System

Abstract

A system is provided.SOLUTION: A system comprising: means for digitizing a book; means for generating a synopsis from book data; means for generating a prompt for an image generation model based on the generated synopsis; means for generating an illustration with the image generation model using the generated prompt; and means for integrating the generated illustration into electronic book data.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] There is a problem that many people who are interested in reading find it difficult to get into books that are full of text, or find it difficult to imagine the contents of a novel. This reduces the enjoyment of reading, and there are concerns that people will stop reading. In particular, while visual aids can deepen understanding, existing books have few illustrations, making it difficult for readers to grasp the content. [Means for solving the problem]

[0005] The present invention provides a system for providing illustrated documents by digitizing books and combining summaries and automatically generated illustrations. The present invention includes the following means:

[0006] The system includes a means for digitizing books, a means for generating a synopsis from book data, a means for generating prompts for an image generation model based on the generated synopsis, a means for generating illustrations in the image generation model using the generated prompts, and a means for integrating the generated illustrations into e-book data. This allows readers to enjoy a more intuitive and interesting reading experience while receiving visual aids. Furthermore, the system allows users to select illustrations at either a pictorial or manga level depending on their needs, making it suitable for a wide range of readers. Furthermore, the system can be applied to a variety of documents other than books, such as instruction manuals and dictionaries, and can complement parts that are visually difficult to understand.

[0007] "Means for digitizing books" refers to means for converting paper books into electronic files, and specifically includes scanning with a scanner and OCR (optical character recognition) technology.

[0008] "Book data" refers to data including text data and image data of electronic books.

[0009] A "means for generating a synopsis" is a means for analyzing book data and generating a summary of the contents, specifically using a generation AI (e.g., GPT-4).

[0010] The "means for generating prompts for an image generation model" refers to a means for automatically creating text instructions for causing an image generation AI model to generate appropriate images based on the generated synopsis.

[0011] An "image generation model" is an AI model that generates images based on text prompts, and specifically includes generation AI such as DALL-E 3.

[0012] The "means for generating an illustration" is a means for creating a visual illustration based on the generated prompt using an image generation model.

[0013] An "illustrated document" is an electronic document that combines text and images, created by integrating digitized book data with generated illustrations.

[0014] "At the user's request" means customizing the system's output to suit the particular needs and preferences of the system user.

[0015] The terms "illustration level and cartoon level" refer to different levels of complexity and continuity of illustration, with the illustration level referring to simple illustrations and the cartoon level referring to detailed, continuous image formats.

[0016] "Instruction manuals and dictionaries" refer to documents other than books that contain many explanations and explanations, and in which visual aids may aid the reader's understanding. [Brief explanation of the drawings]

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

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

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

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention relates to a system for providing illustrated documents by digitizing books and combining summaries and automatically generated illustrations. The system is composed of a server, user terminals, and data exchanged between them.

[0039] System configuration

[0040] 1. Electronic means

[0041] Users use their device to scan books and generate PDF files, using a traditional scanner or OCR technology.

[0042] The user uploads the generated PDF to the system.

[0043] 2. Book Data Processing

[0044] The server receives the uploaded PDF and uses OCR technology to extract the text data, converting the paper book into digital text.

[0045] 3. Synopsis Generation

[0046] The server analyzes the extracted text data and automatically generates a summary using a generative AI model, which creates a summary that gives an overview of the entire book.

[0047] 4. Prompt Generation

[0048] The server generates prompts for the image generation model based on the generated synopsis, for example, prompts containing descriptions of specific scenes or characters.

[0049] 5. Illustration Generation

[0050] The server uses an image generation model (e.g., DALL-E 3) to automatically generate illustrations based on the generated prompts, which provide visual content related to the summary.

[0051] 6. Generating Illustrated Documents

[0052] The server combines the text data with the generated illustrations to generate an illustrated document, which is then output in an e-book format (e.g., PDF or EPUB).

[0053] 7. Output and Distribution

[0054] The server transmits the generated illustrated document to the user's terminal, where the user can download and view it.

[0055] Specific examples

[0056] 1. Examples of digitization

[0057] Scan the book "Example Story" entered by the user, generate a PDF using the scanner and OCR, and upload it to the system.

[0058] 2. Book Data Processing

[0059] The server receives the PDF and extracts the text using OCR, resulting in the text data of "The Example Story."

[0060] 3. Synopsis Generation

[0061] The server uses a generative AI model to create a concise summary from the text data, including key plot points such as the scene where the protagonist goes on an adventure and the climax of the story.

[0062] 4. Prompt Generation

[0063] Based on the generated synopsis, the server creates specific prompts such as "a scene where the protagonist climbs a mountain" or "a scene where the protagonist finds a magic sword."

[0064] 5. Illustration Generation

[0065] The server uses these prompts to generate corresponding illustrations from an image generation model, such as the protagonist climbing a mountain or finding a magic sword.

[0066] 6. Generating Illustrated Documents

[0067] The server inserts the generated illustrations into the corresponding text sections to generate an illustrated document, which is saved in PDF format.

[0068] 7. Output and Distribution

[0069] The server sends the PDF to the user's device, and the user downloads and views the illustrated document "The Example Story" on the device.

[0070] This system allows text-only books to be presented in a visually enjoyable format, reducing the barrier to reading. Additionally, users can select the level of illustrations to suit their needs, allowing them to freely enjoy detailed or simple illustrations. This invention is also effective in helping users understand a variety of documents, including instruction manuals and dictionaries, in addition to books.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] A user scans a book using a device and creates a PDF file. For example, a user scans each page of a book using a scanner and saves them as a single PDF file.

[0074] Step 2:

[0075] A user uploads a PDF file from their device to the system, and then sends the PDF to the system's server using a dedicated application or web browser on the device.

[0076] Step 3:

[0077] The server receives and records the uploaded PDF file, and then prepares the PDF file for OCR processing.

[0078] Step 4:

[0079] The server extracts text data from PDF files using OCR (Optical Character Recognition) technology, which captures character information from scanned images and saves them as text files.

[0080] Step 5:

[0081] The server analyzes the extracted text data and automatically generates a summary of the book using a generative AI, such as GPT-4, to generate a key plot summary from the text data.

[0082] Step 6:

[0083] Based on the generated summary, the server creates a prompt for the image generation model (e.g., DALL-E 3). For example, it generates a specific prompt such as "A scene where the main character goes on an adventure."

[0084] Step 7:

[0085] The server sends a prompt to DALL-E 3, which automatically generates a corresponding illustration. Based on the generated prompt, DALL-E 3 generates an illustration of the specified scene or item.

[0086] Step 8:

[0087] The server receives and saves the generated illustration, adjusting the size and resolution of the illustration as needed.

[0088] Step 9:

[0089] The server integrates the text data with the generated illustrations to generate an illustrated document, for example, by inserting illustrations corresponding to each generated plot summary at appropriate positions in the text to create an integrated document.

[0090] Step 10:

[0091] The server saves the completed illustrated document in PDF or EPUB format and prepares it for delivery to the user's device.

[0092] Step 11:

[0093] The server sends the illustrated document to the user's terminal, where the user can download and view the document.

[0094] This process flow allows users to enjoy text-only books in a visually appealing format, reducing the barrier to reading. Additionally, users can select the level of illustrations to suit their needs, allowing them to freely enjoy detailed or simple illustrations. The present invention is also effective in helping users understand a variety of documents, including instruction manuals and dictionaries, in addition to books.

[0095] Example 1

[0096] 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."

[0097] Conventional e-book systems simply digitize books, resulting in a lack of visual elements that attract readers' attention. Furthermore, adding visual illustrations is often done manually, requiring a significant amount of time and effort. Furthermore, they lack the versatility to handle documents other than books and the ability to customize them to meet user needs.

[0098] 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.

[0099] In this invention, the server includes means for digitizing books, means for extracting text data from the digitized book data, means for generating a synopsis from the extracted text data, means for generating prompts for image generation based on the generated synopsis, means for creating illustrations using an image generation model using the generated prompts, means for integrating the generated illustrations with text data to generate an illustrated document, and means for transmitting the generated illustrated document to a user terminal. This automates the entire process from digitizing books to synopsis generation, automatic generation of illustrations, and generation and distribution of the illustrated document, making it possible to provide visually appealing e-books. Furthermore, this allows for flexible customization according to user needs and support for documents other than books.

[0100] "Means for digitizing books" refers to devices and software that scan books and generate their contents as digital data, such as PDF files.

[0101] "Means for extracting text data from digitized book data" refers to devices or software that utilize OCR technology to extract text information from digitized book data.

[0102] "Means for generating a synopsis from extracted text data" refers to a device or software for analyzing digitized text data and generating a summary of the main contents or key points.

[0103] "Means for generating prompts for image generation based on the generated synopsis" refers to a device or software that creates specific instructions (prompts) for generating illustrations from the generated synopsis.

[0104] "Means for creating an illustration in an image generation model using the generated prompt" refers to a device or software for inputting a prompt into an image generation model and automatically generating an illustration based on the prompt.

[0105] "Means for integrating generated illustrations into text data to generate an illustrated document" refers to devices or software for inserting generated illustrations into corresponding text sections to create a visually rich illustrated electronic document.

[0106] "Means for transmitting the generated illustrated document to a user terminal" refers to a device or software for transmitting the generated illustrated document to a user's electronic device via a network.

[0107] "The level of illustration can be selected according to the user's request" refers to a function that allows the user to choose to generate more detailed illustrations or simple illustrations according to their own preferences.

[0108] "It can be applied to a variety of documents other than books, such as instruction manuals and dictionaries" refers to the system's ability to be applied not only to the digitization of books, but also to a variety of other documents, and to effectively add visual information to each document.

[0109] This invention relates to a system that provides visually appealing illustrated documents by digitizing books and combining summaries and automatically generated illustrations. This system is composed of a server, user terminals, and the data exchanged between them.

[0110] The main configuration and processing steps of the system are as follows.

[0111] electronic means

[0112] A user scans a book with a scanner and generates a PDF file. Specifically, a USB-connected scanner is used to extract text data using OCR technology (e.g., Tesseract OCR). The generated PDF file is then uploaded to the system through a web interface. For example, a user scans a book called "The Example Story," saves it as a PDF, and uploads it to the system.

[0113] Book data processing

[0114] The server receives the uploaded PDF file and stores it in a temporary directory. Then, it uses OCR technology to extract text data from the PDF. Specifically, it uses Tesseract OCR to analyze the text information in the PDF and obtain it as digital text. This digitizes the paper book.

[0115] Synopsis generation

[0116] The server analyzes the extracted text data and generates a synopsis of the book using a generative AI model (e.g., GPT-4). Specifically, by inputting the text data into the AI ​​model, a summary containing the main content is obtained. For example, a summary is generated of the scene in which the protagonist goes on an adventure and the climax of the story.

[0117] Prompt Generation

[0118] Based on the generated synopsis, the server creates prompts for the image generation model, including descriptions of specific scenes and characters, such as "Please describe the scene where the main character climbs the mountain" or "Please describe the scene where the main character finds the magic sword."

[0119] Illustration generation

[0120] The server inputs the prompt sentence into an image generation model (e.g., DALL-E 3) and generates a corresponding illustration. Specifically, the prompt is input into the generation model to obtain a visually detailed illustration. For example, an illustration of "the scene where the protagonist climbs a mountain" or "the scene where the protagonist finds a magic sword" is generated.

[0121] Generating illustrated documents

[0122] The server then integrates the generated illustrations with the text data to generate an illustrated document. Specifically, it inserts the illustrations at appropriate points in the summary and saves them as a PDF or EPUB file. For example, a PDF file with illustrations inserted at appropriate times along with the summary of "The Example Story" is created.

[0123] Output and Delivery

[0124] The server sends the generated illustrated document to the user's device, and the user can download and view the e-book through the system interface. For example, the user can download an illustrated PDF of "The Example Story" and open it on their device to read.

[0125] This system allows books to be presented in a visually enjoyable format, enhancing reader interest and understanding.It can also handle documents other than books (such as instruction manuals and dictionaries), making it a system that can be used for a wide range of purposes.

[0126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0127] Step 1: Scan your eBook

[0128] A user scans a book with a scanner and generates a PDF file. In this operation, the pages of the book are placed one by one on the scanner, and scanning begins by pressing the scanner's start button. The scanned data is automatically saved in PDF format. The input data is the paper book, and the output data is a PDF file.

[0129] Step 2: Upload your PDF file

[0130] The user opens the web interface to upload the generated PDF file to the system. The user clicks the Upload button, selects the target PDF file from the file selection dialog, and presses the Send button to complete the process. The input data is a PDF file, and the output data is a PDF file saved on the server.

[0131] Step 3: Receive and save the PDF file

[0132] The server receives the PDF file uploaded by the user. In this process, the PDF file is saved in a temporary directory on the server. The input data is the PDF file sent to the server, and the output data is the PDF file saved in the temporary directory.

[0133] Step 4: Extract text data

[0134] The server uses OCR technology to extract text data from PDF files. Specifically, it uses the Tesseract OCR engine to analyze image data from PDFs and extract character information. The input data is the PDF file, and the output data is the extracted text data.

[0135] Step 5: Generate a summary

[0136] The server analyzes the extracted text data and generates a summary using a generative AI model (e.g., GPT-4). The text data is input into the AI ​​model, which outputs a summary that extracts the main content and key points. In this step, the input data is the text data, and the output data is the summary text.

[0137] Step 6: Create a prompt

[0138] The server creates prompts for image generation based on the generated summary. Specifically, it selects important scenes and characters in the summary and generates specific prompt sentences to depict them. The input data is the summary text, and the output data is the prompt sentences.

[0139] Step 7: Creating an illustration

[0140] The server inputs the prompt sentence into an image generation model (e.g., DALL-E 3) and generates a corresponding illustration. The server inputs the prompt sentence into the model and receives the generated illustration. The input data is the prompt sentence, and the output data is the generated illustration.

[0141] Step 8: Generating the illustrated document

[0142] The server integrates the generated illustrations with the text data to generate an illustrated document. Specifically, it inserts the illustrations at appropriate positions in the summary text and saves it as a PDF or EPUB file. The input data is the summary text and illustrations, and the output data is the illustrated document.

[0143] Step 9: Distribution of illustrated documents

[0144] The server sends the generated illustrated document to the user's terminal, and the user can download and read the e-book through the system interface. The input data is the illustrated document, and the output data is the illustrated document stored in the user's terminal.

[0145] (Application example 1)

[0146] 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."

[0147] Traditional books relied on paper media, making it difficult to digitize or visually enhance their content. This limited opportunities for readers to visually enjoy the book's contents. Even with digitized books, the increase in text data made it difficult to efficiently grasp the content, and visual aids were lacking. Furthermore, the process of converting paper books into digital format was complicated, making it difficult for the average user to easily use.

[0148] 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.

[0149] In this invention, the server includes means for digitizing books, means for generating a synopsis from book data, means for generating prompts for an image generation model based on the generated synopsis, means for generating illustrations with the image generation model using the generated prompts, means for integrating the generated illustrations into e-book data, means for automatically scanning books using a smartphone camera, means for extracting text using OCR technology, and means for generating e-books in PDF format and making them available for download. This simplifies the process of digitizing books and enables readers to efficiently grasp the contents of books and enjoy them visually through summaries and visual complements.

[0150] "Digitizing a book" means converting a physical book into a digital format, which may involve the use of a scanner or camera.

[0151] "Means for generating synopsis from book data" refers to a technology that summarizes important content from extracted book text data, and uses generative AI and natural language processing technology.

[0152] "Means for generating prompts for an image generation model based on the generated synopsis" refers to a technology for creating instructions for generating images based on the summary.

[0153] An "image generation model" is an artificial intelligence system that generates images based on text prompts.

[0154] "Means for generating illustrations using an image generation model using the generated prompts" refers to the process by which AI creates illustrations using generated instructions.

[0155] The "means for integrating the generated illustrations into the e-book data" is a technology that combines the generated illustrations with text data to create a single e-book format file.

[0156] "Method for automatically scanning books using a smartphone camera" is a technology that uses a smartphone's built-in camera to photograph the pages of a book and digitize the data.

[0157] "Means of extracting text using OCR technology" refers to optical character recognition (OCR), a technology that recognizes and extracts character information from image data.

[0158] "Means for generating and making downloadable e-books in PDF format" refers to technology that converts the generated text and illustrations into a PDF file that can be downloaded by users.

[0159] "Users can choose between illustrations at the level of illustrations and comics, depending on their needs" refers to the ability for users to choose the level of detail and style of the illustrations to be generated.

[0160] "Can be applied to a variety of documents, such as instruction manuals and dictionaries" means that this system can be applied to various types of documents other than books.

[0161] The present invention relates to a system for providing illustrated documents by digitizing books and combining summaries and automatically generated illustrations. Specific embodiments of the present invention will be described below.

[0162] The system consists of a user device, a server, and the data exchanged between them. First, the user device automatically scans the book pages using a smartphone camera. This image is converted to grayscale, and text information is extracted using OCR technology. The OCR technology uses Tesseract, a high-performance character recognition software. At this stage, the book's text data is obtained.

[0163] The server receives this text data and uses a generative AI model (e.g., GPT-3.5) to generate a summary, using the following prompt:

[0164] Summarize the following text:

[0165] (Text data)

[0166] Based on the generated summary, a prompt is created for the image generation model. For example, specific prompts such as "a scene where the main character climbs a mountain" or "a scene where the magic sword is found" are generated. Based on this prompt, an image generation model (such as "DALL-E") is used to generate an illustration.

[0167] The generated illustrations are integrated with the corresponding text to generate an illustrated e-book. The e-book data is saved in PDF format and made available for download to users. This download function allows users to view the generated illustrated book on their own devices.

[0168] The server handles all processes from generating summaries to creating prompts, generating images, and finally integrating them all at once, allowing users to easily digitize books and enjoy them visually without any special technical knowledge.

[0169] As a concrete example, a user scans a book called "The Book of Adventure," and the server extracts the text using OCR and generates a summary using a generative AI model.

[0170] The scene where the main character fights a dragon

[0171] The system generates prompt sentences such as "What is the meaning of this book?" and uses an image generation model to create corresponding illustrations. Finally, the illustrations and summaries are integrated to create an attractive illustrated book.

[0172] This system simplifies the process of digitizing paper books, and allows readers to efficiently understand the content and enjoy it more through summaries and visual complements.

[0173] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0174] Step 1:

[0175] The user scans each page of a book using the smartphone camera. The input is the physical page of the book, and the output is a digital image file. The smartphone's camera application captures it and saves it as a grayscale JPEG or PNG file. This data is then ready for subsequent OCR processing.

[0176] Step 2:

[0177] The user converts the scanned image into text data using OCR technology. The input is a grayscale image, and the output is text data (String). Specifically, OCR software (e.g., Tesseract) installed on the device analyzes the image and extracts text information. The result of the OCR process is text data extracted from the book page.

[0178] Step 3:

[0179] The server receives the extracted text data and generates a summary using a generative AI model (e.g., GPT-3.5). The input is the text data, and the output is the summary (String). The server passes the text data to the generative AI model and sends a prompt like the following:

[0180] Summarize the following text:

[0181] (Text data)

[0182] The generative AI model returns a summary, which is then retrieved by the server.

