system

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a slide in image or PDF format; means for extracting text from the content of the received slide using optical character recognition (OCR); means for content analyzing the extracted text using natural language processing (NLP); means for applying a uniformed design template based on the analysis results to generate a new slide; and means for exporting and providing the generated slide to a user in PDF or image format.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] When evaluating slide presentations, the quality of the design can have an excessive influence on the intrinsic value of the idea. As a result, excellent ideas may not be properly evaluated due to poor design, or people may hesitate to present their ideas due to their lack of design skills. Furthermore, creating a design can take a lot of time, which can hinder the creation of efficient presentations. To solve this problem, it is necessary to standardize slide designs and provide a method for fairly evaluating ideas without being influenced by the quality of the design. [Means for solving the problem]

[0005] This invention provides a system that includes: a means for receiving slides in image or PDF format; a means for extracting text from the received slides using optical character recognition (OCR); a means for content analysis of the extracted text using natural language processing (NLP); a means for generating new slides by applying a standardized design template based on the analysis results; and a means for exporting the generated slides in PDF or image format and providing them to users. This unifies slide designs, enabling fair evaluation of the inherent value of ideas. This system also significantly reduces the time spent on design creation, supporting efficient presentation creation.

[0006] A "slide" is a presentation document or image file used to visually convey information or ideas.

[0007] "Image or PDF format" refers to the format of the slide file, where image formats include JPEG and PNG, and PDF format is a common file format for storing and viewing documents.

[0008] The "means for receiving" refers to the function of the server receiving and temporarily storing the slide file provided by the user.

[0009] Optical character recognition (OCR) is a technology that extracts text from images and PDF files as digital data, and uses this technology to obtain text information from slides.

[0010] "Means for extracting text" refers to the ability to use OCR to identify text data within a slide and extract it in digital form.

[0011] "Natural Language Processing (NLP)" is a technology that allows computers to understand and analyze human language, analyzing the content within slides to understand their meaning and themes.

[0012] "Means for content analysis" refers to the function of analyzing the content of extracted text data using natural language processing technology and understanding the argument and theme of the text.

[0013] A "design template" refers to a design template with predefined slide layout, font style, color scheme, etc.

[0014] "Means of application" refers to the function of placing content into a design template based on the analysis results and generating unified, aesthetically pleasing slides.

[0015] "Means to export" refers to the ability to save the new slides generated in PDF or image format and make them available to users.

[0016] "User" refers to an entity that uses the system to upload slides and receive new slides with a standardized design. [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] Overall system configuration

[0039] The system of this invention receives slides uploaded by users and executes a process to regenerate them with a standardized design. In this system, the server, terminals, and users each have specific roles and work together.

[0040] Upload procedure

[0041] Users select slide files (PDF or image format) from their own devices and upload them through the system's web interface. The server receives the uploaded files and temporarily stores them.

[0042] Content analysis procedure

[0043] The server passes the saved slide files to the image processing module, which uses OCR technology to extract the text from the slides, identifying the text on each slide page and extracting the text data, including numbers and special characters.

[0044] The server then passes the extracted text data to a natural language processing (NLP) module for content analysis, using topic modeling techniques to extract the main themes of the slide content and conducting contextual analysis to understand the argument and key information in the text.

[0045] Uniform design generation procedure

[0046] After the analysis is complete, the server selects an appropriate uniform design template from the template library. When placing text and images into the template, the server uniforms the following design elements:

[0047] Font style and font size

[0048] Text color scheme

[0049] Image placement and size

[0050] Redrawing graphs and charts

[0051] A consistent design is applied to all slide pages, creating layouts that maximize visibility and readability.

[0052] Export and provisioning procedures

[0053] The server exports the newly generated slides as a PDF or image file, temporarily stores the file, and then generates a download link for the user, who then downloads the flattened slide file via this link.

[0054] Specific examples

[0055] For example, consider a case where a user uploads their presentation slides to a university evaluation system. The server receives the slides and uses OCR to extract the text from the slides. It then analyzes the extracted text with an NLP module to identify the main themes and arguments of the presentation. It then applies a uniform design template to generate new slides with consistent font styles, colors, and other elements. Finally, the server exports the new slides in PDF format, which the user can download and submit to the evaluation committee.

[0056] This system not only allows for fair evaluation of the quality of ideas regardless of the design, but also reduces the time required to create designs and supports efficient presentation creation.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The user selects the slide file (PDF or image format) from their device and starts uploading. The file is sent through the web interface and reaches the server.

[0060] Step 2:

[0061] The server receives the uploaded slide files and temporarily stores them in a designated directory or database.

[0062] Step 3:

[0063] The server passes the saved slide files to an image processing module, which checks the file format (PDF or image) and splits them into individual pages if necessary.

[0064] Step 4:

[0065] The server uses OCR (Optical Character Recognition) technology to extract the text from the slides, performs image analysis on each page, and generates text data.

[0066] Step 5:

[0067] The server passes the extracted text data to a natural language processing (NLP) module to perform content analysis, using topic modeling to extract the main themes of the slides and semantic analysis to understand the gist of the text.

[0068] Step 6:

[0069] The server selects an appropriate uniform design template from the template library based on analysis results and automatically determines the template that best suits the slide content.

[0070] Step 7:

[0071] The server applies the extracted text and images to a template, adjusting font style, font size, color scheme, and page layout.

[0072] Step 8:

[0073] The server redraws graphs and charts as needed to ensure a consistent visual design, and images are sized and positioned appropriately according to the template.

[0074] Step 9:

[0075] The server exports the newly generated slides as PDFs or images, resulting in a uniformly designed slide file.

[0076] Step 10:

[0077] The server temporarily stores the generated slide files and generates a download link, which is notified to the user.

[0078] Step 11:

[0079] Users can access the provided download link and download the uniformed slide file, which can then be used for evaluation or presentation.

[0080] Example 1

[0081] 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."

[0082] Maintaining consistency in design and layout when creating slides is a significant burden for users, and is particularly time-consuming and laborious for presentations that contain many slides. Furthermore, users without specialized design knowledge or tools have difficulty creating attractive slides. To address this issue, it is necessary to provide a system that automatically analyzes the content of slides and regenerates them into a consistent design.

[0083] 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.

[0084] In this invention, the server includes: means for a user to select a slide file from their own terminal and upload it to the system; means for the server to receive and temporarily store the uploaded slide file; means for the server to pass the saved slide file to an image processing module and extract text from the slide using optical character recognition; means for the server to pass the extracted text data to a natural language processing module and perform content analysis; means for the server to select an appropriate uniform design template from a template library based on the analysis result and generate a new slide; and means for the server to export the generated slide in PDF or image format and provide it to the user. This enables users to efficiently create consistent slides with excellent visibility and readability without much effort.

[0085] "User" refers to a person who utilizes the system to upload slide files and receive regenerated slides.

[0086] "Terminal" means the device used by a User to access the System and upload Slide Files, including, but not limited to, a PC, tablet, or smartphone.

[0087] "Server" refers to a central computer system for slide generation that receives, stores, analyzes, and regenerates slide files.

[0088] "Slide file" means an electronic file containing elements of a presentation, either in PDF or image format.

[0089] "Web interface" refers to a web page or web application that allows a user to access and operate the system via a browser.

[0090] "Image Processing Module" refers to software or libraries for analyzing text and images within slide files.

[0091] "Optical Character Recognition (OCR)" refers to the technology that mechanically or electronically recognizes text in an image and converts it into digital text data.

[0092] "Text data" refers to information extracted by OCR and stored as a string of characters.

[0093] A "natural language processing (NLP) module" refers to software or libraries for analyzing text data and understanding its meaning and context.

[0094] "Template library" refers to a database or group of files that stores multiple slide design templates.

[0095] A "standardized design template" refers to a presentation slide template that is constructed with a consistent design style.

[0096] "Export" refers to outputting a file in a specific format after processing is complete.

[0097] "Download link" refers to the URL for obtaining a file from the server to the user's device.

[0098] The system of this invention receives slides uploaded by users and executes a process to regenerate them with a standardized design. In this system, the server, terminals, and users each have specific roles and work together.

[0099] First, a user selects a slide file (PDF or image format) from their device and uploads it through the system's web interface. The device used by the user can be a PC, tablet, smartphone, etc. Once the upload is complete, the server receives the slide file and temporarily stores it. This storage is performed in a specific folder in a temporary directory (e.g., / tmp), and the file name is renamed with a unique identifier (e.g., UUID) to avoid duplication.

[0100] The server then passes the saved slide files to an image processing module (e.g., Tesseract OCR) and uses OCR technology to extract the text from the slides. The OCR process identifies the text on each slide page and extracts the text data, including numbers and special characters. The OCR process uses PNG images of each page as input, and outputs the text data for each page in JSON format.

[0101] The server passes the extracted text data to a natural language processing (NLP) module (e.g., SpaCy or NLTK) for content analysis. Topic modeling techniques (e.g., Latent Dirichlet Allocation) are used to extract the main themes of the slide content, and contextual analysis (e.g., dependency analysis) is performed to understand the argument and key information of the text.

[0102] After the analysis is complete, the server selects an appropriate uniform design template (e.g., Canva or a self-developed template) from the template library. When placing text and images into the template, it standardizes the following design elements:

[0103] Font style and font size (e.g., Arial 12pt)

[0104] Text color scheme (e.g., black with blue highlight)

[0105] Image placement and size (e.g., centered, 80% width)

[0106] Redrawing graphs and charts (e.g., using Excel or D3.js)

[0107] The server loads the template file and automatically runs a script (e.g., Python-PowerPoint) to rearrange each element of the slide based on the analysis results. A consistent design is applied to all slide pages, and a layout is generated that maximizes visibility and readability.

[0108] Finally, the server exports the newly generated slides in PDF or image format. This file is saved in a temporary directory, and the server generates a download link and notifies the user. The user can download the new slides from the provided link and use them as a presentation.

[0109] Specific examples

[0110] For example, consider a user uploading their presentation slides to a university evaluation system. Here's the process:

[0111] 1. The user uploads the presentation slides (e.g., PDF format) for the evaluation system to the system.

[0112] 2. The server receives and stores the file.

[0113] 3. The server runs the saved file through Tesseract OCR to extract the text for each page.

[0114] 4. The server analyzes the extracted text using SpaCy to understand the main themes and arguments of the presentation.

[0115] 5. The server selects a uniform design for the evaluation presentation from its template library and regenerates it with consistent font styles, color schemes, etc.

[0116] 6. The server exports the newly generated slides in PDF format and provides the user with a download link.

[0117] 7. Users can download the new slides from this link and submit them to the evaluation committee.

[0118] Prompt Sentence Examples

[0119] The following prompts are examples of inputs to a generative AI model (e.g., GPT-4):

[0120] Please explain in detail the process steps of the system that regenerates presentation slides uploaded by users with a standardized design. Please specify the roles of the user, server, and device, and explain each process step in detail.

[0121] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0122] Step 1:

[0123] The user selects a slide file (PDF or image format) from their own device and uploads it through the system's web interface. Specifically, a file selection dialog appears in the browser, the user selects the appropriate file, and clicks the "Upload" button. The input is the slide file, and the output is a file upload request to the system.

