

[0301] "Providing an electronic content file" means providing the created illustrated electronic content file in a form that can be accessed by the user.

[0302] The present invention is a system in which a user uploads an electronic content file, a server converts the file into text data, analyzes it, generates appropriate illustrations using a generative AI model, and provides the final illustrated electronic content file to the user. This system aims to provide an easy-to-understand reading experience, especially for foreign language learners.

[0303] System Overview

[0304] 1. User Operation

[0305] A user accesses the system's web interface through a terminal. The user selects the electronic content file (e.g., PDF or EPUB) they want to read and clicks the upload button. For example, a user uploads an EPUB file of "The Great Gatsby" by dragging and dropping it.

[0306] 2. Server Processing

[0307] The server receives the electronic content file uploaded by the user. After receiving it, the server converts the electronic content file into text data using software such as Adobe Acrobat or Calibre. The converted text data is stored in an internal database (e.g., MySQL or PostgreSQL).

[0308] 3. Text Analysis

[0309] The server uses Python's NLTK library and SpaCy to analyze the text data. Specifically, it tokenizes sentences and extracts scenes and keywords. This analysis extracts specific scenes and important phrases. For example, it extracts keywords corresponding to the scene "Gatsby gazes at Daisy."

[0310] 4. Emotion Recognition by Emotion Engine

[0311] While the user goes through the above process on the device, the emotion engine analyzes data collected from the user's camera and microphone to recognize the user's emotions in real time. For example, it can detect excitement or sadness from the user's facial expressions and voice. This is done using Microsoft Azure's Emotion API and Google Cloud's Natural Language API.

[0312] 5. Illustration generation request

[0313] The server generates a prompt to send to the generative AI model based on the extracted scenes and keywords, as well as the user's emotions detected by the emotion engine. This prompt includes a specific description of the scene and keywords, along with emotional information. For example, it could be in the format "The scene where Gatsby looks at Daisy, the user feels joy."

[0314] 6. Generative AI behavior

[0315] A generative AI model (e.g., OpenAI's DALLE or other similar technologies) generates an illustration based on the prompt received from the server. The generated illustration is then sent back to the server. For example, a specific illustration is generated based on the prompt, "The scene where Gatsby is looking at Daisy, and the user feels joy."

[0316] 7. Integration of illustrations

[0317] The server receives the illustration images returned by the generative AI model and inserts them into the original text data at the appropriate locations. This generates an electronic content file in picture book format. For example, the server inserts the corresponding illustration image immediately after the text "Gatsby gazing at Daisy."

[0318] 8. Provision to Users

[0319] The server generates a link to provide the generated picture book format electronic content file to the user, who can then download the electronic content via the link and begin reading it.

[0320] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0321] Step 1: A user uploads an electronic content file.

[0322] Input: An electronic content file (e.g., PDF or EPUB format) selected by the user through the device.

[0323] Specific behavior: A user accesses the system's web interface and clicks a button to upload an electronic content file they want to read. This displays a file selection dialog, where the user selects the desired file and confirms the upload.

[0324] Output: The file is sent to the server.

[0325] Step 2: The server converts the electronic content file into text data.

[0326] Input: Electronic content files uploaded by users.

[0327] What happens: The server recognizes the received file and converts it to text using software like Adobe Acrobat or Calibre. The conversion process involves sequentially parsing the pages of the file and extracting strings of characters.

[0328] Output: The converted text data is generated and stored in the server's internal database (e.g. MySQL or PostgreSQL).

[0329] Step 3: The server analyzes the text data using natural language processing algorithms.

[0330] Input: The converted text data.

[0331] How it works: The server uses natural language processing tools such as Python's NLTK library or SpaCy to analyze the text data. This analysis involves tokenizing the text and extracting key scenes and keywords. Each scene and keyword is ranked based on a specific importance.

[0332] Output: Extracted scenes and keyword list.

[0333] Step 4: The server uses an emotion engine to recognize the user's emotions in real time.

[0334] Input: Real-time data from the user's camera and microphone.

[0335] How it works: The server analyzes the user's facial expressions and voice data via Microsoft Azure's Emotion API and Google Cloud's Natural Language API, which allows it to identify the emotions the user is expressing in each scene.

[0336] Output: User emotion data.

[0337] Step 5: The server generates a prompt sentence for the generative AI model and sends a request to generate an illustration.

[0338] Input: A list of scenes and keywords, and user emotion data.

[0339] Specific operation: The server generates prompts based on this data. Specifically, it constructs prompts for each scene by combining detailed descriptions and emotion data. For example, the prompt might be, "In the scene where Gatsby looks at Daisy, the user feels joy."

[0340] Output: The prompt sent to the generative AI model.

[0341] Step 6: The generative AI model generates an illustration image and sends it back to the server.

[0342] Input: The prompt text sent by the server.

[0343] What it does: A generative AI model (e.g., OpenAI's DALLE or similar technology) analyzes the prompt and generates a corresponding illustration image, which is then sent back to the server in its raw form.

[0344] Output: The generated illustration image.

[0345] Step 7: The server integrates the illustration images into the text data.

[0346] Input: Illustration images returned from the generative AI model, original text data.

[0347] Specific operation: The server inserts the generated illustration images into the text data at the appropriate places, specifically by applying an algorithm to integrate the images, such as immediately after each scene.

[0348] Output: Integrated picture book format electronic content file.

[0349] Step 8: The server provides the generated illustrated electronic content file to the user.

[0350] Input: Integrated picture book format electronic content file.

[0351] What happens: The server uploads this file to a cloud storage location accessible to the user (e.g. AWS S3) and generates a download link, which is sent to the user's dashboard or email.

[0352] Output: A downloadable link for the user.

[0353] (Application example 2)

[0354] 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."

[0355] The lack of visual elements in e-books can make learning and reading difficult, especially for language learners. Furthermore, content provided without considering the user's emotional state can reduce user satisfaction and concentration. Therefore, there is a need for a system that can dynamically generate illustrations based on the user's emotions, providing a visually and emotionally rich reading experience.

[0356] 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.

[0357] In this invention, the server includes: a means for a user to upload an e-book file; a means for the server to convert the e-book file into text data; a means for the server to extract scenes and keywords from the text data; a means for analyzing the user's emotions in real time using an emotion analysis engine; a means for the server to send to the generation AI a request to generate illustrations based on the scenes, keywords, and the user's emotions; a means for the generation AI to generate illustration images and return them to the server; a means for the server to integrate the illustration images into the text data; and a means for the server to provide the user with the generated illustrated e-book file. This makes it possible to dynamically generate illustrations according to the user's emotions and provide a visually and emotionally rich reading experience, especially for language learners.

[0358] "User" means an individual or legal entity who uses the System to upload e-book files and receive illustrated e-books.

[0359] An "e-book file" is a reading material that stores information such as text and images in digital format, and typical formats include PDF and EPUB.

[0360] The "server" is a computer system that analyzes e-book files, converts them into text data, extracts scenes and keywords, and integrates the illustrations generated by the generation AI to generate e-book files, which are then provided to users.

[0361] "Text data" refers to text data extracted from an e-book file and processed and analyzed by the server.

[0362] A "scene" is a portion of text data that indicates a specific scene or situation, and serves as basic information for generating an illustration image.

[0363] "Keywords" are important words or phrases in the text data, and are information that clarifies a scene.

[0364] An "emotion analysis engine" is software that analyzes data collected from the user's camera and microphone and recognizes the user's emotional state.

[0365] "Generative AI" is an artificial intelligence model that generates illustration images based on given scenes, keywords, and the user's emotions.

[0366] An "illustration generation request" is an instruction sent by the server to the generation AI, and includes the scene, keywords, and the user's emotional information.

[0367] "Illustrated images" are illustrations or visuals generated by generative AI and integrated into text data.

[0368] "Integration" refers to the process of incorporating the generated illustration images into the original text data at the appropriate locations.

[0369] "Generating an e-book file" refers to the process of creating a new e-book file containing illustrations.

[0370] "Providing to the user" refers to delivering the generated illustrated e-book file to the user via a downloadable link.

[0371] The present invention is a system that provides illustrations based on scenes, keywords, and user emotions to enrich the user's reading experience, especially for language learners. This system is realized through a series of processes: analyzing e-book files, performing emotion recognition, and generating illustrations.

[0372] Program Overview

[0373] 1. User Interface

[0374] Users upload e-book files to the web from their smartphones or PCs.

[0375] It is also possible to set it so that when a user purchases an e-book, it is automatically uploaded.

[0376] 2. E-book analysis

[0377] The uploaded e-book file (PDF, EPUB, etc.) is sent to the server.

[0378] The server uses an appropriate conversion algorithm to convert the e-book file into text data.

[0379] 3. Natural Language Processing and Keyword Extraction

[0380] The server uses natural language processing algorithms (e.g., spaCy or NLTK) to extract scenes and keywords from the text data.

[0381] This process provides important information needed for subsequent illustration generation.

[0382] 4. Emotion analysis

[0383] A sentiment analysis engine (for example, Microsoft Azure's Cognitive Services) uses data from the user's camera and microphone to analyze the user's emotions in real time.

[0384] 5. Creating an illustration generation request

[0385] The server sends an illustration generation request to the generative AI model based on the extracted scenes, keywords, and emotion analysis results.

[0386] The illustration generation request includes an appropriate prompt, such as:

[0387] "Scene: Gatsby and Daisy gazing at each other. User emotion: Joy. Generates illustrations containing warm, romantic images."

[0388] 6. Use of generative AI

[0389] A generative AI model (e.g., OpenAI GPT-4-based model) generates an illustrated image based on the prompt and analysis data.

[0390] The generated illustration is sent back to the server.

[0391] 7. Integration of illustrations

[0392] The server uses an algorithm to appropriately integrate the illustrations into the text data.

[0393] This process generates an illustrated e-book file.

[0394] 8. Provision to Users

[0395] The server generates a link for providing the generated illustrated e-book file to the user.

[0396] Users can then download the generated e-book via that link and begin reading.

[0397] This allows illustrations to be dynamically generated based on the user's emotions, providing a visually and emotionally rich reading experience, especially for language learners.

[0398] Hardware and software used

[0399] Hardware: Smartphones, PCs, cloud servers

[0400] Software: Web application frameworks (Flask / Django), natural language processing libraries (spaCy, NLTK), sentiment analysis API (Microsoft Azure Cognitive Services), generative AI (OpenAI GPT-4 based model)

[0401] This system is extremely useful for users as it can perform all processes from analyzing e-book files to providing new e-book files containing the generated illustrations.

[0402] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0403] Step 1:

[0404] A user uploads an e-book file (e.g., PDF or EPUB) using a smartphone or PC. To do this, the user accesses the system's web interface, selects the e-book file, and clicks the upload button. The input is the e-book file, and the output is sending that file to the server.

[0405] Step 2:

[0406] The server receives the uploaded e-book file. It uses an appropriate conversion algorithm (e.g., a PDF to text conversion library) to convert the received file into text data. The input is the e-book file, and the output is text data.

[0407] Step 3:

[0408] The server analyzes the text data using a natural language processing algorithm (e.g., spaCy or NLTK). It extracts scenes and keywords from the text data. The input is the text data, and the output is the extracted scenes and keywords.

[0409] Step 4:

[0410] The server sends data collected from the user's device via the camera and microphone to an emotion analysis engine (for example, Microsoft Azure's Cognitive Services). This allows the user's emotions to be analyzed in real time. The input is the audio and image data collected from the camera and microphone, and the output is analyzed emotion data.

[0411] Step 5:

[0412] The server sends an illustration generation request to the generative AI model based on the extracted scenes, keywords, and emotion analysis results. This request includes a specific description of the scene and keywords, as well as emotional information. An example prompt is: "Scene: Gatsby and Daisy gazing at each other. User's emotion: Joy. Generate an illustration that includes a warm, romantic image." The input is the scene, keywords, and emotion data, and the output is an illustration generation request.

[0413] Step 6:

[0414] The generative AI model generates an illustration image based on the received illustration generation request. Using a generative AI model (e.g., OpenAI GPT-4 based model), an illustration is generated according to the presented prompt sentence. The input is the illustration generation request, and the output is the generated illustration image.

[0415] Step 7:

[0416] The server receives the illustration images returned by the generative AI model. The server uses an integration algorithm to properly integrate the illustration images into the text data. The input is the text data and the illustration images, and the output is an illustrated e-book file.

[0417] Step 8:

[0418] The server generates a link to provide the generated illustrated e-book file to the user, who can then download the generated e-book and begin reading it. The input is the illustrated e-book file, and the output is the download link.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] [Second embodiment]

[0423] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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."

[0435] The present invention is a system in which a user uploads an e-book file, a server converts the e-book file into text data, extracts scenes and keywords, and generates illustrations based on the data. This system aims to provide an easy-to-understand reading experience, especially for foreign language learners.

[0436] System Overview

[0437] 1. User Action:

[0438] The user accesses the system's web interface through a terminal, selects the e-book file (e.g., PDF or EPUB) they want to read, and uploads it.

[0439] 2. Server Processing:

[0440] The server receives the electronic book file uploaded by the user, and then converts the electronic book file into text data.

[0441] 3. Text Analysis:

[0442] The server uses natural language processing algorithms to analyze the text data and extract scenes and keywords, providing the basis for generating illustrations.

[0443] 4. Illustration generation request:

[0444] The server sends an illustration generation request to the AI ​​based on the extracted scenes and keywords. The request includes specific scenes and keywords.

[0445] 5. Generative AI behavior:

[0446] The AI ​​generates illustration images based on requests received from the server, and the generated illustration images are sent back to the server.

[0447] 6. Illustration Integration:

[0448] The server receives the illustration images returned by the generation AI and inserts them into the original text data in the appropriate places, thereby generating an e-book file in picture book format.

[0449] 7. User Offerings:

[0450] The server generates a link to provide the generated e-book file in picture book format to the user, who can then download the e-book via the link and begin reading.

[0451] Specific examples

[0452] For example, let's say a user wants to read "The Great Gatsby." Here's the specific flow:

[0453] 1. The user selects the e-book file (EPUB format) of "The Great Gatsby" and uploads it through the system's web interface.

[0454] 2. The server receives the file and converts the EPUB file into text data.

[0455] 3. The server uses a natural language processing algorithm to extract scenes and keywords such as "the scene where Gatsby looks at Daisy" and "description of the party" from the text data.

[0456] 4. The server sends an illustration generation request to the generation AI based on the extracted scenes and keywords.

[0457] 5. The AI ​​generates an illustration based on the scene of Gatsby gazing at Daisy and sends the image back to the server.

[0458] 6. The server inserts the received illustration images into the original text data in the appropriate locations to generate a new picture book format e-book file of "The Great Gatsby."

[0459] 7. The user can click on the link provided by the system to download the generated picture book version of "The Great Gatsby" and begin reading.

[0460] The above is a specific embodiment for carrying out the present invention. This system allows users to easily understand the content visually through illustrations, providing an effective reading experience, especially for foreign language learning.

[0461] The processing flow will be explained below.

[0462] Step 1:

[0463] A user accesses the system's web interface using a terminal, selects the e-book file (e.g., PDF or EPUB) they want to read, and clicks the upload button.

[0464] Step 2:

[0465] The server receives the e-book file sent by the user and stores the received e-book file in temporary storage.

[0466] Step 3:

[0467] The server analyzes the e-book file and converts it into text data in an appropriate way. For example, if it is an EPUB file, it analyzes the XML data inside it and extracts the text data.

[0468] Step 4:

[0469] The server applies a natural language processing algorithm to the converted text data, allowing the server to extract important scenes and keywords from the text data.

[0470] Step 5:

[0471] The server generates an illustration generation request based on the extracted scenes and keywords. The request includes specific descriptions of the scenes and keywords.

[0472] Step 6:

[0473] The server sends an illustration generation request to the generation AI. The request is sent via the generation AI's API.

[0474] Step 7:

[0475] The AI ​​generates an illustration image based on the request it receives, taking into account the scene and keywords, and creating the most suitable illustration.

[0476] Step 8:

[0477] The generated illustration image is sent back to the server by the AI ​​generator, and is then temporarily stored in the server.