[0183] Step 4:

[0184] The server generates a prompt for the image generation model based on the generated summary. The input is the summary, and the output is a prompt (String) for the image generation model. Specifically, the server writes out details about important scenes and characters in the summary and generates a prompt such as "The scene where the main character climbs a mountain."

[0185] Step 5:

[0186] The server passes the generated prompt to an image generation model (e.g., DALL-E) to generate an illustration. The input is the prompt, and the output is the generated illustration (image file). The server inputs the prompt into the image generation model and obtains the generated illustration. This illustration is saved for use in the next step.

[0187] Step 6:

[0188] The server integrates the generated summaries and illustrations to generate illustrated e-book data. The input is the summaries and illustrations, and the output is an e-book file (PDF). The server arranges the summary text and the corresponding illustrations in an orderly manner and converts them into a unified PDF file. Specifically, this is done using a PDF generation library (e.g., FPDF).

[0189] Step 7:

[0190] The server sends the generated e-book file to the user's device for download. The input is the e-book file, and the output is the file downloaded to the user's device. The server generates a link to provide the generated PDF file to the user, and the user clicks on this link to complete the e-book download.

[0191] The specific actions taken at each step efficiently digitize books and generate visually enjoyable content.

[0192] 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.

[0193] This invention relates to a system that provides illustrated documents by digitizing books and combining summaries and automatically generated illustrations. At the same time, by combining an emotion engine that recognizes the user's emotions, it is possible to display illustrations and text that correspond to the user's real-time emotions. This system is composed of a server, a user terminal, and the data exchanged between them.

[0194] System configuration

[0195] 1. Electronic means

[0196] A user can use a device to scan a book and create a PDF file, for example, by using a scanner to scan each page of a book and saving it as a single PDF file.

[0197] The user uploads the generated PDF to the system.

[0198] 2. Book Data Processing

[0199] The server receives the uploaded PDF and extracts the text data using OCR technology, which captures the text information from the scanned image and saves it as a text file.

[0200] 3. Synopsis Generation

[0201] The server analyzes the extracted text data and automatically generates a summary using a generative AI model, which creates a summary that gives an overview of the entire book.

[0202] 4. Prompt Generation

[0203] Based on the generated summary, the server creates prompts for the image generation model (e.g., DALL-E 3), such as prompts containing descriptions of specific scenes or characters.

[0204] 5. Illustration Generation

[0205] The server uses an image generation model to automatically generate illustrations based on the generated prompts, which provide visual content related to the summary.

[0206] 6. Emotion Engine Functions

[0207] The device is equipped with an emotion engine that analyzes the user's facial expressions, tone of voice, etc. This emotion engine recognizes the user's emotions in real time.

[0208] The server receives data from the emotion engine and adjusts the tone and style of the illustrations it generates based on the user's emotions. For example, if the user looks sad, the system can generate illustrations with brighter colors to soothe the user.

[0209] 7. Generating Illustrated Documents

[0210] The server combines the text data with the generated illustrations to generate an illustrated document, which is then output in an e-book format (e.g., PDF or EPUB).

[0211] 8. Output and Distribution

[0212] The server transmits the generated illustrated document to the user's terminal, where the user can download and view it.

[0213] Specific examples

[0214] 1. Examples of digitization

[0215] Scan the book "Example Story" entered by the user, generate a PDF using the scanner and OCR, and upload it to the system.

[0216] 2. Book Data Processing

[0217] The server receives the PDF and extracts the text using OCR, resulting in the text data of "The Example Story."

[0218] 3. Synopsis Generation

[0219] The server uses a generative AI model to create a concise summary from the text data, including key plot points such as the scene where the protagonist goes on an adventure and the climax of the story.

[0220] 4. Prompt Generation

[0221] Based on the generated synopsis, the server creates specific prompts such as "a scene where the protagonist climbs a mountain" or "a scene where the protagonist finds a magic sword."

[0222] 5. Illustration Generation

[0223] The server uses these prompts to generate corresponding illustrations from an image generation model, such as the protagonist climbing a mountain or finding a magic sword.

[0224] 6. Applying the Emotion Engine

[0225] While a user is browsing a book on their device, the emotion engine analyzes their facial expressions and voice in real time. For example, if the user shows a surprised expression, the system will change the next illustration to emphasize the element of surprise.

[0226] 7. Generating Illustrated Documents

[0227] The server inserts the generated illustrations into the corresponding text sections to generate an illustrated document, which is saved in PDF format.

[0228] 8. Output and Distribution

[0229] The server sends the PDF to the user's device, and the user downloads and views the illustrated document "The Example Story" on the device.

[0230] This system allows users to enjoy text-only books in a visually appealing format, reducing the barrier to reading. Furthermore, responding to the user's real-time emotions provides a more personalized reading experience. Additionally, the level of illustrations can be selected according to the user's needs, allowing them to freely enjoy detailed or simple illustrations. The present invention is also effective in helping users understand a variety of documents, including instruction manuals and dictionaries, in addition to books.

[0231] The processing flow will be explained below.

[0232] Step 1:

[0233] A user scans a book using a device and creates a PDF file. For example, a user scans each page of a book using a scanner and saves them as a single PDF file.

[0234] Step 2:

[0235] A user uploads a PDF file from their device to the system, and then sends the PDF to the system's server using a dedicated application or web browser on the device.

[0236] Step 3:

[0237] The server receives and records the uploaded PDF file, and then prepares the PDF file for OCR processing.

[0238] Step 4:

[0239] The server extracts text data from PDF files using OCR (Optical Character Recognition) technology, which captures character information from scanned images and saves them as text files.

[0240] Step 5:

[0241] The server analyzes the extracted text data and automatically generates a summary of the book using a generative AI, such as GPT-4, to generate a key plot summary from the text data.

[0242] Step 6:

[0243] Based on the generated summary, the server creates a prompt for the image generation model (e.g., DALL-E 3). For example, it generates a specific prompt such as "A scene where the main character goes on an adventure."

[0244] Step 7:

[0245] The server sends a prompt to DALL-E 3, which automatically generates a corresponding illustration. Based on the generated prompt, DALL-E 3 generates an illustration of the specified scene or item.

[0246] Step 8:

[0247] The server receives and saves the generated illustration, adjusting the size and resolution of the illustration as needed.

[0248] Step 9:

[0249] The device uses an emotion engine to recognize the user's facial expressions and tone of voice, and transmits this information to a server in real time. For example, the device can collect user emotion data using a camera or microphone.

[0250] Step 10:

[0251] The server changes the tone and style of the illustration based on the emotional data it receives: if the user has a sad expression, the system adjusts to generate illustrations with brighter colors and more joyful scenes.

[0252] Step 11:

[0253] The server integrates the text data with the generated illustrations to generate an illustrated document, for example, by inserting illustrations corresponding to each generated plot summary at appropriate positions in the text to create an integrated document.

[0254] Step 12:

[0255] The server saves the completed illustrated document in PDF or EPUB format and prepares it for delivery to the user's device.

[0256] Step 13:

[0257] The server sends the illustrated document to the user's device. The user can download and view the received document. For example, it becomes possible to view "The Story of the Day" with illustrations corresponding to emotions.

[0258] This process flow allows users to enjoy text-only books in a visually appealing format, lowering the barrier to entry and enabling a more personalized reading experience by adapting content based on the user's real-time emotions.

[0259] Example 2

[0260] 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."

[0261] Because traditional books exist in physical form, reading generally requires a certain amount of effort, and text-only books in particular lack visual aids. Furthermore, existing e-book systems lack the ability to automatically generate illustrations, resulting in a uniformly flat reading experience. Furthermore, personalized content display based on user emotions has yet to be realized, leaving a need for improved user experience.

[0262] 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.

[0263] In this invention, the server includes means for digitizing books, means for generating a synopsis from book data, means for generating prompts for an image generation model based on the generated synopsis, means for generating illustrations with the image generation model using the generated prompts, means for integrating the generated illustrations into electronic book data, and means for adjusting the tone and style of the illustrations based on user emotion data. This allows books and various documents to be provided in a visually enjoyable format, and further enables a personalized reading experience that responds to the user's real-time emotions.

[0264] "Book digitization tools" refers to a set of equipment and software used to convert physical books into digital files (e.g., PDF).

[0265] "Means for generating a synopsis from book data" refers to technology that analyzes the text data of a book and extracts the main content as a summary.

[0266] "Means for generating prompts for an image generation model based on the generated synopsis" refers to a technology that creates explanatory text (prompts) to be input into an image generation model based on summarized text.

[0267] "Means for generating an illustration using an image generation model using the generated prompt" refers to a technology for creating an illustration of a specific scene or character by using an image generation model with the prompt as input.

[0268] "Means for integrating the generated illustrations into e-book data" refers to the technology for placing the created illustrations into the corresponding text parts and converting them into a unified e-book format (e.g., PDF, EPUB).

[0269] "Means for adjusting the tone and style of an illustration based on the user's emotional data" refers to technology that recognizes the user's emotional state from their facial expressions and tone of voice, and uses that data to dynamically change the color and design of the illustration.

[0270] The system of the present invention provides illustrated documents by digitizing books and combining summaries with automatically generated illustrations. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it is possible to display illustrations and text in response to the user's real-time emotions. This system is comprised of a server, user terminals, and the data exchanged between them.

[0271] First, the user scans a book using a scanner connected to their device and generates a PDF file. For example, they can use a general-purpose device as the scanner and Tesseract OCR as the OCR software to scan each page of the book and compile them into a single PDF file. The user then uploads this PDF file to the server through the system's upload interface.

[0272] The server receives the uploaded PDF file and extracts the text data using OCR technology, such as Tesseract OCR, which extracts the text information from the scanned image and saves it as a text file.

[0273] The server then analyzes the extracted text data and automatically generates a summary using a generative AI model. Specifically, it uses a natural language processing library (e.g., spaCy) to tokenize the text and extract key nouns and verbs. It then uses a generative AI model (e.g., OpenAI GPT-3) to extract important parts from the long text and create a concise summary.

[0274] Based on the generated summary, the server creates prompts for the image generation model. The prompts include specific descriptions of specific scenes or characters from the summarized content. For example, a prompt might be generated such as "the scene where the main character climbs a mountain" or "the scene where he finds the magic sword."

[0275] The server then uses the generated prompts to generate illustrations using an image generation model (e.g., DALL-E 3). Based on the prompts, the model uses the learned data to generate high-quality illustrations. The generated illustrations are saved as visual content related to the summary.

[0276] Additionally, the device is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine includes a camera and microphone to analyze the user's facial expressions and tone of voice. For analysis, Microsoft Azure's emotion recognition API is used, for example. The server receives data from the emotion engine and adjusts the tone and style of the illustration based on the user's emotional state.

[0277] Finally, the server integrates the generated text data with the illustrations to generate an illustrated document, which is then exported as an e-book in PDF or EPUB format.The generated document is then sent to the user's device via the server, where the user can download and view the document on their device.

[0278] Specific examples are shown below.

[0279] For example, a user scans a book called "The Great Story," generates a PDF, and uploads it to the system. The server receives the PDF and uses OCR technology to extract the text data. Using a generative AI model, the server generates plot summaries, such as "the scene where the protagonist goes on an adventure" and "the climax of the story." Based on these plot summaries, the server then creates prompts, such as "the scene where the protagonist climbs a mountain" and "the scene where the protagonist finds the magic sword." The server then uses the DALL-E 3 model to generate illustrations based on these prompts. As the user browses the book, the device's built-in emotion engine analyzes the user's facial expressions and voice in real time, adjusting the tone of the visual content according to their emotions. Finally, the generated text data and illustrations are integrated to generate an illustrated document, which is then sent to the user's device. The user can then download and view it.

[0280] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0281] Step 1:

[0282] A user scans a book using a scanner connected to a device and generates a PDF file. The input is the physical book and the output is the PDF file. For example, a user scans each page of a book using a scanner and saves them as a PDF file.

[0283] Step 2:

[0284] The user uploads the generated PDF file from the terminal to the server through the system's web interface. The input is a PDF file, and the output is a PDF file stored in the server's storage. Specifically, the user accesses the web application using a browser, clicks the upload button, and selects the file.

[0285] Step 3:

[0286] The server receives the uploaded PDF file and extracts the text data using OCR technology. The input is a PDF file and the output is a text file. Specifically, it uses the Tesseract OCR engine to extract text information from each page of the PDF and saves it as a text file.

[0287] Step 4:

[0288] The server analyzes the extracted text data and automatically generates a summary using a generative AI model. The input is a text file, and the output is the summary text. Specifically, a natural language processing library (e.g., spaCy) is used to tokenize the text and extract key nouns and verbs. A generative AI model (e.g., OpenAI GPT-3) is then used to extract important parts from the long text and create a concise summary.

[0289] Step 5:

[0290] The server generates prompts for the image generation model based on the generated summary. The input is the summary text, and the output is the prompt. Specifically, it generates specific descriptions of specific scenes and characters from the summarized content. For example, it creates prompts such as "the scene where the main character climbs the mountain" or "the scene where he finds the magic sword."

[0291] Step 6:

[0292] The server uses the generated prompt to generate an illustration using an image generation model. The input is the prompt sentence, and the output is an illustration image. Specifically, the prompt is input into an image generation model (e.g., DALL-E 3), which generates a high-quality illustration based on the prompt.

[0293] Step 7:

[0294] The device is equipped with a camera and microphone to analyze the user's emotions, and the emotion engine analyzes the user's facial expressions and tone of voice in real time. The input is the user's facial expression data and voice data, and the output is emotion data. For example, Microsoft Azure's emotion recognition API is used for the analysis.

[0295] Step 8:

[0296] The server receives emotion data from the emotion engine and adjusts the tone and style of the illustration based on the user's emotion. The input is emotion data and an illustration image, and the output is the adjusted illustration image. For example, if the user looks sad, the server generates an illustration with brighter colors.

[0297] Step 9:

[0298] The server integrates the generated text data and illustrations to generate an illustrated document. The input is the text data and illustration images, and the output is an integrated e-book in PDF or EPUB format. The document is formatted using tools such as LaTeX or Pandoc.

[0299] Step 10:

[0300] The server sends the generated illustrated document to the user's device. The input is the integrated e-book file, and the output is the completed transmission to the user's device. The user can download and view the received document on their device.

[0301] (Application example 2)

[0302] 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."

[0303] With the recent spread of e-books, there is a demand for methods to digitize books and enhance their visual appeal. However, current e-readers only provide text information, making it difficult to provide personalized content that responds to the reader's emotions. There is also a need for a deeper reading experience that responds to the reader's real-time emotions.

[0304] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for digitizing a book, means for generating a synopsis from book data, means for generating prompts for an image generation model based on the generated synopsis, means for generating illustrations with the image generation model using the generated prompts, means for integrating the generated illustrations with e-book data, an emotion engine that recognizes a user's emotions in real time, and means for adjusting the tone and style of the illustrations based on the emotion engine. This not only enhances the visual appeal of the book but also enables a personalized reading experience that corresponds to the user's emotions.

[0305] "Means of digitizing books" refers to devices or methods for converting physical books into digital form, such as using scanners or OCR technology.

[0306] "Means for generating synopses from book data" refers to a technology that summarizes and extracts key content from the full text of an digitized book, and refers to the use of a generative AI model.

[0307] "Means for generating prompts for an image generation model" refers to technology that automatically creates specific instructions for generating illustrations from summarized text.

[0308] "Means for generating illustrations using an image generation model" refers to a technology that uses image generation AI to create corresponding illustrations based on the generated prompts.

[0309] "Means for integrating generated illustrations into e-book data" refers to a technology for inserting generated illustrations into corresponding text portions to create a visually appealing e-book.

[0310] An "emotion engine that recognizes user emotions in real time" refers to software or hardware that analyzes a user's facial expressions and tone of voice to estimate their emotions at any given time.

[0311] "Means for adjusting the tone and style of illustrations based on an emotion engine" refers to technology that dynamically changes the color and design of illustrations according to the user's recognized emotions.

[0312] This invention is a system for providing visually rich illustrated e-books by digitizing books and combining summaries and automatically generated illustrations, and customizes the tone and style of the content based on the user's emotions. Specific embodiments for realizing this system are described below.

[0313] First, a user digitizes a physical book using a scanner. Each page of the book is extracted from the scanned image and saved as a PDF file. Next, the user uploads the generated PDF to the system. The server receives the uploaded PDF and extracts the text data using OCR technology. An example of OCR software used here is pytesseract.

[0314] The server analyzes the extracted text data and automatically generates a summary using a generative AI model, such as Hugging Face transformers. Based on the generated summary, the server automatically generates prompts for the image generation model, which include descriptions of specific scenes and characters.

[0315] For example, the following prompts are generated from the summarized text:

[0316] "Generate an illustration of an epic battle scene based on the hero's final battle with the dragon."

[0317] The server then uses an image generation model (e.g., OpenAI's DALL-E) to generate a corresponding illustration based on the prompt, which is then used to combine with the summary as visual content.

[0318] The app also includes an emotion engine that recognizes users' emotions in real time. The emotion engine analyzes the user's facial expressions and tone of voice, using software such as EmotionRecognizer. Emotional data is sent from the user's device to a server, which then adjusts the tone and style of the illustrations it generates based on this data. For example, if the user expresses sadness, it can generate an illustration with bright colors to soothe the user's feelings.

[0319] Finally, the server integrates the text data with the generated illustrations to generate an illustrated e-book, which can be output in PDF or EPUB format and can be downloaded and read by users. The output e-book contains personalized illustrations based on the user's emotions, providing a deeper reading experience.

[0320] This system enriches the reader's experience by providing e-books that incorporate visual elements in addition to text information. It also provides a personalized reading experience by customizing illustrations based on real-time emotion recognition.

[0321] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0322] Step 1:

[0323] A user digitizes a physical book using a scanner, scanning each page of the book as an image and saving it as a PDF file. The input is the paper book, and the output is a PDF file.

[0324] Step 2:

[0325] The user uploads the generated PDF to the system. The server receives the PDF and starts processing it. The input is a PDF file, and the output is PDF data stored in the server.

[0326] Step 3:

[0327] The server uses OCR technology to extract text data from PDFs. For this process, we use OCR software such as pytesseract. The input is a PDF file and the output is text data.

[0328] Step 4:

[0329] The server analyzes the extracted text data and automatically generates a summary using a generative AI model, such as the Hugging Face transformers. The input is text data, and the output is the summarized text.

[0330] Step 5:

[0331] The server generates prompts for the image generation model based on the generated summary. These prompts include descriptions of specific scenes and characters. The input is the summarized text, and the output is the prompt.

[0332] Step 6:

[0333] For example, the summary generates the following prompt:

[0334] "Generate an illustration of an epic battle scene based on the hero's final battle with the dragon."

[0335] Step 7:

[0336] The server uses the generated prompt to pass it to an image generation model (e.g., OpenAI's DALL-E) and generates a corresponding illustration. The input is the prompt text, and the output is the illustration.

[0337] Step 8:

[0338] The user's device is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine analyzes the user's facial expressions and tone of voice and sends the emotion data to the server. The input is the user's facial expressions and tone of voice, and the output is emotion data.

[0339] Step 9:

[0340] The server receives the emotion data and adjusts the tone and style of the generated illustration based on that data. For example, if the user expresses sadness, the illustration will be changed to a brighter color scheme. The input is the emotion data, and the output is the adjusted illustration.