[0124] Step 2:

[0125] The server receives the uploaded slide file and temporarily stores it. The file is saved in a specific folder in a temporary directory (e.g., / tmp), and the file name is renamed with a unique identifier such as a UUID to avoid duplication. The input is a file upload request from the user, and the output is the saved slide file.

[0126] Step 3:

[0127] The server passes the saved slide files to an image processing module (e.g., Tesseract OCR) and extracts the text from the slides using OCR technology. Specifically, the server invokes the Tesseract command and passes the PNG-formatted images for each page as input. The OCR process outputs the text data for each page in JSON format. The input is the saved slide files, and the output is the extracted text data.

[0128] Step 4:

[0129] The server passes the extracted text data to a natural language processing (NLP) module (e.g., SpaCy or NLTK) for content analysis. Specifically, the server loads the text data into the NLP module and uses topic modeling techniques (e.g., Latent Dirichlet Allocation) and context analysis (e.g., dependency analysis) to identify major themes, arguments, and important information. The input is the text data obtained by OCR, and the output is the analysis results.

[0130] Step 5:

[0131] The server selects an appropriate uniform design template from the template library based on the analysis results. The template file is loaded, and a script (e.g., Python-PowerPoint) is automatically executed to rearrange each element of the slide based on the analysis results. The input is the analysis results, and the output is new slide data with the template applied.

[0132] Step 6:

[0133] The server exports the regenerated slides in PDF or image format. It passes the generated slide data to a PDF generation library (e.g., ReportLab) to create a PDF file. This file is saved in a temporary directory. The input is the new slide data with the template applied, and the output is the exported PDF file.

[0134] Step 7:

[0135] The server generates a download link for the new slide file and notifies the user of the link. Specifically, it converts the file path into a URL and provides it to the user via a notification email or web notification. The input is the exported PDF file, and the output is the download link provided to the user.

[0136] Step 8:

[0137] The user downloads the uniformed slide file via this link. Specifically, they click the provided URL to download the slide file and save it on their device. The input is the download link, and the output is the downloaded slide file.

[0138] (Application example 1)

[0139] 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."

[0140] In today's brick-and-mortar stores, product explanation materials, advertising materials, menus, and other items have different designs for each store, creating a visual inconsistency problem. Furthermore, using specialized software to unify designs requires specialized knowledge, making it difficult for store staff to use. This increases the cost and effort required to achieve a unified design, and risks damaging the brand image of the entire store. The present invention aims to solve these problems by providing a system that allows store staff to easily regenerate slides and advertising materials into a unified design.

[0141] 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.

[0142] In this invention, the server includes a means for receiving slides in image or PDF format, a means for extracting text from the content of the received slides using optical character recognition (OCR), and a means for content analysis of the extracted text using natural language processing (NLP). This allows store staff to easily upload slides and advertising materials using smartphones or tablets and regenerate them with a unified and consistent design. The system also unifies the generated slides with a unique design and regenerates them into in-store materials and advertising materials, thereby maintaining visual consistency throughout the store.

[0143] "Slides" refers to images or PDF pages provided in the form of presentations, advertisements, handouts, etc.

[0144] "Image Format" refers to bitmap image file formats such as JPEG, PNG, and GIF.

[0145] "PDF Format" refers to a file format known as Portable Document Format (PDF) for storing and sharing documents electronically.

[0146] "Optical Character Recognition (OCR)" refers to the technology that recognizes characters in an image and converts them into digital text data.

[0147] "Natural Language Processing (NLP)" refers to the technology that enables computers to understand, interpret, and generate human language.

[0148] "Standardized design template" refers to a design template that is standardized with specific font styles, font sizes, color schemes, and layouts.

[0149] "Smartphone" and "tablet" refer to a mobile information terminal equipped with a mobile operating system, capable of connecting to the Internet and using applications.

[0150] A "server" refers to a computer system that stores files and processes information via a network.

[0151] "Export" refers to the operation of saving or outputting generated data in a specified file format.

[0152] The system of this invention aims to standardize the design of materials and advertising materials used in brick-and-mortar stores to create a professional impression. The system allows users to upload slides using a smartphone or tablet and regenerates them into a standardized design.

[0153] Hardware and Software Configuration

[0154] The system uses the following hardware and software:

[0155] Smartphones and tablets: Mobile devices that perform upload operations.

[0156] Server: The computer system that handles all processes such as file storage, OCR processing, NLP analysis, design template application, and exporting.

[0157] OCR engine (pytesseract): Optical character recognition technology for extracting text from images.

[0158] NLP module (spaCy): Natural language processing technology to analyze extracted text and understand key themes and arguments.

[0159] PDF processing library (pdf2image, PIL): A library for converting PDF files to images and performing OCR.

[0160] Overview of data processing and calculation

[0161] Users select slide files (PDF or image format) from their smartphones or tablets and upload them through the system's web interface, where the server receives and temporarily stores them.

[0162] Next, the server passes the saved slide files to the image processing module, which uses an OCR engine (pytesseract) to extract the text from the slides, identifying the text on each slide page and obtaining text data including numbers and special characters.

[0163] The server then passes the extracted text data to a natural language processing module (spaCy) for content analysis, using topic modeling techniques to extract the main themes of the slides and then conducting contextual analysis to understand the text's arguments and key information.

[0164] After the analysis is complete, the server selects an appropriate uniform design template from the template library. When placing text and images into the template, the server uniforms the following design elements:

[0165] Font style and font size

[0166] Text color scheme

[0167] Image placement and size

[0168] Redrawing graphs and charts

[0169] A consistent design is applied to all slide pages, creating layouts that maximize visibility and readability.

[0170] Specific examples

[0171] For example, consider the case where a user uploads a slide file called "New Product Information.pdf." The server receives the slides uploaded by the user and uses an OCR engine to extract the text within the slides. Next, the extracted text is analyzed using an NLP module to identify the main themes and arguments of the presentation. A standardized design template is then applied to generate new slides with consistent font styles, color schemes, etc. Finally, the server exports the new slides in PDF format, which the user can download and use for in-store advertising materials.

[0172] Prompt Sentence Examples

[0173] "Please upload the new product information document.pdf and convert it into a unified design. The file has 10 pages, and each page contains different product information. Please unify the font style and make the design easy to read."

[0174] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0175] Step 1:

[0176] The user selects a slide file (PDF or image format) on a smartphone or tablet and uploads it through the system's web interface. At this stage, the file's format and content are not important and it is sent to the server through the specified interface. The input is the slide file, and the output is the identifier (ID) of the uploaded file.

[0177] Step 2:

[0178] The server receives the uploaded slide file and temporarily stores it. The saved file is stored in the file system and used for subsequent processing. The input is the identifier of the uploaded file, and the output is the path of the saved file.

[0179] Step 3:

[0180] The server passes the saved slide files to the image processing module, which uses an OCR engine (pytesseract) to extract the text in the slides. This process converts the PDF file into an image and identifies the text on each page. The input is the path of the saved file, and the output is the extracted text data.

[0181] Step 4:

[0182] The server passes the extracted text data to a natural language processing (NLP) module (spaCy) for content analysis. This process uses topic modeling techniques to extract major themes and understand the text's argument and important information. The input is text data, and the output is analyzed topics and contextual information.

[0183] Step 5:

[0184] The server selects an appropriate uniform design template from the template library and places the extracted and analyzed text and images into the template. In this step, it standardizes the font style, font size, color scheme, image placement, etc. The input is the analysis result and the template, and the output is new slide data with a uniform design.

[0185] Step 6:

[0186] The server exports the generated slides in PDF or image format and provides them to the user. In this process, a download link for the exported file is generated and notified to the user. The input is the new slide data with a unified design, and the output is the download link.

[0187] Specific examples

[0188] For example, if a user uploads "New Product Presentation.pdf," the server processes the file with OCR, extracts text, performs NLP analysis, applies a uniform design template, and then exports the new slides in PDF format and provides a download link to the user.

[0189] 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.

[0190] Overall system configuration

[0191] This system receives slides uploaded by users, reproduces them with a standardized design, and recognizes the user's emotions and reflects them in the slide design. This system works in cooperation with the server, terminal, and user, each of which has a specific role.

[0192] Upload procedure

[0193] Users select slide files (PDF or image format) from their own devices and upload them through the system's web interface. The server receives the uploaded files and temporarily stores them.

[0194] Emotion Recognition Procedure

[0195] The server activates an emotion engine to recognize the user's emotion when a slide is uploaded or when analyzing the slide's content. The emotion engine analyzes the user's facial expressions, voice tone, text input, etc., and generates the user's emotion data. This data includes emotion information such as joy, sadness, and surprise.

[0196] Content analysis procedure

[0197] The server passes the saved slide files to the image processing module, which uses OCR technology to extract the text from the slides, identifying the text on each slide page and extracting the text data, including numbers and special characters.

[0198] The server then passes the extracted text data to a natural language processing (NLP) module for content analysis, using topic modeling techniques to extract the main themes of the slide content and conducting contextual analysis to understand the argument and key information in the text.

[0199] Uniform design generation procedure

[0200] After the analysis is complete, the server selects an appropriate uniform design template from the template library. The most suitable template is automatically determined based on the emotional information detected by the user's emotion engine. For example, if the user is depressed, a calm color scheme and design will be applied.

[0201] The server applies the extracted text and images to a template, adjusting font style, font size, color scheme, and page layout. It may also adjust design elements based on emotion data.

[0202] Export and provisioning procedures

[0203] The server exports the newly generated slides as a PDF or image file, temporarily stores the file, and then generates a download link for the user, who then downloads the flattened slide file via this link.

[0204] Specific examples

[0205] For example, consider a case where a user uploads their presentation slides to a university evaluation system. During upload, an emotion engine analyzes the user's facial expressions and tone of voice to obtain emotional data indicating nervousness. The server receives the slides uploaded by the user and extracts the text within them using OCR. Next, the extracted text is analyzed using an NLP module to identify the main themes and arguments of the presentation. Taking the emotional data into account, a calming design template is applied to reduce tension. New slides are generated with a consistent font style and color scheme. Finally, the server exports the new slides in PDF format, which the user can download and submit to the evaluation committee.

[0206] This system not only allows for fair evaluation of the quality of ideas regardless of their design, but also allows for more effective presentations by taking into account the user's emotions.

[0207] The processing flow will be explained below.

[0208] Step 1:

[0209] Users select slide files (PDF or image format) from their device and initiate the upload through the web interface.

[0210] Step 2:

[0211] The server receives the uploaded slide files and temporarily stores them in a designated directory or database.

[0212] Step 3:

[0213] The server passes the saved slide files to an image processing module, which, in the case of PDF format, converts each individual page into an image so that each page can be processed independently.

[0214] Step 4:

[0215] The server starts an emotion engine while the user is uploading. The emotion engine analyzes the video captured from the user's webcam, the audio captured from the microphone, and the text input to recognize the user's emotional state (e.g., joy, sadness, surprise, tension).

[0216] Step 5:

[0217] The server uses OCR (Optical Character Recognition) technology to extract text from slides. It performs image analysis on each page and generates text data. It performs accurate text extraction, taking into account differences in font and character size.