[0478] Step 9:

[0479] The server integrates the received illustration images into the original text data, inserts the illustrations into specific positions in the text data, and generates an e-book file in picture book format.

[0480] Step 10:

[0481] The server generates a download link for providing the generated e-book file in picture book format to the user, and the link is displayed on the web interface so that the user can access it.

[0482] Step 11:

[0483] The user uses the device to click the provided download link to download the generated e-book file in picture book format. The user can then open the downloaded e-book and begin reading it with the illustrations.

[0484] Example 1

[0485] 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."

[0486] Conventional e-books lack visual information, and lack illustrations to aid comprehension, especially in foreign language learning, limiting their effectiveness. Furthermore, manually inserting illustrations takes time and effort, making it difficult to provide efficient learning support.

[0487] 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.

[0488] In this invention, the server includes means for a user to upload a book file, means for the server to convert the book file into document data, means for the server to extract scenes and key phrases from the document data, means for the server to send an illustration generation request to a generation algorithm, means for the generation algorithm to generate illustration images and return them to the server, means for the server to integrate the illustration images into the document data, and means for the server to provide the user with the generated illustrated book file. This allows users to efficiently use books with added visual information, which can aid understanding, particularly in foreign language learning.

[0489] "User" refers to an individual who uses the System to upload Book Files and download Illustrated Book Files.

[0490] "Server" refers to a computer system that receives book files, converts them into document data, extracts scenes and key phrases, sends illustration generation requests, and integrates illustration images.

[0491] "Book File" refers to a digital file of an electronically recorded book, including formats such as PDF and EPUB.

[0492] "Document data" refers to text data extracted from a book file.

[0493] "Scene" refers to a description within document data that requires a specific scene or illustration.

[0494] "Key words" refer to notable keywords or phrases within document data.

[0495] "Generation algorithm" refers to an algorithm for generating an illustration image based on an illustration generation request.

[0496] An "illustration generation request" refers to a request to create an illustration image sent to the generation algorithm based on a scene or key phrases.

[0497] "Illustrated Image" refers to a visual image generated by a generative algorithm.

[0498] "Integration into book file" refers to the process of placing the generated illustration images in the appropriate positions in the original document data.

[0499] "Illustrated book file" refers to a book file in which the generated illustrated images have been integrated.

[0500] This system allows users to upload book files, converts them into document data, extracts scenes and key phrases, and generates illustrations based on them. This system aims to provide an easy-to-understand reading experience, especially for foreign language learners.

[0501] First, the user accesses the system's web interface using a terminal. Through this interface, the user selects and uploads the book file (e.g., PDF or EPUB format) they want to read. The upload operation can be performed in the same way as selecting a file.

[0502] The server receives the book file uploaded by the user. The server uses software libraries such as "Apache PDFBox" and "EPUB.js" to convert the book file into document data. For example, in the case of an EPUB format file, EPUB.js is used to extract the text data.

[0503] Next, the server analyzes the document data using natural language processing technology. Examples of software used for this analysis include "spaCy" and "NLTK." The server uses these libraries to extract scenes and key phrases from the document data. For example, "the scene where Gatsby looks at Daisy" or "description of the party" may be extracted.

[0504] The server sends an illustration generation request to the generation algorithm based on the extracted scenes and key phrases. This prompt includes a specific scene and key phrases. Examples of prompts include "The Great Gatsby: Generate an illustration of the scene where Gatsby looks at Daisy" and "The Great Gatsby: Generate an illustration based on the description of the party."

[0505] The generation algorithms used are "Stable Diffusion" and "DALL-E." The generation algorithm generates an illustration image based on the received prompt text and sends the illustration image back to the server.

[0506] The server then inserts the received illustration into the document at the appropriate location, possibly using the Pillow library. For example, the server could place the illustration immediately after the text "Gatsby looking at Daisy."

[0507] Finally, the server generates a link to provide the generated illustrated book file to the user, who can then download the book file and begin reading.

[0508] This allows users to efficiently use books with added visual information, which can aid comprehension, especially in foreign language learning. The system of the present invention provides a visual interface for users, supporting a more effective learning experience.

[0509] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0510] Step 1:

[0511] A user accesses the system's web interface through a terminal. The user clicks the "Upload" button, selects the book file (e.g., PDF or EPUB format) they want to read, and uploads it. The input is the book file selected by the user, and the output is the book file sent to the server.

[0512] Step 2:

[0513] The server receives the book file uploaded by the user. Based on the received file, it converts it into document data using "Apache PDFBox" or "EPUB.js". This conversion process involves reading the file and converting it into text data. The input is the book file, and the output is the converted document data.

[0514] Step 3:

[0515] The server analyzes the converted document data using natural language processing technology (such as "spaCy" or "NLTK"). This analysis identifies and extracts scenes and key phrases. Specific processing involves morphological analysis of the text and scene identification. The input is the document data, and the output is the extracted scenes and key phrases.

[0516] Step 4:

[0517] The server sends an illustration generation request to a generation algorithm (such as "Stable Diffusion" or "DALL-E") based on the extracted scenes and key phrases. The prompt sentence includes a specific example, "Please generate an illustration of the scene in The Great Gatsby where Gatsby is gazing at Daisy." The input is the scene and key phrases, and the output is the prompt sentence sent to the generation AI.

[0518] Step 5:

[0519] The generation AI receives the prompt text and generates an illustration image based on it. The generated illustration image is then sent back to the server. In this process, the AI ​​analyzes the received prompt text and generates a visual image. The input is the prompt text and the output is the generated illustration image.

[0520] Step 6:

[0521] The server inserts the received illustration images into the original document data at the appropriate position. Here, the "Pillow" library is used to integrate the illustration images at the appropriate position and generate a book file in picture book format. The input is the illustration images and document data, and the output is a book file with the illustrations integrated.

[0522] Step 7:

[0523] The server generates a link to provide the generated illustrated book file to the user, who can use this link to download and view the new book file. The input is the final book file, and the output is the download link.

[0524] (Application example 1)

[0525] 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."

[0526] Conventional e-books have been difficult to understand, especially for foreign language learners. Visual aids are especially needed for situations and keywords that are difficult to understand with text alone. However, no system currently available can efficiently accomplish this. Furthermore, there is a need for a system that allows users to upload digital document files, generate visual aids, and easily download and view them.

[0527] 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.

[0528] In this invention, the server includes means for a user to upload a digital document file, means for the server to convert the digital document file into text data, means for the server to extract scenes and keywords from the text data, means for the server to send an illustration generation request to a generative AI model, means for the generative AI model to generate an illustration image and return it to the server, means for the server to integrate the illustration image into the text data, means for the server to provide the generated illustrated digital document file to the user, and means for the user to download and view the digital document file from a smartphone application. This allows users to view digital documents in a more understandable format, enabling effective learning, particularly for foreign language learners.

[0529] "User" refers to any individual or entity that uses the System.

[0530] "Digital document file" refers to a document that is stored and displayed electronically, including, for example, PDF and EPUB.

[0531] "Server" refers to a computer system that processes and stores data.

[0532] "Text data" refers to textual information extracted from a digital document file.

[0533] A "scene" refers to a specific event or situation in text data.

[0534] A "keyword" refers to a word or short phrase that has an important meaning within text data.

[0535] A "generative artificial intelligence model" refers to a machine learning algorithm for generating images or text based on specified input data.

[0536] "Illustration images" refer to illustrations and figures created to visually complement the content of text data.

[0537] "Smartphone application" refers to a program that runs on a smartphone.

[0538] A "natural language processing algorithm" refers to a computational method for analyzing text data and extracting meaning.

[0539] "Visual aid format" refers to a format that combines visual information (such as illustrations) with text.

[0540] The present invention is a system in which a user uploads a digital document file, a server converts the digital document file into text data, extracts scenes and keywords, and generates illustrations based on the text data.The system also integrates the generated illustrations into the text data, and provides a function that allows users to download and view the digital document file in visual aid format from a smartphone application.

[0541] System Overview

[0542] 1. User Action:

[0543] A user accesses the system interface using a smartphone, selects the digital document file (e.g., PDF or EPUB) they want to read, and uploads it.

[0544] 2. Server Processing:

[0545] The server receives the digital document file uploaded by the user, and then converts the digital document file into text data.

[0546] 3. Text Analysis:

[0547] The server analyzes the text data using natural language processing algorithms (e.g., spaCy or NLTK) to extract scenes and keywords, thereby obtaining the information needed to generate illustrations.

[0548] 4. Illustration generation request:

[0549] The server sends an illustration generation request to a generative AI model (e.g., OpenAI API) based on the extracted scenes and keywords. The request includes specific scenes and keywords.

[0550] 5. Generative AI behavior:

[0551] The generative AI model generates illustration images based on requests received from the server, and the generated illustration images are sent back to the server.

[0552] 6. Illustration Integration:

[0553] The server receives the illustration images returned by the generative AI model and inserts them into the original text data at the appropriate locations, generating a digital document file in the form of a visual aid.

[0554] 7. Provision to Users:

[0555] The server provides the generated illustrated digital document file to the user, who can then download the visually-aided digital document file through a smartphone application and begin viewing it.

[0556] Specific examples

[0557] For example, suppose a user wants to read a digital document file of "The Great Gatsby." Here's the specific flow:

[0558] 1. The user selects a digital document file (EPUB format) for "The Great Gatsby" and uploads it from the smartphone application.

[0559] 2. The server receives the file and converts the EPUB file into text data.

[0560] 3. The server uses a natural language processing algorithm to extract scenes and keywords from the text data, such as "the scene where Gatsby looks at Daisy" and "description of the party."

[0561] 4. The server sends an illustration generation request to the generative AI model based on the extracted scene and keywords. For example, the prompt sentence may include the following:

[0562] "A scene where Gatsby gazes at Daisy."

[0563] 5. The generative AI model generates an illustration based on the prompt and sends it back to the server.

[0564] 6. The server inserts the received illustrations into the original text data in the appropriate locations, generating a new visually-aided "The Great Gatsby" digital document file.

[0565] 7. The user can click on the link provided by the smartphone application to download and begin reading "The Great Gatsby" with the generated illustrations.

[0566] The above system allows users to view digital documents with added illustrations in a visually easy-to-understand format, making learning particularly effective for foreign language learners.

[0567] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0568] Program processing steps

[0569] Step 1: Upload your eBook file

[0570] Specific behavior:

[0571] A user starts a smartphone application, selects the digital document file (PDF, EPUB, etc.) they want to read, and selects the file on the upload screen. The selected file is then sent to the server within the application.

[0572] input:

[0573] User-selected digital document file (PDF or EPUB)

[0574] output:

[0575] Digital document files sent to the server

[0576] Step 2: Convert eBook files to text

[0577] Specific behavior:

[0578] The server converts the received digital document file into text data using a dedicated library (e.g., PyMuPDF, EPUB-lib). For PDF files, the text is extracted page by page, and for EPUB files, the text is extracted chapter by chapter.

[0579] input:

[0580] Digital document files (PDF and EPUB)

[0581] output:

[0582] Text data

[0583] Step 3: Analyzing the text data

[0584] Specific behavior:

[0585] The server uses natural language processing algorithms (e.g., spaCy or NLTK) to analyze the text data and extract scenes and keywords. This involves performing semantic and syntactic analysis of the text to identify important scenes and keywords.

[0586] input:

[0587] Text data

[0588] output:

[0589] Extracted scenes and keywords

[0590] Step 4: Create and submit an illustration generation request

[0591] Specific behavior:

[0592] The server generates a prompt based on the extracted scene and keywords, and sends a request to generate an illustration to a generative artificial intelligence model (e.g., OpenAI API). The prompt is created by including a description of the scene and keywords.

[0593] input:

[0594] Extracted scenes and keywords

[0595] output:

[0596] An illustration generation request sent to the generative AI model

[0597] Step 5: Creating and receiving illustrations

[0598] Specific behavior:

[0599] The generative artificial intelligence model generates an illustration image based on the request received from the server. The generated illustration image is sent back to the server, which receives and stores this image.

[0600] input:

[0601] Illustration generation request

[0602] output:

[0603] Generated illustration image

[0604] Step 6: Integrating illustrations into text data

[0605] Specific behavior:

[0606] The server inserts the generated illustrations into the original text data at the appropriate locations. The insertion position of the illustrations is determined based on the extracted scenes and keywords. A text editing library (e.g., PyMuPDF, EPUB-lib) is used for the processing.

[0607] input:

[0608] Text data, generated illustration images

[0609] output:

[0610] Illustrated digital document file

[0611] Step 7: Provide illustrated digital document files

[0612] Specific behavior:

[0613] The server generates a link for providing the created digital document file with illustrations to the user, and provides the link to the user through a smartphone application. The user can click the link to download and view the digital document file.

[0614] input:

[0615] Illustrated digital document file

[0616] output:

[0617] Download link provided to users

[0618] Specific examples

[0619] For example, suppose a user wants to read a digital document file of "The Great Gatsby." Here's the specific flow:

[0620] 1. The user selects a digital document file (EPUB format) for "The Great Gatsby" and uploads it from the smartphone application.

[0621] 2. The server receives the file and converts the EPUB file into text data.

[0622] 3. The server uses a natural language processing algorithm to extract scenes and keywords from the text data, such as "the scene where Gatsby looks at Daisy" and "description of the party."

[0623] 4. The server sends an illustration generation request to the generative AI model based on the extracted scene and keywords. For example, the prompt sentence may include the following:

[0624] "A scene where Gatsby gazes at Daisy."

[0625] 5. The generative AI model generates an illustration based on the prompt and sends it back to the server.

[0626] 6. The server inserts the received illustrations into the original text data in the appropriate locations, generating a new visually-aided "The Great Gatsby" digital document file.

[0627] 7. The user can click on the link provided by the smartphone application to download and begin reading "The Great Gatsby" with the generated illustrations.

[0628] 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.

[0629] This system allows users to upload e-book files, converts them into text data, extracts scenes and keywords, and generates appropriate illustrations using a generative AI and emotion engine. This system aims to provide an easy-to-understand reading experience, especially for foreign language learners.

[0630] System Overview

[0631] 1. User Action:

[0632] The user accesses the system's web interface through a terminal, selects the e-book file (e.g., PDF or EPUB) they want to read, and clicks the upload button.

[0633] 2. Server Processing:

[0634] The server receives the electronic book file uploaded by the user, and then converts the electronic book file into text data.

[0635] 3. Text Analysis:

[0636] The server uses natural language processing algorithms to analyze the text data and extract scenes and keywords, providing the basis for generating illustrations.

[0637] 4. Emotion Recognition with Emotion Engine:

[0638] While the user goes through the above process through the device, the emotion engine analyzes data collected from the user's camera and microphone to recognize the user's emotions in real time.

[0639] 5. Illustration generation request:

[0640] The server generates an illustration generation request based on the extracted scenes and keywords and the user's emotions detected by the emotion engine. The request includes specific descriptions of the scenes and keywords and emotion information.

[0641] 6. Generative AI behavior:

[0642] The AI ​​generates illustration images based on requests received from the server, and the generated illustration images are sent back to the server.

[0643] 7. Illustration Integration:

[0644] The server receives the illustration images returned by the generation AI and inserts them into the original text data in the appropriate places, thereby generating an e-book file in picture book format.

[0645] 8. User Offerings:

[0646] The server generates a link to provide the generated e-book file in picture book format to the user, who can then download the e-book via the link and begin reading.

[0647] Specific examples

[0648] For example, let's say a user wants to read "The Great Gatsby." Here's the specific flow:

[0649] 1. The user selects the e-book file (EPUB format) of "The Great Gatsby" and uploads it through the system's web interface.

[0650] 2. The server receives the file and converts the EPUB file into text data.

[0651] 3. The server uses a natural language processing algorithm to extract scenes and keywords such as "the scene where Gatsby looks at Daisy" and "description of the party" from the text data.

[0652] 4. The emotion engine recognizes emotions from the user's facial expressions and voice, detecting when the user is expressing emotions such as excitement or sadness in a particular scene.