[0341] Step 10:

[0342] The server then combines the adjusted illustrations with the text data to generate an illustrated e-book, which is then output in PDF or EPUB format. The input is the text data and adjusted illustrations, and the output is the illustrated e-book.

[0343] Step 11:

[0344] Users download and read the generated illustrated e-books, providing a visually rich content and a personalized reading experience based on real-time emotions. The input is the illustrated e-book, and the output is the user's reading experience.

[0345] 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.

[0346] 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.

[0347] 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.

[0348] [Second embodiment]

[0349] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0350] 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.

[0351] 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).

[0352] 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.

[0353] 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.

[0354] 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).

[0355] 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.

[0356] 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.

[0357] 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.

[0358] 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.

[0359] In the smart glasses 214, 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.

[0360] 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."

[0361] The present invention relates to a system for providing illustrated documents by digitizing books and combining summaries and automatically generated illustrations. The system is composed of a server, user terminals, and data exchanged between them.

[0362] System configuration

[0363] 1. Electronic means

[0364] Users use their device to scan books and generate PDF files, using a traditional scanner or OCR technology.

[0365] The user uploads the generated PDF to the system.

[0366] 2. Book Data Processing

[0367] The server receives the uploaded PDF and uses OCR technology to extract the text data, converting the paper book into digital text.

[0368] 3. Synopsis Generation

[0369] The server analyzes the extracted text data and automatically generates a summary using a generative AI model, which creates a summary that gives an overview of the entire book.

[0370] 4. Prompt Generation

[0371] The server generates prompts for the image generation model based on the generated synopsis, for example, prompts containing descriptions of specific scenes or characters.

[0372] 5. Illustration Generation

[0373] The server uses an image generation model (e.g., DALL-E 3) to automatically generate illustrations based on the generated prompts, which provide visual content related to the summary.

[0374] 6. Generating Illustrated Documents

[0375] The server combines the text data with the generated illustrations to generate an illustrated document, which is then output in an e-book format (e.g., PDF or EPUB).

[0376] 7. Output and Distribution

[0377] The server transmits the generated illustrated document to the user's terminal, where the user can download and view it.

[0378] Specific examples

[0379] 1. Examples of digitization

[0380] Scan the book "Example Story" entered by the user, generate a PDF using the scanner and OCR, and upload it to the system.

[0381] 2. Book Data Processing

[0382] The server receives the PDF and extracts the text using OCR, resulting in the text data of "The Example Story."

[0383] 3. Synopsis Generation

[0384] The server uses a generative AI model to create a concise summary from the text data, including key plot points such as the scene where the protagonist goes on an adventure and the climax of the story.

[0385] 4. Prompt Generation

[0386] Based on the generated synopsis, the server creates specific prompts such as "a scene where the protagonist climbs a mountain" or "a scene where the protagonist finds a magic sword."

[0387] 5. Illustration Generation

[0388] The server uses these prompts to generate corresponding illustrations from an image generation model, such as the protagonist climbing a mountain or finding a magic sword.

[0389] 6. Generating Illustrated Documents

[0390] The server inserts the generated illustrations into the corresponding text sections to generate an illustrated document, which is saved in PDF format.

[0391] 7. Output and Distribution

[0392] The server sends the PDF to the user's device, and the user downloads and views the illustrated document "The Example Story" on the device.

[0393] This system allows text-only books to be presented in a visually enjoyable format, reducing the barrier to reading. Additionally, users can select the level of illustrations to suit their needs, allowing them to freely enjoy detailed or simple illustrations. This invention is also effective in helping users understand a variety of documents, including instruction manuals and dictionaries, in addition to books.

[0394] The processing flow will be explained below.

[0395] Step 1:

[0396] A user scans a book using a device and creates a PDF file. For example, a user scans each page of a book using a scanner and saves them as a single PDF file.

[0397] Step 2:

[0398] A user uploads a PDF file from their device to the system, and then sends the PDF to the system's server using a dedicated application or web browser on the device.

[0399] Step 3:

[0400] The server receives and records the uploaded PDF file, and then prepares the PDF file for OCR processing.

[0401] Step 4:

[0402] The server extracts text data from PDF files using OCR (Optical Character Recognition) technology, which captures character information from scanned images and saves them as text files.

[0403] Step 5:

[0404] The server analyzes the extracted text data and automatically generates a summary of the book using a generative AI, such as GPT-4, to generate a key plot summary from the text data.

[0405] Step 6:

[0406] Based on the generated summary, the server creates a prompt for the image generation model (e.g., DALL-E 3). For example, it generates a specific prompt such as "A scene where the main character goes on an adventure."

[0407] Step 7:

[0408] The server sends a prompt to DALL-E 3, which automatically generates a corresponding illustration. Based on the generated prompt, DALL-E 3 generates an illustration of the specified scene or item.

[0409] Step 8:

[0410] The server receives and saves the generated illustration, adjusting the size and resolution of the illustration as needed.

[0411] Step 9:

[0412] The server integrates the text data with the generated illustrations to generate an illustrated document, for example, by inserting illustrations corresponding to each generated plot summary at appropriate positions in the text to create an integrated document.

[0413] Step 10:

[0414] The server saves the completed illustrated document in PDF or EPUB format and prepares it for delivery to the user's device.

[0415] Step 11:

[0416] The server sends the illustrated document to the user's terminal, where the user can download and view the document.

[0417] This process flow allows users to enjoy text-only books in a visually appealing format, reducing the barrier to reading. Additionally, users can select the level of illustrations to suit their needs, allowing them to freely enjoy detailed or simple illustrations. The present invention is also effective in helping users understand a variety of documents, including instruction manuals and dictionaries, in addition to books.

[0418] Example 1

[0419] 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."

[0420] Conventional e-book systems simply digitize books, resulting in a lack of visual elements that attract readers' attention. Furthermore, adding visual illustrations is often done manually, requiring a significant amount of time and effort. Furthermore, they lack the versatility to handle documents other than books and the ability to customize them to meet user needs.

[0421] 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.

[0422] In this invention, the server includes means for digitizing books, means for extracting text data from the digitized book data, means for generating a synopsis from the extracted text data, means for generating prompts for image generation based on the generated synopsis, means for creating illustrations using an image generation model using the generated prompts, means for integrating the generated illustrations with text data to generate an illustrated document, and means for transmitting the generated illustrated document to a user terminal. This automates the entire process from digitizing books to synopsis generation, automatic generation of illustrations, and generation and distribution of the illustrated document, making it possible to provide visually appealing e-books. Furthermore, this allows for flexible customization according to user needs and support for documents other than books.

[0423] "Means for digitizing books" refers to devices and software that scan books and generate their contents as digital data, such as PDF files.

[0424] "Means for extracting text data from digitized book data" refers to devices or software that utilize OCR technology to extract text information from digitized book data.

[0425] "Means for generating a synopsis from extracted text data" refers to a device or software for analyzing digitized text data and generating a summary of the main contents or key points.

[0426] "Means for generating prompts for image generation based on the generated synopsis" refers to a device or software that creates specific instructions (prompts) for generating illustrations from the generated synopsis.

[0427] "Means for creating an illustration in an image generation model using the generated prompt" refers to a device or software for inputting a prompt into an image generation model and automatically generating an illustration based on the prompt.

[0428] "Means for integrating generated illustrations into text data to generate an illustrated document" refers to devices or software for inserting generated illustrations into corresponding text sections to create a visually rich illustrated electronic document.

[0429] "Means for transmitting the generated illustrated document to a user terminal" refers to a device or software for transmitting the generated illustrated document to a user's electronic device via a network.

[0430] "The level of illustration can be selected according to the user's request" refers to a function that allows the user to choose to generate more detailed illustrations or simple illustrations according to their own preferences.

[0431] "It can be applied to a variety of documents other than books, such as instruction manuals and dictionaries" refers to the system's ability to be applied not only to the digitization of books, but also to a variety of other documents, and to effectively add visual information to each document.

[0432] This invention relates to a system that provides visually appealing illustrated documents by digitizing books and combining summaries and automatically generated illustrations. This system is composed of a server, user terminals, and the data exchanged between them.

[0433] The main configuration and processing steps of the system are as follows.

[0434] electronic means

[0435] A user scans a book with a scanner and generates a PDF file. Specifically, a USB-connected scanner is used to extract text data using OCR technology (e.g., Tesseract OCR). The generated PDF file is then uploaded to the system through a web interface. For example, a user scans a book called "The Example Story," saves it as a PDF, and uploads it to the system.

[0436] Book data processing

[0437] The server receives the uploaded PDF file and stores it in a temporary directory. Then, it uses OCR technology to extract text data from the PDF. Specifically, it uses Tesseract OCR to analyze the text information in the PDF and obtain it as digital text. This digitizes the paper book.

[0438] Synopsis generation

[0439] The server analyzes the extracted text data and generates a synopsis of the book using a generative AI model (e.g., GPT-4). Specifically, by inputting the text data into the AI ​​model, a summary containing the main content is obtained. For example, a summary is generated of the scene in which the protagonist goes on an adventure and the climax of the story.

[0440] Prompt Generation

[0441] Based on the generated synopsis, the server creates prompts for the image generation model, including descriptions of specific scenes and characters, such as "Please describe the scene where the main character climbs the mountain" or "Please describe the scene where the main character finds the magic sword."

[0442] Illustration generation

[0443] The server inputs the prompt sentence into an image generation model (e.g., DALL-E 3) and generates a corresponding illustration. Specifically, the prompt is input into the generation model to obtain a visually detailed illustration. For example, an illustration of "the scene where the protagonist climbs a mountain" or "the scene where the protagonist finds a magic sword" is generated.

[0444] Generating illustrated documents

[0445] The server then integrates the generated illustrations with the text data to generate an illustrated document. Specifically, it inserts the illustrations at appropriate points in the summary and saves them as a PDF or EPUB file. For example, a PDF file with illustrations inserted at appropriate times along with the summary of "The Example Story" is created.

[0446] Output and Delivery

[0447] The server sends the generated illustrated document to the user's device, and the user can download and view the e-book through the system interface. For example, the user can download an illustrated PDF of "The Example Story" and open it on their device to read.

[0448] This system allows books to be presented in a visually enjoyable format, enhancing reader interest and understanding.It can also handle documents other than books (such as instruction manuals and dictionaries), making it a system that can be used for a wide range of purposes.

[0449] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0450] Step 1: Scan your eBook

[0451] A user scans a book with a scanner and generates a PDF file. In this operation, the pages of the book are placed one by one on the scanner, and scanning begins by pressing the scanner's start button. The scanned data is automatically saved in PDF format. The input data is the paper book, and the output data is a PDF file.

[0452] Step 2: Upload your PDF file

[0453] The user opens the web interface to upload the generated PDF file to the system. The user clicks the Upload button, selects the target PDF file from the file selection dialog, and presses the Send button to complete the process. The input data is a PDF file, and the output data is a PDF file saved on the server.

[0454] Step 3: Receive and save the PDF file

[0455] The server receives the PDF file uploaded by the user. In this process, the PDF file is saved in a temporary directory on the server. The input data is the PDF file sent to the server, and the output data is the PDF file saved in the temporary directory.

[0456] Step 4: Extract text data

[0457] The server uses OCR technology to extract text data from PDF files. Specifically, it uses the Tesseract OCR engine to analyze image data from PDFs and extract character information. The input data is the PDF file, and the output data is the extracted text data.

[0458] Step 5: Generate a summary

[0459] The server analyzes the extracted text data and generates a summary using a generative AI model (e.g., GPT-4). The text data is input into the AI ​​model, which outputs a summary that extracts the main content and key points. In this step, the input data is the text data, and the output data is the summary text.

[0460] Step 6: Create a prompt

[0461] The server creates prompts for image generation based on the generated summary. Specifically, it selects important scenes and characters in the summary and generates specific prompt sentences to depict them. The input data is the summary text, and the output data is the prompt sentences.

[0462] Step 7: Creating an illustration

[0463] The server inputs the prompt sentence into an image generation model (e.g., DALL-E 3) and generates a corresponding illustration. The server inputs the prompt sentence into the model and receives the generated illustration. The input data is the prompt sentence, and the output data is the generated illustration.

[0464] Step 8: Generating the illustrated document

[0465] The server integrates the generated illustrations with the text data to generate an illustrated document. Specifically, it inserts the illustrations at appropriate positions in the summary text and saves it as a PDF or EPUB file. The input data is the summary text and illustrations, and the output data is the illustrated document.

[0466] Step 9: Distribution of illustrated documents

[0467] The server sends the generated illustrated document to the user's terminal, and the user can download and read the e-book through the system interface. The input data is the illustrated document, and the output data is the illustrated document stored in the user's terminal.

[0468] (Application example 1)

[0469] 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."

[0470] Traditional books relied on paper media, making it difficult to digitize or visually enhance their content. This limited opportunities for readers to visually enjoy the book's contents. Even with digitized books, the increase in text data made it difficult to efficiently grasp the content, and visual aids were lacking. Furthermore, the process of converting paper books into digital format was complicated, making it difficult for the average user to easily use.

[0471] 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.

[0472] In this invention, the server includes means for digitizing books, means for generating a synopsis from book data, means for generating prompts for an image generation model based on the generated synopsis, means for generating illustrations with the image generation model using the generated prompts, means for integrating the generated illustrations into e-book data, means for automatically scanning books using a smartphone camera, means for extracting text using OCR technology, and means for generating e-books in PDF format and making them available for download. This simplifies the process of digitizing books and enables readers to efficiently grasp the contents of books and enjoy them visually through summaries and visual complements.

[0473] "Digitizing a book" means converting a physical book into a digital format, which may involve the use of a scanner or camera.

[0474] "Means for generating synopsis from book data" refers to a technology that summarizes important content from extracted book text data, and uses generative AI and natural language processing technology.

[0475] "Means for generating prompts for an image generation model based on the generated synopsis" refers to a technology for creating instructions for generating images based on the summary.

[0476] An "image generation model" is an artificial intelligence system that generates images based on text prompts.

[0477] "Means for generating illustrations using an image generation model using the generated prompts" refers to the process by which AI creates illustrations using generated instructions.

[0478] The "means for integrating the generated illustrations into the e-book data" is a technology that combines the generated illustrations with text data to create a single e-book format file.

[0479] "Method for automatically scanning books using a smartphone camera" is a technology that uses a smartphone's built-in camera to photograph the pages of a book and digitize the data.

[0480] "Means of extracting text using OCR technology" refers to optical character recognition (OCR), a technology that recognizes and extracts character information from image data.

[0481] "Means for generating and making downloadable e-books in PDF format" refers to technology that converts the generated text and illustrations into a PDF file that can be downloaded by users.

[0482] "Users can choose between illustrations at the level of illustrations and comics, depending on their needs" refers to the ability for users to choose the level of detail and style of the illustrations to be generated.

[0483] "Can be applied to a variety of documents, such as instruction manuals and dictionaries" means that this system can be applied to various types of documents other than books.

[0484] The present invention relates to a system for providing illustrated documents by digitizing books and combining summaries and automatically generated illustrations. Specific embodiments of the present invention will be described below.

[0485] The system consists of a user device, a server, and the data exchanged between them. First, the user device automatically scans the book pages using a smartphone camera. This image is converted to grayscale, and text information is extracted using OCR technology. The OCR technology uses Tesseract, a high-performance character recognition software. At this stage, the book's text data is obtained.

[0486] The server receives this text data and uses a generative AI model (e.g., GPT-3.5) to generate a summary, using the following prompt:

[0487] Summarize the following text:

[0488] (Text data)

[0489] Based on the generated summary, a prompt is created for the image generation model. For example, specific prompts such as "a scene where the main character climbs a mountain" or "a scene where the magic sword is found" are generated. Based on this prompt, an image generation model (such as "DALL-E") is used to generate an illustration.

[0490] The generated illustrations are integrated with the corresponding text to generate an illustrated e-book. The e-book data is saved in PDF format and made available for download to users. This download function allows users to view the generated illustrated book on their own devices.

[0491] The server handles all processes from generating summaries to creating prompts, generating images, and finally integrating them all at once, allowing users to easily digitize books and enjoy them visually without any special technical knowledge.

[0492] As a concrete example, a user scans a book called "The Book of Adventure," and the server extracts the text using OCR and generates a summary using a generative AI model.

[0493] The scene where the main character fights a dragon

[0494] The system generates prompt sentences such as "What is the meaning of this book?" and uses an image generation model to create corresponding illustrations. Finally, the illustrations and summaries are integrated to create an attractive illustrated book.

[0495] This system simplifies the process of digitizing paper books, and allows readers to efficiently understand the content and enjoy it more through summaries and visual complements.

[0496] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0497] Step 1:

[0498] The user scans each page of a book using the smartphone camera. The input is the physical page of the book, and the output is a digital image file. The smartphone's camera application captures it and saves it as a grayscale JPEG or PNG file. This data is then ready for subsequent OCR processing.

[0499] Step 2:

[0500] The user converts the scanned image into text data using OCR technology. The input is a grayscale image, and the output is text data (String). Specifically, OCR software (e.g., Tesseract) installed on the device analyzes the image and extracts text information. The result of the OCR process is text data extracted from the book page.

[0501] Step 3:

[0502] The server receives the extracted text data and generates a summary using a generative AI model (e.g., GPT-3.5). The input is the text data, and the output is the summary (String). The server passes the text data to the generative AI model and sends a prompt like the following:

[0503] Summarize the following text:

[0504] (Text data)

[0505] The generative AI model returns a summary, which is then retrieved by the server.

[0506] Step 4:

[0507] The server generates a prompt for the image generation model based on the generated summary. The input is the summary, and the output is a prompt (String) for the image generation model. Specifically, the server writes out details about important scenes and characters in the summary and generates a prompt such as "The scene where the main character climbs a mountain."

[0508] Step 5:

[0509] The server passes the generated prompt to an image generation model (e.g., DALL-E) to generate an illustration. The input is the prompt, and the output is the generated illustration (image file). The server inputs the prompt into the image generation model and obtains the generated illustration. This illustration is saved for use in the next step.

[0510] Step 6:

[0511] The server integrates the generated summaries and illustrations to generate illustrated e-book data. The input is the summaries and illustrations, and the output is an e-book file (PDF). The server arranges the summary text and the corresponding illustrations in an orderly manner and converts them into a unified PDF file. Specifically, this is done using a PDF generation library (e.g., FPDF).

[0512] Step 7:

[0513] The server sends the generated e-book file to the user's device for download. The input is the e-book file, and the output is the file downloaded to the user's device. The server generates a link to provide the generated PDF file to the user, and the user clicks on this link to complete the e-book download.

[0514] The specific actions taken at each step efficiently digitize books and generate visually enjoyable content.

[0515] 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.

[0516] This invention relates to a system that provides illustrated documents by digitizing books and combining summaries and automatically generated illustrations. At the same time, by combining an emotion engine that recognizes the user's emotions, it is possible to display illustrations and text that correspond to the user's real-time emotions. This system is composed of a server, a user terminal, and the data exchanged between them.

[0517] System configuration

[0518] 1. Electronic means

[0519] A user can use a device to scan a book and create a PDF file, for example, by using a scanner to scan each page of a book and saving it as a single PDF file.