[0218] Step 6:

[0219] The server passes the extracted text data to a natural language processing (NLP) module to perform content analysis, using topic modeling to extract the main themes of the slides and semantic analysis to understand the argument and key information of the text.

[0220] Step 7:

[0221] The server selects an appropriate uniform design template from the template library, taking into account the emotional information recognized by the user's emotion engine. For example, if the user is nervous, a template with a calm color scheme will be selected.

[0222] Step 8:

[0223] The server applies the extracted text and images to a template, adjusting the font style, font size, and color scheme, and adjusting design elements within the template based on the emotion data.

[0224] Step 9:

[0225] The server redraws graphs and charts as needed to maintain a consistent visual design, including adjusting image size and placement.

[0226] Step 10:

[0227] The server exports the newly generated slides as PDFs or images, creating a uniformly designed slide file.

[0228] Step 11:

[0229] The server temporarily stores the generated slide files, generates a download link, and notifies the user via email or a web interface.

[0230] Step 12:

[0231] Users can access the provided download link and download the uniformed slide file, which can then be used for evaluation or presentation.

[0232] Example 2

[0233] 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."

[0234] Conventional slide generation systems not only lack design consistency, but also have the problem of being unable to propose appropriate designs that take the user's emotional state into account. Furthermore, while they are capable of analyzing the content of text and images within slides, there are no systems that can understand the user's emotions and modify the design based on those emotions. Furthermore, there is a need to achieve more effective communication in presentations by using designs that reflect the user's emotions.

[0235] 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.

[0236] In this invention, the server includes means for receiving slides in image or PDF format, means for extracting text from the content of the received slides using optical character recognition (OCR), means for content analysis of the extracted text using natural language processing (NLP), means for recognizing and acquiring user emotion data, means for selecting a standardized design template based on the acquired emotion data, means for generating new slides using the selected template, and means for exporting the generated slides in PDF or image format and providing them to the user, thereby enabling effective slide design that reflects the user's emotions.

[0237] "Slides" are images or PDF documents used in presentations and information presentations.

[0238] Optical character recognition (OCR) is a technology that extracts character information from an image as digital text.

[0239] "Natural language processing (NLP)" is a technology that analyzes the content of text data and understands the subject matter and context.

[0240] "Emotion data" refers to emotional information recognized from the user's facial expression, tone of voice, text input, and the like.

[0241] A "template library" is a database that collects slide design templates.

[0242] A "standardized design template" is a slide template with a consistent overall design style and format.

[0243] "Export" means converting data or files into a specific format and outputting it to an external device.

[0244] A "user" is a person who uses the system to upload slides and receive generated slides.

[0245] A "server" is a computer system that manages the overall processing of the system and stores, analyzes, and generates various types of data.

[0246] This invention is a system that receives slides uploaded by users, reproduces them with a standardized design, and recognizes the user's emotions and reflects them in the slide design. The system works in cooperation with the server, terminal, and user, each of which has a specific role.

[0247] Overall structure

[0248] The hardware and software used to implement the invention are as follows: The server receives and temporarily stores slide files uploaded from the user's device. It also analyzes the slide content using OCR technology and a natural language processing (NLP) module. Furthermore, it acquires the user's emotional data using an emotion recognition module, selects an emotion-based design template from a template library, generates new slides, and exports them.

[0249] Upload procedure

[0250] Users select slide files (PDF or image format) from their own devices and upload them through the system's web interface. The server receives the uploaded files and temporarily stores them, checking the file format and size.

[0251] Emotion Recognition Procedure

[0252] The server starts the emotion engine when the user uploads a slide. The emotion engine used here is software that captures and analyzes the user's facial expressions and voice through a webcam and microphone. The captured data is sent to an emotion recognition module, which generates the user's emotion data, which includes emotional information such as joy, sadness, and surprise.

[0253] Content analysis procedure

[0254] The server passes the saved slide files to an image processing module, which uses OCR (Optical Character Recognition) technology to extract the text from the slides. The extracted text data, including numbers and special characters, is saved as text data. This data is then passed to a natural language processing (NLP) module for content analysis. Specifically, topic modeling technology is used to extract the main themes from the slides, and contextual analysis is used to understand the main points and key points of the text.

[0255] Uniform design generation procedure

[0256] After analysis, the server selects an appropriate uniform design template from its template library. The optimal template is automatically selected based on the emotional data acquired by the emotion recognition module. For example, if the user is nervous, a calming color scheme and design will be applied. The server then applies the extracted text and images to the template, unifying the font style, size, and color scheme, and adjusting the page layout. This generates a new uniform slide.

[0257] Export and provisioning procedures

[0258] The server exports the newly generated slides in PDF or image format, temporarily saves the file, and then generates a download link via which users can download and use the generated slides.

[0259] Examples of specific examples and prompts

[0260] For example, when a user uploads presentation slides for a university evaluation system, an emotion recognition engine may detect emotional data indicating "nervousness" from the user's facial expressions and tone of voice. The server receives the slides uploaded by the user, extracts text using OCR technology, and analyzes the content using an NLP module. It then applies a design template to reduce tension and standardizes fonts and color schemes to generate new slides. The final slides are exported as PDFs for users to download.

[0261] An example prompt is:

[0262] "Create a presentation slide design template for when users are feeling down"

[0263] "Generate slide designs that reflect the emotion of joy"

[0264] By using such prompts, it becomes possible to design effective slides that take emotional data into account.

[0265] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0266] Step 1:

[0267] Users select slide files (PDF or image format) from their terminal and upload them through the system's web interface.

[0268] Specifically, the user opens a browser, accesses the system's web page, selects a file, and presses the upload button.

[0269] Input: Slide file selected from the user's device

[0270] Output: Uploaded slide files are transferred to the server.

[0271] Step 2:

[0272] The server receives the uploaded slide files and temporarily stores them.

[0273] Specifically, the server verifies the file format and size and stores it in the appropriate folder.

[0274] Input: Uploaded slide files

[0275] Output: Temporarily saved slide files

[0276] Step 3:

[0277] The server starts the emotion engine when the slides are uploaded.

[0278] Specifically, the server captures the user's facial expressions and voice via a webcam and microphone connected to the user's device.

[0279] Input: User's facial expression data and voice data

[0280] Output: Obtained user emotion data

[0281] Step 4:

[0282] The server passes the saved slide files to an image processing module, which uses OCR technology to extract the text in the slides.

[0283] Specifically, the server analyzes each page of the slides and performs character recognition.

[0284] Input: Temporarily saved slide file

[0285] Output: Extracted text data

[0286] Step 5:

[0287] The server passes the extracted text data to a natural language processing (NLP) module for content analysis.

[0288] Specifically, the server uses topic modeling technology to extract themes from the slide content and understands the arguments and key points through contextual analysis.

[0289] Input: Extracted text data

[0290] Output: Analyzed content data (theme, argument, key points)

[0291] Step 6:

[0292] The server considers the emotion data obtained by the emotion recognition module and selects an appropriate uniform design template from the template library.

[0293] Specifically, the server searches the template library and automatically selects the most suitable template based on the emotion information.

[0294] Input: Analyzed content data, emotion data

[0295] Output: Selected design template

[0296] Step 7:

[0297] The server generates a new slide using the selected design template.

[0298] Specifically, the server applies text and images to templates, unifies font styles, sizes, and colors, and adjusts page layouts.

[0299] Input: Selected design template, parsed content data

[0300] Output: The new slide file that is generated.

[0301] Step 8:

[0302] The server exports the newly generated slides in PDF or image format and provides them to the user.

[0303] Specifically, the server converts the generated slides into a specified format, temporarily stores them, and generates a download link.

[0304] Input: The new slide file that was generated

[0305] Output: Exported slide files, generated download link

[0306] Step 9:

[0307] The user downloads the uniformed slide file via the provided download link.

[0308] Specifically, the user clicks on the link provided by the system and saves the file to their device.

[0309] Input: Generated download link

[0310] Output: Slide files downloaded to the user's device

[0311] (Application example 2)

[0312] 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."

[0313] In virtual stores and online presentations, a system is needed to provide personalized content in real time that responds to user emotions. Conventional slide production systems and advertising display systems are not designed with user emotions in mind, and the challenge was how to utilize emotional data to improve the user experience.

[0314] 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 receiving slides in image or PDF format, means for extracting text from the content of the received slides using optical character recognition (OCR), means for content analysis of the extracted text using natural language processing (NLP), means for generating new slides by applying a standardized design template based on the analysis results and user emotion data, means for exporting the generated slides in PDF or image format and providing them to the user, means for analyzing the user's facial expressions and tone of voice to generate emotion data, and means for generating and displaying advertisements according to the user's emotions. This makes it possible to provide personalized content according to the user's emotions in real time.

[0315] "Slides" are images or PDF format data used as presentation materials.

[0316] Optical character recognition (OCR) is a technology that detects characters in an image and extracts them as text data.

[0317] "Natural language processing (NLP)" is a technology that analyzes text data and understands and interprets its content.

[0318] A "uniform design template" is a template that provides a uniform layout and style used to ensure design consistency.

[0319] "Emotion data" is emotional information analyzed from the user's facial expressions, tone of voice, and the like.

[0320] "Advertisement generation means" is a technology that generates optimal advertisements according to the user's interests and emotions.

[0321] "Server" means the computer system that has the central function of data processing, receiving, analyzing, applying designs, generating advertisements and exporting slides.

[0322] "User facial expression analysis" is a technology that uses a camera to capture a user's face and infer their emotions from their facial expressions.

[0323] "Voice tone analysis" is a technology that uses a microphone to collect a user's voice and analyzes emotions from the tone of the voice.

[0324] "Personalized content" is content that is custom-made to suit a user's individual needs and feelings.

[0325] A "virtual store" is a virtual sales location that offers products and services over the Internet.

[0326] To implement this invention, it is necessary to build a system that receives slides uploaded by users, regenerates them with a standardized design, and recognizes users' emotions and reflects them in designs and advertisements. A specific embodiment of this system is shown below.

[0327] Hardware and Software Configuration

[0328] First, users use a web interface to upload slide files (image or PDF format) from their device, which then sends the uploaded slides to the server.

[0329] The server has the following features:

[0330] 1. Receive and temporarily store the slides.

[0331] 2. Use OCR technology to extract text from the slide content (specifically, an OCR engine such as Tesseract).

[0332] 3. Analyze the extracted text with a natural language processing (NLP) module (for example, using a library such as SpaCy or NLTK).

[0333] 4. Launch an emotion recognition engine (using a facial recognition library such as OpenCV or dlib, and a voice analysis engine) to analyze the user's facial expressions and voice tone to generate emotion data.

[0334] 5. Based on the analysis results and sentiment data, a uniform design template is applied to generate new slides.

[0335] 6. Generate and display ads based on user emotions (e.g., using ad templates that correspond to specific topics or emotions).

[0336] 7. Export the newly generated slides as PDF or images and provide them to the user.

[0337] Processing flow

[0338] When a user uploads a slide, the server receives and temporarily stores the slide. Then, OCR technology is used to extract the text from the slide. The extracted text is then analyzed by an NLP module to identify the main themes and arguments of the slide.