[0653] 5. The server sends an illustration generation request to the generation AI based on the extracted scenes and keywords, and the user's emotions detected by the emotion engine.

[0654] 6. The AI ​​generates an illustration based on the scene of Gatsby gazing at Daisy and sends the image back to the server. It may also include elements corresponding to emotions.

[0655] 7. The server inserts the received illustration images into the original text data in the appropriate locations to generate a new picture book format e-book file of "The Great Gatsby."

[0656] 8. The user can click on the link provided by the system to download and start reading the generated picture book version of "The Great Gatsby."

[0657] The above is a concrete example of how to implement the present invention. This system not only allows users to visually understand the content through illustrations, but also allows them to enjoy a reading experience that is customized to their emotions. This provides an effective reading experience, especially for foreign language learning.

[0658] The processing flow will be explained below.

[0659] Step 1:

[0660] A user accesses the system's web interface using a terminal, selects the e-book file (e.g., PDF or EPUB) they want to read, and clicks the upload button.

[0661] Step 2:

[0662] The server receives the e-book file sent by the user and stores the received e-book file in temporary storage.

[0663] Step 3:

[0664] The server analyzes the e-book file and converts it into text data in an appropriate way. For example, if it is an EPUB file, it analyzes the XML data inside it and extracts the text data.

[0665] Step 4:

[0666] The server applies a natural language processing algorithm to the converted text data, allowing the server to extract important scenes and keywords from the text data.

[0667] Step 5:

[0668] While the user is operating the system through the device, the emotion engine collects data from the device's camera and microphone, analyzes the user's facial expressions and voice, and recognizes emotions in real time.

[0669] Step 6:

[0670] The server generates an illustration generation request based on the extracted scenes and keywords, and the user's emotions recognized by the emotion engine. The request includes specific descriptions of the scenes and keywords, as well as emotional information.

[0671] Step 7:

[0672] The server sends an illustration generation request to the generation AI. The request is sent via the generation AI's API.

[0673] Step 8:

[0674] The AI ​​generates an illustration based on the request it receives, taking into account the scene, keywords, and the user's emotions to create the most appropriate illustration.

[0675] Step 9:

[0676] The generated illustration image is sent back to the server by the AI ​​generator, and is then temporarily stored in the server.

[0677] Step 10:

[0678] The server integrates the received illustration images into the original text data, inserts the illustrations into specific positions in the text data, and generates an e-book file in picture book format.

[0679] Step 11:

[0680] The server generates a download link for providing the generated e-book file in picture book format to the user, and the link is displayed on the web interface so that the user can access it.

[0681] Step 12:

[0682] The user uses the device to click the provided download link to download the generated e-book file in picture book format. The user can then open the downloaded e-book and begin reading it with the illustrations.

[0683] Example 2

[0684] 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."

[0685] Current electronic content systems lack the ability to create appropriate illustrations to visually support the text being read, and lack a means to provide a customized reading experience based on the user's emotions, making it difficult to provide effective reading support, especially for foreign language learners.

[0686] 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.

[0687] In this invention, the server includes: a means for a user to upload an electronic content file; a means for the server to convert the electronic content file into text data; a means for the server to extract scenes and keywords from the text data using a natural language processing algorithm; a means for the server to use an emotion engine that recognizes the user's emotions in real time; a means for the server to generate a prompt sentence for a generative AI model and send an illustration generation request; a means for the generative AI model to generate an illustration image and return it to the server; a means for the server to integrate the illustration image into the text data; and a means for the server to provide the user with the generated illustrated electronic content file, thereby enabling a reading experience with illustrations customized according to the user's emotions.

[0688] A "user" is an entity that uploads electronic content files to the system and receives the resulting illustrated electronic content files.

[0689] An "electronic content file" is a digital file containing text data, such as a PDF or EPUB file.

[0690] "Server" refers to the hardware and software that processes electronic content files uploaded by users and converts them into text data.

[0691] "Text data" is character string data extracted from an electronic content file, and is the subject of analysis and processing.

[0692] "Natural language processing algorithms" are technologies that include machine learning and statistical methods for extracting scenes and keywords from text data.

[0693] "Scenes and keywords" refer to specific scenes and important words within the text data, and are the information that forms the basis for generating illustrations.

[0694] The "emotion engine" is a technology that recognizes a user's emotions by analyzing data collected from a camera or microphone.

[0695] A "generative AI model" is an artificial intelligence algorithm that generates images based on a prompt, and examples include deep learning techniques.

[0696] A "prompt sentence" is a specific explanatory sentence used when sending an illustration generation request to the generative AI model.

[0697] An "illustrated image" is a visual picture generated by a generative AI model based on a prompt sentence.

[0698] "Integrating" means inserting the generated illustration image into the original text data at an appropriate location.

[0699] "Providing an electronic content file" means providing the created illustrated electronic content file in a form that can be accessed by the user.

[0700] The present invention is a system in which a user uploads an electronic content file, a server converts the file into text data, analyzes it, generates appropriate illustrations using a generative AI model, and provides the final illustrated electronic content file to the user. This system aims to provide an easy-to-understand reading experience, especially for foreign language learners.

[0701] System Overview

[0702] 1. User Operation

[0703] A user accesses the system's web interface through a terminal. The user selects the electronic content file (e.g., PDF or EPUB) they want to read and clicks the upload button. For example, a user uploads an EPUB file of "The Great Gatsby" by dragging and dropping it.

[0704] 2. Server Processing

[0705] The server receives the electronic content file uploaded by the user. After receiving it, the server converts the electronic content file into text data using software such as Adobe Acrobat or Calibre. The converted text data is stored in an internal database (e.g., MySQL or PostgreSQL).

[0706] 3. Text Analysis

[0707] The server uses Python's NLTK library and SpaCy to analyze the text data. Specifically, it tokenizes sentences and extracts scenes and keywords. This analysis extracts specific scenes and important phrases. For example, it extracts keywords corresponding to the scene "Gatsby gazes at Daisy."

[0708] 4. Emotion Recognition by Emotion Engine

[0709] While the user goes through the above process on the device, the emotion engine analyzes data collected from the user's camera and microphone to recognize the user's emotions in real time. For example, it can detect excitement or sadness from the user's facial expressions and voice. This is done using Microsoft Azure's Emotion API and Google Cloud's Natural Language API.

[0710] 5. Illustration generation request

[0711] The server generates a prompt to send to the generative AI model based on the extracted scenes and keywords, as well as the user's emotions detected by the emotion engine. This prompt includes a specific description of the scene and keywords, along with emotional information. For example, it could be in the format "The scene where Gatsby looks at Daisy, the user feels joy."

[0712] 6. Generative AI behavior

[0713] A generative AI model (e.g., OpenAI's DALLE or other similar technologies) generates an illustration based on the prompt received from the server. The generated illustration is then sent back to the server. For example, a specific illustration is generated based on the prompt, "The scene where Gatsby is looking at Daisy, and the user feels joy."

[0714] 7. Integration of illustrations

[0715] The server receives the illustration images returned by the generative AI model and inserts them into the original text data at the appropriate locations. This generates an electronic content file in picture book format. For example, the server inserts the corresponding illustration image immediately after the text "Gatsby gazing at Daisy."

[0716] 8. Provision to Users

[0717] The server generates a link to provide the generated picture book format electronic content file to the user, who can then download the electronic content via the link and begin reading it.

[0718] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0719] Step 1: A user uploads an electronic content file.

[0720] Input: An electronic content file (e.g., PDF or EPUB format) selected by the user through the device.

[0721] Specific behavior: A user accesses the system's web interface and clicks a button to upload an electronic content file they want to read. This displays a file selection dialog, where the user selects the desired file and confirms the upload.

[0722] Output: The file is sent to the server.

[0723] Step 2: The server converts the electronic content file into text data.

[0724] Input: Electronic content files uploaded by users.

[0725] What happens: The server recognizes the received file and converts it to text using software like Adobe Acrobat or Calibre. The conversion process involves sequentially parsing the pages of the file and extracting strings of characters.

[0726] Output: The converted text data is generated and stored in the server's internal database (e.g. MySQL or PostgreSQL).

[0727] Step 3: The server analyzes the text data using natural language processing algorithms.

[0728] Input: The converted text data.

[0729] How it works: The server uses natural language processing tools such as Python's NLTK library or SpaCy to analyze the text data. This analysis involves tokenizing the text and extracting key scenes and keywords. Each scene and keyword is ranked based on a specific importance.

[0730] Output: Extracted scenes and keyword list.

[0731] Step 4: The server uses an emotion engine to recognize the user's emotions in real time.

[0732] Input: Real-time data from the user's camera and microphone.

[0733] How it works: The server analyzes the user's facial expressions and voice data via Microsoft Azure's Emotion API and Google Cloud's Natural Language API, which allows it to identify the emotions the user is expressing in each scene.

[0734] Output: User emotion data.

[0735] Step 5: The server generates a prompt sentence for the generative AI model and sends a request to generate an illustration.

[0736] Input: A list of scenes and keywords, and user emotion data.

[0737] Specific operation: The server generates prompts based on this data. Specifically, it constructs prompts for each scene by combining detailed descriptions and emotion data. For example, the prompt might be, "In the scene where Gatsby looks at Daisy, the user feels joy."

[0738] Output: The prompt sent to the generative AI model.

[0739] Step 6: The generative AI model generates an illustration image and sends it back to the server.

[0740] Input: The prompt text sent by the server.

[0741] What it does: A generative AI model (e.g., OpenAI's DALLE or similar technology) analyzes the prompt and generates a corresponding illustration image, which is then sent back to the server in its raw form.

[0742] Output: The generated illustration image.

[0743] Step 7: The server integrates the illustration images into the text data.

[0744] Input: Illustration images returned from the generative AI model, original text data.

[0745] Specific operation: The server inserts the generated illustration images into the text data at the appropriate places, specifically by applying an algorithm to integrate the images, such as immediately after each scene.

[0746] Output: Integrated picture book format electronic content file.

[0747] Step 8: The server provides the generated illustrated electronic content file to the user.

[0748] Input: Integrated picture book format electronic content file.

[0749] What happens: The server uploads this file to a cloud storage location accessible to the user (e.g. AWS S3) and generates a download link, which is sent to the user's dashboard or email.

[0750] Output: A downloadable link for the user.

[0751] (Application example 2)

[0752] 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."

[0753] The lack of visual elements in e-books can make learning and reading difficult, especially for language learners. Furthermore, content provided without considering the user's emotional state can reduce user satisfaction and concentration. Therefore, there is a need for a system that can dynamically generate illustrations based on the user's emotions, providing a visually and emotionally rich reading experience.

[0754] 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.

[0755] In this invention, the server includes: a means for a user to upload an e-book file; a means for the server to convert the e-book file into text data; a means for the server to extract scenes and keywords from the text data; a means for analyzing the user's emotions in real time using an emotion analysis engine; a means for the server to send to the generation AI a request to generate illustrations based on the scenes, keywords, and the user's emotions; a means for the generation AI to generate illustration images and return them to the server; a means for the server to integrate the illustration images into the text data; and a means for the server to provide the user with the generated illustrated e-book file. This makes it possible to dynamically generate illustrations according to the user's emotions and provide a visually and emotionally rich reading experience, especially for language learners.

[0756] "User" means an individual or legal entity who uses the System to upload e-book files and receive illustrated e-books.

[0757] An "e-book file" is a reading material that stores information such as text and images in digital format, and typical formats include PDF and EPUB.

[0758] The "server" is a computer system that analyzes e-book files, converts them into text data, extracts scenes and keywords, and integrates the illustrations generated by the generation AI to generate e-book files, which are then provided to users.

[0759] "Text data" refers to text data extracted from an e-book file and processed and analyzed by the server.

[0760] A "scene" is a portion of text data that indicates a specific scene or situation, and serves as basic information for generating an illustration image.

[0761] "Keywords" are important words or phrases in the text data, and are information that clarifies a scene.

[0762] An "emotion analysis engine" is software that analyzes data collected from the user's camera and microphone and recognizes the user's emotional state.

[0763] "Generative AI" is an artificial intelligence model that generates illustration images based on given scenes, keywords, and the user's emotions.

[0764] An "illustration generation request" is an instruction sent by the server to the generation AI, and includes the scene, keywords, and the user's emotional information.

[0765] "Illustrated images" are illustrations or visuals generated by generative AI and integrated into text data.

[0766] "Integration" refers to the process of incorporating the generated illustration images into the original text data at the appropriate locations.

[0767] "Generating an e-book file" refers to the process of creating a new e-book file containing illustrations.

[0768] "Providing to the user" refers to delivering the generated illustrated e-book file to the user via a downloadable link.

[0769] The present invention is a system that provides illustrations based on scenes, keywords, and user emotions to enrich the user's reading experience, especially for language learners. This system is realized through a series of processes: analyzing e-book files, performing emotion recognition, and generating illustrations.

[0770] Program Overview

[0771] 1. User Interface

[0772] Users upload e-book files to the web from their smartphones or PCs.

[0773] It is also possible to set it so that when a user purchases an e-book, it is automatically uploaded.

[0774] 2. E-book analysis

[0775] The uploaded e-book file (PDF, EPUB, etc.) is sent to the server.

[0776] The server uses an appropriate conversion algorithm to convert the e-book file into text data.

[0777] 3. Natural Language Processing and Keyword Extraction

[0778] The server uses natural language processing algorithms (e.g., spaCy or NLTK) to extract scenes and keywords from the text data.

[0779] This process provides important information needed for subsequent illustration generation.

[0780] 4. Emotion analysis

[0781] A sentiment analysis engine (for example, Microsoft Azure's Cognitive Services) uses data from the user's camera and microphone to analyze the user's emotions in real time.

[0782] 5. Creating an illustration generation request

[0783] The server sends an illustration generation request to the generative AI model based on the extracted scenes, keywords, and emotion analysis results.

[0784] The illustration generation request includes an appropriate prompt, such as:

[0785] "Scene: Gatsby and Daisy gazing at each other. User emotion: Joy. Generates illustrations containing warm, romantic images."

[0786] 6. Use of generative AI

[0787] A generative AI model (e.g., OpenAI GPT-4-based model) generates an illustrated image based on the prompt and analysis data.

[0788] The generated illustration is sent back to the server.

[0789] 7. Integration of illustrations

[0790] The server uses an algorithm to appropriately integrate the illustrations into the text data.

[0791] This process generates an illustrated e-book file.

[0792] 8. Provision to Users

[0793] The server generates a link for providing the generated illustrated e-book file to the user.

[0794] Users can then download the generated e-book via that link and begin reading.

[0795] This allows illustrations to be dynamically generated based on the user's emotions, providing a visually and emotionally rich reading experience, especially for language learners.

[0796] Hardware and software used

[0797] Hardware: Smartphones, PCs, cloud servers

[0798] Software: Web application frameworks (Flask / Django), natural language processing libraries (spaCy, NLTK), sentiment analysis API (Microsoft Azure Cognitive Services), generative AI (OpenAI GPT-4 based model)

[0799] This system is extremely useful for users as it can perform all processes from analyzing e-book files to providing new e-book files containing the generated illustrations.

[0800] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0801] Step 1:

[0802] A user uploads an e-book file (e.g., PDF or EPUB) using a smartphone or PC. To do this, the user accesses the system's web interface, selects the e-book file, and clicks the upload button. The input is the e-book file, and the output is sending that file to the server.

[0803] Step 2:

[0804] The server receives the uploaded e-book file. It uses an appropriate conversion algorithm (e.g., a PDF to text conversion library) to convert the received file into text data. The input is the e-book file, and the output is text data.

[0805] Step 3:

[0806] The server analyzes the text data using a natural language processing algorithm (e.g., spaCy or NLTK). It extracts scenes and keywords from the text data. The input is the text data, and the output is the extracted scenes and keywords.

[0807] Step 4:

[0808] The server sends data collected from the user's device via the camera and microphone to an emotion analysis engine (for example, Microsoft Azure's Cognitive Services). This allows the user's emotions to be analyzed in real time. The input is the audio and image data collected from the camera and microphone, and the output is analyzed emotion data.