[0520] The user uploads the generated PDF to the system.

[0521] 2. Book Data Processing

[0522] The server receives the uploaded PDF and extracts the text data using OCR technology, which captures the text information from the scanned image and saves it as a text file.

[0523] 3. Synopsis Generation

[0524] The server analyzes the extracted text data and automatically generates a summary using a generative AI model, which creates a summary that gives an overview of the entire book.

[0525] 4. Prompt Generation

[0526] Based on the generated summary, the server creates prompts for the image generation model (e.g., DALL-E 3), such as prompts containing descriptions of specific scenes or characters.

[0527] 5. Illustration Generation

[0528] The server uses an image generation model to automatically generate illustrations based on the generated prompts, which provide visual content related to the summary.

[0529] 6. Emotion Engine Functions

[0530] The device is equipped with an emotion engine that analyzes the user's facial expressions, tone of voice, etc. This emotion engine recognizes the user's emotions in real time.

[0531] The server receives data from the emotion engine and adjusts the tone and style of the illustrations it generates based on the user's emotions. For example, if the user looks sad, the system can generate illustrations with brighter colors to soothe the user.

[0532] 7. Generating Illustrated Documents

[0533] The server combines the text data with the generated illustrations to generate an illustrated document, which is then output in an e-book format (e.g., PDF or EPUB).

[0534] 8. Output and Distribution

[0535] The server transmits the generated illustrated document to the user's terminal, where the user can download and view it.

[0536] Specific examples

[0537] 1. Examples of digitization

[0538] Scan the book "Example Story" entered by the user, generate a PDF using the scanner and OCR, and upload it to the system.

[0539] 2. Book Data Processing

[0540] The server receives the PDF and extracts the text using OCR, resulting in the text data of "The Example Story."

[0541] 3. Synopsis Generation

[0542] The server uses a generative AI model to create a concise summary from the text data, including key plot points such as the scene where the protagonist goes on an adventure and the climax of the story.

[0543] 4. Prompt Generation

[0544] Based on the generated synopsis, the server creates specific prompts such as "a scene where the protagonist climbs a mountain" or "a scene where the protagonist finds a magic sword."

[0545] 5. Illustration Generation

[0546] The server uses these prompts to generate corresponding illustrations from an image generation model, such as the protagonist climbing a mountain or finding a magic sword.

[0547] 6. Applying the Emotion Engine

[0548] While a user is browsing a book on their device, the emotion engine analyzes their facial expressions and voice in real time. For example, if the user shows a surprised expression, the system will change the next illustration to emphasize the element of surprise.

[0549] 7. Generating Illustrated Documents

[0550] The server inserts the generated illustrations into the corresponding text sections to generate an illustrated document, which is saved in PDF format.

[0551] 8. Output and Distribution

[0552] The server sends the PDF to the user's device, and the user downloads and views the illustrated document "The Example Story" on the device.

[0553] This system allows users to enjoy text-only books in a visually appealing format, reducing the barrier to reading. Furthermore, responding to the user's real-time emotions provides a more personalized reading experience. Additionally, the level of illustrations can be selected according to the user's needs, allowing them to freely enjoy detailed or simple illustrations. The present invention is also effective in helping users understand a variety of documents, including instruction manuals and dictionaries, in addition to books.

[0554] The processing flow will be explained below.

[0555] Step 1:

[0556] A user scans a book using a device and creates a PDF file. For example, a user scans each page of a book using a scanner and saves them as a single PDF file.

[0557] Step 2:

[0558] A user uploads a PDF file from their device to the system, and then sends the PDF to the system's server using a dedicated application or web browser on the device.

[0559] Step 3:

[0560] The server receives and records the uploaded PDF file, and then prepares the PDF file for OCR processing.

[0561] Step 4:

[0562] The server extracts text data from PDF files using OCR (Optical Character Recognition) technology, which captures character information from scanned images and saves them as text files.

[0563] Step 5:

[0564] The server analyzes the extracted text data and automatically generates a summary of the book using a generative AI, such as GPT-4, to generate a key plot summary from the text data.

[0565] Step 6:

[0566] Based on the generated summary, the server creates a prompt for the image generation model (e.g., DALL-E 3). For example, it generates a specific prompt such as "A scene where the main character goes on an adventure."

[0567] Step 7:

[0568] The server sends a prompt to DALL-E 3, which automatically generates a corresponding illustration. Based on the generated prompt, DALL-E 3 generates an illustration of the specified scene or item.

[0569] Step 8:

[0570] The server receives and saves the generated illustration, adjusting the size and resolution of the illustration as needed.

[0571] Step 9:

[0572] The device uses an emotion engine to recognize the user's facial expressions and tone of voice, and transmits this information to a server in real time. For example, the device can collect user emotion data using a camera or microphone.

[0573] Step 10:

[0574] The server changes the tone and style of the illustration based on the emotional data it receives: if the user has a sad expression, the system adjusts to generate illustrations with brighter colors and more joyful scenes.

[0575] Step 11:

[0576] The server integrates the text data with the generated illustrations to generate an illustrated document, for example, by inserting illustrations corresponding to each generated plot summary at appropriate positions in the text to create an integrated document.

[0577] Step 12:

[0578] The server saves the completed illustrated document in PDF or EPUB format and prepares it for delivery to the user's device.

[0579] Step 13:

[0580] The server sends the illustrated document to the user's device. The user can download and view the received document. For example, it becomes possible to view "The Story of the Day" with illustrations corresponding to emotions.

[0581] This process flow allows users to enjoy text-only books in a visually appealing format, lowering the barrier to entry and enabling a more personalized reading experience by adapting content based on the user's real-time emotions.

[0582] Example 2

[0583] 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."

[0584] Because traditional books exist in physical form, reading generally requires a certain amount of effort, and text-only books in particular lack visual aids. Furthermore, existing e-book systems lack the ability to automatically generate illustrations, resulting in a uniformly flat reading experience. Furthermore, personalized content display based on user emotions has yet to be realized, leaving a need for improved user experience.

[0585] 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.

[0586] In this invention, the server includes means for digitizing books, means for generating a synopsis from book data, means for generating prompts for an image generation model based on the generated synopsis, means for generating illustrations with the image generation model using the generated prompts, means for integrating the generated illustrations into electronic book data, and means for adjusting the tone and style of the illustrations based on user emotion data. This allows books and various documents to be provided in a visually enjoyable format, and further enables a personalized reading experience that responds to the user's real-time emotions.

[0587] "Book digitization tools" refers to a set of equipment and software used to convert physical books into digital files (e.g., PDF).

[0588] "Means for generating a synopsis from book data" refers to technology that analyzes the text data of a book and extracts the main content as a summary.

[0589] "Means for generating prompts for an image generation model based on the generated synopsis" refers to a technology that creates explanatory text (prompts) to be input into an image generation model based on summarized text.

[0590] "Means for generating an illustration using an image generation model using the generated prompt" refers to a technology for creating an illustration of a specific scene or character by using an image generation model with the prompt as input.

[0591] "Means for integrating the generated illustrations into e-book data" refers to the technology for placing the created illustrations into the corresponding text parts and converting them into a unified e-book format (e.g., PDF, EPUB).

[0592] "Means for adjusting the tone and style of an illustration based on the user's emotional data" refers to technology that recognizes the user's emotional state from their facial expressions and tone of voice, and uses that data to dynamically change the color and design of the illustration.

[0593] The system of the present invention provides illustrated documents by digitizing books and combining summaries with automatically generated illustrations. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it is possible to display illustrations and text in response to the user's real-time emotions. This system is comprised of a server, user terminals, and the data exchanged between them.

[0594] First, the user scans a book using a scanner connected to their device and generates a PDF file. For example, they can use a general-purpose device as the scanner and Tesseract OCR as the OCR software to scan each page of the book and compile them into a single PDF file. The user then uploads this PDF file to the server through the system's upload interface.

[0595] The server receives the uploaded PDF file and extracts the text data using OCR technology, such as Tesseract OCR, which extracts the text information from the scanned image and saves it as a text file.

[0596] The server then analyzes the extracted text data and automatically generates a summary using a generative AI model. Specifically, it uses a natural language processing library (e.g., spaCy) to tokenize the text and extract key nouns and verbs. It then uses a generative AI model (e.g., OpenAI GPT-3) to extract important parts from the long text and create a concise summary.

[0597] Based on the generated summary, the server creates prompts for the image generation model. The prompts include specific descriptions of specific scenes or characters from the summarized content. For example, a prompt might be generated such as "the scene where the main character climbs a mountain" or "the scene where he finds the magic sword."

[0598] The server then uses the generated prompts to generate illustrations using an image generation model (e.g., DALL-E 3). Based on the prompts, the model uses the learned data to generate high-quality illustrations. The generated illustrations are saved as visual content related to the summary.

[0599] Additionally, the device is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine includes a camera and microphone to analyze the user's facial expressions and tone of voice. For analysis, Microsoft Azure's emotion recognition API is used, for example. The server receives data from the emotion engine and adjusts the tone and style of the illustration based on the user's emotional state.

[0600] Finally, the server integrates the generated text data with the illustrations to generate an illustrated document, which is then exported as an e-book in PDF or EPUB format.The generated document is then sent to the user's device via the server, where the user can download and view the document on their device.

[0601] Specific examples are shown below.

[0602] For example, a user scans a book called "The Great Story," generates a PDF, and uploads it to the system. The server receives the PDF and uses OCR technology to extract the text data. Using a generative AI model, the server generates plot summaries, such as "the scene where the protagonist goes on an adventure" and "the climax of the story." Based on these plot summaries, the server then creates prompts, such as "the scene where the protagonist climbs a mountain" and "the scene where the protagonist finds the magic sword." The server then uses the DALL-E 3 model to generate illustrations based on these prompts. As the user browses the book, the device's built-in emotion engine analyzes the user's facial expressions and voice in real time, adjusting the tone of the visual content according to their emotions. Finally, the generated text data and illustrations are integrated to generate an illustrated document, which is then sent to the user's device. The user can then download and view it.

[0603] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0604] Step 1:

[0605] A user scans a book using a scanner connected to a device and generates a PDF file. The input is the physical book and the output is the PDF file. For example, a user scans each page of a book using a scanner and saves them as a PDF file.

[0606] Step 2:

[0607] The user uploads the generated PDF file from the terminal to the server through the system's web interface. The input is a PDF file, and the output is a PDF file stored in the server's storage. Specifically, the user accesses the web application using a browser, clicks the upload button, and selects the file.

[0608] Step 3:

[0609] The server receives the uploaded PDF file and extracts the text data using OCR technology. The input is a PDF file and the output is a text file. Specifically, it uses the Tesseract OCR engine to extract text information from each page of the PDF and saves it as a text file.

[0610] Step 4:

[0611] The server analyzes the extracted text data and automatically generates a summary using a generative AI model. The input is a text file, and the output is the summary text. Specifically, a natural language processing library (e.g., spaCy) is used to tokenize the text and extract key nouns and verbs. A generative AI model (e.g., OpenAI GPT-3) is then used to extract important parts from the long text and create a concise summary.

[0612] Step 5:

[0613] The server generates prompts for the image generation model based on the generated summary. The input is the summary text, and the output is the prompt. Specifically, it generates specific descriptions of specific scenes and characters from the summarized content. For example, it creates prompts such as "the scene where the main character climbs the mountain" or "the scene where he finds the magic sword."

[0614] Step 6:

[0615] The server uses the generated prompt to generate an illustration using an image generation model. The input is the prompt sentence, and the output is an illustration image. Specifically, the prompt is input into an image generation model (e.g., DALL-E 3), which generates a high-quality illustration based on the prompt.

[0616] Step 7:

[0617] The device is equipped with a camera and microphone to analyze the user's emotions, and the emotion engine analyzes the user's facial expressions and tone of voice in real time. The input is the user's facial expression data and voice data, and the output is emotion data. For example, Microsoft Azure's emotion recognition API is used for the analysis.

[0618] Step 8:

[0619] The server receives emotion data from the emotion engine and adjusts the tone and style of the illustration based on the user's emotion. The input is emotion data and an illustration image, and the output is the adjusted illustration image. For example, if the user looks sad, the server generates an illustration with brighter colors.

[0620] Step 9:

[0621] The server integrates the generated text data and illustrations to generate an illustrated document. The input is the text data and illustration images, and the output is an integrated e-book in PDF or EPUB format. The document is formatted using tools such as LaTeX or Pandoc.

[0622] Step 10:

[0623] The server sends the generated illustrated document to the user's device. The input is the integrated e-book file, and the output is the completed transmission to the user's device. The user can download and view the received document on their device.

[0624] (Application example 2)

[0625] 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."

[0626] With the recent spread of e-books, there is a demand for methods to digitize books and enhance their visual appeal. However, current e-readers only provide text information, making it difficult to provide personalized content that responds to the reader's emotions. There is also a need for a deeper reading experience that responds to the reader's real-time emotions.

[0627] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for digitizing a book, means for generating a synopsis from book data, means for generating prompts for an image generation model based on the generated synopsis, means for generating illustrations with the image generation model using the generated prompts, means for integrating the generated illustrations with e-book data, an emotion engine that recognizes a user's emotions in real time, and means for adjusting the tone and style of the illustrations based on the emotion engine. This not only enhances the visual appeal of the book but also enables a personalized reading experience that corresponds to the user's emotions.

[0628] "Means of digitizing books" refers to devices or methods for converting physical books into digital form, such as using scanners or OCR technology.

[0629] "Means for generating synopses from book data" refers to a technology that summarizes and extracts key content from the full text of an digitized book, and refers to the use of a generative AI model.

[0630] "Means for generating prompts for an image generation model" refers to technology that automatically creates specific instructions for generating illustrations from summarized text.

[0631] "Means for generating illustrations using an image generation model" refers to a technology that uses image generation AI to create corresponding illustrations based on the generated prompts.

[0632] "Means for integrating generated illustrations into e-book data" refers to a technology for inserting generated illustrations into corresponding text portions to create a visually appealing e-book.

[0633] An "emotion engine that recognizes user emotions in real time" refers to software or hardware that analyzes a user's facial expressions and tone of voice to estimate their emotions at any given time.

[0634] "Means for adjusting the tone and style of illustrations based on an emotion engine" refers to technology that dynamically changes the color and design of illustrations according to the user's recognized emotions.

[0635] This invention is a system for providing visually rich illustrated e-books by digitizing books and combining summaries and automatically generated illustrations, and customizes the tone and style of the content based on the user's emotions. Specific embodiments for realizing this system are described below.

[0636] First, a user digitizes a physical book using a scanner. Each page of the book is extracted from the scanned image and saved as a PDF file. Next, the user uploads the generated PDF to the system. The server receives the uploaded PDF and extracts the text data using OCR technology. An example of OCR software used here is pytesseract.

[0637] The server analyzes the extracted text data and automatically generates a summary using a generative AI model, such as Hugging Face transformers. Based on the generated summary, the server automatically generates prompts for the image generation model, which include descriptions of specific scenes and characters.

[0638] For example, the following prompts are generated from the summarized text:

[0639] "Generate an illustration of an epic battle scene based on the hero's final battle with the dragon."

[0640] The server then uses an image generation model (e.g., OpenAI's DALL-E) to generate a corresponding illustration based on the prompt, which is then used to combine with the summary as visual content.

[0641] The app also includes an emotion engine that recognizes users' emotions in real time. The emotion engine analyzes the user's facial expressions and tone of voice, using software such as EmotionRecognizer. Emotional data is sent from the user's device to a server, which then adjusts the tone and style of the illustrations it generates based on this data. For example, if the user expresses sadness, it can generate an illustration with bright colors to soothe the user's feelings.

[0642] Finally, the server integrates the text data with the generated illustrations to generate an illustrated e-book, which can be output in PDF or EPUB format and can be downloaded and read by users. The output e-book contains personalized illustrations based on the user's emotions, providing a deeper reading experience.

[0643] This system enriches the reader's experience by providing e-books that incorporate visual elements in addition to text information. It also provides a personalized reading experience by customizing illustrations based on real-time emotion recognition.

[0644] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0645] Step 1:

[0646] A user digitizes a physical book using a scanner, scanning each page of the book as an image and saving it as a PDF file. The input is the paper book, and the output is a PDF file.

[0647] Step 2:

[0648] The user uploads the generated PDF to the system. The server receives the PDF and starts processing it. The input is a PDF file, and the output is PDF data stored in the server.

[0649] Step 3:

[0650] The server uses OCR technology to extract text data from PDFs. For this process, we use OCR software such as pytesseract. The input is a PDF file and the output is text data.

[0651] Step 4:

[0652] The server analyzes the extracted text data and automatically generates a summary using a generative AI model, such as the Hugging Face transformers. The input is text data, and the output is the summarized text.

[0653] Step 5:

[0654] The server generates prompts for the image generation model based on the generated summary. These prompts include descriptions of specific scenes and characters. The input is the summarized text, and the output is the prompt.

[0655] Step 6:

[0656] For example, the summary generates the following prompt:

[0657] "Generate an illustration of an epic battle scene based on the hero's final battle with the dragon."

[0658] Step 7:

[0659] The server uses the generated prompt to pass it to an image generation model (e.g., OpenAI's DALL-E) and generates a corresponding illustration. The input is the prompt text, and the output is the illustration.

[0660] Step 8:

[0661] The user's device is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine analyzes the user's facial expressions and tone of voice and sends the emotion data to the server. The input is the user's facial expressions and tone of voice, and the output is emotion data.

[0662] Step 9:

[0663] The server receives the emotion data and adjusts the tone and style of the generated illustration based on that data. For example, if the user expresses sadness, the illustration will be changed to a brighter color scheme. The input is the emotion data, and the output is the adjusted illustration.

[0664] Step 10:

[0665] The server then combines the adjusted illustrations with the text data to generate an illustrated e-book, which is then output in PDF or EPUB format. The input is the text data and adjusted illustrations, and the output is the illustrated e-book.

[0666] Step 11:

[0667] Users download and read the generated illustrated e-books, providing a visually rich content and a personalized reading experience based on real-time emotions. The input is the illustrated e-book, and the output is the user's reading experience.

[0668] 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.

[0669] 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.

[0670] 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.

[0671] [Third embodiment]

[0672] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0673] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0674] 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).

[0675] 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.

[0676] 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.

[0677] 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).

[0678] 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.

[0679] 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.

[0680] 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.

[0681] 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.

[0682] 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.

[0683] 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."

[0684] The present invention relates to a system for providing illustrated documents by digitizing books and combining summaries and automatically generated illustrations. The system is composed of a server, user terminals, and data exchanged between them.

[0685] System configuration

[0686] 1. Electronic means

[0687] Users use their device to scan books and generate PDF files, using a traditional scanner or OCR technology.

[0688] The user uploads the generated PDF to the system.

[0689] 2. Book Data Processing

[0690] The server receives the uploaded PDF and uses OCR technology to extract the text data, converting the paper book into digital text.

[0691] 3. Synopsis Generation

[0692] The server analyzes the extracted text data and automatically generates a summary using a generative AI model, which creates a summary that gives an overview of the entire book.

[0693] 4. Prompt Generation

[0694] The server generates prompts for the image generation model based on the generated synopsis, for example, prompts containing descriptions of specific scenes or characters.