[0339] The server then activates an emotion recognition engine to analyze the user's facial expressions and tone of voice, generating emotional data for the user. Based on this emotional data and the analysis of the slides, an optimal design template is selected and a new slide is generated.

[0340] The generated slides have a uniform font style, size, color scheme, and layout, and personalized advertisements are generated based on the user's emotional data and displayed on the user's smart glasses or device.

[0341] Specific examples

[0342] For example, if a user visits a virtual store and wears smart glasses, the camera and microphone analyze the user's facial expressions and voice to obtain emotional information. If the user is smiling, the emotional data is analyzed as "joy." Next, the text of the slides uploaded by the user is analyzed using OCR and NLP technology, and the main theme is determined to be "Newly Released 4K TVs."

[0343] Example prompt sentence:

[0344] "Text extracted from slide: 'New 4K TVs'"

[0345] "User Emotion: 'Delight'"

[0346] "Generates ad: 'Special offer now! Check out our 4K TV!'"

[0347] Based on this information, the server generates advertising content that is likely to attract the user's interest and displays it on the smart glasses, improving the user experience.

[0348] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0349] Step 1:

[0350] Users upload slide files (image or PDF format) from their own devices through a web interface. The uploaded slide files are received by the server and temporarily stored.

[0351] Input: Slide files (image or PDF format) uploaded by the user

[0352] Output: Slide files saved on the server

[0353] Step 2:

[0354] The server uses OCR technology to extract the text from the uploaded slides. Specifically, it uses an OCR engine such as Tesseract to analyze the character information from each page of the slides and extract it as text data.

[0355] Input: Slide files stored on the server

[0356] Data processing: Analysis of character information using OCR technology

[0357] Output: Extracted text data

[0358] Step 3:

[0359] The server then analyzes the extracted text data using a natural language processing (NLP) module, which uses NLP techniques to identify the main themes and arguments of the text. Specifically, it uses libraries such as SpaCy and NLTK.

[0360] Input: Text data extracted by OCR technology

[0361] Data processing: Text analysis using NLP technology

[0362] Output: Data on the analyzed themes and arguments

[0363] Step 4:

[0364] The server starts an emotion recognition engine to analyze the user's facial expressions and voice tone. Libraries such as OpenCV and dlib are used for facial expression analysis, and a dedicated voice analysis engine is used for voice analysis. The user's emotional data is generated.

[0365] Input: User's facial expression (camera image) and voice (microphone audio)

[0366] Data processing: Emotion recognition by facial expression analysis and voice tone analysis

[0367] Output: Generated emotion data

[0368] Step 5:

[0369] Based on the analysis results and emotion data, the server selects a uniform design template and generates new slides, which have a uniform font style, size, color scheme, and layout.

[0370] Input: NLP analysis data and emotion data

[0371] Data processing: Template selection and application

[0372] Output: The newly generated slide

[0373] Step 6:

[0374] The server exports the new slides in PDF or image format and generates a download link for the user, who can use this link to download the slides.

[0375] Input: The newly generated slide

[0376] Data processing: Export in PDF or image format

[0377] Output: Download link

[0378] Step 7:

[0379] The server generates personalized advertisements based on the user's emotions and displays them on the user's device or smart glasses. The advertisement generation engine generates optimal advertisement content based on the emotion data and slide theme data.

[0380] Input: Sentiment and Theme Data

[0381] Data processing: Ad content generation by the ad generation engine

[0382] Output: Generated personalized ads

[0383] 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.

[0384] 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.

[0385] 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.

[0386] [Second embodiment]

[0387] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0398] 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."

[0399] Overall system configuration

[0400] The system of this invention receives slides uploaded by users and executes a process to regenerate them with a standardized design. In this system, the server, terminals, and users each have specific roles and work together.

[0401] Upload procedure

[0402] Users select slide files (PDF or image format) from their own devices and upload them through the system's web interface. The server receives the uploaded files and temporarily stores them.

[0403] Content analysis procedure

[0404] The server passes the saved slide files to the image processing module, which uses OCR technology to extract the text from the slides, identifying the text on each slide page and extracting the text data, including numbers and special characters.

[0405] The server then passes the extracted text data to a natural language processing (NLP) module for content analysis, using topic modeling techniques to extract the main themes of the slide content and conducting contextual analysis to understand the text's argument and key information.

[0406] Uniform design generation procedure

[0407] After the analysis is complete, the server selects an appropriate uniform design template from the template library. When placing text and images into the template, the server uniforms the following design elements:

[0408] Font style and font size

[0409] Text color scheme

[0410] Image placement and size

[0411] Redrawing graphs and charts

[0412] A consistent design is applied to all slide pages, creating layouts that maximize visibility and readability.

[0413] Export and provisioning procedures

[0414] The server exports the newly generated slides as a PDF or image file, temporarily stores the file, and then generates a download link for the user, who then downloads the flattened slide file via this link.

[0415] Specific examples

[0416] For example, consider a case where a user uploads their presentation slides to a university evaluation system. The server receives the slides and uses OCR to extract the text from the slides. It then analyzes the extracted text with an NLP module to identify the main themes and arguments of the presentation. It then applies a uniform design template to generate new slides with consistent font styles, colors, and other elements. Finally, the server exports the new slides in PDF format, which the user can download and submit to the evaluation committee.

[0417] This system not only allows for fair evaluation of the quality of ideas regardless of the design, but also reduces the time required to create designs and supports efficient presentation creation.

[0418] The processing flow will be explained below.

[0419] Step 1:

[0420] The user selects the slide file (PDF or image format) from their device and starts uploading. The file is sent through the web interface and reaches the server.

[0421] Step 2:

[0422] The server receives the uploaded slide files and temporarily stores them in a designated directory or database.

[0423] Step 3:

[0424] The server passes the saved slide files to an image processing module, which checks the file format (PDF or image) and splits them into individual pages if necessary.

[0425] Step 4:

[0426] The server uses OCR (Optical Character Recognition) technology to extract the text from the slides, performs image analysis on each page, and generates text data.

[0427] Step 5:

[0428] The server passes the extracted text data to a natural language processing (NLP) module to perform content analysis, using topic modeling to extract the main themes of the slides and semantic analysis to understand the gist of the text.

[0429] Step 6:

[0430] The server selects an appropriate uniform design template from the template library based on analysis results, automatically determining the template that best suits the slide content.

[0431] Step 7:

[0432] The server applies the extracted text and images to a template, adjusting font style, font size, color scheme, and page layout.

[0433] Step 8:

[0434] The server redraws graphs and charts as needed to ensure a consistent visual design, and images are sized and positioned appropriately according to the template.

[0435] Step 9:

[0436] The server exports the newly generated slides as PDFs or images, resulting in a uniformly designed slide file.

[0437] Step 10:

[0438] The server temporarily stores the generated slide files and generates a download link, which is notified to the user.

[0439] Step 11:

[0440] Users can access the provided download link and download the uniformed slide file, which can then be used for evaluation or presentation.

[0441] Example 1

[0442] 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."

[0443] Maintaining consistency in design and layout when creating slides is a significant burden for users, and is particularly time-consuming and laborious for presentations that contain many slides. Furthermore, users without specialized design knowledge or tools have difficulty creating attractive slides. To address this issue, it is necessary to provide a system that automatically analyzes the content of slides and regenerates them into a consistent design.

[0444] 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.

[0445] In this invention, the server includes: means for a user to select a slide file from their own terminal and upload it to the system; means for the server to receive and temporarily store the uploaded slide file; means for the server to pass the saved slide file to an image processing module and extract text from the slide using optical character recognition; means for the server to pass the extracted text data to a natural language processing module and perform content analysis; means for the server to select an appropriate uniform design template from a template library based on the analysis result and generate a new slide; and means for the server to export the generated slide in PDF or image format and provide it to the user. This enables users to efficiently create consistent slides with excellent visibility and readability without much effort.

[0446] "User" refers to a person who utilizes the system to upload slide files and receive regenerated slides.

[0447] "Terminal" means the device used by a User to access the System and upload Slide Files, including, but not limited to, a PC, tablet, or smartphone.

[0448] "Server" refers to a central computer system for slide generation that receives, stores, analyzes, and regenerates slide files.

[0449] "Slide file" means an electronic file containing elements of a presentation, either in PDF or image format.

[0450] "Web interface" refers to a web page or web application that allows a user to access and operate the system via a browser.

[0451] "Image Processing Module" refers to software or libraries for analyzing text and images within slide files.

[0452] "Optical Character Recognition (OCR)" refers to the technology that mechanically or electronically recognizes text in an image and converts it into digital text data.

[0453] "Text data" refers to information extracted by OCR and stored as a string of characters.

[0454] A "natural language processing (NLP) module" refers to software or libraries for analyzing text data and understanding its meaning and context.

[0455] "Template library" refers to a database or group of files that stores multiple slide design templates.

[0456] A "standardized design template" refers to a presentation slide template that is constructed with a consistent design style.

[0457] "Export" refers to outputting a file in a specific format after processing is complete.

[0458] "Download link" refers to the URL for obtaining a file from the server to the user's device.

[0459] The system of this invention receives slides uploaded by users and executes a process to regenerate them with a standardized design. In this system, the server, terminals, and users each have specific roles and work together.

[0460] First, a user selects a slide file (PDF or image format) from their device and uploads it through the system's web interface. The device used by the user can be a PC, tablet, smartphone, etc. Once the upload is complete, the server receives the slide file and temporarily stores it. This storage is performed in a specific folder in a temporary directory (e.g., / tmp), and the file name is renamed with a unique identifier (e.g., UUID) to avoid duplication.

[0461] The server then passes the saved slide files to an image processing module (e.g., Tesseract OCR) and uses OCR technology to extract the text from the slides. The OCR process identifies the text on each slide page and extracts the text data, including numbers and special characters. The OCR process uses PNG images of each page as input, and outputs the text data for each page in JSON format.

[0462] The server passes the extracted text data to a natural language processing (NLP) module (e.g., SpaCy or NLTK) for content analysis. Topic modeling techniques (e.g., Latent Dirichlet Allocation) are used to extract the main themes of the slide content, and contextual analysis (e.g., dependency analysis) is performed to understand the argument and key information of the text.

[0463] After the analysis is complete, the server selects an appropriate uniform design template (e.g., Canva or a self-developed template) from the template library. When placing text and images into the template, it standardizes the following design elements:

[0464] Font style and font size (e.g., Arial 12pt)

[0465] Text color scheme (e.g., black with blue highlight)

[0466] Image placement and size (e.g., centered, 80% width)

[0467] Redrawing graphs and charts (e.g., using Excel or D3.js)

[0468] The server loads the template file and automatically runs a script (e.g., Python-PowerPoint) to rearrange each element of the slide based on the analysis results. A consistent design is applied to all slide pages, and a layout is generated that maximizes visibility and readability.

[0469] Finally, the server exports the newly generated slides in PDF or image format. This file is saved in a temporary directory, and the server generates a download link and notifies the user. The user can download the new slides from the provided link and use them as a presentation.

[0470] Specific examples

[0471] For example, consider a user uploading their presentation slides to a university evaluation system. Here's the process:

[0472] 1. The user uploads the presentation slides (e.g., PDF format) for the evaluation system to the system.