[0809] Step 5:

[0810] The server sends an illustration generation request to the generative AI model based on the extracted scenes, keywords, and emotion analysis results. This request includes a specific description of the scene and keywords, as well as emotional information. An example prompt is: "Scene: Gatsby and Daisy gazing at each other. User's emotion: Joy. Generate an illustration that includes a warm, romantic image." The input is the scene, keywords, and emotion data, and the output is an illustration generation request.

[0811] Step 6:

[0812] The generative AI model generates an illustration image based on the received illustration generation request. Using a generative AI model (e.g., OpenAI GPT-4 based model), an illustration is generated according to the presented prompt sentence. The input is the illustration generation request, and the output is the generated illustration image.

[0813] Step 7:

[0814] The server receives the illustration images returned by the generative AI model. The server uses an integration algorithm to properly integrate the illustration images into the text data. The input is the text data and the illustration images, and the output is an illustrated e-book file.

[0815] Step 8:

[0816] The server generates a link to provide the generated illustrated e-book file to the user, who can then download the generated e-book and begin reading it. The input is the illustrated e-book file, and the output is the download link.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] [Third embodiment]

[0821] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0822] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0823] 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).

[0824] 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.

[0825] 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.

[0826] 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).

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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."

[0833] The present invention is a system in which a user uploads an e-book file, a server converts the e-book file into text data, extracts scenes and keywords, and generates illustrations based on the data. This system aims to provide an easy-to-understand reading experience, especially for foreign language learners.

[0834] System Overview

[0835] 1. User Action:

[0836] The user accesses the system's web interface through a terminal, selects the e-book file (e.g., PDF or EPUB) they want to read, and uploads it.

[0837] 2. Server Processing:

[0838] The server receives the electronic book file uploaded by the user, and then converts the electronic book file into text data.

[0839] 3. Text Analysis:

[0840] The server uses natural language processing algorithms to analyze the text data and extract scenes and keywords, providing the basis for generating illustrations.

[0841] 4. Illustration generation request:

[0842] The server sends an illustration generation request to the AI ​​based on the extracted scenes and keywords. The request includes specific scenes and keywords.

[0843] 5. Generative AI behavior:

[0844] The AI ​​generates illustration images based on requests received from the server, and the generated illustration images are sent back to the server.

[0845] 6. Illustration Integration:

[0846] The server receives the illustration images returned by the generation AI and inserts them into the original text data in the appropriate places, thereby generating an e-book file in picture book format.

[0847] 7. Provision to Users:

[0848] The server generates a link to provide the generated e-book file in picture book format to the user, who can then download the e-book via the link and begin reading.

[0849] Specific examples

[0850] For example, let's say a user wants to read "The Great Gatsby." Here's the specific flow:

[0851] 1. The user selects the e-book file (EPUB format) of "The Great Gatsby" and uploads it through the system's web interface.

[0852] 2. The server receives the file and converts the EPUB file into text data.

[0853] 3. The server uses a natural language processing algorithm to extract scenes and keywords such as "the scene where Gatsby looks at Daisy" and "description of the party" from the text data.

[0854] 4. The server sends an illustration generation request to the generation AI based on the extracted scenes and keywords.

[0855] 5. The AI ​​generates an illustration based on the scene of Gatsby gazing at Daisy and sends the image back to the server.

[0856] 6. The server inserts the received illustration images into the original text data in the appropriate locations to generate a new picture book format e-book file of "The Great Gatsby."

[0857] 7. The user can click on the link provided by the system to download the generated picture book version of "The Great Gatsby" and begin reading.

[0858] The above is a specific embodiment for carrying out the present invention. This system allows users to easily understand the content visually through illustrations, providing an effective reading experience, especially for foreign language learning.

[0859] The processing flow will be explained below.

[0860] Step 1:

[0861] A user accesses the system's web interface using a terminal, selects the e-book file (e.g., PDF or EPUB) they want to read, and clicks the upload button.

[0862] Step 2:

[0863] The server receives the e-book file sent by the user and stores the received e-book file in temporary storage.

[0864] Step 3:

[0865] The server analyzes the e-book file and converts it into text data in an appropriate way. For example, if it is an EPUB file, it analyzes the XML data inside it and extracts the text data.

[0866] Step 4:

[0867] The server applies a natural language processing algorithm to the converted text data, allowing the server to extract important scenes and keywords from the text data.

[0868] Step 5:

[0869] The server generates an illustration generation request based on the extracted scenes and keywords. The request includes specific descriptions of the scenes and keywords.

[0870] Step 6:

[0871] The server sends an illustration generation request to the generation AI. The request is sent via the generation AI's API.

[0872] Step 7:

[0873] The AI ​​generates an illustration image based on the request it receives, taking into account the scene and keywords, and creating the most suitable illustration.

[0874] Step 8:

[0875] The generated illustration image is sent back to the server by the AI ​​generator, and is then temporarily stored in the server.

[0876] Step 9:

[0877] The server integrates the received illustration images into the original text data, inserts the illustrations into specific positions in the text data, and generates an e-book file in picture book format.

[0878] Step 10:

[0879] The server generates a download link for providing the generated e-book file in picture book format to the user, and the link is displayed on the web interface so that the user can access it.

[0880] Step 11:

[0881] The user uses the device to click the provided download link to download the generated e-book file in picture book format. The user can then open the downloaded e-book and begin reading it with the illustrations.

[0882] Example 1

[0883] 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."

[0884] Conventional e-books lack visual information, and lack illustrations to aid comprehension, especially in foreign language learning, limiting their effectiveness. Furthermore, manually inserting illustrations takes time and effort, making it difficult to provide efficient learning support.

[0885] 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.

[0886] In this invention, the server includes means for a user to upload a book file, means for the server to convert the book file into document data, means for the server to extract scenes and key phrases from the document data, means for the server to send an illustration generation request to a generation algorithm, means for the generation algorithm to generate illustration images and return them to the server, means for the server to integrate the illustration images into the document data, and means for the server to provide the user with the generated illustrated book file. This allows users to efficiently use books with added visual information, which can aid understanding, particularly in foreign language learning.

[0887] "User" refers to an individual who uses the System to upload Book Files and download Illustrated Book Files.

[0888] "Server" refers to a computer system that receives book files, converts them into document data, extracts scenes and key phrases, sends illustration generation requests, and integrates illustration images.

[0889] "Book File" refers to a digital file of an electronically recorded book, including formats such as PDF and EPUB.

[0890] "Document data" refers to text data extracted from a book file.

[0891] "Scene" refers to a description within document data that requires a specific scene or illustration.

[0892] "Key words" refer to notable keywords or phrases within document data.

[0893] "Generation algorithm" refers to an algorithm for generating an illustration image based on an illustration generation request.

[0894] An "illustration generation request" refers to a request to create an illustration image sent to the generation algorithm based on a scene or key phrases.

[0895] "Illustrated Image" refers to a visual image generated by a generative algorithm.

[0896] "Integration into book file" refers to the process of placing the generated illustration images in the appropriate positions in the original document data.

[0897] "Illustrated book file" refers to a book file in which the generated illustrated images have been integrated.

[0898] This system allows users to upload book files, converts them into document data, extracts scenes and key phrases, and generates illustrations based on them. This system aims to provide an easy-to-understand reading experience, especially for foreign language learners.

[0899] First, the user accesses the system's web interface using a terminal. Through this interface, the user selects and uploads the book file (e.g., PDF or EPUB format) they want to read. The upload operation can be performed in the same way as selecting a file.

[0900] The server receives the book file uploaded by the user. The server uses software libraries such as "Apache PDFBox" and "EPUB.js" to convert the book file into document data. For example, in the case of an EPUB format file, EPUB.js is used to extract the text data.

[0901] Next, the server analyzes the document data using natural language processing technology. Examples of software used for this analysis include "spaCy" and "NLTK." The server uses these libraries to extract scenes and key phrases from the document data. For example, "the scene where Gatsby looks at Daisy" or "description of the party" may be extracted.

[0902] The server sends an illustration generation request to the generation algorithm based on the extracted scenes and key phrases. This prompt includes a specific scene and key phrases. Examples of prompts include "The Great Gatsby: Generate an illustration of the scene where Gatsby looks at Daisy" and "The Great Gatsby: Generate an illustration based on the description of the party."

[0903] The generation algorithms used are "Stable Diffusion" and "DALL-E." The generation algorithm generates an illustration image based on the received prompt text and sends the illustration image back to the server.

[0904] The server then inserts the received illustration into the document at the appropriate location, possibly using the Pillow library. For example, the server could place the illustration immediately after the text "Gatsby looking at Daisy."

[0905] Finally, the server generates a link to provide the generated illustrated book file to the user, who can then download the book file and begin reading.

[0906] This allows users to efficiently use books with added visual information, which can aid comprehension, especially in foreign language learning. The system of the present invention provides a visual interface for users, supporting a more effective learning experience.

[0907] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0908] Step 1:

[0909] A user accesses the system's web interface through a terminal. The user clicks the "Upload" button, selects the book file (e.g., PDF or EPUB format) they want to read, and uploads it. The input is the book file selected by the user, and the output is the book file sent to the server.

[0910] Step 2:

[0911] The server receives the book file uploaded by the user. Based on the received file, it converts it into document data using "Apache PDFBox" or "EPUB.js". This conversion process involves reading the file and converting it into text data. The input is the book file, and the output is the converted document data.

[0912] Step 3:

[0913] The server analyzes the converted document data using natural language processing technology (such as "spaCy" or "NLTK"). This analysis identifies and extracts scenes and key phrases. Specific processing involves morphological analysis of the text and scene identification. The input is the document data, and the output is the extracted scenes and key phrases.

[0914] Step 4:

[0915] The server sends an illustration generation request to a generation algorithm (such as "Stable Diffusion" or "DALL-E") based on the extracted scenes and key phrases. The prompt sentence includes a specific example, "Please generate an illustration of the scene in The Great Gatsby where Gatsby is gazing at Daisy." The input is the scene and key phrases, and the output is the prompt sentence sent to the generation AI.

[0916] Step 5:

[0917] The generation AI receives the prompt text and generates an illustration image based on it. The generated illustration image is then sent back to the server. In this process, the AI ​​analyzes the received prompt text and generates a visual image. The input is the prompt text and the output is the generated illustration image.

[0918] Step 6:

[0919] The server inserts the received illustration images into the original document data at the appropriate position. Here, the "Pillow" library is used to integrate the illustration images at the appropriate position and generate a book file in picture book format. The input is the illustration images and document data, and the output is a book file with the illustrations integrated.

[0920] Step 7:

[0921] The server generates a link to provide the generated illustrated book file to the user, who can use this link to download and view the new book file. The input is the final book file, and the output is the download link.

[0922] (Application example 1)

[0923] 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."

[0924] Conventional e-books have been difficult to understand, especially for foreign language learners. Visual aids are especially needed for situations and keywords that are difficult to understand with text alone. However, no system currently available can efficiently accomplish this. Furthermore, there is a need for a system that allows users to upload digital document files, generate visual aids, and easily download and view them.

[0925] 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.

[0926] In this invention, the server includes means for a user to upload a digital document file, means for the server to convert the digital document file into text data, means for the server to extract scenes and keywords from the text data, means for the server to send an illustration generation request to a generative AI model, means for the generative AI model to generate an illustration image and return it to the server, means for the server to integrate the illustration image into the text data, means for the server to provide the generated illustrated digital document file to the user, and means for the user to download and view the digital document file from a smartphone application. This allows users to view digital documents in a more understandable format, enabling effective learning, particularly for foreign language learners.

[0927] "User" refers to any individual or entity that uses the System.

[0928] "Digital document file" refers to a document that is stored and displayed electronically, including, for example, PDF and EPUB.

[0929] "Server" refers to a computer system that processes and stores data.

[0930] "Text data" refers to textual information extracted from a digital document file.

[0931] A "scene" refers to a specific event or situation in text data.

[0932] A "keyword" refers to a word or short phrase that has an important meaning within text data.

[0933] A "generative artificial intelligence model" refers to a machine learning algorithm for generating images or text based on specified input data.

[0934] "Illustration images" refer to illustrations and figures created to visually complement the content of text data.

[0935] "Smartphone application" refers to a program that runs on a smartphone.

[0936] A "natural language processing algorithm" refers to a computational method for analyzing text data and extracting meaning.

[0937] "Visual aid format" refers to a format that combines visual information (such as illustrations) with text.

[0938] The present invention is a system in which a user uploads a digital document file, a server converts the digital document file into text data, extracts scenes and keywords, and generates illustrations based on the text data.The system also integrates the generated illustrations into the text data, and provides a function that allows users to download and view the digital document file in visual aid format from a smartphone application.

[0939] System Overview

[0940] 1. User Action:

[0941] A user accesses the system interface using a smartphone, selects the digital document file (e.g., PDF or EPUB) they want to read, and uploads it.

[0942] 2. Server Processing:

[0943] The server receives the digital document file uploaded by the user, and then converts the digital document file into text data.

[0944] 3. Text Analysis:

[0945] The server analyzes the text data using natural language processing algorithms (e.g., spaCy or NLTK) to extract scenes and keywords, thereby obtaining the information needed to generate illustrations.

[0946] 4. Illustration generation request:

[0947] The server sends an illustration generation request to a generative AI model (e.g., OpenAI API) based on the extracted scenes and keywords. The request includes specific scenes and keywords.

[0948] 5. Generative AI behavior:

[0949] The generative AI model generates illustration images based on requests received from the server, and the generated illustration images are sent back to the server.

[0950] 6. Illustration Integration:

[0951] The server receives the illustration images returned by the generative AI model and inserts them into the original text data at the appropriate locations, generating a digital document file in the form of a visual aid.

[0952] 7. Provision to Users:

[0953] The server provides the generated illustrated digital document file to the user, who can then download the visually-aided digital document file through a smartphone application and begin viewing it.

[0954] Specific examples

[0955] For example, suppose a user wants to read a digital document file of "The Great Gatsby." Here's the specific flow:

[0956] 1. The user selects a digital document file (EPUB format) for "The Great Gatsby" and uploads it from the smartphone application.

[0957] 2. The server receives the file and converts the EPUB file into text data.

[0958] 3. The server uses a natural language processing algorithm to extract scenes and keywords from the text data, such as "the scene where Gatsby looks at Daisy" and "description of the party."

[0959] 4. The server sends an illustration generation request to the generative AI model based on the extracted scene and keywords. For example, the prompt sentence may include the following:

[0960] "A scene where Gatsby gazes at Daisy."

[0961] 5. The generative AI model generates an illustration based on the prompt and sends it back to the server.

[0962] 6. The server inserts the received illustrations into the original text data in the appropriate locations, generating a new visually-aided "The Great Gatsby" digital document file.

[0963] 7. The user can click on the link provided by the smartphone application to download and begin reading "The Great Gatsby" with the generated illustrations.

[0964] The above system allows users to view digital documents with added illustrations in a visually easy-to-understand format, making learning particularly effective for foreign language learners.

[0965] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0966] Program processing steps

[0967] Step 1: Upload your eBook file

[0968] Specific behavior:

[0969] A user starts a smartphone application, selects the digital document file (PDF, EPUB, etc.) they want to read, and selects the file on the upload screen. The selected file is then sent to the server within the application.

[0970] input:

[0971] User-selected digital document file (PDF or EPUB)

[0972] output:

[0973] Digital document files sent to the server

[0974] Step 2: Convert eBook files to text

[0975] Specific behavior:

[0976] The server converts the received digital document file into text data using a dedicated library (e.g., PyMuPDF, EPUB-lib). For PDF files, the text is extracted page by page, and for EPUB files, the text is extracted chapter by chapter.

[0977] input:

[0978] Digital document files (PDF and EPUB)

[0979] output:

[0980] Text data

[0981] Step 3: Analyzing the text data

[0982] Specific behavior:

[0983] The server uses natural language processing algorithms (e.g., spaCy or NLTK) to analyze the text data and extract scenes and keywords. This involves performing semantic and syntactic analysis of the text to identify important scenes and keywords.