[0695] 5. Illustration Generation

[0696] The server uses an image generation model (e.g., DALL-E 3) to automatically generate illustrations based on the generated prompts, which provide visual content related to the summary.

[0697] 6. Generating Illustrated Documents

[0698] The server combines the text data with the generated illustrations to generate an illustrated document, which is then output in an e-book format (e.g., PDF or EPUB).

[0699] 7. Output and Distribution

[0700] The server transmits the generated illustrated document to the user's terminal, where the user can download and view it.

[0701] Specific examples

[0702] 1. Examples of digitization

[0703] Scan the book "Example Story" entered by the user, generate a PDF using the scanner and OCR, and upload it to the system.

[0704] 2. Book Data Processing

[0705] The server receives the PDF and extracts the text using OCR, resulting in the text data of "The Example Story."

[0706] 3. Synopsis Generation

[0707] The server uses a generative AI model to create a concise summary from the text data, including key plot points such as the scene where the protagonist goes on an adventure and the climax of the story.

[0708] 4. Prompt Generation

[0709] Based on the generated synopsis, the server creates specific prompts such as "a scene where the protagonist climbs a mountain" or "a scene where the protagonist finds a magic sword."

[0710] 5. Illustration Generation

[0711] The server uses these prompts to generate corresponding illustrations from an image generation model, such as the protagonist climbing a mountain or finding a magic sword.

[0712] 6. Generating Illustrated Documents

[0713] The server inserts the generated illustrations into the corresponding text sections to generate an illustrated document, which is saved in PDF format.

[0714] 7. Output and Distribution

[0715] The server sends the PDF to the user's device, and the user downloads and views the illustrated document "The Example Story" on the device.

[0716] This system allows text-only books to be presented in a visually enjoyable format, reducing the barrier to reading. Additionally, users can select the level of illustrations to suit their needs, allowing them to freely enjoy detailed or simple illustrations. This invention is also effective in helping users understand a variety of documents, including instruction manuals and dictionaries, in addition to books.

[0717] The processing flow will be explained below.

[0718] Step 1:

[0719] A user scans a book using a device and creates a PDF file. For example, a user scans each page of a book using a scanner and saves them as a single PDF file.

[0720] Step 2:

[0721] A user uploads a PDF file from their device to the system, and then sends the PDF to the system's server using a dedicated application or web browser on the device.

[0722] Step 3:

[0723] The server receives and records the uploaded PDF file, and then prepares the PDF file for OCR processing.

[0724] Step 4:

[0725] The server extracts text data from PDF files using OCR (Optical Character Recognition) technology, which captures character information from scanned images and saves them as text files.

[0726] Step 5:

[0727] The server analyzes the extracted text data and automatically generates a summary of the book using a generative AI, such as GPT-4, to generate a key plot summary from the text data.

[0728] Step 6:

[0729] Based on the generated summary, the server creates a prompt for the image generation model (e.g., DALL-E 3). For example, it generates a specific prompt such as "A scene where the main character goes on an adventure."

[0730] Step 7:

[0731] The server sends a prompt to DALL-E 3, which automatically generates a corresponding illustration. Based on the generated prompt, DALL-E 3 generates an illustration of the specified scene or item.

[0732] Step 8:

[0733] The server receives and saves the generated illustration, adjusting the size and resolution of the illustration as needed.

[0734] Step 9:

[0735] The server integrates the text data with the generated illustrations to generate an illustrated document, for example, by inserting illustrations corresponding to each generated plot summary at appropriate positions in the text to create an integrated document.

[0736] Step 10:

[0737] The server saves the completed illustrated document in PDF or EPUB format and prepares it for delivery to the user's device.

[0738] Step 11:

[0739] The server sends the illustrated document to the user's terminal, where the user can download and view the document.

[0740] This process flow allows users to enjoy text-only books in a visually appealing format, reducing the barrier to reading. Additionally, users can select the level of illustrations to suit their needs, allowing them to freely enjoy detailed or simple illustrations. The present invention is also effective in helping users understand a variety of documents, including instruction manuals and dictionaries, in addition to books.

[0741] Example 1

[0742] 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."

[0743] Conventional e-book systems simply digitize books, resulting in a lack of visual elements that attract readers' attention. Furthermore, adding visual illustrations is often done manually, requiring a significant amount of time and effort. Furthermore, they lack the versatility to handle documents other than books and the ability to customize them to meet user needs.

[0744] 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.

[0745] In this invention, the server includes means for digitizing books, means for extracting text data from the digitized book data, means for generating a synopsis from the extracted text data, means for generating prompts for image generation based on the generated synopsis, means for creating illustrations using an image generation model using the generated prompts, means for integrating the generated illustrations with text data to generate an illustrated document, and means for transmitting the generated illustrated document to a user terminal. This automates the entire process from digitizing books to synopsis generation, automatic generation of illustrations, and generation and distribution of the illustrated document, making it possible to provide visually appealing e-books. Furthermore, this allows for flexible customization according to user needs and support for documents other than books.

[0746] "Means for digitizing books" refers to devices and software that scan books and generate their contents as digital data, such as PDF files.

[0747] "Means for extracting text data from digitized book data" refers to devices or software that utilize OCR technology to extract text information from digitized book data.

[0748] "Means for generating a synopsis from extracted text data" refers to a device or software for analyzing digitized text data and generating a summary of the main contents or key points.

[0749] "Means for generating prompts for image generation based on the generated synopsis" refers to a device or software that creates specific instructions (prompts) for generating illustrations from the generated synopsis.

[0750] "Means for creating an illustration in an image generation model using the generated prompt" refers to a device or software for inputting a prompt into an image generation model and automatically generating an illustration based on the prompt.

[0751] "Means for integrating generated illustrations into text data to generate an illustrated document" refers to devices or software for inserting generated illustrations into corresponding text sections to create a visually rich illustrated electronic document.

[0752] "Means for transmitting the generated illustrated document to a user terminal" refers to a device or software for transmitting the generated illustrated document to a user's electronic device via a network.

[0753] "The level of illustration can be selected according to the user's request" refers to a function that allows the user to choose to generate more detailed illustrations or simple illustrations according to their own preferences.

[0754] "It can be applied to a variety of documents other than books, such as instruction manuals and dictionaries" refers to the system's ability to be applied not only to the digitization of books, but also to a variety of other documents, and to effectively add visual information to each document.

[0755] This invention relates to a system that provides visually appealing illustrated documents by digitizing books and combining summaries and automatically generated illustrations. This system is composed of a server, user terminals, and the data exchanged between them.

[0756] The main configuration and processing steps of the system are as follows.

[0757] electronic means

[0758] A user scans a book with a scanner and generates a PDF file. Specifically, a USB-connected scanner is used to extract text data using OCR technology (e.g., Tesseract OCR). The generated PDF file is then uploaded to the system through a web interface. For example, a user scans a book called "The Example Story," saves it as a PDF, and uploads it to the system.

[0759] Book data processing

[0760] The server receives the uploaded PDF file and stores it in a temporary directory. Then, it uses OCR technology to extract text data from the PDF. Specifically, it uses Tesseract OCR to analyze the text information in the PDF and obtain it as digital text. This digitizes the paper book.

[0761] Synopsis generation

[0762] The server analyzes the extracted text data and generates a synopsis of the book using a generative AI model (e.g., GPT-4). Specifically, by inputting the text data into the AI ​​model, a summary containing the main content is obtained. For example, a summary is generated of the scene in which the protagonist goes on an adventure and the climax of the story.

[0763] Prompt Generation

[0764] Based on the generated synopsis, the server creates prompts for the image generation model, including descriptions of specific scenes and characters, such as "Please describe the scene where the main character climbs the mountain" or "Please describe the scene where the main character finds the magic sword."

[0765] Illustration generation

[0766] The server inputs the prompt sentence into an image generation model (e.g., DALL-E 3) and generates a corresponding illustration. Specifically, the prompt is input into the generation model to obtain a visually detailed illustration. For example, an illustration of "the scene where the protagonist climbs a mountain" or "the scene where the protagonist finds a magic sword" is generated.

[0767] Generating illustrated documents

[0768] The server then integrates the generated illustrations with the text data to generate an illustrated document. Specifically, it inserts the illustrations at appropriate points in the summary and saves them as a PDF or EPUB file. For example, a PDF file with illustrations inserted at appropriate times along with the summary of "The Example Story" is created.

[0769] Output and Delivery

[0770] The server sends the generated illustrated document to the user's device, and the user can download and view the e-book through the system interface. For example, the user can download an illustrated PDF of "The Example Story" and open it on their device to read.

[0771] This system allows books to be presented in a visually enjoyable format, enhancing reader interest and understanding.It can also handle documents other than books (such as instruction manuals and dictionaries), making it a system that can be used for a wide range of purposes.

[0772] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0773] Step 1: Scan your eBook

[0774] A user scans a book with a scanner and generates a PDF file. In this operation, the pages of the book are placed one by one on the scanner, and scanning begins by pressing the scanner's start button. The scanned data is automatically saved in PDF format. The input data is the paper book, and the output data is a PDF file.

[0775] Step 2: Upload your PDF file

[0776] The user opens the web interface to upload the generated PDF file to the system. The user clicks the Upload button, selects the target PDF file from the file selection dialog, and presses the Send button to complete the process. The input data is a PDF file, and the output data is a PDF file saved on the server.

[0777] Step 3: Receive and save the PDF file

[0778] The server receives the PDF file uploaded by the user. In this process, the PDF file is saved in a temporary directory on the server. The input data is the PDF file sent to the server, and the output data is the PDF file saved in the temporary directory.

[0779] Step 4: Extract text data

[0780] The server uses OCR technology to extract text data from PDF files. Specifically, it uses the Tesseract OCR engine to analyze image data from PDFs and extract character information. The input data is the PDF file, and the output data is the extracted text data.

[0781] Step 5: Generate a summary

[0782] The server analyzes the extracted text data and generates a summary using a generative AI model (e.g., GPT-4). The text data is input into the AI ​​model, which outputs a summary that extracts the main content and key points. In this step, the input data is the text data, and the output data is the summary text.

[0783] Step 6: Create a prompt

[0784] The server creates prompts for image generation based on the generated summary. Specifically, it selects important scenes and characters in the summary and generates specific prompt sentences to depict them. The input data is the summary text, and the output data is the prompt sentences.

[0785] Step 7: Creating an illustration

[0786] The server inputs the prompt sentence into an image generation model (e.g., DALL-E 3) and generates a corresponding illustration. The server inputs the prompt sentence into the model and receives the generated illustration. The input data is the prompt sentence, and the output data is the generated illustration.

[0787] Step 8: Generating the illustrated document

[0788] The server integrates the generated illustrations with the text data to generate an illustrated document. Specifically, it inserts the illustrations at appropriate positions in the summary text and saves it as a PDF or EPUB file. The input data is the summary text and illustrations, and the output data is the illustrated document.

[0789] Step 9: Distribution of illustrated documents

[0790] The server sends the generated illustrated document to the user's terminal, and the user can download and read the e-book through the system interface. The input data is the illustrated document, and the output data is the illustrated document stored in the user's terminal.

[0791] (Application example 1)

[0792] 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."

[0793] Traditional books relied on paper media, making it difficult to digitize or visually enhance their content. This limited opportunities for readers to visually enjoy the book's contents. Even with digitized books, the increase in text data made it difficult to efficiently grasp the content, and visual aids were lacking. Furthermore, the process of converting paper books into digital format was complicated, making it difficult for the average user to easily use.

[0794] 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.

[0795] In this invention, the server includes means for digitizing books, means for generating a synopsis from book data, means for generating prompts for an image generation model based on the generated synopsis, means for generating illustrations with the image generation model using the generated prompts, means for integrating the generated illustrations into e-book data, means for automatically scanning books using a smartphone camera, means for extracting text using OCR technology, and means for generating e-books in PDF format and making them available for download. This simplifies the process of digitizing books and enables readers to efficiently grasp the contents of books and enjoy them visually through summaries and visual complements.

[0796] "Digitizing a book" means converting a physical book into a digital format, which may involve the use of a scanner or camera.

[0797] "Means for generating synopsis from book data" refers to a technology that summarizes important content from extracted book text data, and uses generative AI and natural language processing technology.

[0798] "Means for generating prompts for an image generation model based on the generated synopsis" refers to a technology for creating instructions for generating images based on the summary.

[0799] An "image generation model" is an artificial intelligence system that generates images based on text prompts.

[0800] "Means for generating illustrations using an image generation model using the generated prompts" refers to the process by which AI creates illustrations using generated instructions.

[0801] The "means for integrating the generated illustrations into the e-book data" is a technology that combines the generated illustrations with text data to create a single e-book format file.

[0802] "Method for automatically scanning books using a smartphone camera" is a technology that uses a smartphone's built-in camera to photograph the pages of a book and digitize the data.

[0803] "Means of extracting text using OCR technology" refers to optical character recognition (OCR), a technology that recognizes and extracts character information from image data.

[0804] "Means for generating and making downloadable e-books in PDF format" refers to technology that converts the generated text and illustrations into a PDF file that can be downloaded by users.

[0805] "Users can choose between illustrations at the level of illustrations and comics, depending on their needs" refers to the ability for users to choose the level of detail and style of the illustrations to be generated.

[0806] "Can be applied to a variety of documents, such as instruction manuals and dictionaries" means that this system can be applied to various types of documents other than books.

[0807] The present invention relates to a system for providing illustrated documents by digitizing books and combining summaries and automatically generated illustrations. Specific embodiments of the present invention will be described below.

[0808] The system consists of a user device, a server, and the data exchanged between them. First, the user device automatically scans the book pages using a smartphone camera. This image is converted to grayscale, and text information is extracted using OCR technology. The OCR technology uses Tesseract, a high-performance character recognition software. At this stage, the book's text data is obtained.

[0809] The server receives this text data and uses a generative AI model (e.g., GPT-3.5) to generate a summary, using the following prompt:

[0810] Summarize the following text:

[0811] (Text data)

[0812] Based on the generated summary, a prompt is created for the image generation model. For example, specific prompts such as "a scene where the main character climbs a mountain" or "a scene where the magic sword is found" are generated. Based on this prompt, an image generation model (such as "DALL-E") is used to generate an illustration.

[0813] The generated illustrations are integrated with the corresponding text to generate an illustrated e-book. The e-book data is saved in PDF format and made available for download to users. This download function allows users to view the generated illustrated book on their own devices.

[0814] The server handles all processes from generating summaries to creating prompts, generating images, and finally integrating them all at once, allowing users to easily digitize books and enjoy them visually without any special technical knowledge.

[0815] As a concrete example, a user scans a book called "The Book of Adventure," and the server extracts the text using OCR and generates a summary using a generative AI model.

[0816] The scene where the main character fights a dragon

[0817] The system generates prompt sentences such as "What is the meaning of this book?" and uses an image generation model to create corresponding illustrations. Finally, the illustrations and summaries are integrated to create an attractive illustrated book.

[0818] This system simplifies the process of digitizing paper books, and allows readers to efficiently understand the content and enjoy it more through summaries and visual complements.

[0819] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0820] Step 1:

[0821] The user scans each page of a book using the smartphone camera. The input is the physical page of the book, and the output is a digital image file. The smartphone's camera application captures it and saves it as a grayscale JPEG or PNG file. This data is then ready for subsequent OCR processing.

[0822] Step 2:

[0823] The user converts the scanned image into text data using OCR technology. The input is a grayscale image, and the output is text data (String). Specifically, OCR software (e.g., Tesseract) installed on the device analyzes the image and extracts text information. The result of the OCR process is text data extracted from the book page.

[0824] Step 3:

[0825] The server receives the extracted text data and generates a summary using a generative AI model (e.g., GPT-3.5). The input is the text data, and the output is the summary (String). The server passes the text data to the generative AI model and sends a prompt like the following:

[0826] Summarize the following text:

[0827] (Text data)

[0828] The generative AI model returns a summary, which is then retrieved by the server.

[0829] Step 4:

[0830] The server generates a prompt for the image generation model based on the generated summary. The input is the summary, and the output is a prompt (String) for the image generation model. Specifically, the server writes out details about important scenes and characters in the summary and generates a prompt such as "The scene where the main character climbs a mountain."

[0831] Step 5:

[0832] The server passes the generated prompt to an image generation model (e.g., DALL-E) to generate an illustration. The input is the prompt, and the output is the generated illustration (image file). The server inputs the prompt into the image generation model and obtains the generated illustration. This illustration is saved for use in the next step.

[0833] Step 6:

[0834] The server integrates the generated summaries and illustrations to generate illustrated e-book data. The input is the summaries and illustrations, and the output is an e-book file (PDF). The server arranges the summary text and the corresponding illustrations in an orderly manner and converts them into a unified PDF file. Specifically, this is done using a PDF generation library (e.g., FPDF).

[0835] Step 7:

[0836] The server sends the generated e-book file to the user's device for download. The input is the e-book file, and the output is the file downloaded to the user's device. The server generates a link to provide the generated PDF file to the user, and the user clicks on this link to complete the e-book download.

[0837] The specific actions taken at each step efficiently digitize books and generate visually enjoyable content.

[0838] 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.

[0839] This invention relates to a system that provides illustrated documents by digitizing books and combining summaries and automatically generated illustrations. At the same time, by combining an emotion engine that recognizes the user's emotions, it is possible to display illustrations and text that correspond to the user's real-time emotions. This system is composed of a server, a user terminal, and the data exchanged between them.

[0840] System configuration

[0841] 1. Electronic means

[0842] A user can use a device to scan a book and create a PDF file, for example, by using a scanner to scan each page of a book and saving it as a single PDF file.

[0843] The user uploads the generated PDF to the system.

[0844] 2. Book Data Processing

[0845] The server receives the uploaded PDF and extracts the text data using OCR technology, which captures the text information from the scanned image and saves it as a text file.

[0846] 3. Synopsis Generation

[0847] The server analyzes the extracted text data and automatically generates a summary using a generative AI model, which creates a summary that gives an overview of the entire book.

[0848] 4. Prompt Generation

[0849] Based on the generated summary, the server creates prompts for the image generation model (e.g., DALL-E 3), such as prompts containing descriptions of specific scenes or characters.

[0850] 5. Illustration Generation

[0851] The server uses an image generation model to automatically generate illustrations based on the generated prompts, which provide visual content related to the summary.

[0852] 6. Emotion Engine Functions

[0853] The device is equipped with an emotion engine that analyzes the user's facial expressions, tone of voice, etc. This emotion engine recognizes the user's emotions in real time.

[0854] The server receives data from the emotion engine and adjusts the tone and style of the illustrations it generates based on the user's emotions. For example, if the user looks sad, the system can generate illustrations with brighter colors to soothe the user.

[0855] 7. Generating Illustrated Documents

[0856] The server combines the text data with the generated illustrations to generate an illustrated document, which is then output in an e-book format (e.g., PDF or EPUB).

[0857] 8. Output and Distribution

[0858] The server transmits the generated illustrated document to the user's terminal, where the user can download and view it.

[0859] Specific examples

[0860] 1. Examples of digitization

[0861] Scan the book "Example Story" entered by the user, generate a PDF using the scanner and OCR, and upload it to the system.

[0862] 2. Book Data Processing

[0863] The server receives the PDF and extracts the text using OCR, resulting in the text data of "The Example Story."