[0473] 2. The server receives and stores the file.

[0474] 3. The server runs the saved file through Tesseract OCR to extract the text for each page.

[0475] 4. The server analyzes the extracted text using SpaCy to understand the main themes and arguments of the presentation.

[0476] 5. The server selects a uniform design for the evaluation presentation from its template library and regenerates it with consistent font styles, color schemes, etc.

[0477] 6. The server exports the newly generated slides in PDF format and provides the user with a download link.

[0478] 7. Users can download the new slides from this link and submit them to the evaluation committee.

[0479] Prompt Sentence Examples

[0480] The following prompts are examples of inputs to a generative AI model (e.g., GPT-4):

[0481] Please explain in detail the process steps of the system that regenerates presentation slides uploaded by users with a standardized design. Please specify the roles of the user, server, and device, and explain each process step in detail.

[0482] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0483] Step 1:

[0484] The user selects a slide file (PDF or image format) from their own device and uploads it through the system's web interface. Specifically, a file selection dialog appears in the browser, the user selects the appropriate file, and clicks the "Upload" button. The input is the slide file, and the output is a file upload request to the system.

[0485] Step 2:

[0486] The server receives the uploaded slide file and temporarily stores it. The file is saved in a specific folder in a temporary directory (e.g., / tmp), and the file name is renamed with a unique identifier such as a UUID to avoid duplication. The input is a file upload request from the user, and the output is the saved slide file.

[0487] Step 3:

[0488] The server passes the saved slide files to an image processing module (e.g., Tesseract OCR) and extracts the text from the slides using OCR technology. Specifically, the server invokes the Tesseract command and passes the PNG-formatted images for each page as input. The OCR process outputs the text data for each page in JSON format. The input is the saved slide files, and the output is the extracted text data.

[0489] Step 4:

[0490] The server passes the extracted text data to a natural language processing (NLP) module (e.g., SpaCy or NLTK) for content analysis. Specifically, the server loads the text data into the NLP module and uses topic modeling techniques (e.g., Latent Dirichlet Allocation) and context analysis (e.g., dependency analysis) to identify major themes, arguments, and important information. The input is the text data obtained by OCR, and the output is the analysis results.

[0491] Step 5:

[0492] The server selects an appropriate uniform design template from the template library based on the analysis results. The template file is loaded, and a script (e.g., Python-PowerPoint) is automatically executed to rearrange each element of the slide based on the analysis results. The input is the analysis results, and the output is new slide data with the template applied.

[0493] Step 6:

[0494] The server exports the regenerated slides in PDF or image format. It passes the generated slide data to a PDF generation library (e.g., ReportLab) to create a PDF file. This file is saved in a temporary directory. The input is the new slide data with the template applied, and the output is the exported PDF file.

[0495] Step 7:

[0496] The server generates a download link for the new slide file and notifies the user of the link. Specifically, it converts the file path into a URL and provides it to the user via a notification email or web notification. The input is the exported PDF file, and the output is the download link provided to the user.

[0497] Step 8:

[0498] The user downloads the uniformed slide file via this link. Specifically, they click the provided URL to download the slide file and save it on their device. The input is the download link, and the output is the downloaded slide file.

[0499] (Application example 1)

[0500] 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."

[0501] In today's brick-and-mortar stores, product explanation materials, advertising materials, menus, and other items have different designs for each store, creating a visual inconsistency problem. Furthermore, using specialized software to unify designs requires specialized knowledge, making it difficult for store staff to use. This increases the cost and effort required to achieve a unified design, and risks damaging the brand image of the entire store. The present invention aims to solve these problems by providing a system that allows store staff to easily regenerate slides and advertising materials into a unified design.

[0502] 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.

[0503] In this invention, the server includes a means for receiving slides in image or PDF format, a means for extracting text from the content of the received slides using optical character recognition (OCR), and a means for content analysis of the extracted text using natural language processing (NLP). This allows store staff to easily upload slides and advertising materials using smartphones or tablets and regenerate them with a unified and consistent design. The system also unifies the generated slides with a unique design and regenerates them into in-store materials and advertising materials, thereby maintaining visual consistency throughout the store.

[0504] "Slides" refers to images or PDF pages provided in the form of presentations, advertisements, handouts, etc.

[0505] "Image Format" refers to bitmap image file formats such as JPEG, PNG, and GIF.

[0506] "PDF Format" refers to a file format known as Portable Document Format (PDF) for storing and sharing documents electronically.

[0507] "Optical Character Recognition (OCR)" refers to the technology that recognizes characters in an image and converts them into digital text data.

[0508] "Natural Language Processing (NLP)" refers to the technology that enables computers to understand, interpret, and generate human language.

[0509] A "standardized design template" refers to a design template that is standardized in specific font styles, font sizes, color schemes, and layouts.

[0510] "Smartphone" and "tablet" refer to devices that are portable information terminals equipped with a mobile operating system and that can connect to the Internet and use applications.

[0511] A "server" refers to a computer system that stores files and processes information via a network.

[0512] "Export" refers to the operation of saving or outputting generated data in a specified file format.

[0513] The system of this invention aims to standardize the design of materials and advertising materials used in physical stores to create a professional impression. The system allows users to upload slides using a smartphone or tablet and regenerates them into a standardized design.

[0514] Hardware and Software Configuration

[0515] The system uses the following hardware and software:

[0516] Smartphones and tablets: Mobile devices that perform upload operations.

[0517] Server: The computer system that handles all processes such as file storage, OCR processing, NLP analysis, design template application, and exporting.

[0518] OCR engine (pytesseract): Optical character recognition technology for extracting text from images.

[0519] NLP module (spaCy): Natural language processing technology to analyze extracted text and understand key themes and arguments.

[0520] PDF processing library (pdf2image, PIL): A library for converting PDF files to images and performing OCR processing.

[0521] Overview of data processing and calculation

[0522] Users select slide files (PDF or image format) from their smartphones or tablets and upload them through the system's web interface, where the server receives and temporarily stores them.

[0523] Next, the server passes the saved slide files to the image processing module, which uses an OCR engine (pytesseract) to extract the text from the slides, identifying the text on each slide page and obtaining text data including numbers and special characters.

[0524] The server then passes the extracted text data to a natural language processing module (spaCy) for content analysis, using topic modeling techniques to extract the main themes of the slides and then conducting contextual analysis to understand the text's arguments and key information.

[0525] After the analysis is complete, the server selects an appropriate uniform design template from the template library. When placing text and images into the template, the server uniforms the following design elements:

[0526] Font style and font size

[0527] Text color scheme

[0528] Image placement and size

[0529] Redrawing graphs and charts

[0530] A consistent design is applied to all slide pages, creating layouts that maximize visibility and readability.

[0531] Specific examples

[0532] For example, consider the case where a user uploads a slide file called "New Product Information.pdf." The server receives the slides uploaded by the user and uses an OCR engine to extract the text within the slides. Next, the extracted text is analyzed using an NLP module to identify the main themes and arguments of the presentation. A standardized design template is then applied to generate new slides with consistent font styles, color schemes, etc. Finally, the server exports the new slides in PDF format, which the user can download and use for in-store advertising materials.

[0533] Prompt Sentence Examples

[0534] "Please upload the new product information document.pdf and convert it into a unified design. The file has 10 pages, and each page contains different product information. Please unify the font style and make the design easy to read."

[0535] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0536] Step 1:

[0537] The user selects a slide file (PDF or image format) on a smartphone or tablet and uploads it through the system's web interface. At this stage, the file's format and content are not important and it is sent to the server through the specified interface. The input is the slide file, and the output is the identifier (ID) of the uploaded file.

[0538] Step 2:

[0539] The server receives the uploaded slide file and temporarily stores it. The saved file is stored in the file system and used for subsequent processing. The input is the identifier of the uploaded file, and the output is the path of the saved file.

[0540] Step 3:

[0541] The server passes the saved slide files to the image processing module, which uses an OCR engine (pytesseract) to extract the text in the slides. This process converts the PDF file into an image and identifies the text on each page. The input is the path of the saved file, and the output is the extracted text data.

[0542] Step 4:

[0543] The server passes the extracted text data to a natural language processing (NLP) module (spaCy) for content analysis. This process uses topic modeling techniques to extract major themes and understand the text's argument and important information. The input is text data, and the output is analyzed topics and contextual information.

[0544] Step 5:

[0545] The server selects an appropriate uniform design template from the template library and places the extracted and analyzed text and images into the template. In this step, it standardizes the font style, font size, color scheme, image placement, etc. The input is the analysis result and the template, and the output is new slide data with a uniform design.

[0546] Step 6:

[0547] The server exports the generated slides in PDF or image format and provides them to the user. In this process, a download link for the exported file is generated and notified to the user. The input is the new slide data with a unified design, and the output is the download link.

[0548] Specific examples

[0549] For example, if a user uploads "New Product Presentation.pdf," the server processes the file with OCR, extracts text, performs NLP analysis, applies a uniform design template, and then exports the new slides in PDF format and provides a download link to the user.

[0550] 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.

[0551] Overall system configuration

[0552] This system receives slides uploaded by users, reproduces them with a standardized design, and recognizes the user's emotions and reflects them in the slide design. This system works in cooperation with the server, terminal, and user, each of which has a specific role.

[0553] Upload procedure

[0554] Users select slide files (PDF or image format) from their own devices and upload them through the system's web interface. The server receives the uploaded files and temporarily stores them.

[0555] Emotion Recognition Procedure

[0556] The server activates an emotion engine to recognize the user's emotion when a slide is uploaded or when analyzing the slide's content. The emotion engine analyzes the user's facial expressions, voice tone, text input, etc., and generates the user's emotion data. This data includes emotion information such as joy, sadness, and surprise.

[0557] Content analysis procedure

[0558] The server passes the saved slide files to the image processing module, which uses OCR technology to extract the text from the slides, identifying the text on each slide page and extracting the text data, including numbers and special characters.

[0559] The server then passes the extracted text data to a natural language processing (NLP) module for content analysis, using topic modeling techniques to extract the main themes of the slide content and conducting contextual analysis to understand the text's argument and key information.

[0560] Uniform design generation procedure

[0561] After the analysis is complete, the server selects an appropriate uniform design template from the template library. The most suitable template is automatically determined based on the emotional information detected by the user's emotion engine. For example, if the user is depressed, a calm color scheme and design will be applied.

[0562] The server applies the extracted text and images to a template, adjusting font style, font size, color scheme, and page layout. It may also adjust design elements based on emotion data.

[0563] Export and provisioning procedures

[0564] The server exports the newly generated slides as a PDF or image file, temporarily stores the file, and then generates a download link for the user, who then downloads the flattened slide file via this link.

[0565] Specific examples

[0566] For example, consider a case where a user uploads their presentation slides to a university evaluation system. During upload, an emotion engine analyzes the user's facial expressions and tone of voice to obtain emotional data indicating nervousness. The server receives the slides uploaded by the user and extracts the text within them using OCR. Next, the extracted text is analyzed using an NLP module to identify the main themes and arguments of the presentation. Taking the emotional data into account, a calming design template is applied to reduce tension. New slides are generated with a consistent font style and color scheme. Finally, the server exports the new slides in PDF format, which the user can download and submit to the evaluation committee.