[0984] input:

[0985] Text data

[0986] output:

[0987] Extracted scenes and keywords

[0988] Step 4: Create and submit an illustration generation request

[0989] Specific behavior:

[0990] The server generates a prompt based on the extracted scene and keywords, and sends a request to generate an illustration to a generative artificial intelligence model (e.g., OpenAI API). The prompt is created by including a description of the scene and keywords.

[0991] input:

[0992] Extracted scenes and keywords

[0993] output:

[0994] An illustration generation request sent to the generative AI model

[0995] Step 5: Creating and receiving illustrations

[0996] Specific behavior:

[0997] The generative artificial intelligence model generates an illustration image based on the request received from the server. The generated illustration image is sent back to the server, which receives and stores this image.

[0998] input:

[0999] Illustration generation request

[1000] output:

[1001] Generated illustration image

[1002] Step 6: Integrating illustrations into text data

[1003] Specific behavior:

[1004] The server inserts the generated illustrations into the original text data at the appropriate locations. The insertion position of the illustrations is determined based on the extracted scenes and keywords. A text editing library (e.g., PyMuPDF, EPUB-lib) is used for the processing.

[1005] input:

[1006] Text data, generated illustration images

[1007] output:

[1008] Illustrated digital document file

[1009] Step 7: Provide illustrated digital document files

[1010] Specific behavior:

[1011] The server generates a link for providing the created digital document file with illustrations to the user, and provides the link to the user through a smartphone application. The user can click the link to download and view the digital document file.

[1012] input:

[1013] Illustrated digital document file

[1014] output:

[1015] Download link provided to users

[1016] Specific examples

[1017] For example, suppose a user wants to read a digital document file of "The Great Gatsby." Here's the specific flow:

[1018] 1. The user selects a digital document file (EPUB format) for "The Great Gatsby" and uploads it from the smartphone application.

[1019] 2. The server receives the file and converts the EPUB file into text data.

[1020] 3. The server uses a natural language processing algorithm to extract scenes and keywords from the text data, such as "the scene where Gatsby looks at Daisy" and "description of the party."

[1021] 4. The server sends an illustration generation request to the generative AI model based on the extracted scene and keywords. For example, the prompt sentence may include the following:

[1022] "A scene where Gatsby gazes at Daisy."

[1023] 5. The generative AI model generates an illustration based on the prompt and sends it back to the server.

[1024] 6. The server inserts the received illustrations into the original text data in the appropriate locations, generating a new visually-aided "The Great Gatsby" digital document file.

[1025] 7. The user can click on the link provided by the smartphone application to download and begin reading "The Great Gatsby" with the generated illustrations.

[1026] 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.

[1027] This system allows users to upload e-book files, converts them into text data, extracts scenes and keywords, and generates appropriate illustrations using a generative AI and emotion engine. This system aims to provide an easy-to-understand reading experience, especially for foreign language learners.

[1028] System Overview

[1029] 1. User Action:

[1030] The user accesses the system's web interface through a terminal, selects the e-book file (e.g., PDF or EPUB) they want to read, and clicks the upload button.

[1031] 2. Server Processing:

[1032] The server receives the electronic book file uploaded by the user, and then converts the electronic book file into text data.

[1033] 3. Text Analysis:

[1034] The server uses natural language processing algorithms to analyze the text data and extract scenes and keywords, providing the basis for generating illustrations.

[1035] 4. Emotion Recognition with Emotion Engine:

[1036] While the user goes through the above process through the device, the emotion engine analyzes data collected from the user's camera and microphone to recognize the user's emotions in real time.

[1037] 5. Illustration generation request:

[1038] The server generates an illustration generation request based on the extracted scenes and keywords and the user's emotions detected by the emotion engine. The request includes specific descriptions of the scenes and keywords and emotion information.

[1039] 6. Generative AI behavior:

[1040] The AI ​​generates illustration images based on requests received from the server, and the generated illustration images are sent back to the server.

[1041] 7. Illustration Integration:

[1042] The server receives the illustration images returned by the generation AI and inserts them into the original text data in the appropriate places, thereby generating an e-book file in picture book format.

[1043] 8. User Offerings:

[1044] The server generates a link to provide the generated e-book file in picture book format to the user, who can then download the e-book via the link and begin reading.

[1045] Specific examples

[1046] For example, let's say a user wants to read "The Great Gatsby." Here's the specific flow:

[1047] 1. The user selects the e-book file (EPUB format) of "The Great Gatsby" and uploads it through the system's web interface.

[1048] 2. The server receives the file and converts the EPUB file into text data.

[1049] 3. The server uses a natural language processing algorithm to extract scenes and keywords such as "the scene where Gatsby looks at Daisy" and "description of the party" from the text data.

[1050] 4. The emotion engine recognizes emotions from the user's facial expressions and voice, detecting when the user is expressing emotions such as excitement or sadness in a particular scene.

[1051] 5. The server sends an illustration generation request to the generation AI based on the extracted scenes and keywords, and the user's emotions detected by the emotion engine.

[1052] 6. The AI ​​generates an illustration based on the scene of Gatsby gazing at Daisy and sends the image back to the server. It may also include elements corresponding to emotions.

[1053] 7. The server inserts the received illustration images into the original text data in the appropriate locations to generate a new picture book format e-book file of "The Great Gatsby."

[1054] 8. The user can click on the link provided by the system to download and start reading the generated picture book version of "The Great Gatsby."

[1055] The above is a concrete example of how to implement the present invention. This system not only allows users to visually understand the content through illustrations, but also allows them to enjoy a reading experience that is customized to their emotions. This provides an effective reading experience, especially for foreign language learning.

[1056] The processing flow will be explained below.

[1057] Step 1:

[1058] A user accesses the system's web interface using a terminal, selects the e-book file (e.g., PDF or EPUB) they want to read, and clicks the upload button.

[1059] Step 2:

[1060] The server receives the e-book file sent by the user and stores the received e-book file in temporary storage.

[1061] Step 3:

[1062] The server analyzes the e-book file and converts it into text data in an appropriate way. For example, if it is an EPUB file, it analyzes the XML data inside it and extracts the text data.

[1063] Step 4:

[1064] The server applies a natural language processing algorithm to the converted text data, allowing the server to extract important scenes and keywords from the text data.

[1065] Step 5:

[1066] While the user is operating the system through the device, the emotion engine collects data from the device's camera and microphone, analyzes the user's facial expressions and voice, and recognizes emotions in real time.

[1067] Step 6:

[1068] The server generates an illustration generation request based on the extracted scenes and keywords, and the user's emotions recognized by the emotion engine. The request includes specific descriptions of the scenes and keywords, as well as emotional information.

[1069] Step 7:

[1070] The server sends an illustration generation request to the generation AI. The request is sent via the generation AI's API.

[1071] Step 8:

[1072] The AI ​​generates an illustration based on the request it receives, taking into account the scene, keywords, and the user's emotions to create the most appropriate illustration.

[1073] Step 9:

[1074] The generated illustration image is sent back to the server by the AI ​​generator, and is then temporarily stored in the server.

[1075] Step 10:

[1076] The server integrates the received illustration images into the original text data, inserts the illustrations into specific positions in the text data, and generates an e-book file in picture book format.

[1077] Step 11:

[1078] The server generates a download link for providing the generated e-book file in picture book format to the user, and the link is displayed on the web interface so that the user can access it.

[1079] Step 12:

[1080] The user uses the device to click the provided download link to download the generated e-book file in picture book format. The user can then open the downloaded e-book and begin reading it with the illustrations.

[1081] Example 2

[1082] 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."

[1083] Current electronic content systems lack the ability to create appropriate illustrations to visually support the text being read, and lack a means to provide a customized reading experience based on the user's emotions, making it difficult to provide effective reading support, especially for foreign language learners.

[1084] 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.

[1085] In this invention, the server includes: a means for a user to upload an electronic content file; a means for the server to convert the electronic content file into text data; a means for the server to extract scenes and keywords from the text data using a natural language processing algorithm; a means for the server to use an emotion engine that recognizes the user's emotions in real time; a means for the server to generate a prompt sentence for a generative AI model and send an illustration generation request; a means for the generative AI model to generate an illustration image and return it to the server; a means for the server to integrate the illustration image into the text data; and a means for the server to provide the user with the generated illustrated electronic content file, thereby enabling a reading experience with illustrations customized according to the user's emotions.

[1086] A "user" is an entity that uploads electronic content files to the system and receives the resulting illustrated electronic content files.

[1087] An "electronic content file" is a digital file containing text data, such as a PDF or EPUB file.

[1088] "Server" refers to the hardware and software that processes electronic content files uploaded by users and converts them into text data.

[1089] "Text data" is character string data extracted from an electronic content file, and is the subject of analysis and processing.

[1090] "Natural language processing algorithms" are technologies that include machine learning and statistical methods for extracting scenes and keywords from text data.

[1091] "Scenes and keywords" refer to specific scenes and important words within the text data, and are the information that forms the basis for generating illustrations.

[1092] The "emotion engine" is a technology that recognizes a user's emotions by analyzing data collected from a camera or microphone.

[1093] A "generative AI model" is an artificial intelligence algorithm that generates images based on a prompt, and examples include deep learning techniques.

[1094] A "prompt sentence" is a specific explanatory sentence used when sending an illustration generation request to the generative AI model.

[1095] An "illustrated image" is a visual picture generated by a generative AI model based on a prompt sentence.

[1096] "Integrating" means inserting the generated illustration image into the original text data at an appropriate location.

[1097] "Providing an electronic content file" means providing the created illustrated electronic content file in a form that can be accessed by the user.

[1098] The present invention is a system in which a user uploads an electronic content file, a server converts the file into text data, analyzes it, generates appropriate illustrations using a generative AI model, and provides the final illustrated electronic content file to the user. This system aims to provide an easy-to-understand reading experience, especially for foreign language learners.

[1099] System Overview

[1100] 1. User Operation

[1101] A user accesses the system's web interface through a terminal. The user selects the electronic content file (e.g., PDF or EPUB) they want to read and clicks the upload button. For example, a user uploads an EPUB file of "The Great Gatsby" by dragging and dropping it.

[1102] 2. Server Processing

[1103] The server receives the electronic content file uploaded by the user. After receiving it, the server converts the electronic content file into text data using software such as Adobe Acrobat or Calibre. The converted text data is stored in an internal database (e.g., MySQL or PostgreSQL).

[1104] 3. Text Analysis

[1105] The server uses Python's NLTK library and SpaCy to analyze the text data. Specifically, it tokenizes sentences and extracts scenes and keywords. This analysis extracts specific scenes and important phrases. For example, it extracts keywords corresponding to the scene "Gatsby gazes at Daisy."

[1106] 4. Emotion Recognition by Emotion Engine

[1107] While the user goes through the above process on the device, the emotion engine analyzes data collected from the user's camera and microphone to recognize the user's emotions in real time. For example, it can detect excitement or sadness from the user's facial expressions and voice. This is done using Microsoft Azure's Emotion API and Google Cloud's Natural Language API.

[1108] 5. Illustration generation request

[1109] The server generates a prompt to send to the generative AI model based on the extracted scenes and keywords, as well as the user's emotions detected by the emotion engine. This prompt includes a specific description of the scene and keywords, along with emotional information. For example, it could be in the format "The scene where Gatsby looks at Daisy, the user feels joy."

[1110] 6. Generative AI behavior

[1111] A generative AI model (e.g., OpenAI's DALLE or other similar technologies) generates an illustration based on the prompt received from the server. The generated illustration is then sent back to the server. For example, a specific illustration is generated based on the prompt, "The scene where Gatsby is looking at Daisy, and the user feels joy."

[1112] 7. Integration of illustrations

[1113] The server receives the illustration images returned by the generative AI model and inserts them into the original text data at the appropriate locations. This generates an electronic content file in picture book format. For example, the server inserts the corresponding illustration image immediately after the text "Gatsby gazing at Daisy."

[1114] 8. Provision to Users

[1115] The server generates a link to provide the generated picture book format electronic content file to the user, who can then download the electronic content via the link and begin reading it.

[1116] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1117] Step 1: A user uploads an electronic content file.

[1118] Input: An electronic content file (e.g., PDF or EPUB format) selected by the user through the device.

[1119] Specific behavior: A user accesses the system's web interface and clicks a button to upload an electronic content file they want to read. This displays a file selection dialog, where the user selects the desired file and confirms the upload.

[1120] Output: The file is sent to the server.

[1121] Step 2: The server converts the electronic content file into text data.

[1122] Input: Electronic content files uploaded by users.

[1123] What happens: The server recognizes the received file and converts it to text using software like Adobe Acrobat or Calibre. The conversion process involves sequentially parsing the pages of the file and extracting strings of characters.

[1124] Output: The converted text data is generated and stored in the server's internal database (e.g. MySQL or PostgreSQL).

[1125] Step 3: The server analyzes the text data using natural language processing algorithms.

[1126] Input: The converted text data.

[1127] How it works: The server uses natural language processing tools such as Python's NLTK library or SpaCy to analyze the text data. This analysis involves tokenizing the text and extracting key scenes and keywords. Each scene and keyword is ranked based on a specific importance.

[1128] Output: Extracted scenes and keyword list.

[1129] Step 4: The server uses an emotion engine to recognize the user's emotions in real time.

[1130] Input: Real-time data from the user's camera and microphone.

[1131] How it works: The server analyzes the user's facial expressions and voice data via Microsoft Azure's Emotion API and Google Cloud's Natural Language API, which allows it to identify the emotions the user is expressing in each scene.

[1132] Output: User emotion data.

[1133] Step 5: The server generates a prompt sentence for the generative AI model and sends a request to generate an illustration.

[1134] Input: A list of scenes and keywords, and user emotion data.

[1135] Specific operation: The server generates prompts based on this data. Specifically, it constructs prompts for each scene by combining detailed descriptions and emotion data. For example, the prompt might be, "In the scene where Gatsby looks at Daisy, the user feels joy."

[1136] Output: The prompt sent to the generative AI model.

[1137] Step 6: The generative AI model generates an illustration image and sends it back to the server.

[1138] Input: The prompt text sent by the server.

[1139] What it does: A generative AI model (e.g., OpenAI's DALLE or similar technology) analyzes the prompt and generates a corresponding illustration image, which is then sent back to the server in its raw form.

[1140] Output: The generated illustration image.

[1141] Step 7: The server integrates the illustration images into the text data.

[1142] Input: Illustration images returned from the generative AI model, original text data.

[1143] Specific operation: The server inserts the generated illustration images into the text data at the appropriate places, specifically by applying an algorithm to integrate the images, such as immediately after each scene.

[1144] Output: Integrated picture book format electronic content file.

[1145] Step 8: The server provides the generated illustrated electronic content file to the user.

[1146] Input: Integrated picture book format electronic content file.

[1147] What happens: The server uploads this file to a cloud storage location accessible to the user (e.g. AWS S3) and generates a download link, which is sent to the user's dashboard or email.

[1148] Output: A downloadable link for the user.

[1149] (Application example 2)

[1150] 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."

[1151] The lack of visual elements in e-books can make learning and reading difficult, especially for language learners. Furthermore, content provided without considering the user's emotional state can reduce user satisfaction and concentration. Therefore, there is a need for a system that can dynamically generate illustrations based on the user's emotions, providing a visually and emotionally rich reading experience.

[1152] 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.

[1153] In this invention, the server includes: a means for a user to upload an e-book file; a means for the server to convert the e-book file into text data; a means for the server to extract scenes and keywords from the text data; a means for analyzing the user's emotions in real time using an emotion analysis engine; a means for the server to send to the generation AI a request to generate illustrations based on the scenes, keywords, and the user's emotions; a means for the generation AI to generate illustration images and return them to the server; a means for the server to integrate the illustration images into the text data; and a means for the server to provide the user with the generated illustrated e-book file. This makes it possible to dynamically generate illustrations according to the user's emotions and provide a visually and emotionally rich reading experience, especially for language learners.

[1154] "User" means an individual or legal entity who uses the System to upload e-book files and receive illustrated e-books.

[1155] An "e-book file" is a reading material that stores information such as text and images in digital format, and typical formats include PDF and EPUB.