[0864] 3. Synopsis Generation

[0865] The server uses a generative AI model to create a concise summary from the text data, including key plot points such as the scene where the protagonist goes on an adventure and the climax of the story.

[0866] 4. Prompt Generation

[0867] Based on the generated synopsis, the server creates specific prompts such as "a scene where the protagonist climbs a mountain" or "a scene where the protagonist finds a magic sword."

[0868] 5. Illustration Generation

[0869] The server uses these prompts to generate corresponding illustrations from an image generation model, such as the protagonist climbing a mountain or finding a magic sword.

[0870] 6. Applying the Emotion Engine

[0871] While a user is browsing a book on their device, the emotion engine analyzes their facial expressions and voice in real time. For example, if the user shows a surprised expression, the system will change the next illustration to emphasize the element of surprise.

[0872] 7. Generating Illustrated Documents

[0873] The server inserts the generated illustrations into the corresponding text sections to generate an illustrated document, which is saved in PDF format.

[0874] 8. Output and Distribution

[0875] The server sends the PDF to the user's device, and the user downloads and views the illustrated document "The Example Story" on the device.

[0876] This system allows users to enjoy text-only books in a visually appealing format, reducing the barrier to reading. Furthermore, responding to the user's real-time emotions provides a more personalized reading experience. Additionally, the level of illustrations can be selected according to the user's needs, allowing them to freely enjoy detailed or simple illustrations. The present invention is also effective in helping users understand a variety of documents, including instruction manuals and dictionaries, in addition to books.

[0877] The processing flow will be explained below.

[0878] Step 1:

[0879] A user scans a book using a device and creates a PDF file. For example, a user scans each page of a book using a scanner and saves them as a single PDF file.

[0880] Step 2:

[0881] A user uploads a PDF file from their device to the system, and then sends the PDF to the system's server using a dedicated application or web browser on the device.

[0882] Step 3:

[0883] The server receives and records the uploaded PDF file, and then prepares the PDF file for OCR processing.

[0884] Step 4:

[0885] The server extracts text data from PDF files using OCR (Optical Character Recognition) technology, which captures character information from scanned images and saves them as text files.

[0886] Step 5:

[0887] The server analyzes the extracted text data and automatically generates a summary of the book using a generative AI, such as GPT-4, to generate a key plot summary from the text data.

[0888] Step 6:

[0889] Based on the generated summary, the server creates a prompt for the image generation model (e.g., DALL-E 3). For example, it generates a specific prompt such as "A scene where the main character goes on an adventure."

[0890] Step 7:

[0891] The server sends a prompt to DALL-E 3, which automatically generates a corresponding illustration. Based on the generated prompt, DALL-E 3 generates an illustration of the specified scene or item.

[0892] Step 8:

[0893] The server receives and saves the generated illustration, adjusting the size and resolution of the illustration as needed.

[0894] Step 9:

[0895] The device uses an emotion engine to recognize the user's facial expressions and tone of voice, and transmits this information to a server in real time. For example, the device can collect user emotion data using a camera or microphone.

[0896] Step 10:

[0897] The server changes the tone and style of the illustration based on the emotional data it receives: if the user has a sad expression, the system adjusts to generate illustrations with brighter colors and more joyful scenes.

[0898] Step 11:

[0899] The server integrates the text data with the generated illustrations to generate an illustrated document, for example, by inserting illustrations corresponding to each generated plot summary at appropriate positions in the text to create an integrated document.

[0900] Step 12:

[0901] The server saves the completed illustrated document in PDF or EPUB format and prepares it for delivery to the user's device.

[0902] Step 13:

[0903] The server sends the illustrated document to the user's device. The user can download and view the received document. For example, it becomes possible to view "The Story of the Day" with illustrations corresponding to emotions.

[0904] This process flow allows users to enjoy text-only books in a visually appealing format, lowering the barrier to entry and enabling a more personalized reading experience by adapting content based on the user's real-time emotions.

[0905] Example 2

[0906] 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."

[0907] Because traditional books exist in physical form, reading generally requires a certain amount of effort, and text-only books in particular lack visual aids. Furthermore, existing e-book systems lack the ability to automatically generate illustrations, resulting in a uniformly flat reading experience. Furthermore, personalized content display based on user emotions has yet to be realized, leaving a need for improved user experience.

[0908] 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.

[0909] In this invention, the server includes means for digitizing books, means for generating a synopsis from book data, means for generating prompts for an image generation model based on the generated synopsis, means for generating illustrations with the image generation model using the generated prompts, means for integrating the generated illustrations into electronic book data, and means for adjusting the tone and style of the illustrations based on user emotion data. This allows books and various documents to be provided in a visually enjoyable format, and further enables a personalized reading experience that responds to the user's real-time emotions.

[0910] "Book digitization tools" refers to a set of equipment and software used to convert physical books into digital files (e.g., PDF).

[0911] "Means for generating a synopsis from book data" refers to technology that analyzes the text data of a book and extracts the main content as a summary.

[0912] "Means for generating prompts for an image generation model based on the generated synopsis" refers to a technology that creates explanatory text (prompts) to be input into an image generation model based on summarized text.

[0913] "Means for generating an illustration using an image generation model using the generated prompt" refers to a technology for creating an illustration of a specific scene or character by using an image generation model with the prompt as input.

[0914] "Means for integrating the generated illustrations into e-book data" refers to the technology for placing the created illustrations into the corresponding text parts and converting them into a unified e-book format (e.g., PDF, EPUB).

[0915] "Means for adjusting the tone and style of an illustration based on the user's emotional data" refers to technology that recognizes the user's emotional state from their facial expressions and tone of voice, and uses that data to dynamically change the color and design of the illustration.

[0916] The system of the present invention provides illustrated documents by digitizing books and combining summaries with automatically generated illustrations. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it is possible to display illustrations and text in response to the user's real-time emotions. This system is comprised of a server, user terminals, and the data exchanged between them.

[0917] First, the user scans a book using a scanner connected to their device and generates a PDF file. For example, they can use a general-purpose device as the scanner and Tesseract OCR as the OCR software to scan each page of the book and compile them into a single PDF file. The user then uploads this PDF file to the server through the system's upload interface.

[0918] The server receives the uploaded PDF file and extracts the text data using OCR technology, such as Tesseract OCR, which extracts the text information from the scanned image and saves it as a text file.

[0919] The server then analyzes the extracted text data and automatically generates a summary using a generative AI model. Specifically, it uses a natural language processing library (e.g., spaCy) to tokenize the text and extract key nouns and verbs. It then uses a generative AI model (e.g., OpenAI GPT-3) to extract important parts from the long text and create a concise summary.

[0920] Based on the generated summary, the server creates prompts for the image generation model. The prompts include specific descriptions of specific scenes or characters from the summarized content. For example, a prompt might be generated such as "the scene where the main character climbs a mountain" or "the scene where he finds the magic sword."

[0921] The server then uses the generated prompts to generate illustrations using an image generation model (e.g., DALL-E 3). Based on the prompts, the model uses the learned data to generate high-quality illustrations. The generated illustrations are saved as visual content related to the summary.

[0922] Additionally, the device is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine includes a camera and microphone to analyze the user's facial expressions and tone of voice. For analysis, Microsoft Azure's emotion recognition API is used, for example. The server receives data from the emotion engine and adjusts the tone and style of the illustration based on the user's emotional state.

[0923] Finally, the server integrates the generated text data with the illustrations to generate an illustrated document, which is then exported as an e-book in PDF or EPUB format.The generated document is then sent to the user's device via the server, where the user can download and view the document on their device.

[0924] Specific examples are shown below.

[0925] For example, a user scans a book called "The Great Story," generates a PDF, and uploads it to the system. The server receives the PDF and uses OCR technology to extract the text data. Using a generative AI model, the server generates plot summaries, such as "the scene where the protagonist goes on an adventure" and "the climax of the story." Based on these plot summaries, the server then creates prompts, such as "the scene where the protagonist climbs a mountain" and "the scene where the protagonist finds the magic sword." The server then uses the DALL-E 3 model to generate illustrations based on these prompts. As the user browses the book, the device's built-in emotion engine analyzes the user's facial expressions and voice in real time, adjusting the tone of the visual content according to their emotions. Finally, the generated text data and illustrations are integrated to generate an illustrated document, which is then sent to the user's device. The user can then download and view it.

[0926] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0927] Step 1:

[0928] A user scans a book using a scanner connected to a device and generates a PDF file. The input is the physical book and the output is the PDF file. For example, a user scans each page of a book using a scanner and saves them as a PDF file.

[0929] Step 2:

[0930] The user uploads the generated PDF file from the terminal to the server through the system's web interface. The input is a PDF file, and the output is a PDF file stored in the server's storage. Specifically, the user accesses the web application using a browser, clicks the upload button, and selects the file.

[0931] Step 3:

[0932] The server receives the uploaded PDF file and extracts the text data using OCR technology. The input is a PDF file and the output is a text file. Specifically, it uses the Tesseract OCR engine to extract text information from each page of the PDF and saves it as a text file.

[0933] Step 4:

[0934] The server analyzes the extracted text data and automatically generates a summary using a generative AI model. The input is a text file, and the output is the summary text. Specifically, a natural language processing library (e.g., spaCy) is used to tokenize the text and extract key nouns and verbs. A generative AI model (e.g., OpenAI GPT-3) is then used to extract important parts from the long text and create a concise summary.

[0935] Step 5:

[0936] The server generates prompts for the image generation model based on the generated summary. The input is the summary text, and the output is the prompt. Specifically, it generates specific descriptions of specific scenes and characters from the summarized content. For example, it creates prompts such as "the scene where the main character climbs the mountain" or "the scene where he finds the magic sword."

[0937] Step 6:

[0938] The server uses the generated prompt to generate an illustration using an image generation model. The input is the prompt sentence, and the output is an illustration image. Specifically, the prompt is input into an image generation model (e.g., DALL-E 3), which generates a high-quality illustration based on the prompt.

[0939] Step 7:

[0940] The device is equipped with a camera and microphone to analyze the user's emotions, and the emotion engine analyzes the user's facial expressions and tone of voice in real time. The input is the user's facial expression data and voice data, and the output is emotion data. For example, Microsoft Azure's emotion recognition API is used for the analysis.

[0941] Step 8:

[0942] The server receives emotion data from the emotion engine and adjusts the tone and style of the illustration based on the user's emotion. The input is emotion data and an illustration image, and the output is the adjusted illustration image. For example, if the user looks sad, the server generates an illustration with brighter colors.

[0943] Step 9:

[0944] The server integrates the generated text data and illustrations to generate an illustrated document. The input is the text data and illustration images, and the output is an integrated e-book in PDF or EPUB format. The document is formatted using tools such as LaTeX or Pandoc.

[0945] Step 10:

[0946] The server sends the generated illustrated document to the user's device. The input is the integrated e-book file, and the output is the completed transmission to the user's device. The user can download and view the received document on their device.

[0947] (Application example 2)

[0948] 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."

[0949] With the recent spread of e-books, there is a demand for methods to digitize books and enhance their visual appeal. However, current e-readers only provide text information, making it difficult to provide personalized content that responds to the reader's emotions. There is also a need for a deeper reading experience that responds to the reader's real-time emotions.

[0950] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for digitizing a book, means for generating a synopsis from book data, means for generating prompts for an image generation model based on the generated synopsis, means for generating illustrations with the image generation model using the generated prompts, means for integrating the generated illustrations with e-book data, an emotion engine that recognizes a user's emotions in real time, and means for adjusting the tone and style of the illustrations based on the emotion engine. This not only enhances the visual appeal of the book but also enables a personalized reading experience that corresponds to the user's emotions.

[0951] "Means of digitizing books" refers to devices or methods for converting physical books into digital form, such as using scanners or OCR technology.

[0952] "Means for generating synopses from book data" refers to a technology that summarizes and extracts key content from the full text of an digitized book, and refers to the use of a generative AI model.

[0953] "Means for generating prompts for an image generation model" refers to technology that automatically creates specific instructions for generating illustrations from summarized text.

[0954] "Means for generating illustrations using an image generation model" refers to a technology that uses image generation AI to create corresponding illustrations based on the generated prompts.

[0955] "Means for integrating generated illustrations into e-book data" refers to a technology for inserting generated illustrations into corresponding text portions to create a visually appealing e-book.

[0956] An "emotion engine that recognizes user emotions in real time" refers to software or hardware that analyzes a user's facial expressions and tone of voice to estimate their emotions at any given time.

[0957] "Means for adjusting the tone and style of illustrations based on an emotion engine" refers to technology that dynamically changes the color and design of illustrations according to the user's recognized emotions.

[0958] This invention is a system for providing visually rich illustrated e-books by digitizing books and combining summaries and automatically generated illustrations, and customizes the tone and style of the content based on the user's emotions. Specific embodiments for realizing this system are described below.

[0959] First, a user digitizes a physical book using a scanner. Each page of the book is extracted from the scanned image and saved as a PDF file. Next, the user uploads the generated PDF to the system. The server receives the uploaded PDF and extracts the text data using OCR technology. An example of OCR software used here is pytesseract.

[0960] The server analyzes the extracted text data and automatically generates a summary using a generative AI model, such as Hugging Face transformers. Based on the generated summary, the server automatically generates prompts for the image generation model, which include descriptions of specific scenes and characters.

[0961] For example, the following prompts are generated from the summarized text:

[0962] "Generate an illustration of an epic battle scene based on the hero's final battle with the dragon."

[0963] The server then uses an image generation model (e.g., OpenAI's DALL-E) to generate a corresponding illustration based on the prompt, which is then used to combine with the summary as visual content.

[0964] The app also includes an emotion engine that recognizes users' emotions in real time. The emotion engine analyzes the user's facial expressions and tone of voice, using software such as EmotionRecognizer. Emotional data is sent from the user's device to a server, which then adjusts the tone and style of the illustrations it generates based on this data. For example, if the user expresses sadness, it can generate an illustration with bright colors to soothe the user's feelings.

[0965] Finally, the server integrates the text data with the generated illustrations to generate an illustrated e-book, which can be output in PDF or EPUB format and can be downloaded and read by users. The output e-book contains personalized illustrations based on the user's emotions, providing a deeper reading experience.

[0966] This system enriches the reader's experience by providing e-books that incorporate visual elements in addition to text information. It also provides a personalized reading experience by customizing illustrations based on real-time emotion recognition.

[0967] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0968] Step 1:

[0969] A user digitizes a physical book using a scanner, scanning each page of the book as an image and saving it as a PDF file. The input is the paper book, and the output is a PDF file.

[0970] Step 2:

[0971] The user uploads the generated PDF to the system. The server receives the PDF and starts processing it. The input is a PDF file, and the output is PDF data stored in the server.

[0972] Step 3:

[0973] The server uses OCR technology to extract text data from PDFs. For this process, we use OCR software such as pytesseract. The input is a PDF file and the output is text data.

[0974] Step 4:

[0975] The server analyzes the extracted text data and automatically generates a summary using a generative AI model, such as the Hugging Face transformers. The input is text data, and the output is the summarized text.

[0976] Step 5:

[0977] The server generates prompts for the image generation model based on the generated summary. These prompts include descriptions of specific scenes and characters. The input is the summarized text, and the output is the prompt.

[0978] Step 6:

[0979] For example, the summary generates the following prompt:

[0980] "Generate an illustration of an epic battle scene based on the hero's final battle with the dragon."

[0981] Step 7:

[0982] The server uses the generated prompt to pass it to an image generation model (e.g., OpenAI's DALL-E) and generates a corresponding illustration. The input is the prompt text, and the output is the illustration.

[0983] Step 8:

[0984] The user's device is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine analyzes the user's facial expressions and tone of voice and sends the emotion data to the server. The input is the user's facial expressions and tone of voice, and the output is emotion data.

[0985] Step 9:

[0986] The server receives the emotion data and adjusts the tone and style of the generated illustration based on that data. For example, if the user expresses sadness, the illustration will be changed to a brighter color scheme. The input is the emotion data, and the output is the adjusted illustration.

[0987] Step 10:

[0988] The server then combines the adjusted illustrations with the text data to generate an illustrated e-book, which is then output in PDF or EPUB format. The input is the text data and adjusted illustrations, and the output is the illustrated e-book.

[0989] Step 11:

[0990] Users download and read the generated illustrated e-books, providing a visually rich content and a personalized reading experience based on real-time emotions. The input is the illustrated e-book, and the output is the user's reading experience.

[0991] 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.

[0992] 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.

[0993] 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.

[0994] [Fourth embodiment]

[0995] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0996] 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.

[0997] 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).

[0998] 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.

[0999] 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.

[1000] 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).

[1001] 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.

[1002] 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.

[1003] 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.

[1004] 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.

[1005] 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.

[1006] 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.

[1007] 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."

[1008] The present invention relates to a system for providing illustrated documents by digitizing books and combining summaries and automatically generated illustrations. The system is composed of a server, user terminals, and data exchanged between them.

[1009] System configuration

[1010] 1. Electronic means

[1011] Users use their device to scan books and generate PDF files, using a traditional scanner or OCR technology.

[1012] The user uploads the generated PDF to the system.

[1013] 2. Book Data Processing

[1014] The server receives the uploaded PDF and uses OCR technology to extract the text data, converting the paper book into digital text.

[1015] 3. Synopsis Generation

[1016] The server analyzes the extracted text data and automatically generates a summary using a generative AI model, which creates a summary that gives an overview of the entire book.

[1017] 4. Prompt Generation

[1018] The server generates prompts for the image generation model based on the generated synopsis, for example, prompts containing descriptions of specific scenes or characters.

[1019] 5. Illustration Generation

[1020] The server uses an image generation model (e.g., DALL-E 3) to automatically generate illustrations based on the generated prompts, which provide visual content related to the summary.

[1021] 6. Generating Illustrated Documents

[1022] The server combines the text data with the generated illustrations to generate an illustrated document, which is then output in an e-book format (e.g., PDF or EPUB).

[1023] 7. Output and Distribution

[1024] The server transmits the generated illustrated document to the user's terminal, where the user can download and view it.

[1025] Specific examples

[1026] 1. Examples of digitization

[1027] Scan the book "Example Story" entered by the user, generate a PDF using the scanner and OCR, and upload it to the system.

[1028] 2. Book Data Processing

[1029] The server receives the PDF and extracts the text using OCR, resulting in the text data of "The Example Story."

[1030] 3. Synopsis Generation

[1031] The server uses a generative AI model to create a concise summary from the text data, including key plot points such as the scene where the protagonist goes on an adventure and the climax of the story.

[1032] 4. Prompt Generation

[1033] Based on the generated synopsis, the server creates specific prompts such as "a scene where the protagonist climbs a mountain" or "a scene where the protagonist finds a magic sword."

[1034] 5. Illustration Generation

[1035] The server uses these prompts to generate corresponding illustrations from an image generation model, such as the protagonist climbing a mountain or finding a magic sword.

[1036] 6. Generating Illustrated Documents

[1037] The server inserts the generated illustrations into the corresponding text sections to generate an illustrated document, which is saved in PDF format.

[1038] 7. Output and Distribution

[1039] The server sends the PDF to the user's device, and the user downloads and views the illustrated document "The Example Story" on the device.