[0567] This system not only allows for fair evaluation of the quality of ideas regardless of their design, but also allows for more effective presentations by taking into account the user's emotions.

[0568] The processing flow will be explained below.

[0569] Step 1:

[0570] Users select slide files (PDF or image format) from their device and initiate the upload through the web interface.

[0571] Step 2:

[0572] The server receives the uploaded slide files and temporarily stores them in a designated directory or database.

[0573] Step 3:

[0574] The server passes the saved slide files to an image processing module, which, in the case of PDF format, converts each individual page into an image so that each page can be processed independently.

[0575] Step 4:

[0576] The server starts an emotion engine while the user is uploading. The emotion engine analyzes the video captured from the user's webcam, the audio captured from the microphone, and the text input to recognize the user's emotional state (e.g., joy, sadness, surprise, tension).

[0577] Step 5:

[0578] The server uses OCR (Optical Character Recognition) technology to extract text from slides. It performs image analysis on each page and generates text data. It performs accurate text extraction, taking into account differences in font and character size.

[0579] Step 6:

[0580] The server passes the extracted text data to a natural language processing (NLP) module to perform content analysis, using topic modeling to extract the main themes of the slides and semantic analysis to understand the argument and key information of the text.

[0581] Step 7:

[0582] The server selects an appropriate uniform design template from the template library, taking into account the emotional information recognized by the user's emotion engine. For example, if the user is nervous, a template with a calm color scheme will be selected.

[0583] Step 8:

[0584] The server applies the extracted text and images to a template, adjusting the font style, font size, and color scheme, and adjusting design elements within the template based on the emotion data.

[0585] Step 9:

[0586] The server redraws graphs and charts as needed to maintain a consistent visual design, including adjusting image size and placement.

[0587] Step 10:

[0588] The server exports the newly generated slides as PDFs or images, creating a uniformly designed slide file.

[0589] Step 11:

[0590] The server temporarily stores the generated slide files, generates a download link, and notifies the user via email or a web interface.

[0591] Step 12:

[0592] Users can access the provided download link and download the uniformed slide file, which can then be used for evaluation or presentation.

[0593] Example 2

[0594] 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."

[0595] Conventional slide generation systems not only lack design consistency, but also have the problem of being unable to propose appropriate designs that take the user's emotional state into account. Furthermore, while they are capable of analyzing the content of text and images within slides, there are no systems that can understand the user's emotions and modify the design based on those emotions. Furthermore, there is a need to achieve more effective communication in presentations by using designs that reflect the user's emotions.

[0596] 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.

[0597] In this invention, the server includes means for receiving slides in image or PDF format, means for extracting text from the content of the received slides using optical character recognition (OCR), means for content analysis of the extracted text using natural language processing (NLP), means for recognizing and acquiring user emotion data, means for selecting a standardized design template based on the acquired emotion data, means for generating new slides using the selected template, and means for exporting the generated slides in PDF or image format and providing them to the user, thereby enabling effective slide design that reflects the user's emotions.

[0598] "Slides" are images or PDF documents used in presentations and information presentations.

[0599] Optical character recognition (OCR) is a technology that extracts character information from an image as digital text.

[0600] "Natural language processing (NLP)" is a technology that analyzes the content of text data and understands the subject matter and context.

[0601] "Emotion data" refers to emotional information recognized from the user's facial expression, tone of voice, text input, and the like.

[0602] A "template library" is a database that collects slide design templates.

[0603] A "standardized design template" is a slide template with a consistent overall design style and format.

[0604] "Export" means converting data or files into a specific format and outputting it to an external device.

[0605] A "user" is a person who uses the system to upload slides and receive generated slides.

[0606] A "server" is a computer system that manages the overall processing of the system and stores, analyzes, and generates various types of data.

[0607] This invention is a system that receives slides uploaded by users, reproduces them with a standardized design, and recognizes the user's emotions and reflects them in the slide design. The system works in cooperation with the server, terminal, and user, each of which has a specific role.

[0608] Overall structure

[0609] The hardware and software used to implement the invention are as follows: The server receives and temporarily stores slide files uploaded from the user's device. It also analyzes the slide content using OCR technology and a natural language processing (NLP) module. Furthermore, it acquires the user's emotional data using an emotion recognition module, selects an emotion-based design template from a template library, generates new slides, and exports them.

[0610] Upload procedure

[0611] Users select slide files (PDF or image format) from their own devices and upload them through the system's web interface. The server receives the uploaded files and temporarily stores them, checking the file format and size.

[0612] Emotion Recognition Procedure

[0613] The server starts the emotion engine when the user uploads a slide. The emotion engine used here is software that captures and analyzes the user's facial expressions and voice through a webcam and microphone. The captured data is sent to an emotion recognition module, which generates the user's emotion data, which includes emotional information such as joy, sadness, and surprise.

[0614] Content analysis procedure

[0615] The server passes the saved slide files to an image processing module, which uses OCR (Optical Character Recognition) technology to extract the text from the slides. The extracted text data, including numbers and special characters, is saved as text data. This data is then passed to a natural language processing (NLP) module for content analysis. Specifically, topic modeling technology is used to extract the main themes from the slides, and contextual analysis is used to understand the main points and key points of the text.

[0616] Uniform design generation procedure

[0617] After analysis, the server selects an appropriate uniform design template from its template library. The optimal template is automatically selected based on the emotional data acquired by the emotion recognition module. For example, if the user is nervous, a calming color scheme and design will be applied. The server then applies the extracted text and images to the template, unifying the font style, size, and color scheme, and adjusting the page layout. This generates a new uniform slide.

[0618] Export and provisioning procedures

[0619] The server exports the newly generated slides in PDF or image format, temporarily saves the file, and then generates a download link via which users can download and use the generated slides.

[0620] Examples of concrete examples and prompts

[0621] For example, when a user uploads presentation slides for a university evaluation system, an emotion recognition engine may detect emotional data indicating "nervousness" from the user's facial expressions and tone of voice. The server receives the slides uploaded by the user, extracts text using OCR technology, and analyzes the content using an NLP module. It then applies a design template to reduce tension and standardizes fonts and color schemes to generate new slides. The final slides are exported as PDFs for users to download.

[0622] An example prompt is:

[0623] "Create a presentation slide design template for when users are feeling down"

[0624] "Generate slide designs that reflect the emotion of joy"

[0625] By using such prompts, it becomes possible to design effective slides that take emotional data into account.

[0626] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0627] Step 1:

[0628] Users select slide files (PDF or image format) from their terminal and upload them through the system's web interface.

[0629] Specifically, the user opens a browser, accesses the system's web page, selects a file, and presses the upload button.

[0630] Input: Slide file selected from the user's device

[0631] Output: Uploaded slide files are transferred to the server.

[0632] Step 2:

[0633] The server receives the uploaded slide files and temporarily stores them.

[0634] Specifically, the server verifies the file format and size and stores it in the appropriate folder.

[0635] Input: Uploaded slide files

[0636] Output: Temporarily saved slide files

[0637] Step 3:

[0638] The server starts the emotion engine when the slides are uploaded.

[0639] Specifically, the server captures the user's facial expressions and voice via a webcam and microphone connected to the user's device.

[0640] Input: User's facial expression data and voice data

[0641] Output: Obtained user emotion data

[0642] Step 4:

[0643] The server passes the saved slide files to an image processing module, which uses OCR technology to extract the text in the slides.

[0644] Specifically, the server analyzes each page of the slides and performs character recognition.

[0645] Input: Temporarily saved slide file

[0646] Output: Extracted text data

[0647] Step 5:

[0648] The server passes the extracted text data to a natural language processing (NLP) module for content analysis.

[0649] Specifically, the server uses topic modeling technology to extract themes from the slide content and understands the arguments and key points through contextual analysis.

[0650] Input: Extracted text data

[0651] Output: Analyzed content data (theme, argument, key points)

[0652] Step 6:

[0653] The server considers the emotion data obtained by the emotion recognition module and selects an appropriate uniform design template from the template library.

[0654] Specifically, the server searches the template library and automatically selects the most suitable template based on the emotion information.

[0655] Input: Analyzed content data, emotion data

[0656] Output: Selected design template

[0657] Step 7:

[0658] The server generates a new slide using the selected design template.

[0659] Specifically, the server applies text and images to templates, unifies font styles, sizes, and colors, and adjusts page layouts.

[0660] Input: Selected design template, parsed content data

[0661] Output: The new slide file that is generated.

[0662] Step 8:

[0663] The server exports the newly generated slides in PDF or image format and provides them to the user.

[0664] Specifically, the server converts the generated slides into a specified format, temporarily stores them, and generates a download link.

[0665] Input: The new slide file that was generated

[0666] Output: Exported slide files, generated download link

[0667] Step 9:

[0668] The user downloads the uniformed slide file via the provided download link.

[0669] Specifically, the user clicks on the link provided by the system and saves the file to their device.

[0670] Input: Generated download link

[0671] Output: Slide files downloaded to the user's device

[0672] (Application example 2)

[0673] 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."

[0674] In virtual stores and online presentations, a system is needed to provide personalized content in real time that responds to user emotions. Conventional slide production systems and advertising display systems are not designed with user emotions in mind, and the challenge was how to utilize emotional data to improve the user experience.

[0675] 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 receiving slides in image or PDF format, means for extracting text from the content of the received slides using optical character recognition (OCR), means for content analysis of the extracted text using natural language processing (NLP), means for generating new slides by applying a standardized design template based on the analysis results and user emotion data, means for exporting the generated slides in PDF or image format and providing them to the user, means for analyzing the user's facial expressions and tone of voice to generate emotion data, and means for generating and displaying advertisements according to the user's emotions. This makes it possible to provide personalized content according to the user's emotions in real time.

[0676] "Slides" are images or PDF format data used as presentation materials.

[0677] Optical character recognition (OCR) is a technology that detects characters in an image and extracts them as text data.

[0678] "Natural language processing (NLP)" is a technology that analyzes text data and understands and interprets its content.

[0679] A "uniform design template" is a template that provides a uniform layout and style used to ensure design consistency.

[0680] "Emotion data" is emotional information analyzed from the user's facial expressions, tone of voice, and the like.

[0681] "Advertisement generation means" is a technology that generates optimal advertisements according to the user's interests and emotions.

[0682] "Server" means the computer system that has the central function of data processing, receiving, analyzing, applying designs, generating advertisements and exporting slides.

[0683] "User facial expression analysis" is a technology that uses a camera to capture a user's face and infer their emotions from their facial expressions.

[0684] "Voice tone analysis" is a technology that uses a microphone to collect a user's voice and analyzes emotions from the tone of the voice.

[0685] "Personalized content" is content that is custom-made to suit a user's individual needs and feelings.

[0686] A "virtual store" is a virtual sales location that offers products and services over the Internet.

[0687] To implement this invention, it is necessary to build a system that receives slides uploaded by users, regenerates them with a standardized design, and recognizes users' emotions and reflects them in designs and advertisements. A specific embodiment of this system is shown below.

[0688] Hardware and Software Configuration

[0689] First, users use a web interface to upload slide files (image or PDF format) from their device, which then sends the uploaded slides to the server.