[1156] The "server" is a computer system that analyzes e-book files, converts them into text data, extracts scenes and keywords, and integrates the illustrations generated by the generation AI to generate e-book files, which are then provided to users.

[1157] "Text data" refers to text data extracted from an e-book file and processed and analyzed by the server.

[1158] A "scene" is a portion of text data that indicates a specific scene or situation, and serves as basic information for generating an illustration image.

[1159] "Keywords" are important words or phrases in the text data, and are information that clarifies a scene.

[1160] An "emotion analysis engine" is software that analyzes data collected from the user's camera and microphone and recognizes the user's emotional state.

[1161] "Generative AI" is an artificial intelligence model that generates illustration images based on given scenes, keywords, and the user's emotions.

[1162] An "illustration generation request" is an instruction sent by the server to the generation AI, and includes the scene, keywords, and the user's emotional information.

[1163] "Illustrated images" are illustrations or visuals generated by generative AI and integrated into text data.

[1164] "Integration" refers to the process of incorporating the generated illustration images into the original text data at the appropriate locations.

[1165] "Generating an e-book file" refers to the process of creating a new e-book file containing illustrations.

[1166] "Providing to the user" refers to delivering the generated illustrated e-book file to the user via a downloadable link.

[1167] The present invention is a system that provides illustrations based on scenes, keywords, and user emotions to enrich the user's reading experience, especially for language learners. This system is realized through a series of processes: analyzing e-book files, performing emotion recognition, and generating illustrations.

[1168] Program Overview

[1169] 1. User Interface

[1170] Users upload e-book files to the web from their smartphones or PCs.

[1171] It is also possible to set it so that when a user purchases an e-book, it is automatically uploaded.

[1172] 2. E-book analysis

[1173] The uploaded e-book file (PDF, EPUB, etc.) is sent to the server.

[1174] The server uses an appropriate conversion algorithm to convert the e-book file into text data.

[1175] 3. Natural Language Processing and Keyword Extraction

[1176] The server uses natural language processing algorithms (e.g., spaCy or NLTK) to extract scenes and keywords from the text data.

[1177] This process provides important information needed for subsequent illustration generation.

[1178] 4. Emotion analysis

[1179] A sentiment analysis engine (for example, Microsoft Azure's Cognitive Services) uses data from the user's camera and microphone to analyze the user's emotions in real time.

[1180] 5. Creating an illustration generation request

[1181] The server sends an illustration generation request to the generative AI model based on the extracted scenes, keywords, and emotion analysis results.

[1182] The illustration generation request includes an appropriate prompt, such as:

[1183] "Scene: Gatsby and Daisy gazing at each other. User emotion: Joy. Generates illustrations containing warm, romantic images."

[1184] 6. Use of generative AI

[1185] A generative AI model (e.g., OpenAI GPT-4-based model) generates an illustrated image based on the prompt and analysis data.

[1186] The generated illustration is sent back to the server.

[1187] 7. Integration of illustrations

[1188] The server uses an algorithm to appropriately integrate the illustrations into the text data.

[1189] This process generates an illustrated e-book file.

[1190] 8. Provision to Users

[1191] The server generates a link for providing the generated illustrated e-book file to the user.

[1192] Users can then download the generated e-book via that link and begin reading.

[1193] This allows illustrations to be dynamically generated based on the user's emotions, providing a visually and emotionally rich reading experience, especially for language learners.

[1194] Hardware and software used

[1195] Hardware: Smartphones, PCs, cloud servers

[1196] Software: Web application frameworks (Flask / Django), natural language processing libraries (spaCy, NLTK), sentiment analysis API (Microsoft Azure Cognitive Services), generative AI (OpenAI GPT-4 based model)

[1197] This system is extremely useful for users as it can perform all processes from analyzing e-book files to providing new e-book files containing the generated illustrations.

[1198] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1199] Step 1:

[1200] A user uploads an e-book file (e.g., PDF or EPUB) using a smartphone or PC. To do this, the user accesses the system's web interface, selects the e-book file, and clicks the upload button. The input is the e-book file, and the output is sending that file to the server.

[1201] Step 2:

[1202] The server receives the uploaded e-book file. It uses an appropriate conversion algorithm (e.g., a PDF to text conversion library) to convert the received file into text data. The input is the e-book file, and the output is text data.

[1203] Step 3:

[1204] The server analyzes the text data using a natural language processing algorithm (e.g., spaCy or NLTK). It extracts scenes and keywords from the text data. The input is the text data, and the output is the extracted scenes and keywords.

[1205] Step 4:

[1206] The server sends data collected from the user's device via the camera and microphone to an emotion analysis engine (for example, Microsoft Azure's Cognitive Services). This allows the user's emotions to be analyzed in real time. The input is the audio and image data collected from the camera and microphone, and the output is analyzed emotion data.

[1207] Step 5:

[1208] The server sends an illustration generation request to the generative AI model based on the extracted scenes, keywords, and emotion analysis results. This request includes a specific description of the scene and keywords, as well as emotional information. An example prompt is: "Scene: Gatsby and Daisy gazing at each other. User's emotion: Joy. Generate an illustration that includes a warm, romantic image." The input is the scene, keywords, and emotion data, and the output is an illustration generation request.

[1209] Step 6:

[1210] The generative AI model generates an illustration image based on the received illustration generation request. Using a generative AI model (e.g., OpenAI GPT-4 based model), an illustration is generated according to the presented prompt sentence. The input is the illustration generation request, and the output is the generated illustration image.

[1211] Step 7:

[1212] The server receives the illustration images returned by the generative AI model. The server uses an integration algorithm to properly integrate the illustration images into the text data. The input is the text data and the illustration images, and the output is an illustrated e-book file.

[1213] Step 8:

[1214] The server generates a link to provide the generated illustrated e-book file to the user, who can then download the generated e-book and begin reading it. The input is the illustrated e-book file, and the output is the download link.

[1215] 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.

[1216] 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.

[1217] 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.

[1218] [Fourth embodiment]

[1219] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1220] 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.

[1221] 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).

[1222] 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.

[1223] 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.

[1224] 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).

[1225] 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.

[1226] 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.

[1227] 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.

[1228] 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.

[1229] 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.

[1230] 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.

[1231] 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."

[1232] The present invention is a system in which a user uploads an e-book file, a server converts the e-book file into text data, extracts scenes and keywords, and generates illustrations based on the data. This system aims to provide an easy-to-understand reading experience, especially for foreign language learners.

[1233] System Overview

[1234] 1. User Action:

[1235] The user accesses the system's web interface through a terminal, selects the e-book file (e.g., PDF or EPUB) they want to read, and uploads it.

[1236] 2. Server Processing:

[1237] The server receives the electronic book file uploaded by the user, and then converts the electronic book file into text data.

[1238] 3. Text Analysis:

[1239] The server uses natural language processing algorithms to analyze the text data and extract scenes and keywords, providing the basis for generating illustrations.

[1240] 4. Illustration generation request:

[1241] The server sends an illustration generation request to the AI ​​based on the extracted scenes and keywords. The request includes specific scenes and keywords.

[1242] 5. Generative AI behavior:

[1243] The AI ​​generates illustration images based on requests received from the server, and the generated illustration images are sent back to the server.

[1244] 6. Illustration Integration:

[1245] The server receives the illustration images returned by the generation AI and inserts them into the original text data in the appropriate places, thereby generating an e-book file in picture book format.

[1246] 7. Provision to Users:

[1247] The server generates a link to provide the generated e-book file in picture book format to the user, who can then download the e-book via the link and begin reading.

[1248] Specific examples

[1249] For example, let's say a user wants to read "The Great Gatsby." Here's the specific flow:

[1250] 1. The user selects the e-book file (EPUB format) of "The Great Gatsby" and uploads it through the system's web interface.

[1251] 2. The server receives the file and converts the EPUB file into text data.

[1252] 3. The server uses a natural language processing algorithm to extract scenes and keywords such as "the scene where Gatsby looks at Daisy" and "description of the party" from the text data.

[1253] 4. The server sends an illustration generation request to the generation AI based on the extracted scenes and keywords.

[1254] 5. The AI ​​generates an illustration based on the scene of Gatsby gazing at Daisy and sends the image back to the server.

[1255] 6. The server inserts the received illustration images into the original text data in the appropriate locations to generate a new picture book format e-book file of "The Great Gatsby."

[1256] 7. The user can click on the link provided by the system to download the generated picture book version of "The Great Gatsby" and begin reading.

[1257] The above is a specific embodiment for carrying out the present invention. This system allows users to easily understand the content visually through illustrations, providing an effective reading experience, especially for foreign language learning.

[1258] The processing flow will be explained below.

[1259] Step 1:

[1260] A user accesses the system's web interface using a terminal, selects the e-book file (e.g., PDF or EPUB) they want to read, and clicks the upload button.

[1261] Step 2:

[1262] The server receives the e-book file sent by the user and stores the received e-book file in temporary storage.

[1263] Step 3:

[1264] The server analyzes the e-book file and converts it into text data in an appropriate way. For example, if it is an EPUB file, it analyzes the XML data inside it and extracts the text data.

[1265] Step 4:

[1266] The server applies a natural language processing algorithm to the converted text data, allowing the server to extract important scenes and keywords from the text data.

[1267] Step 5:

[1268] The server generates an illustration generation request based on the extracted scenes and keywords. The request includes specific descriptions of the scenes and keywords.

[1269] Step 6:

[1270] The server sends an illustration generation request to the generation AI. The request is sent via the generation AI's API.

[1271] Step 7:

[1272] The AI ​​generates an illustration image based on the request it receives, taking into account the scene and keywords, and creating the most suitable illustration.

[1273] Step 8:

[1274] The generated illustration image is sent back to the server by the AI ​​generator, and is then temporarily stored in the server.

[1275] Step 9:

[1276] The server integrates the received illustration images into the original text data, inserts the illustrations into specific positions in the text data, and generates an e-book file in picture book format.

[1277] Step 10:

[1278] The server generates a download link for providing the generated e-book file in picture book format to the user, and the link is displayed on the web interface so that the user can access it.

[1279] Step 11:

[1280] The user uses the device to click the provided download link to download the generated e-book file in picture book format. The user can then open the downloaded e-book and begin reading it with the illustrations.

[1281] Example 1

[1282] 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."

[1283] Conventional e-books lack visual information, and lack illustrations to aid comprehension, especially in foreign language learning, limiting their effectiveness. Furthermore, manually inserting illustrations takes time and effort, making it difficult to provide efficient learning support.

[1284] 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.

[1285] In this invention, the server includes means for a user to upload a book file, means for the server to convert the book file into document data, means for the server to extract scenes and key phrases from the document data, means for the server to send an illustration generation request to a generation algorithm, means for the generation algorithm to generate illustration images and return them to the server, means for the server to integrate the illustration images into the document data, and means for the server to provide the user with the generated illustrated book file. This allows users to efficiently use books with added visual information, which can aid understanding, particularly in foreign language learning.

[1286] "User" refers to an individual who uses the System to upload Book Files and download Illustrated Book Files.

[1287] "Server" refers to a computer system that receives book files, converts them into document data, extracts scenes and key phrases, sends illustration generation requests, and integrates illustration images.

[1288] "Book File" refers to a digital file of an electronically recorded book, including formats such as PDF and EPUB.

[1289] "Document data" refers to text data extracted from a book file.

[1290] "Scene" refers to a description within document data that requires a specific scene or illustration.

[1291] "Key words" refer to notable keywords or phrases within document data.

[1292] "Generation algorithm" refers to an algorithm for generating an illustration image based on an illustration generation request.

[1293] An "illustration generation request" refers to a request to create an illustration image sent to the generation algorithm based on a scene or key phrases.

[1294] "Illustrated Image" refers to a visual image generated by a generative algorithm.

[1295] "Integration into book file" refers to the process of placing the generated illustration images in the appropriate positions in the original document data.

[1296] "Illustrated book file" refers to a book file in which the generated illustrated images have been integrated.

[1297] This system allows users to upload book files, converts them into document data, extracts scenes and key phrases, and generates illustrations based on them. This system aims to provide an easy-to-understand reading experience, especially for foreign language learners.

[1298] First, the user accesses the system's web interface using a terminal. Through this interface, the user selects and uploads the book file (e.g., PDF or EPUB format) they want to read. The upload operation can be performed in the same way as selecting a file.

[1299] The server receives the book file uploaded by the user. The server uses software libraries such as "Apache PDFBox" and "EPUB.js" to convert the book file into document data. For example, in the case of an EPUB format file, EPUB.js is used to extract the text data.

[1300] Next, the server analyzes the document data using natural language processing technology. Examples of software used for this analysis include "spaCy" and "NLTK." The server uses these libraries to extract scenes and key phrases from the document data. For example, "the scene where Gatsby looks at Daisy" or "description of the party" may be extracted.

[1301] The server sends an illustration generation request to the generation algorithm based on the extracted scenes and key phrases. This prompt includes a specific scene and key phrases. Examples of prompts include "The Great Gatsby: Generate an illustration of the scene where Gatsby looks at Daisy" and "The Great Gatsby: Generate an illustration based on the description of the party."

[1302] The generation algorithms used are "Stable Diffusion" and "DALL-E." The generation algorithm generates an illustration image based on the received prompt text and sends the illustration image back to the server.

[1303] The server then inserts the received illustration into the document at the appropriate location, possibly using the Pillow library. For example, the server could place the illustration immediately after the text "Gatsby looking at Daisy."

[1304] Finally, the server generates a link to provide the generated illustrated book file to the user, who can then download the book file and begin reading.

[1305] This allows users to efficiently use books with added visual information, which can aid comprehension, especially in foreign language learning. The system of the present invention provides a visual interface for users, supporting a more effective learning experience.

[1306] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1307] Step 1:

[1308] A user accesses the system's web interface through a terminal. The user clicks the "Upload" button, selects the book file (e.g., PDF or EPUB format) they want to read, and uploads it. The input is the book file selected by the user, and the output is the book file sent to the server.

[1309] Step 2:

[1310] The server receives the book file uploaded by the user. Based on the received file, it converts it into document data using "Apache PDFBox" or "EPUB.js". This conversion process involves reading the file and converting it into text data. The input is the book file, and the output is the converted document data.

[1311] Step 3:

[1312] The server analyzes the converted document data using natural language processing technology (such as "spaCy" or "NLTK"). This analysis identifies and extracts scenes and key phrases. Specific processing involves morphological analysis of the text and scene identification. The input is the document data, and the output is the extracted scenes and key phrases.

[1313] Step 4:

[1314] The server sends an illustration generation request to a generation algorithm (such as "Stable Diffusion" or "DALL-E") based on the extracted scenes and key phrases. The prompt sentence includes a specific example, "Please generate an illustration of the scene in The Great Gatsby where Gatsby is gazing at Daisy." The input is the scene and key phrases, and the output is the prompt sentence sent to the generation AI.

[1315] Step 5:

[1316] The generation AI receives the prompt text and generates an illustration image based on it. The generated illustration image is then sent back to the server. In this process, the AI ​​analyzes the received prompt text and generates a visual image. The input is the prompt text and the output is the generated illustration image.

[1317] Step 6:

[1318] The server inserts the received illustration images into the original document data at the appropriate position. Here, the "Pillow" library is used to integrate the illustration images at the appropriate position and generate a book file in picture book format. The input is the illustration images and document data, and the output is a book file with the illustrations integrated.

[1319] Step 7:

[1320] The server generates a link to provide the generated illustrated book file to the user, who can use this link to download and view the new book file. The input is the final book file, and the output is the download link.

[1321] (Application example 1)

[1322] 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."

[1323] Conventional e-books have been difficult to understand, especially for foreign language learners. Visual aids are especially needed for situations and keywords that are difficult to understand with text alone. However, no system currently available can efficiently accomplish this. Furthermore, there is a need for a system that allows users to upload digital document files, generate visual aids, and easily download and view them.

[1324] 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.

[1325] In this invention, the server includes means for a user to upload a digital document file, means for the server to convert the digital document file into text data, means for the server to extract scenes and keywords from the text data, means for the server to send an illustration generation request to a generative AI model, means for the generative AI model to generate an illustration image and return it to the server, means for the server to integrate the illustration image into the text data, means for the server to provide the generated illustrated digital document file to the user, and means for the user to download and view the digital document file from a smartphone application. This allows users to view digital documents in a more understandable format, enabling effective learning, particularly for foreign language learners.