[1040] This system allows text-only books to be presented in a visually enjoyable format, reducing the barrier to reading. Additionally, users can select the level of illustrations to suit their needs, allowing them to freely enjoy detailed or simple illustrations. This invention is also effective in helping users understand a variety of documents, including instruction manuals and dictionaries, in addition to books.

[1041] The processing flow will be explained below.

[1042] Step 1:

[1043] A user scans a book using a device and creates a PDF file. For example, a user scans each page of a book using a scanner and saves them as a single PDF file.

[1044] Step 2:

[1045] A user uploads a PDF file from their device to the system, and then sends the PDF to the system's server using a dedicated application or web browser on the device.

[1046] Step 3:

[1047] The server receives and records the uploaded PDF file, and then prepares the PDF file for OCR processing.

[1048] Step 4:

[1049] The server extracts text data from PDF files using OCR (Optical Character Recognition) technology, which captures character information from scanned images and saves them as text files.

[1050] Step 5:

[1051] The server analyzes the extracted text data and automatically generates a summary of the book using a generative AI, such as GPT-4, to generate a key plot summary from the text data.

[1052] Step 6:

[1053] Based on the generated summary, the server creates a prompt for the image generation model (e.g., DALL-E 3). For example, it generates a specific prompt such as "A scene where the main character goes on an adventure."

[1054] Step 7:

[1055] The server sends a prompt to DALL-E 3, which automatically generates a corresponding illustration. Based on the generated prompt, DALL-E 3 generates an illustration of the specified scene or item.

[1056] Step 8:

[1057] The server receives and saves the generated illustration, adjusting the size and resolution of the illustration as needed.

[1058] Step 9:

[1059] The server integrates the text data with the generated illustrations to generate an illustrated document, for example, by inserting illustrations corresponding to each generated plot summary at appropriate positions in the text to create an integrated document.

[1060] Step 10:

[1061] The server saves the completed illustrated document in PDF or EPUB format and prepares it for delivery to the user's device.

[1062] Step 11:

[1063] The server sends the illustrated document to the user's terminal, where the user can download and view the document.

[1064] This process flow allows users to enjoy text-only books in a visually appealing format, reducing the barrier to reading. Additionally, users can select the level of illustrations to suit their needs, allowing them to freely enjoy detailed or simple illustrations. The present invention is also effective in helping users understand a variety of documents, including instruction manuals and dictionaries, in addition to books.

[1065] Example 1

[1066] 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."

[1067] Conventional e-book systems simply digitize books, resulting in a lack of visual elements that attract readers' attention. Furthermore, adding visual illustrations is often done manually, requiring a significant amount of time and effort. Furthermore, they lack the versatility to handle documents other than books and the ability to customize them to meet user needs.

[1068] 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.

[1069] In this invention, the server includes means for digitizing books, means for extracting text data from the digitized book data, means for generating a synopsis from the extracted text data, means for generating prompts for image generation based on the generated synopsis, means for creating illustrations using an image generation model using the generated prompts, means for integrating the generated illustrations with text data to generate an illustrated document, and means for transmitting the generated illustrated document to a user terminal. This automates the entire process from digitizing books to synopsis generation, automatic generation of illustrations, and generation and distribution of the illustrated document, making it possible to provide visually appealing e-books. Furthermore, this allows for flexible customization according to user needs and support for documents other than books.

[1070] "Means for digitizing books" refers to devices and software that scan books and generate their contents as digital data, such as PDF files.

[1071] "Means for extracting text data from digitized book data" refers to devices or software that utilize OCR technology to extract text information from digitized book data.

[1072] "Means for generating a synopsis from extracted text data" refers to a device or software for analyzing digitized text data and generating a summary of the main contents or key points.

[1073] "Means for generating prompts for image generation based on the generated synopsis" refers to a device or software that creates specific instructions (prompts) for generating illustrations from the generated synopsis.

[1074] "Means for creating an illustration in an image generation model using the generated prompt" refers to a device or software for inputting a prompt into an image generation model and automatically generating an illustration based on the prompt.

[1075] "Means for integrating generated illustrations into text data to generate an illustrated document" refers to devices or software for inserting generated illustrations into corresponding text sections to create a visually rich illustrated electronic document.

[1076] "Means for transmitting the generated illustrated document to a user terminal" refers to a device or software for transmitting the generated illustrated document to a user's electronic device via a network.

[1077] "The level of illustration can be selected according to the user's request" refers to a function that allows the user to choose to generate more detailed illustrations or simple illustrations according to their own preferences.

[1078] "It can be applied to a variety of documents other than books, such as instruction manuals and dictionaries" refers to the system's ability to be applied not only to the digitization of books, but also to a variety of other documents, and to effectively add visual information to each document.

[1079] This invention relates to a system that provides visually appealing illustrated documents by digitizing books and combining summaries and automatically generated illustrations. This system is composed of a server, user terminals, and the data exchanged between them.

[1080] The main configuration and processing steps of the system are as follows.

[1081] electronic means

[1082] A user scans a book with a scanner and generates a PDF file. Specifically, a USB-connected scanner is used to extract text data using OCR technology (e.g., Tesseract OCR). The generated PDF file is then uploaded to the system through a web interface. For example, a user scans a book called "The Example Story," saves it as a PDF, and uploads it to the system.

[1083] Book data processing

[1084] The server receives the uploaded PDF file and stores it in a temporary directory. Then, it uses OCR technology to extract text data from the PDF. Specifically, it uses Tesseract OCR to analyze the text information in the PDF and obtain it as digital text. This digitizes the paper book.

[1085] Synopsis generation

[1086] The server analyzes the extracted text data and generates a synopsis of the book using a generative AI model (e.g., GPT-4). Specifically, by inputting the text data into the AI ​​model, a summary containing the main content is obtained. For example, a summary is generated of the scene in which the protagonist goes on an adventure and the climax of the story.

[1087] Prompt Generation

[1088] Based on the generated synopsis, the server creates prompts for the image generation model, including descriptions of specific scenes and characters, such as "Please describe the scene where the main character climbs the mountain" or "Please describe the scene where the main character finds the magic sword."

[1089] Illustration generation

[1090] The server inputs the prompt sentence into an image generation model (e.g., DALL-E 3) and generates a corresponding illustration. Specifically, the prompt is input into the generation model to obtain a visually detailed illustration. For example, an illustration of "the scene where the protagonist climbs a mountain" or "the scene where the protagonist finds a magic sword" is generated.

[1091] Generating illustrated documents

[1092] The server then integrates the generated illustrations with the text data to generate an illustrated document. Specifically, it inserts the illustrations at appropriate points in the summary and saves them as a PDF or EPUB file. For example, a PDF file with illustrations inserted at appropriate times along with the summary of "The Example Story" is created.

[1093] Output and Delivery

[1094] The server sends the generated illustrated document to the user's device, and the user can download and view the e-book through the system interface. For example, the user can download an illustrated PDF of "The Example Story" and open it on their device to read.

[1095] This system allows books to be presented in a visually enjoyable format, enhancing reader interest and understanding.It can also handle documents other than books (such as instruction manuals and dictionaries), making it a system that can be used for a wide range of purposes.

[1096] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1097] Step 1: Scan your eBook

[1098] A user scans a book with a scanner and generates a PDF file. In this operation, the pages of the book are placed one by one on the scanner, and scanning begins by pressing the scanner's start button. The scanned data is automatically saved in PDF format. The input data is the paper book, and the output data is a PDF file.

[1099] Step 2: Upload your PDF file

[1100] The user opens the web interface to upload the generated PDF file to the system. The user clicks the Upload button, selects the target PDF file from the file selection dialog, and presses the Send button to complete the process. The input data is a PDF file, and the output data is a PDF file saved on the server.

[1101] Step 3: Receive and save the PDF file

[1102] The server receives the PDF file uploaded by the user. In this process, the PDF file is saved in a temporary directory on the server. The input data is the PDF file sent to the server, and the output data is the PDF file saved in the temporary directory.

[1103] Step 4: Extract text data

[1104] The server uses OCR technology to extract text data from PDF files. Specifically, it uses the Tesseract OCR engine to analyze image data from PDFs and extract character information. The input data is the PDF file, and the output data is the extracted text data.

[1105] Step 5: Generate a summary

[1106] The server analyzes the extracted text data and generates a summary using a generative AI model (e.g., GPT-4). The text data is input into the AI ​​model, which outputs a summary that extracts the main content and key points. In this step, the input data is the text data, and the output data is the summary text.

[1107] Step 6: Create a prompt

[1108] The server creates prompts for image generation based on the generated summary. Specifically, it selects important scenes and characters in the summary and generates specific prompt sentences to depict them. The input data is the summary text, and the output data is the prompt sentences.

[1109] Step 7: Creating an illustration

[1110] The server inputs the prompt sentence into an image generation model (e.g., DALL-E 3) and generates a corresponding illustration. The server inputs the prompt sentence into the model and receives the generated illustration. The input data is the prompt sentence, and the output data is the generated illustration.

[1111] Step 8: Generating the illustrated document

[1112] The server integrates the generated illustrations with the text data to generate an illustrated document. Specifically, it inserts the illustrations at appropriate positions in the summary text and saves it as a PDF or EPUB file. The input data is the summary text and illustrations, and the output data is the illustrated document.

[1113] Step 9: Distribution of illustrated documents

[1114] The server sends the generated illustrated document to the user's terminal, and the user can download and read the e-book through the system interface. The input data is the illustrated document, and the output data is the illustrated document stored in the user's terminal.

[1115] (Application example 1)

[1116] 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."

[1117] Traditional books relied on paper media, making it difficult to digitize or visually enhance their content. This limited opportunities for readers to visually enjoy the book's contents. Even with digitized books, the increase in text data made it difficult to efficiently grasp the content, and visual aids were lacking. Furthermore, the process of converting paper books into digital format was complicated, making it difficult for the average user to easily use.

[1118] 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.

[1119] In this invention, the server includes means for digitizing books, means for generating a synopsis from book data, means for generating prompts for an image generation model based on the generated synopsis, means for generating illustrations with the image generation model using the generated prompts, means for integrating the generated illustrations into e-book data, means for automatically scanning books using a smartphone camera, means for extracting text using OCR technology, and means for generating e-books in PDF format and making them available for download. This simplifies the process of digitizing books and enables readers to efficiently grasp the contents of books and enjoy them visually through summaries and visual complements.

[1120] "Digitizing a book" means converting a physical book into a digital format, which may involve the use of a scanner or camera.

[1121] "Means for generating synopsis from book data" refers to a technology that summarizes important content from extracted book text data, and uses generative AI and natural language processing technology.

[1122] "Means for generating prompts for an image generation model based on the generated synopsis" refers to a technology for creating instructions for generating images based on the summary.

[1123] An "image generation model" is an artificial intelligence system that generates images based on text prompts.

[1124] "Means for generating illustrations using an image generation model using the generated prompts" refers to the process by which AI creates illustrations using generated instructions.

[1125] The "means for integrating the generated illustrations into the e-book data" is a technology that combines the generated illustrations with text data to create a single e-book format file.

[1126] "Method for automatically scanning books using a smartphone camera" is a technology that uses a smartphone's built-in camera to photograph the pages of a book and digitize the data.

[1127] "Means of extracting text using OCR technology" refers to optical character recognition (OCR), a technology that recognizes and extracts character information from image data.

[1128] "Means for generating and making downloadable e-books in PDF format" refers to technology that converts the generated text and illustrations into a PDF file that can be downloaded by users.

[1129] "Users can choose between illustrations at the level of illustrations and comics, depending on their needs" refers to the ability for users to choose the level of detail and style of the illustrations to be generated.

[1130] "Can be applied to a variety of documents, such as instruction manuals and dictionaries" means that this system can be applied to various types of documents other than books.

[1131] The present invention relates to a system for providing illustrated documents by digitizing books and combining summaries and automatically generated illustrations. Specific embodiments of the present invention will be described below.

[1132] The system consists of a user device, a server, and the data exchanged between them. First, the user device automatically scans the book pages using a smartphone camera. This image is converted to grayscale, and text information is extracted using OCR technology. The OCR technology uses Tesseract, a high-performance character recognition software. At this stage, the book's text data is obtained.

[1133] The server receives this text data and uses a generative AI model (e.g., GPT-3.5) to generate a summary, using the following prompt:

[1134] Summarize the following text:

[1135] (Text data)

[1136] Based on the generated summary, a prompt is created for the image generation model. For example, specific prompts such as "a scene where the main character climbs a mountain" or "a scene where the magic sword is found" are generated. Based on this prompt, an image generation model (such as "DALL-E") is used to generate an illustration.

[1137] The generated illustrations are integrated with the corresponding text to generate an illustrated e-book. The e-book data is saved in PDF format and made available for download to users. This download function allows users to view the generated illustrated book on their own devices.

[1138] The server handles all processes from generating summaries to creating prompts, generating images, and finally integrating them all at once, allowing users to easily digitize books and enjoy them visually without any special technical knowledge.

[1139] As a concrete example, a user scans a book called "The Book of Adventure," and the server extracts the text using OCR and generates a summary using a generative AI model.

[1140] The scene where the main character fights a dragon

[1141] The system generates prompt sentences such as "What is the meaning of this book?" and uses an image generation model to create corresponding illustrations. Finally, the illustrations and summaries are integrated to create an attractive illustrated book.

[1142] This system simplifies the process of digitizing paper books, and allows readers to efficiently understand the content and enjoy it more through summaries and visual complements.

[1143] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1144] Step 1:

[1145] The user scans each page of a book using the smartphone camera. The input is the physical page of the book, and the output is a digital image file. The smartphone's camera application captures it and saves it as a grayscale JPEG or PNG file. This data is then ready for subsequent OCR processing.

[1146] Step 2:

[1147] The user converts the scanned image into text data using OCR technology. The input is a grayscale image, and the output is text data (String). Specifically, OCR software (e.g., Tesseract) installed on the device analyzes the image and extracts text information. The result of the OCR process is text data extracted from the book page.

[1148] Step 3:

[1149] The server receives the extracted text data and generates a summary using a generative AI model (e.g., GPT-3.5). The input is the text data, and the output is the summary (String). The server passes the text data to the generative AI model and sends a prompt like the following:

[1150] Summarize the following text:

[1151] (Text data)

[1152] The generative AI model returns a summary, which is then retrieved by the server.

[1153] Step 4:

[1154] The server generates a prompt for the image generation model based on the generated summary. The input is the summary, and the output is a prompt (String) for the image generation model. Specifically, the server writes out details about important scenes and characters in the summary and generates a prompt such as "The scene where the main character climbs a mountain."

[1155] Step 5:

[1156] The server passes the generated prompt to an image generation model (e.g., DALL-E) to generate an illustration. The input is the prompt, and the output is the generated illustration (image file). The server inputs the prompt into the image generation model and obtains the generated illustration. This illustration is saved for use in the next step.

[1157] Step 6:

[1158] The server integrates the generated summaries and illustrations to generate illustrated e-book data. The input is the summaries and illustrations, and the output is an e-book file (PDF). The server arranges the summary text and the corresponding illustrations in an orderly manner and converts them into a unified PDF file. Specifically, this is done using a PDF generation library (e.g., FPDF).

[1159] Step 7:

[1160] The server sends the generated e-book file to the user's device for download. The input is the e-book file, and the output is the file downloaded to the user's device. The server generates a link to provide the generated PDF file to the user, and the user clicks on this link to complete the e-book download.

[1161] The specific actions taken at each step efficiently digitize books and generate visually enjoyable content.

[1162] 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.

[1163] This invention relates to a system that provides illustrated documents by digitizing books and combining summaries and automatically generated illustrations. At the same time, by combining an emotion engine that recognizes the user's emotions, it is possible to display illustrations and text that correspond to the user's real-time emotions. This system is composed of a server, a user terminal, and the data exchanged between them.

[1164] System configuration

[1165] 1. Electronic means

[1166] A user can use a device to scan a book and create a PDF file, for example, by using a scanner to scan each page of a book and saving it as a single PDF file.

[1167] The user uploads the generated PDF to the system.

[1168] 2. Book Data Processing

[1169] The server receives the uploaded PDF and extracts the text data using OCR technology, which captures the text information from the scanned image and saves it as a text file.

[1170] 3. Synopsis Generation

[1171] The server analyzes the extracted text data and automatically generates a summary using a generative AI model, which creates a summary that gives an overview of the entire book.

[1172] 4. Prompt Generation

[1173] Based on the generated summary, the server creates prompts for the image generation model (e.g., DALL-E 3), such as prompts containing descriptions of specific scenes or characters.

[1174] 5. Illustration Generation

[1175] The server uses an image generation model to automatically generate illustrations based on the generated prompts, which provide visual content related to the summary.

[1176] 6. Emotion Engine Functions

[1177] The device is equipped with an emotion engine that analyzes the user's facial expressions, tone of voice, etc. This emotion engine recognizes the user's emotions in real time.

[1178] The server receives data from the emotion engine and adjusts the tone and style of the illustrations it generates based on the user's emotions. For example, if the user looks sad, the system can generate illustrations with brighter colors to soothe the user.

[1179] 7. Generating Illustrated Documents

[1180] The server combines the text data with the generated illustrations to generate an illustrated document, which is then output in an e-book format (e.g., PDF or EPUB).

[1181] 8. Output and Distribution

[1182] The server transmits the generated illustrated document to the user's terminal, where the user can download and view it.

[1183] Specific examples

[1184] 1. Examples of digitization

[1185] Scan the book "Example Story" entered by the user, generate a PDF using the scanner and OCR, and upload it to the system.

[1186] 2. Book Data Processing

[1187] The server receives the PDF and extracts the text using OCR, resulting in the text data of "The Example Story."

[1188] 3. Synopsis Generation

[1189] The server uses a generative AI model to create a concise summary from the text data, including key plot points such as the scene where the protagonist goes on an adventure and the climax of the story.

[1190] 4. Prompt Generation

[1191] Based on the generated synopsis, the server creates specific prompts such as "a scene where the protagonist climbs a mountain" or "a scene where the protagonist finds a magic sword."

[1192] 5. Illustration Generation

[1193] The server uses these prompts to generate corresponding illustrations from an image generation model, such as the protagonist climbing a mountain or finding a magic sword.

[1194] 6. Applying the Emotion Engine

[1195] While a user is browsing a book on their device, the emotion engine analyzes their facial expressions and voice in real time. For example, if the user shows a surprised expression, the system will change the next illustration to emphasize the element of surprise.

[1196] 7. Generating Illustrated Documents

[1197] The server inserts the generated illustrations into the corresponding text sections to generate an illustrated document, which is saved in PDF format.

[1198] 8. Output and Distribution

[1199] The server sends the PDF to the user's device, and the user downloads and views the illustrated document "The Example Story" on the device.

[1200] This system allows users to enjoy text-only books in a visually appealing format, reducing the barrier to reading. Furthermore, responding to the user's real-time emotions provides a more personalized reading experience. Additionally, the level of illustrations can be selected according to the user's needs, allowing them to freely enjoy detailed or simple illustrations. The present invention is also effective in helping users understand a variety of documents, including instruction manuals and dictionaries, in addition to books.

[1201] The processing flow will be explained below.

[1202] Step 1:

[1203] A user scans a book using a device and creates a PDF file. For example, a user scans each page of a book using a scanner and saves them as a single PDF file.

[1204] Step 2:

[1205] A user uploads a PDF file from their device to the system, and then sends the PDF to the system's server using a dedicated application or web browser on the device.