[0690] The server has the following features:

[0691] 1. Receive and temporarily store the slides.

[0692] 2. Use OCR technology to extract text from the slide content (specifically, an OCR engine such as Tesseract).

[0693] 3. Analyze the extracted text with a natural language processing (NLP) module (for example, using a library such as SpaCy or NLTK).

[0694] 4. Launch an emotion recognition engine (using a facial recognition library such as OpenCV or dlib, and a voice analysis engine) to analyze the user's facial expressions and voice tone to generate emotion data.

[0695] 5. Based on the analysis results and sentiment data, a uniform design template is applied to generate new slides.

[0696] 6. Generate and display ads based on user emotions (e.g., using ad templates that correspond to specific topics or emotions).

[0697] 7. Export the newly generated slides as PDF or images and provide them to the user.

[0698] Processing flow

[0699] When a user uploads a slide, the server receives and temporarily stores the slide. Then, OCR technology is used to extract the text from the slide. The extracted text is then analyzed by an NLP module to identify the main themes and arguments of the slide.

[0700] The server then activates an emotion recognition engine to analyze the user's facial expressions and tone of voice, generating emotional data for the user. Based on this emotional data and the analysis of the slides, an optimal design template is selected and a new slide is generated.

[0701] The generated slides have a uniform font style, size, color scheme, and layout, and personalized advertisements are generated based on the user's emotional data and displayed on the user's smart glasses or device.

[0702] Specific examples

[0703] For example, if a user visits a virtual store and wears smart glasses, the camera and microphone analyze the user's facial expressions and voice to obtain emotional information. If the user is smiling, the emotional data is analyzed as "joy." Next, the text of the slides uploaded by the user is analyzed using OCR and NLP technology, and the main theme is determined to be "Newly Released 4K TVs."

[0704] Example prompt sentence:

[0705] "Text extracted from slide: 'New 4K TVs'"

[0706] "User Emotion: 'Delight'"

[0707] "Generates ad: 'Special offer now! Check out our 4K TV!'"

[0708] Based on this information, the server generates advertising content that is likely to attract the user's interest and displays it on the smart glasses, improving the user experience.

[0709] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0710] Step 1:

[0711] Users upload slide files (image or PDF format) from their own devices through a web interface. The uploaded slide files are received by the server and temporarily stored.

[0712] Input: Slide files (image or PDF format) uploaded by the user

[0713] Output: Slide files saved on the server

[0714] Step 2:

[0715] The server uses OCR technology to extract the text from the uploaded slides. Specifically, it uses an OCR engine such as Tesseract to analyze the character information from each page of the slides and extract it as text data.

[0716] Input: Slide files stored on the server

[0717] Data processing: Analysis of character information using OCR technology

[0718] Output: Extracted text data

[0719] Step 3:

[0720] The server then analyzes the extracted text data using a natural language processing (NLP) module, which uses NLP techniques to identify the main themes and arguments of the text. Specifically, it uses libraries such as SpaCy and NLTK.

[0721] Input: Text data extracted by OCR technology

[0722] Data processing: Text analysis using NLP technology

[0723] Output: Data on the analyzed themes and arguments

[0724] Step 4:

[0725] The server starts an emotion recognition engine to analyze the user's facial expressions and voice tone. Libraries such as OpenCV and dlib are used for facial expression analysis, and a dedicated voice analysis engine is used for voice analysis. The user's emotional data is generated.

[0726] Input: User's facial expression (camera image) and voice (microphone audio)

[0727] Data processing: Emotion recognition by facial expression analysis and voice tone analysis

[0728] Output: Generated emotion data

[0729] Step 5:

[0730] Based on the analysis results and emotion data, the server selects a uniform design template and generates new slides, which have a uniform font style, size, color scheme, and layout.

[0731] Input: NLP analysis data and emotion data

[0732] Data processing: Template selection and application

[0733] Output: The newly generated slide

[0734] Step 6:

[0735] The server exports the new slides in PDF or image format and generates a download link to provide to the user, who can use this link to download the slides.

[0736] Input: The newly generated slide

[0737] Data processing: Export in PDF or image format

[0738] Output: Download link

[0739] Step 7:

[0740] The server generates personalized advertisements based on the user's emotions and displays them on the user's device or smart glasses. The advertisement generation engine generates optimal advertisement content based on the emotion data and slide theme data.

[0741] Input: Sentiment and Theme Data

[0742] Data processing: Ad content generation by the ad generation engine

[0743] Output: Generated personalized ads

[0744] 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.

[0745] 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.

[0746] 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.

[0747] [Third embodiment]

[0748] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0749] 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.

[0750] 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).

[0751] 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.

[0752] 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.

[0753] 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).

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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."

[0760] Overall system configuration

[0761] The system of this invention receives slides uploaded by users and executes a process to regenerate them with a standardized design. In this system, the server, terminals, and users each have specific roles and work together.

[0762] Upload procedure

[0763] Users select slide files (PDF or image format) from their own devices and upload them through the system's web interface. The server receives the uploaded files and temporarily stores them.

[0764] Content analysis procedure

[0765] The server passes the saved slide files to the image processing module, which uses OCR technology to extract the text from the slides, identifying the text on each slide page and extracting the text data, including numbers and special characters.

[0766] The server then passes the extracted text data to a natural language processing (NLP) module for content analysis, using topic modeling techniques to extract the main themes of the slide content and conducting contextual analysis to understand the text's argument and key information.

[0767] Uniform design generation procedure

[0768] After the analysis is complete, the server selects an appropriate uniform design template from the template library. When placing text and images into the template, the server uniforms the following design elements:

[0769] Font style and font size

[0770] Text color scheme

[0771] Image placement and size

[0772] Redrawing graphs and charts

[0773] A consistent design is applied to all slide pages, creating layouts that maximize visibility and readability.

[0774] Export and provisioning procedures

[0775] The server exports the newly generated slides as a PDF or image file, temporarily stores the file, and then generates a download link for the user, who then downloads the flattened slide file via this link.

[0776] Specific examples

[0777] For example, consider a case where a user uploads their presentation slides to a university evaluation system. The server receives the slides and uses OCR to extract the text from the slides. It then analyzes the extracted text with an NLP module to identify the main themes and arguments of the presentation. It then applies a uniform design template to generate new slides with consistent font styles, colors, and other elements. Finally, the server exports the new slides in PDF format, which the user can download and submit to the evaluation committee.

[0778] This system not only allows for fair evaluation of the quality of ideas regardless of the design, but also reduces the time required to create designs and supports efficient presentation creation.

[0779] The processing flow will be explained below.

[0780] Step 1:

[0781] The user selects the slide file (PDF or image format) from their device and starts uploading. The file is sent through the web interface and reaches the server.

[0782] Step 2:

[0783] The server receives the uploaded slide files and temporarily stores them in a designated directory or database.

[0784] Step 3:

[0785] The server passes the saved slide files to an image processing module, which checks the file format (PDF or image) and splits them into individual pages if necessary.

[0786] Step 4:

[0787] The server uses OCR (Optical Character Recognition) technology to extract the text from the slides, performs image analysis on each page, and generates text data.

[0788] Step 5:

[0789] The server passes the extracted text data to a natural language processing (NLP) module to perform content analysis, using topic modeling to extract the main themes of the slides and semantic analysis to understand the gist of the text.

[0790] Step 6:

[0791] The server selects an appropriate uniform design template from the template library based on analysis results, automatically determining the template that best suits the slide content.

[0792] Step 7:

[0793] The server applies the extracted text and images to a template, adjusting font style, font size, color scheme, and page layout.

[0794] Step 8:

[0795] The server redraws graphs and charts as needed to ensure a consistent visual design, and images are sized and positioned appropriately according to the template.

[0796] Step 9:

[0797] The server exports the newly generated slides as PDFs or images, resulting in a uniformly designed slide file.

[0798] Step 10:

[0799] The server temporarily stores the generated slide files and generates a download link, which is notified to the user.

[0800] Step 11:

[0801] Users can access the provided download link and download the uniformed slide file, which can then be used for evaluation or presentation.

[0802] Example 1

[0803] 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."

[0804] Maintaining consistency in design and layout when creating slides is a significant burden for users, and is particularly time-consuming and laborious for presentations that contain many slides. Furthermore, users without specialized design knowledge or tools have difficulty creating attractive slides. To address this issue, it is necessary to provide a system that automatically analyzes the content of slides and regenerates them into a consistent design.

[0805] 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.

[0806] In this invention, the server includes: means for a user to select a slide file from their own terminal and upload it to the system; means for the server to receive and temporarily store the uploaded slide file; means for the server to pass the saved slide file to an image processing module and extract text from the slide using optical character recognition; means for the server to pass the extracted text data to a natural language processing module and perform content analysis; means for the server to select an appropriate uniform design template from a template library based on the analysis result and generate a new slide; and means for the server to export the generated slide in PDF or image format and provide it to the user. This enables users to efficiently create consistent slides with excellent visibility and readability without much effort.

[0807] "User" refers to a person who utilizes the system to upload slide files and receive regenerated slides.

[0808] "Terminal" means the device used by a User to access the System and upload Slide Files, including, but not limited to, a PC, tablet, or smartphone.

[0809] "Server" refers to a central computer system for slide generation that receives, stores, analyzes, and regenerates slide files.

[0810] "Slide file" means an electronic file containing elements of a presentation, either in PDF or image format.

[0811] "Web interface" refers to a web page or web application that allows a user to access and operate the system via a browser.

[0812] "Image Processing Module" refers to software or libraries for analyzing text and images within slide files.

[0813] "Optical Character Recognition (OCR)" refers to the technology that mechanically or electronically recognizes text in an image and converts it into digital text data.

[0814] "Text data" refers to information extracted by OCR and stored as a string of characters.

[0815] A "natural language processing (NLP) module" refers to software or libraries for analyzing text data and understanding its meaning and context.

[0816] "Template library" refers to a database or group of files that stores multiple slide design templates.

[0817] A "standardized design template" refers to a presentation slide template that is constructed with a consistent design style.

[0818] "Export" refers to outputting a file in a specific format after processing is complete.

[0819] "Download link" refers to the URL for obtaining a file from the server to the user's device.

[0820] The system of this invention receives slides uploaded by users and executes a process to regenerate them with a standardized design. In this system, the server, terminals, and users each have specific roles and work together.

[0821] First, a user selects a slide file (PDF or image format) from their device and uploads it through the system's web interface. The device used by the user can be a PC, tablet, smartphone, etc. Once the upload is complete, the server receives the slide file and temporarily stores it. This storage is performed in a specific folder in a temporary directory (e.g., / tmp), and the file name is renamed with a unique identifier (e.g., UUID) to avoid duplication.

[0822] The server then passes the saved slide files to an image processing module (e.g., Tesseract OCR) and uses OCR technology to extract the text from the slides. The OCR process identifies the text on each slide page and extracts the text data, including numbers and special characters. The OCR process uses PNG images of each page as input, and outputs the text data for each page in JSON format.