[1326] "User" refers to any individual or entity that uses the System.

[1327] "Digital document file" refers to a document that is stored and displayed electronically, including, for example, PDF and EPUB.

[1328] "Server" refers to a computer system that processes and stores data.

[1329] "Text data" refers to textual information extracted from a digital document file.

[1330] A "scene" refers to a specific event or situation in text data.

[1331] A "keyword" refers to a word or short phrase that has an important meaning within text data.

[1332] A "generative artificial intelligence model" refers to a machine learning algorithm for generating images or text based on specified input data.

[1333] "Illustration images" refer to illustrations and figures created to visually complement the content of text data.

[1334] "Smartphone application" refers to a program that runs on a smartphone.

[1335] A "natural language processing algorithm" refers to a computational method for analyzing text data and extracting meaning.

[1336] "Visual aid format" refers to a format that combines visual information (such as illustrations) with text.

[1337] The present invention is a system in which a user uploads a digital document file, a server converts the digital document file into text data, extracts scenes and keywords, and generates illustrations based on the text data.The system also integrates the generated illustrations into the text data, and provides a function that allows users to download and view the digital document file in visual aid format from a smartphone application.

[1338] System Overview

[1339] 1. User Action:

[1340] A user accesses the system interface using a smartphone, selects the digital document file (e.g., PDF or EPUB) they want to read, and uploads it.

[1341] 2. Server Processing:

[1342] The server receives the digital document file uploaded by the user, and then converts the digital document file into text data.

[1343] 3. Text Analysis:

[1344] The server analyzes the text data using natural language processing algorithms (e.g., spaCy or NLTK) to extract scenes and keywords, thereby obtaining the information needed to generate illustrations.

[1345] 4. Illustration generation request:

[1346] The server sends an illustration generation request to a generative AI model (e.g., OpenAI API) based on the extracted scenes and keywords. The request includes specific scenes and keywords.

[1347] 5. Generative AI behavior:

[1348] The generative AI model generates illustration images based on requests received from the server, and the generated illustration images are sent back to the server.

[1349] 6. Illustration Integration:

[1350] The server receives the illustration images returned by the generative AI model and inserts them into the original text data at the appropriate locations, generating a digital document file in the form of a visual aid.

[1351] 7. Provision to Users:

[1352] The server provides the generated illustrated digital document file to the user, who can then download the visually-aided digital document file through a smartphone application and begin viewing it.

[1353] Specific examples

[1354] For example, suppose a user wants to read a digital document file of "The Great Gatsby." Here's the specific flow:

[1355] 1. The user selects a digital document file (EPUB format) for "The Great Gatsby" and uploads it from the smartphone application.

[1356] 2. The server receives the file and converts the EPUB file into text data.

[1357] 3. The server uses a natural language processing algorithm to extract scenes and keywords from the text data, such as "the scene where Gatsby looks at Daisy" and "description of the party."

[1358] 4. The server sends an illustration generation request to the generative AI model based on the extracted scene and keywords. For example, the prompt sentence may include the following:

[1359] "A scene where Gatsby gazes at Daisy."

[1360] 5. The generative AI model generates an illustration based on the prompt and sends it back to the server.

[1361] 6. The server inserts the received illustrations into the original text data in the appropriate locations, generating a new visually-aided "The Great Gatsby" digital document file.

[1362] 7. The user can click on the link provided by the smartphone application to download and begin reading "The Great Gatsby" with the generated illustrations.

[1363] The above system allows users to view digital documents with added illustrations in a visually easy-to-understand format, making learning particularly effective for foreign language learners.

[1364] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1365] Program processing steps

[1366] Step 1: Upload your eBook file

[1367] Specific behavior:

[1368] A user starts a smartphone application, selects the digital document file (PDF, EPUB, etc.) they want to read, and selects the file on the upload screen. The selected file is then sent to the server within the application.

[1369] input:

[1370] User-selected digital document file (PDF or EPUB)

[1371] output:

[1372] Digital document files sent to the server

[1373] Step 2: Convert eBook files to text

[1374] Specific behavior:

[1375] The server converts the received digital document file into text data using a dedicated library (e.g., PyMuPDF, EPUB-lib). For PDF files, the text is extracted page by page, and for EPUB files, the text is extracted chapter by chapter.

[1376] input:

[1377] Digital document files (PDF and EPUB)

[1378] output:

[1379] Text data

[1380] Step 3: Analyzing the text data

[1381] Specific behavior:

[1382] The server uses natural language processing algorithms (e.g., spaCy or NLTK) to analyze the text data and extract scenes and keywords. This involves performing semantic and syntactic analysis of the text to identify important scenes and keywords.

[1383] input:

[1384] Text data

[1385] output:

[1386] Extracted scenes and keywords

[1387] Step 4: Create and submit an illustration generation request

[1388] Specific behavior:

[1389] The server generates a prompt based on the extracted scene and keywords, and sends a request to generate an illustration to a generative artificial intelligence model (e.g., OpenAI API). The prompt is created by including a description of the scene and keywords.

[1390] input:

[1391] Extracted scenes and keywords

[1392] output:

[1393] An illustration generation request sent to the generative AI model

[1394] Step 5: Creating and receiving illustrations

[1395] Specific behavior:

[1396] The generative artificial intelligence model generates an illustration image based on the request received from the server. The generated illustration image is sent back to the server, which receives and stores this image.

[1397] input:

[1398] Illustration generation request

[1399] output:

[1400] Generated illustration image

[1401] Step 6: Integrating illustrations into text data

[1402] Specific behavior:

[1403] The server inserts the generated illustrations into the original text data at the appropriate locations. The insertion position of the illustrations is determined based on the extracted scenes and keywords. A text editing library (e.g., PyMuPDF, EPUB-lib) is used for the processing.

[1404] input:

[1405] Text data, generated illustration images

[1406] output:

[1407] Illustrated digital document file

[1408] Step 7: Provide illustrated digital document files

[1409] Specific behavior:

[1410] The server generates a link for providing the created digital document file with illustrations to the user, and provides the link to the user through a smartphone application. The user can click the link to download and view the digital document file.

[1411] input:

[1412] Illustrated digital document file

[1413] output:

[1414] Download link provided to users

[1415] Specific examples

[1416] For example, suppose a user wants to read a digital document file of "The Great Gatsby." Here's the specific flow:

[1417] 1. The user selects a digital document file (EPUB format) for "The Great Gatsby" and uploads it from the smartphone application.

[1418] 2. The server receives the file and converts the EPUB file into text data.

[1419] 3. The server uses a natural language processing algorithm to extract scenes and keywords from the text data, such as "the scene where Gatsby looks at Daisy" and "description of the party."

[1420] 4. The server sends an illustration generation request to the generative AI model based on the extracted scene and keywords. For example, the prompt sentence may include the following:

[1421] "A scene where Gatsby gazes at Daisy."

[1422] 5. The generative AI model generates an illustration based on the prompt and sends it back to the server.

[1423] 6. The server inserts the received illustrations into the original text data in the appropriate locations, generating a new visually-aided "The Great Gatsby" digital document file.

[1424] 7. The user can click on the link provided by the smartphone application to download and begin reading "The Great Gatsby" with the generated illustrations.

[1425] 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.

[1426] This system allows users to upload e-book files, converts them into text data, extracts scenes and keywords, and generates appropriate illustrations using a generative AI and emotion engine. This system aims to provide an easy-to-understand reading experience, especially for foreign language learners.

[1427] System Overview

[1428] 1. User Action:

[1429] The user accesses the system's web interface through a terminal, selects the e-book file (e.g., PDF or EPUB) they want to read, and clicks the upload button.

[1430] 2. Server Processing:

[1431] The server receives the electronic book file uploaded by the user, and then converts the electronic book file into text data.

[1432] 3. Text Analysis:

[1433] The server uses natural language processing algorithms to analyze the text data and extract scenes and keywords, providing the basis for generating illustrations.

[1434] 4. Emotion Recognition with Emotion Engine:

[1435] While the user goes through the above process through the device, the emotion engine analyzes data collected from the user's camera and microphone to recognize the user's emotions in real time.

[1436] 5. Illustration generation request:

[1437] The server generates an illustration generation request based on the extracted scenes and keywords and the user's emotions detected by the emotion engine. The request includes specific descriptions of the scenes and keywords and emotion information.

[1438] 6. Generative AI behavior:

[1439] The AI ​​generates illustration images based on requests received from the server, and the generated illustration images are sent back to the server.

[1440] 7. Illustration Integration:

[1441] The server receives the illustration images returned by the generation AI and inserts them into the original text data in the appropriate places, thereby generating an e-book file in picture book format.

[1442] 8. User Offerings:

[1443] The server generates a link to provide the generated e-book file in picture book format to the user, who can then download the e-book via the link and begin reading.

[1444] Specific examples

[1445] For example, let's say a user wants to read "The Great Gatsby." Here's the specific flow:

[1446] 1. The user selects the e-book file (EPUB format) of "The Great Gatsby" and uploads it through the system's web interface.

[1447] 2. The server receives the file and converts the EPUB file into text data.

[1448] 3. The server uses a natural language processing algorithm to extract scenes and keywords such as "the scene where Gatsby looks at Daisy" and "description of the party" from the text data.

[1449] 4. The emotion engine recognizes emotions from the user's facial expressions and voice, detecting when the user is expressing emotions such as excitement or sadness in a particular scene.

[1450] 5. The server sends an illustration generation request to the generation AI based on the extracted scenes and keywords, and the user's emotions detected by the emotion engine.

[1451] 6. The AI ​​generates an illustration based on the scene of Gatsby gazing at Daisy and sends the image back to the server. It may also include elements corresponding to emotions.

[1452] 7. The server inserts the received illustration images into the original text data in the appropriate locations to generate a new picture book format e-book file of "The Great Gatsby."

[1453] 8. The user can click on the link provided by the system to download and start reading the generated picture book version of "The Great Gatsby."

[1454] The above is a concrete example of how to implement the present invention. This system not only allows users to visually understand the content through illustrations, but also allows them to enjoy a reading experience that is customized to their emotions. This provides an effective reading experience, especially for foreign language learning.

[1455] The processing flow will be explained below.

[1456] Step 1:

[1457] A user accesses the system's web interface using a terminal, selects the e-book file (e.g., PDF or EPUB) they want to read, and clicks the upload button.

[1458] Step 2:

[1459] The server receives the e-book file sent by the user and stores the received e-book file in temporary storage.

[1460] Step 3:

[1461] The server analyzes the e-book file and converts it into text data in an appropriate way. For example, if it is an EPUB file, it analyzes the XML data inside it and extracts the text data.

[1462] Step 4:

[1463] The server applies a natural language processing algorithm to the converted text data, allowing the server to extract important scenes and keywords from the text data.

[1464] Step 5:

[1465] While the user is operating the system through the device, the emotion engine collects data from the device's camera and microphone, analyzes the user's facial expressions and voice, and recognizes emotions in real time.

[1466] Step 6:

[1467] The server generates an illustration generation request based on the extracted scenes and keywords, and the user's emotions recognized by the emotion engine. The request includes specific descriptions of the scenes and keywords, as well as emotional information.

[1468] Step 7:

[1469] The server sends an illustration generation request to the generation AI. The request is sent via the generation AI's API.

[1470] Step 8:

[1471] The AI ​​generates an illustration based on the request it receives, taking into account the scene, keywords, and the user's emotions to create the most appropriate illustration.

[1472] Step 9:

[1473] The generated illustration image is sent back to the server by the AI ​​generator, and is then temporarily stored in the server.

[1474] Step 10:

[1475] The server integrates the received illustration images into the original text data, inserts the illustrations into specific positions in the text data, and generates an e-book file in picture book format.

[1476] Step 11:

[1477] The server generates a download link for providing the generated e-book file in picture book format to the user, and the link is displayed on the web interface so that the user can access it.

[1478] Step 12:

[1479] The user uses the device to click the provided download link to download the generated e-book file in picture book format. The user can then open the downloaded e-book and begin reading it with the illustrations.

[1480] Example 2

[1481] 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."

[1482] Current electronic content systems lack the ability to create appropriate illustrations to visually support the text being read, and lack a means to provide a customized reading experience based on the user's emotions, making it difficult to provide effective reading support, especially for foreign language learners.

[1483] 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.

[1484] In this invention, the server includes: a means for a user to upload an electronic content file; a means for the server to convert the electronic content file into text data; a means for the server to extract scenes and keywords from the text data using a natural language processing algorithm; a means for the server to use an emotion engine that recognizes the user's emotions in real time; a means for the server to generate a prompt sentence for a generative AI model and send an illustration generation request; a means for the generative AI model to generate an illustration image and return it to the server; a means for the server to integrate the illustration image into the text data; and a means for the server to provide the user with the generated illustrated electronic content file, thereby enabling a reading experience with illustrations customized according to the user's emotions.

[1485] A "user" is an entity that uploads electronic content files to the system and receives the resulting illustrated electronic content files.

[1486] An "electronic content file" is a digital file containing text data, such as a PDF or EPUB file.

[1487] "Server" refers to the hardware and software that processes electronic content files uploaded by users and converts them into text data.

[1488] "Text data" is character string data extracted from an electronic content file, and is the subject of analysis and processing.

[1489] "Natural language processing algorithms" are technologies that include machine learning and statistical methods for extracting scenes and keywords from text data.

[1490] "Scenes and keywords" refer to specific scenes and important words within the text data, and are the information that forms the basis for generating illustrations.

[1491] The "emotion engine" is a technology that recognizes a user's emotions by analyzing data collected from a camera or microphone.

[1492] A "generative AI model" is an artificial intelligence algorithm that generates images based on a prompt, and examples include deep learning techniques.

[1493] A "prompt sentence" is a specific explanatory sentence used when sending an illustration generation request to the generative AI model.

[1494] An "illustrated image" is a visual picture generated by a generative AI model based on a prompt sentence.

[1495] "Integrating" means inserting the generated illustration image into the original text data at an appropriate location.

[1496] "Providing an electronic content file" means providing the created illustrated electronic content file in a form that can be accessed by the user.

[1497] The present invention is a system in which a user uploads an electronic content file, a server converts the file into text data, analyzes it, generates appropriate illustrations using a generative AI model, and provides the final illustrated electronic content file to the user. This system aims to provide an easy-to-understand reading experience, especially for foreign language learners.

[1498] System Overview

[1499] 1. User Operation

[1500] A user accesses the system's web interface through a terminal. The user selects the electronic content file (e.g., PDF or EPUB) they want to read and clicks the upload button. For example, a user uploads an EPUB file of "The Great Gatsby" by dragging and dropping it.

[1501] 2. Server Processing

[1502] The server receives the electronic content file uploaded by the user. After receiving it, the server converts the electronic content file into text data using software such as Adobe Acrobat or Calibre. The converted text data is stored in an internal database (e.g., MySQL or PostgreSQL).

[1503] 3. Text Analysis

[1504] The server uses Python's NLTK library and SpaCy to analyze the text data. Specifically, it tokenizes sentences and extracts scenes and keywords. This analysis extracts specific scenes and important phrases. For example, it extracts keywords corresponding to the scene "Gatsby gazes at Daisy."

[1505] 4. Emotion Recognition by Emotion Engine

[1506] While the user goes through the above process on the device, the emotion engine analyzes data collected from the user's camera and microphone to recognize the user's emotions in real time. For example, it can detect excitement or sadness from the user's facial expressions and voice. This is done using Microsoft Azure's Emotion API and Google Cloud's Natural Language API.

[1507] 5. Illustration generation request

[1508] The server generates a prompt to send to the generative AI model based on the extracted scenes and keywords, as well as the user's emotions detected by the emotion engine. This prompt includes a specific description of the scene and keywords, along with emotional information. For example, it could be in the format "The scene where Gatsby looks at Daisy, the user feels joy."