[1206] Step 3:

[1207] The server receives and records the uploaded PDF file, and then prepares the PDF file for OCR processing.

[1208] Step 4:

[1209] The server extracts text data from PDF files using OCR (Optical Character Recognition) technology, which captures character information from scanned images and saves them as text files.

[1210] Step 5:

[1211] The server analyzes the extracted text data and automatically generates a summary of the book using a generative AI, such as GPT-4, to generate a key plot summary from the text data.

[1212] Step 6:

[1213] Based on the generated summary, the server creates a prompt for the image generation model (e.g., DALL-E 3). For example, it generates a specific prompt such as "A scene where the main character goes on an adventure."

[1214] Step 7:

[1215] The server sends a prompt to DALL-E 3, which automatically generates a corresponding illustration. Based on the generated prompt, DALL-E 3 generates an illustration of the specified scene or item.

[1216] Step 8:

[1217] The server receives and saves the generated illustration, adjusting the size and resolution of the illustration as needed.

[1218] Step 9:

[1219] The device uses an emotion engine to recognize the user's facial expressions and tone of voice, and transmits this information to a server in real time. For example, the device can collect user emotion data using a camera or microphone.

[1220] Step 10:

[1221] The server changes the tone and style of the illustration based on the emotional data it receives: if the user has a sad expression, the system adjusts to generate illustrations with brighter colors and more joyful scenes.

[1222] Step 11:

[1223] The server integrates the text data with the generated illustrations to generate an illustrated document, for example, by inserting illustrations corresponding to each generated plot summary at appropriate positions in the text to create an integrated document.

[1224] Step 12:

[1225] The server saves the completed illustrated document in PDF or EPUB format and prepares it for delivery to the user's device.

[1226] Step 13:

[1227] The server sends the illustrated document to the user's device. The user can download and view the received document. For example, it becomes possible to view "The Story of the Day" with illustrations corresponding to emotions.

[1228] This process flow allows users to enjoy text-only books in a visually appealing format, lowering the barrier to entry and enabling a more personalized reading experience by adapting content based on the user's real-time emotions.

[1229] Example 2

[1230] 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."

[1231] Because traditional books exist in physical form, reading generally requires a certain amount of effort, and text-only books in particular lack visual aids. Furthermore, existing e-book systems lack the ability to automatically generate illustrations, resulting in a uniformly flat reading experience. Furthermore, personalized content display based on user emotions has yet to be realized, leaving a need for improved user experience.

[1232] 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.

[1233] In this invention, the server includes means for digitizing books, means for generating a synopsis from book data, means for generating prompts for an image generation model based on the generated synopsis, means for generating illustrations with the image generation model using the generated prompts, means for integrating the generated illustrations into electronic book data, and means for adjusting the tone and style of the illustrations based on user emotion data. This allows books and various documents to be provided in a visually enjoyable format, and further enables a personalized reading experience that responds to the user's real-time emotions.

[1234] "Book digitization tools" refers to a set of equipment and software used to convert physical books into digital files (e.g., PDF).

[1235] "Means for generating a synopsis from book data" refers to technology that analyzes the text data of a book and extracts the main content as a summary.

[1236] "Means for generating prompts for an image generation model based on the generated synopsis" refers to a technology that creates explanatory text (prompts) to be input into an image generation model based on summarized text.

[1237] "Means for generating an illustration using an image generation model using the generated prompt" refers to a technology for creating an illustration of a specific scene or character by using an image generation model with the prompt as input.

[1238] "Means for integrating the generated illustrations into e-book data" refers to the technology for placing the created illustrations into the corresponding text parts and converting them into a unified e-book format (e.g., PDF, EPUB).

[1239] "Means for adjusting the tone and style of an illustration based on the user's emotional data" refers to technology that recognizes the user's emotional state from their facial expressions and tone of voice, and uses that data to dynamically change the color and design of the illustration.

[1240] The system of the present invention provides illustrated documents by digitizing books and combining summaries with automatically generated illustrations. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it is possible to display illustrations and text in response to the user's real-time emotions. This system is comprised of a server, user terminals, and the data exchanged between them.

[1241] First, the user scans a book using a scanner connected to their device and generates a PDF file. For example, they can use a general-purpose device as the scanner and Tesseract OCR as the OCR software to scan each page of the book and compile them into a single PDF file. The user then uploads this PDF file to the server through the system's upload interface.

[1242] The server receives the uploaded PDF file and extracts the text data using OCR technology, such as Tesseract OCR, which extracts the text information from the scanned image and saves it as a text file.

[1243] The server then analyzes the extracted text data and automatically generates a summary using a generative AI model. Specifically, it uses a natural language processing library (e.g., spaCy) to tokenize the text and extract key nouns and verbs. It then uses a generative AI model (e.g., OpenAI GPT-3) to extract important parts from the long text and create a concise summary.

[1244] Based on the generated summary, the server creates prompts for the image generation model. The prompts include specific descriptions of specific scenes or characters from the summarized content. For example, a prompt might be generated such as "the scene where the main character climbs a mountain" or "the scene where he finds the magic sword."

[1245] The server then uses the generated prompts to generate illustrations using an image generation model (e.g., DALL-E 3). Based on the prompts, the model uses the learned data to generate high-quality illustrations. The generated illustrations are saved as visual content related to the summary.

[1246] Additionally, the device is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine includes a camera and microphone to analyze the user's facial expressions and tone of voice. For analysis, Microsoft Azure's emotion recognition API is used, for example. The server receives data from the emotion engine and adjusts the tone and style of the illustration based on the user's emotional state.

[1247] Finally, the server integrates the generated text data with the illustrations to generate an illustrated document, which is then exported as an e-book in PDF or EPUB format.The generated document is then sent to the user's device via the server, where the user can download and view the document on their device.

[1248] Specific examples are shown below.

[1249] For example, a user scans a book called "The Great Story," generates a PDF, and uploads it to the system. The server receives the PDF and uses OCR technology to extract the text data. Using a generative AI model, the server generates plot summaries, such as "the scene where the protagonist goes on an adventure" and "the climax of the story." Based on these plot summaries, the server then creates prompts, such as "the scene where the protagonist climbs a mountain" and "the scene where the protagonist finds the magic sword." The server then uses the DALL-E 3 model to generate illustrations based on these prompts. As the user browses the book, the device's built-in emotion engine analyzes the user's facial expressions and voice in real time, adjusting the tone of the visual content according to their emotions. Finally, the generated text data and illustrations are integrated to generate an illustrated document, which is then sent to the user's device. The user can then download and view it.

[1250] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1251] Step 1:

[1252] A user scans a book using a scanner connected to a device and generates a PDF file. The input is the physical book and the output is the PDF file. For example, a user scans each page of a book using a scanner and saves them as a PDF file.

[1253] Step 2:

[1254] The user uploads the generated PDF file from the terminal to the server through the system's web interface. The input is a PDF file, and the output is a PDF file stored in the server's storage. Specifically, the user accesses the web application using a browser, clicks the upload button, and selects the file.

[1255] Step 3:

[1256] The server receives the uploaded PDF file and extracts the text data using OCR technology. The input is a PDF file and the output is a text file. Specifically, it uses the Tesseract OCR engine to extract text information from each page of the PDF and saves it as a text file.

[1257] Step 4:

[1258] The server analyzes the extracted text data and automatically generates a summary using a generative AI model. The input is a text file, and the output is the summary text. Specifically, a natural language processing library (e.g., spaCy) is used to tokenize the text and extract key nouns and verbs. A generative AI model (e.g., OpenAI GPT-3) is then used to extract important parts from the long text and create a concise summary.

[1259] Step 5:

[1260] The server generates prompts for the image generation model based on the generated summary. The input is the summary text, and the output is the prompt. Specifically, it generates specific descriptions of specific scenes and characters from the summarized content. For example, it creates prompts such as "the scene where the main character climbs the mountain" or "the scene where he finds the magic sword."

[1261] Step 6:

[1262] The server uses the generated prompt to generate an illustration using an image generation model. The input is the prompt sentence, and the output is an illustration image. Specifically, the prompt is input into an image generation model (e.g., DALL-E 3), which generates a high-quality illustration based on the prompt.

[1263] Step 7:

[1264] The device is equipped with a camera and microphone to analyze the user's emotions, and the emotion engine analyzes the user's facial expressions and tone of voice in real time. The input is the user's facial expression data and voice data, and the output is emotion data. For example, Microsoft Azure's emotion recognition API is used for the analysis.

[1265] Step 8:

[1266] The server receives emotion data from the emotion engine and adjusts the tone and style of the illustration based on the user's emotion. The input is emotion data and an illustration image, and the output is the adjusted illustration image. For example, if the user looks sad, the server generates an illustration with brighter colors.

[1267] Step 9:

[1268] The server integrates the generated text data and illustrations to generate an illustrated document. The input is the text data and illustration images, and the output is an integrated e-book in PDF or EPUB format. The document is formatted using tools such as LaTeX or Pandoc.

[1269] Step 10:

[1270] The server sends the generated illustrated document to the user's device. The input is the integrated e-book file, and the output is the completed transmission to the user's device. The user can download and view the received document on their device.

[1271] (Application example 2)

[1272] 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."

[1273] With the recent spread of e-books, there is a demand for methods to digitize books and enhance their visual appeal. However, current e-readers only provide text information, making it difficult to provide personalized content that responds to the reader's emotions. There is also a need for a deeper reading experience that responds to the reader's real-time emotions.

[1274] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for digitizing a book, means for generating a synopsis from book data, means for generating prompts for an image generation model based on the generated synopsis, means for generating illustrations with the image generation model using the generated prompts, means for integrating the generated illustrations with e-book data, an emotion engine that recognizes a user's emotions in real time, and means for adjusting the tone and style of the illustrations based on the emotion engine. This not only enhances the visual appeal of the book but also enables a personalized reading experience that corresponds to the user's emotions.

[1275] "Means of digitizing books" refers to devices or methods for converting physical books into digital form, such as using scanners or OCR technology.

[1276] "Means for generating synopses from book data" refers to a technology that summarizes and extracts key content from the full text of an digitized book, and refers to the use of a generative AI model.

[1277] "Means for generating prompts for an image generation model" refers to technology that automatically creates specific instructions for generating illustrations from summarized text.

[1278] "Means for generating illustrations using an image generation model" refers to a technology that uses image generation AI to create corresponding illustrations based on the generated prompts.

[1279] "Means for integrating generated illustrations into e-book data" refers to a technology for inserting generated illustrations into corresponding text portions to create a visually appealing e-book.

[1280] An "emotion engine that recognizes user emotions in real time" refers to software or hardware that analyzes a user's facial expressions and tone of voice to estimate their emotions at any given time.

[1281] "Means for adjusting the tone and style of illustrations based on an emotion engine" refers to technology that dynamically changes the color and design of illustrations according to the user's recognized emotions.

[1282] This invention is a system for providing visually rich illustrated e-books by digitizing books and combining summaries and automatically generated illustrations, and customizes the tone and style of the content based on the user's emotions. Specific embodiments for realizing this system are described below.

[1283] First, a user digitizes a physical book using a scanner. Each page of the book is extracted from the scanned image and saved as a PDF file. Next, the user uploads the generated PDF to the system. The server receives the uploaded PDF and extracts the text data using OCR technology. An example of OCR software used here is pytesseract.

[1284] The server analyzes the extracted text data and automatically generates a summary using a generative AI model, such as Hugging Face transformers. Based on the generated summary, the server automatically generates prompts for the image generation model, which include descriptions of specific scenes and characters.

[1285] For example, the following prompts are generated from the summarized text:

[1286] "Generate an illustration of an epic battle scene based on the hero's final battle with the dragon."

[1287] The server then uses an image generation model (e.g., OpenAI's DALL-E) to generate a corresponding illustration based on the prompt, which is then used to combine with the summary as visual content.

[1288] The app also includes an emotion engine that recognizes users' emotions in real time. The emotion engine analyzes the user's facial expressions and tone of voice, using software such as EmotionRecognizer. Emotional data is sent from the user's device to a server, which then adjusts the tone and style of the illustrations it generates based on this data. For example, if the user expresses sadness, it can generate an illustration with bright colors to soothe the user's feelings.

[1289] Finally, the server integrates the text data with the generated illustrations to generate an illustrated e-book, which can be output in PDF or EPUB format and can be downloaded and read by users. The output e-book contains personalized illustrations based on the user's emotions, providing a deeper reading experience.

[1290] This system enriches the reader's experience by providing e-books that incorporate visual elements in addition to text information. It also provides a personalized reading experience by customizing illustrations based on real-time emotion recognition.

[1291] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1292] Step 1:

[1293] A user digitizes a physical book using a scanner, scanning each page of the book as an image and saving it as a PDF file. The input is the paper book, and the output is a PDF file.

[1294] Step 2:

[1295] The user uploads the generated PDF to the system. The server receives the PDF and starts processing it. The input is a PDF file, and the output is PDF data stored in the server.

[1296] Step 3:

[1297] The server uses OCR technology to extract text data from PDFs. For this process, we use OCR software such as pytesseract. The input is a PDF file and the output is text data.

[1298] Step 4:

[1299] The server analyzes the extracted text data and automatically generates a summary using a generative AI model, such as the Hugging Face transformers. The input is text data, and the output is the summarized text.

[1300] Step 5:

[1301] The server generates prompts for the image generation model based on the generated summary. These prompts include descriptions of specific scenes and characters. The input is the summarized text, and the output is the prompt.

[1302] Step 6:

[1303] For example, the summary generates the following prompt:

[1304] "Generate an illustration of an epic battle scene based on the hero's final battle with the dragon."

[1305] Step 7:

[1306] The server uses the generated prompt to pass it to an image generation model (e.g., OpenAI's DALL-E) and generates a corresponding illustration. The input is the prompt text, and the output is the illustration.

[1307] Step 8:

[1308] The user's device is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine analyzes the user's facial expressions and tone of voice and sends the emotion data to the server. The input is the user's facial expressions and tone of voice, and the output is emotion data.

[1309] Step 9:

[1310] The server receives the emotion data and adjusts the tone and style of the generated illustration based on that data. For example, if the user expresses sadness, the illustration will be changed to a brighter color scheme. The input is the emotion data, and the output is the adjusted illustration.

[1311] Step 10:

[1312] The server then combines the adjusted illustrations with the text data to generate an illustrated e-book, which is then output in PDF or EPUB format. The input is the text data and adjusted illustrations, and the output is the illustrated e-book.

[1313] Step 11:

[1314] Users download and read the generated illustrated e-books, providing a visually rich content and a personalized reading experience based on real-time emotions. The input is the illustrated e-book, and the output is the user's reading experience.

[1315] 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.

[1316] 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.

[1317] 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.

[1318] 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.

[1319] 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.

[1320] 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.

[1321] 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).

[1322] 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.

[1323] 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."

[1324] 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.

[1325] 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).

[1326] 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.

[1327] 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.

[1328] 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.

[1329] 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.

[1330] 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.

[1331] 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.

[1332] 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.

[1333] 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.

[1334] 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.

[1335] 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.

[1336] The following is further disclosed regarding the above embodiment.

[1337] (Claim 1)

[1338] A means of digitizing books,

[1339] A means for generating a synopsis from book data;

[1340] means for generating prompts for an image generation model based on the generated synopsis;

[1341] a means for generating an illustration using an image generation model using the generated prompt;

[1342] A means for integrating the generated illustrations into e-book data;

[1343] A system including:

[1344] (Claim 2)

[1345] The system of claim 1, wherein illustrations at a pictorial level and a cartoon level can be selected according to the user's request.

[1346] (Claim 3)

[1347] The system of claim 1 can be applied to a variety of documents, such as instruction manuals and dictionaries, in addition to books.

[1348] "Example 1"

[1349] (Claim 1)

[1350] A means for digitizing books;

[1351] A means for extracting text data from digitized book data;

[1352] means for generating a synopsis from the extracted text data;

[1353] means for generating prompts for image generation based on the generated synopsis;

[1354] A means of creating illustrations using the generated prompts in an image-generating model;

[1355] means for integrating the generated illustrations with text data to generate an illustrated document;

[1356] means for transmitting the generated illustrated document to a user terminal;

[1357] A system including:

[1358] (Claim 2)

[1359] 10. The system of claim 1, wherein the level of illustration is selectable according to the user's needs.

[1360] (Claim 3)

[1361] The system according to claim 1, which can be applied to various documents other than books, such as instruction manuals and dictionaries.

[1362] "Application Example 1"

[1363] (Claim 1)

[1364] A means of digitizing books,

[1365] A means for generating a synopsis from book data;

[1366] means for generating prompts for an image generation model based on the generated synopsis;

[1367] a means for generating an illustration using an image generation model using the generated prompt;

[1368] A means for integrating the generated illustrations into e-book data;

[1369] An automatic book scanning method using a smartphone camera,

[1370] means for extracting text using OCR technology;

[1371] A means to generate and download e-books in PDF format;

[1372] A system including:

[1373] (Claim 2)

[1374] The system of claim 1, wherein illustrations at a pictorial level and a cartoon level can be selected according to the user's request.

[1375] (Claim 3)

[1376] The system of claim 1 can be applied to a variety of documents, such as instruction manuals and dictionaries, in addition to books.

[1377] "Example 2: Combining Emotion Engines"

[1378] (Claim 1)

[1379] A means of digitizing books,

[1380] A means for generating a synopsis from book data;

[1381] means for generating prompts for an image generation model based on the generated synopsis;

[1382] a means for generating an illustration using an image generation model using the generated prompt;

[1383] A means for integrating the generated illustrations into e-book data;

[1384] A means to adjust the tone and style of illustrations based on the user's emotional data;

[1385] A system including:

[1386] (Claim 2)

[1387] The system of claim 1, wherein illustrations at a pictorial level and a cartoon level can be selected according to the user's request.

[1388] (Claim 3)

[1389] The system of claim 1 can be applied to a variety of documents, such as instruction manuals and dictionaries, in addition to books.

[1390] "Application example 2 when combining emotion engines"

[1391] (Claim 1)

[1392] A means of digitizing books,

[1393] A means for generating a synopsis from book data;

[1394] means for generating prompts for an image generation model based on the generated synopsis;

[1395] a means for generating an illustration using an image generation model using the generated prompt;

[1396] A means for integrating the generated illustrations into e-book data;

[1397] An emotion engine that recognizes the user's emotions in real time;

[1398] A means to adjust the tone and style of illustrations based on the emotion engine,

[1399] A system including:

[1400] (Claim 2)

[1401] The system of claim 1, wherein illustrations at a pictorial level and a cartoon level can be selected according to the user's request.

[1402] (Claim 3)

[1403] The system of claim 1 can be applied to a variety of documents, such as instruction manuals and dictionaries, in addition to books. [Explanation of symbols]

[1404] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of digitizing books, A means for generating a synopsis from book data; means for generating prompts for an image generation model based on the generated synopsis; a means for generating an illustration using an image generation model using the generated prompt; A means for integrating the generated illustrations into e-book data; A system including:

2. 2. The system according to claim 1, wherein illustrations at a pictorial level and a cartoon level can be selected according to the user's request.

3. 2. The system according to claim 1, which can be applied to a variety of documents other than books, such as instruction manuals and dictionaries.

Images