[1509] 6. Generative AI behavior

[1510] A generative AI model (e.g., OpenAI's DALLE or other similar technologies) generates an illustration based on the prompt received from the server. The generated illustration is then sent back to the server. For example, a specific illustration is generated based on the prompt, "The scene where Gatsby is looking at Daisy, and the user feels joy."

[1511] 7. Integration of illustrations

[1512] The server receives the illustration images returned by the generative AI model and inserts them into the original text data at the appropriate locations. This generates an electronic content file in picture book format. For example, the server inserts the corresponding illustration image immediately after the text "Gatsby gazing at Daisy."

[1513] 8. Provision to Users

[1514] The server generates a link to provide the generated picture book format electronic content file to the user, who can then download the electronic content via the link and begin reading it.

[1515] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1516] Step 1: A user uploads an electronic content file.

[1517] Input: An electronic content file (e.g., PDF or EPUB format) selected by the user through the device.

[1518] Specific behavior: A user accesses the system's web interface and clicks a button to upload an electronic content file they want to read. This displays a file selection dialog, where the user selects the desired file and confirms the upload.

[1519] Output: The file is sent to the server.

[1520] Step 2: The server converts the electronic content file into text data.

[1521] Input: Electronic content files uploaded by users.

[1522] What happens: The server recognizes the received file and converts it to text using software like Adobe Acrobat or Calibre. The conversion process involves sequentially parsing the pages of the file and extracting strings of characters.

[1523] Output: The converted text data is generated and stored in the server's internal database (e.g. MySQL or PostgreSQL).

[1524] Step 3: The server analyzes the text data using natural language processing algorithms.

[1525] Input: The converted text data.

[1526] How it works: The server uses natural language processing tools such as Python's NLTK library or SpaCy to analyze the text data. This analysis involves tokenizing the text and extracting key scenes and keywords. Each scene and keyword is ranked based on a specific importance.

[1527] Output: Extracted scenes and keyword list.

[1528] Step 4: The server uses an emotion engine to recognize the user's emotions in real time.

[1529] Input: Real-time data from the user's camera and microphone.

[1530] How it works: The server analyzes the user's facial expressions and voice data via Microsoft Azure's Emotion API and Google Cloud's Natural Language API, which allows it to identify the emotions the user is expressing in each scene.

[1531] Output: User emotion data.

[1532] Step 5: The server generates a prompt sentence for the generative AI model and sends a request to generate an illustration.

[1533] Input: A list of scenes and keywords, and user emotion data.

[1534] Specific operation: The server generates prompts based on this data. Specifically, it constructs prompts for each scene by combining detailed descriptions and emotion data. For example, the prompt might be, "In the scene where Gatsby looks at Daisy, the user feels joy."

[1535] Output: The prompt sent to the generative AI model.

[1536] Step 6: The generative AI model generates an illustration image and sends it back to the server.

[1537] Input: The prompt text sent by the server.

[1538] What it does: A generative AI model (e.g., OpenAI's DALLE or similar technology) analyzes the prompt and generates a corresponding illustration image, which is then sent back to the server in its raw form.

[1539] Output: The generated illustration image.

[1540] Step 7: The server integrates the illustration images into the text data.

[1541] Input: Illustration images returned from the generative AI model, original text data.

[1542] Specific operation: The server inserts the generated illustration images into the text data at the appropriate places, specifically by applying an algorithm to integrate the images, such as immediately after each scene.

[1543] Output: Integrated picture book format electronic content file.

[1544] Step 8: The server provides the generated illustrated electronic content file to the user.

[1545] Input: Integrated picture book format electronic content file.

[1546] What happens: The server uploads this file to a cloud storage location accessible to the user (e.g. AWS S3) and generates a download link, which is sent to the user's dashboard or email.

[1547] Output: A downloadable link for the user.

[1548] (Application example 2)

[1549] 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."

[1550] The lack of visual elements in e-books can make learning and reading difficult, especially for language learners. Furthermore, content provided without considering the user's emotional state can reduce user satisfaction and concentration. Therefore, there is a need for a system that can dynamically generate illustrations based on the user's emotions, providing a visually and emotionally rich reading experience.

[1551] 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.

[1552] In this invention, the server includes: a means for a user to upload an e-book file; a means for the server to convert the e-book file into text data; a means for the server to extract scenes and keywords from the text data; a means for analyzing the user's emotions in real time using an emotion analysis engine; a means for the server to send to the generation AI a request to generate illustrations based on the scenes, keywords, and the user's emotions; a means for the generation AI to generate illustration images and return them to the server; a means for the server to integrate the illustration images into the text data; and a means for the server to provide the user with the generated illustrated e-book file. This makes it possible to dynamically generate illustrations according to the user's emotions and provide a visually and emotionally rich reading experience, especially for language learners.

[1553] "User" means an individual or legal entity who uses the System to upload e-book files and receive illustrated e-books.

[1554] An "e-book file" is a reading material that stores information such as text and images in digital format, and typical formats include PDF and EPUB.

[1555] The "server" is a computer system that analyzes e-book files, converts them into text data, extracts scenes and keywords, and integrates the illustrations generated by the generation AI to generate e-book files, which are then provided to users.

[1556] "Text data" refers to text data extracted from an e-book file and processed and analyzed by the server.

[1557] A "scene" is a portion of text data that indicates a specific scene or situation, and serves as basic information for generating an illustration image.

[1558] "Keywords" are important words or phrases in the text data, and are information that clarifies a scene.

[1559] An "emotion analysis engine" is software that analyzes data collected from the user's camera and microphone and recognizes the user's emotional state.

[1560] "Generative AI" is an artificial intelligence model that generates illustration images based on given scenes, keywords, and the user's emotions.

[1561] An "illustration generation request" is an instruction sent by the server to the generation AI, and includes the scene, keywords, and the user's emotional information.

[1562] "Illustrated images" are illustrations or visuals generated by generative AI and integrated into text data.

[1563] "Integration" refers to the process of incorporating the generated illustration images into the original text data at the appropriate locations.

[1564] "Generating an e-book file" refers to the process of creating a new e-book file containing illustrations.

[1565] "Providing to the user" refers to delivering the generated illustrated e-book file to the user via a downloadable link.

[1566] The present invention is a system that provides illustrations based on scenes, keywords, and user emotions to enrich the user's reading experience, especially for language learners. This system is realized through a series of processes: analyzing e-book files, performing emotion recognition, and generating illustrations.

[1567] Program Overview

[1568] 1. User Interface

[1569] Users upload e-book files to the web from their smartphones or PCs.

[1570] It is also possible to set it so that when a user purchases an e-book, it is automatically uploaded.

[1571] 2. E-book analysis

[1572] The uploaded e-book file (PDF, EPUB, etc.) is sent to the server.

[1573] The server uses an appropriate conversion algorithm to convert the e-book file into text data.

[1574] 3. Natural Language Processing and Keyword Extraction

[1575] The server uses natural language processing algorithms (e.g., spaCy or NLTK) to extract scenes and keywords from the text data.

[1576] This process provides important information needed for subsequent illustration generation.

[1577] 4. Emotion analysis

[1578] A sentiment analysis engine (for example, Microsoft Azure's Cognitive Services) uses data from the user's camera and microphone to analyze the user's emotions in real time.

[1579] 5. Creating an illustration generation request

[1580] The server sends an illustration generation request to the generative AI model based on the extracted scenes, keywords, and emotion analysis results.

[1581] The illustration generation request includes an appropriate prompt, such as:

[1582] "Scene: Gatsby and Daisy gazing at each other. User emotion: Joy. Generates illustrations containing warm, romantic images."

[1583] 6. Use of generative AI

[1584] A generative AI model (e.g., OpenAI GPT-4-based model) generates an illustrated image based on the prompt and analysis data.

[1585] The generated illustration is sent back to the server.

[1586] 7. Integration of illustrations

[1587] The server uses an algorithm to appropriately integrate the illustrations into the text data.

[1588] This process generates an illustrated e-book file.

[1589] 8. Provision to Users

[1590] The server generates a link for providing the generated illustrated e-book file to the user.

[1591] Users can then download the generated e-book via that link and begin reading.

[1592] This allows illustrations to be dynamically generated based on the user's emotions, providing a visually and emotionally rich reading experience, especially for language learners.

[1593] Hardware and software used

[1594] Hardware: Smartphones, PCs, cloud servers

[1595] Software: Web application frameworks (Flask / Django), natural language processing libraries (spaCy, NLTK), sentiment analysis API (Microsoft Azure Cognitive Services), generative AI (OpenAI GPT-4 based model)

[1596] This system is extremely useful for users as it can perform all processes from analyzing e-book files to providing new e-book files containing the generated illustrations.

[1597] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1598] Step 1:

[1599] A user uploads an e-book file (e.g., PDF or EPUB) using a smartphone or PC. To do this, the user accesses the system's web interface, selects the e-book file, and clicks the upload button. The input is the e-book file, and the output is sending that file to the server.

[1600] Step 2:

[1601] The server receives the uploaded e-book file. It uses an appropriate conversion algorithm (e.g., a PDF to text conversion library) to convert the received file into text data. The input is the e-book file, and the output is text data.

[1602] Step 3:

[1603] The server analyzes the text data using a natural language processing algorithm (e.g., spaCy or NLTK). It extracts scenes and keywords from the text data. The input is the text data, and the output is the extracted scenes and keywords.

[1604] Step 4:

[1605] The server sends data collected from the user's device via the camera and microphone to an emotion analysis engine (for example, Microsoft Azure's Cognitive Services). This allows the user's emotions to be analyzed in real time. The input is the audio and image data collected from the camera and microphone, and the output is analyzed emotion data.

[1606] Step 5:

[1607] The server sends an illustration generation request to the generative AI model based on the extracted scenes, keywords, and emotion analysis results. This request includes a specific description of the scene and keywords, as well as emotional information. An example prompt is: "Scene: Gatsby and Daisy gazing at each other. User's emotion: Joy. Generate an illustration that includes a warm, romantic image." The input is the scene, keywords, and emotion data, and the output is an illustration generation request.

[1608] Step 6:

[1609] The generative AI model generates an illustration image based on the received illustration generation request. Using a generative AI model (e.g., OpenAI GPT-4 based model), an illustration is generated according to the presented prompt sentence. The input is the illustration generation request, and the output is the generated illustration image.

[1610] Step 7:

[1611] The server receives the illustration images returned by the generative AI model. The server uses an integration algorithm to properly integrate the illustration images into the text data. The input is the text data and the illustration images, and the output is an illustrated e-book file.

[1612] Step 8:

[1613] The server generates a link to provide the generated illustrated e-book file to the user, who can then download the generated e-book and begin reading it. The input is the illustrated e-book file, and the output is the download link.

[1614] 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.

[1615] 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.

[1616] 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.

[1617] 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.

[1618] 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.

[1619] 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.

[1620] 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).

[1621] 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.

[1622] 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."

[1623] 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.

[1624] 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).

[1625] 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.

[1626] 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.

[1627] 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.

[1628] 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.

[1629] 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.

[1630] 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.

[1631] 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.

[1632] 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.

[1633] 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.

[1634] 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.

[1635] The following is further disclosed regarding the above embodiment.

[1636] (Claim 1)

[1637] a means for a user to upload an e-book file;

[1638] A means for the server to convert the e-book file into text data;

[1639] A means for the server to extract scenes and keywords from the text data;

[1640] A means for the server to send an illustration generation request to the generation AI;

[1641] A means for the generating AI to generate illustration images and return them to the server;

[1642] A means for the server to integrate the illustration image into the text data;

[1643] a means for the server to provide the generated illustrated e-book file to the user;

[1644] A system including:

[1645] (Claim 2)

[1646] 10. The system of claim 1, wherein the server uses a natural language processing algorithm to extract scenes and keywords from the text data.

[1647] (Claim 3)

[1648] 10. The system of claim 1, wherein the server applies an algorithm to appropriately insert illustration images into text data to generate an e-book file in picture book format.

[1649] "Example 1"

[1650] (Claim 1)

[1651] a means for users to upload book files;

[1652] A means for the server to convert the book file into document data;

[1653] A means for the server to extract scenes and key phrases from document data;

[1654] A means for the server to send an illustration generation request to the generation algorithm;

[1655] A means for the generation algorithm to generate an illustration image and return it to the server;

[1656] A means for the server to integrate illustration images into document data;

[1657] means for the server to provide the generated illustrated book file to the user;

[1658] A system including:

[1659] (Claim 2)

[1660] 2. The system of claim 1, wherein the server extracts scenes and key phrases from the document data using natural language processing technology.

[1661] (Claim 3)

[1662] 10. The system of claim 1, wherein the server applies an algorithm to appropriately insert illustration images into document data to generate a book file in picture book format.

[1663] "Application Example 1"

[1664] (Claim 1)

[1665] a means for a user to upload a digital document file;

[1666] A means for the server to convert the digital document file into text data;

[1667] A means for the server to extract scenes and keywords from the text data;

[1668] A means for the server to send an illustration generation request to the generating artificial intelligence model;

[1669] A means for the generating artificial intelligence model to generate an illustration image and return it to the server;

[1670] A means for the server to integrate the illustration image into the text data;

[1671] means for the server to provide the generated illustrated digital document file to a user;

[1672] means for a user to download and view digital document files from a smartphone application;

[1673] A system including:

[1674] (Claim 2)

[1675] 10. The system of claim 1, wherein the server uses a natural language processing algorithm to extract scenes and keywords from the text data.

[1676] (Claim 3)

[1677] 10. The system of claim 1, wherein the server applies an algorithm to appropriately insert illustration images into text data to generate a digital document file in a visually-aided format.

[1678] "Example 2: Combining Emotion Engines"

[1679] (Claim 1)

[1680] means for users to upload electronic content files;

[1681] means for the server to convert the electronic content file into text data;

[1682] A means for the server to extract scenes and keywords from the text data using a natural language processing algorithm;

[1683] A means for the server to use an emotion engine that recognizes the user's emotion in real time;

[1684] A means for the server to generate a prompt sentence for the generating AI model and send a request to generate an illustration;

[1685] A means for the generative AI model to generate illustration images and return them to the server;

[1686] A means for the server to integrate the illustration image into the text data;

[1687] means for the server to provide the generated illustrated electronic content file to a user;

[1688] A system including:

[1689] (Claim 2)

[1690] 10. The system of claim 1, wherein the server generates automatically generated prompt sentences and sends illustration generation requests.

[1691] (Claim 3)

[1692] 10. The system of claim 1, wherein the server applies an algorithm to appropriately insert the pictorial images into the text data to generate the electronic content file.

[1693] "Application example 2 when combining emotion engines"

[1694] New Claims

[1695] (Claim 1)

[1696] a means for a user to upload an e-book file;

[1697] A means for the server to convert the e-book file into text data;

[1698] A means for the server to extract scenes and keywords from the text data;

[1699] a means for analyzing user emotions in real time using a sentiment analysis engine;

[1700] A means for the server to send a request for illustration generation to the generation AI based on the scene, keywords, and user's emotions;

[1701] A means for the generating AI to generate illustration images and return them to the server;

[1702] A means for the server to integrate the illustration image into the text data;

[1703] a means for the server to provide the generated illustrated e-book file to the user;

[1704] A system including:

[1705] (Claim 2)

[1706] 10. The system of claim 1, wherein the server uses a natural language processing algorithm to extract scenes and keywords from the text data.

[1707] (Claim 3)

[1708] 10. The system of claim 1, wherein the server applies an algorithm to appropriately insert illustration images into text data to generate an e-book file in picture book format. [Explanation of symbols]

[1709] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to upload an e-book file; A means for the server to convert the e-book file into text data; A means for the server to extract scenes and keywords from the text data; A means for the server to send an illustration generation request to the generation AI; A means for the generating AI to generate illustration images and return them to the server; A means for the server to integrate the illustration image into the text data; a means for the server to provide the generated illustrated e-book file to the user; A system including:

2. The system of claim 1 , wherein the server uses a natural language processing algorithm to extract scenes and keywords from the text data.

3. The system of claim 1 , wherein the server applies an algorithm to appropriately insert illustration images into text data to generate an e-book file in picture book format.

Images