[0823] The server passes the extracted text data to a natural language processing (NLP) module (e.g., SpaCy or NLTK) for content analysis. Topic modeling techniques (e.g., Latent Dirichlet Allocation) are used to extract the main themes of the slide content, and contextual analysis (e.g., dependency analysis) is performed to understand the argument and key information of the text.

[0824] After the analysis is complete, the server selects an appropriate uniform design template (e.g., Canva or a self-developed template) from the template library. When placing text and images into the template, it standardizes the following design elements:

[0825] Font style and font size (e.g., Arial 12pt)

[0826] Text color scheme (e.g., black with blue highlight)

[0827] Image placement and size (e.g., centered, 80% width)

[0828] Redrawing graphs and charts (e.g., using Excel or D3.js)

[0829] The server loads the template file and automatically runs a script (e.g., Python-PowerPoint) to rearrange each element of the slide based on the analysis results. A consistent design is applied to all slide pages, and a layout is generated that maximizes visibility and readability.

[0830] Finally, the server exports the newly generated slides in PDF or image format. This file is saved in a temporary directory, and the server generates a download link and notifies the user. The user can download the new slides from the provided link and use them as a presentation.

[0831] Specific examples

[0832] For example, consider a user uploading their presentation slides to a university evaluation system. Here's the process:

[0833] 1. The user uploads the presentation slides (e.g., PDF format) for the evaluation system to the system.

[0834] 2. The server receives and stores the file.

[0835] 3. The server runs the saved file through Tesseract OCR to extract the text for each page.

[0836] 4. The server analyzes the extracted text using SpaCy to understand the main themes and arguments of the presentation.

[0837] 5. The server selects a uniform design for the evaluation presentation from its template library and regenerates it with consistent font styles, color schemes, etc.

[0838] 6. The server exports the newly generated slides in PDF format and provides the user with a download link.

[0839] 7. Users can download the new slides from this link and submit them to the evaluation committee.

[0840] Prompt Sentence Examples

[0841] The following prompts are examples of inputs to a generative AI model (e.g., GPT-4):

[0842] Please explain in detail the process steps of the system that regenerates presentation slides uploaded by users with a standardized design. Please specify the roles of the user, server, and device, and explain each process step in detail.

[0843] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0844] Step 1:

[0845] The user selects a slide file (PDF or image format) from their own device and uploads it through the system's web interface. Specifically, a file selection dialog appears in the browser, the user selects the appropriate file, and clicks the "Upload" button. The input is the slide file, and the output is a file upload request to the system.

[0846] Step 2:

[0847] The server receives the uploaded slide file and temporarily stores it. The file is saved in a specific folder in a temporary directory (e.g., / tmp), and the file name is renamed with a unique identifier such as a UUID to avoid duplication. The input is a file upload request from the user, and the output is the saved slide file.

[0848] Step 3:

[0849] The server passes the saved slide files to an image processing module (e.g., Tesseract OCR) and extracts the text from the slides using OCR technology. Specifically, the server invokes the Tesseract command and passes the PNG-formatted images for each page as input. The OCR process outputs the text data for each page in JSON format. The input is the saved slide files, and the output is the extracted text data.

[0850] Step 4:

[0851] The server passes the extracted text data to a natural language processing (NLP) module (e.g., SpaCy or NLTK) for content analysis. Specifically, the server loads the text data into the NLP module and uses topic modeling techniques (e.g., Latent Dirichlet Allocation) and context analysis (e.g., dependency analysis) to identify major themes, arguments, and important information. The input is the text data obtained by OCR, and the output is the analysis results.

[0852] Step 5:

[0853] The server selects an appropriate uniform design template from the template library based on the analysis results. The template file is loaded, and a script (e.g., Python-PowerPoint) is automatically executed to rearrange each element of the slide based on the analysis results. The input is the analysis results, and the output is new slide data with the template applied.

[0854] Step 6:

[0855] The server exports the regenerated slides in PDF or image format. It passes the generated slide data to a PDF generation library (e.g., ReportLab) to create a PDF file. This file is saved in a temporary directory. The input is the new slide data with the template applied, and the output is the exported PDF file.

[0856] Step 7:

[0857] The server generates a download link for the new slide file and notifies the user of the link. Specifically, it converts the file path into a URL and provides it to the user via a notification email or web notification. The input is the exported PDF file, and the output is the download link provided to the user.

[0858] Step 8:

[0859] The user downloads the uniformed slide file via this link. Specifically, they click the provided URL to download the slide file and save it on their device. The input is the download link, and the output is the downloaded slide file.

[0860] (Application example 1)

[0861] 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."

[0862] In today's brick-and-mortar stores, product explanation materials, advertising materials, menus, and other items have different designs for each store, creating a visual inconsistency problem. Furthermore, using specialized software to unify designs requires specialized knowledge, making it difficult for store staff to use. This increases the cost and effort required to achieve a unified design, and risks damaging the brand image of the entire store. The present invention aims to solve these problems by providing a system that allows store staff to easily regenerate slides and advertising materials into a unified design.

[0863] 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.

[0864] In this invention, the server includes a means for receiving slides in image or PDF format, a means for extracting text from the content of the received slides using optical character recognition (OCR), and a means for content analysis of the extracted text using natural language processing (NLP). This allows store staff to easily upload slides and advertising materials using smartphones or tablets and regenerate them with a unified and consistent design. The system also unifies the generated slides with a unique design and regenerates them into in-store materials and advertising materials, thereby maintaining visual consistency throughout the store.

[0865] "Slides" refers to images or PDF pages provided in the form of presentations, advertisements, handouts, etc.

[0866] "Image Format" refers to bitmap image file formats such as JPEG, PNG, and GIF.

[0867] "PDF Format" refers to a file format known as Portable Document Format (PDF) for storing and sharing documents electronically.

[0868] "Optical Character Recognition (OCR)" refers to the technology that recognizes characters in an image and converts them into digital text data.

[0869] "Natural Language Processing (NLP)" refers to the technology that enables computers to understand, interpret, and generate human language.

[0870] A "standardized design template" refers to a design template that is standardized in specific font styles, font sizes, color schemes, and layouts.

[0871] "Smartphone" and "tablet" refer to devices that are portable information terminals equipped with a mobile operating system and that can connect to the Internet and use applications.

[0872] A "server" refers to a computer system that stores files and processes information via a network.

[0873] "Export" refers to the operation of saving or outputting generated data in a specified file format.

[0874] The system of this invention aims to standardize the design of materials and advertising materials used in physical stores to create a professional impression. The system allows users to upload slides using a smartphone or tablet and regenerates them into a standardized design.

[0875] Hardware and Software Configuration

[0876] The system uses the following hardware and software:

[0877] Smartphones and tablets: Mobile devices that perform upload operations.

[0878] Server: The computer system that handles all processes such as file storage, OCR processing, NLP analysis, design template application, and exporting.

[0879] OCR engine (pytesseract): Optical character recognition technology for extracting text from images.

[0880] NLP module (spaCy): Natural language processing technology to analyze extracted text and understand key themes and arguments.

[0881] PDF processing library (pdf2image, PIL): A library for converting PDF files to images and performing OCR processing.

[0882] Overview of data processing and calculation

[0883] Users select slide files (PDF or image format) from their smartphones or tablets and upload them through the system's web interface, where the server receives and temporarily stores them.

[0884] Next, the server passes the saved slide files to the image processing module, which uses an OCR engine (pytesseract) to extract the text from the slides, identifying the text on each slide page and obtaining text data including numbers and special characters.

[0885] The server then passes the extracted text data to a natural language processing module (spaCy) for content analysis, using topic modeling techniques to extract the main themes of the slides and then conducting contextual analysis to understand the text's arguments and key information.

[0886] After the analysis is complete, the server selects an appropriate uniform design template from the template library. When placing text and images into the template, the server uniforms the following design elements:

[0887] Font style and font size

[0888] Text color scheme

[0889] Image placement and size

[0890] Redrawing graphs and charts

[0891] A consistent design is applied to all slide pages, creating layouts that maximize visibility and readability.

[0892] Specific examples

[0893] For example, consider the case where a user uploads a slide file called "New Product Information.pdf." The server receives the slides uploaded by the user and uses an OCR engine to extract the text within the slides. Next, the extracted text is analyzed using an NLP module to identify the main themes and arguments of the presentation. A standardized design template is then applied to generate new slides with consistent font styles, color schemes, etc. Finally, the server exports the new slides in PDF format, which the user can download and use for in-store advertising materials.

[0894] Prompt Sentence Examples

[0895] "Please upload the new product information document.pdf and convert it into a unified design. The file has 10 pages, and each page contains different product information. Please unify the font style and make the design easy to read."

[0896] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0897] Step 1:

[0898] The user selects a slide file (PDF or image format) on a smartphone or tablet and uploads it through the system's web interface. At this stage, the file's format and content are not important and it is sent to the server through the specified interface. The input is the slide file, and the output is the identifier (ID) of the uploaded file.

[0899] Step 2:

[0900] The server receives the uploaded slide file and temporarily stores it. The saved file is stored in the file system and used for subsequent processing. The input is the identifier of the uploaded file, and the output is the path of the saved file.

[0901] Step 3:

[0902] The server passes the saved slide files to the image processing module, which uses an OCR engine (pytesseract) to extract the text in the slides. This process converts the PDF file into an image and identifies the text on each page. The input is the path of the saved file, and the output is the extracted text data.

[0903] Step 4:

[0904] The server passes the extracted text data to a natural language processing (NLP) module (spaCy) for content analysis. This process uses topic modeling techniques to extract major themes and understand the text's argument and important information. The input is text data, and the output is analyzed topics and contextual information.

[0905] Step 5:

[0906] The server selects an appropriate uniform design template from the template library and places the extracted and analyzed text and images into the template. In this step, it standardizes the font style, font size, color scheme, image placement, etc. The input is the analysis result and the template, and the output is new slide data with a uniform design.

[0907] Step 6:

[0908] The server exports the generated slides in PDF or image format and provides them to the user. In this process, a download link for the exported file is generated and notified to the user. The input is the new slide data with a unified design, and the output is the download link.

[0909] Specific examples

[0910] For example, if a user uploads "New Product Presentation.pdf," the server processes the file with OCR, extracts text, performs NLP analysis, applies a uniform design template, and then exports the new slides in PDF format and provides a download link to the user.

[0911] 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.

[0912] Overall system configuration

[0913] This system receives slides uploaded by users, reproduces them with a standardized design, and recognizes the user's emotions and reflects them in the slide design. This system works in cooperation with the server, terminal, and user, each of which has a specific role.

[0914] Upload procedure

[0915] Users select slide files (PDF or image format) from their own devices and upload them through the system's web interface. The server receives the uploaded files and temporarily stores them.

[0916] Emotion Recognition Procedure

[0917] The server activates an emotion engine to recognize the user's emotion when a slide is uploaded or when analyzing the slide's content. The emotion engine analyzes the user's facial expressions, voice tone, text input, etc., and generates the user's emotion data. This data includes emotion information such as joy, sadness, and surprise.

[0918] Content analysis procedure

[0919] The server passes the saved slide files to the image processing module, which uses OCR technology to extract the text from the slides, identifying the text on each slide page and extracting the text data, including numbers and special characters.

[0920] The server then passes the extracted text data to a natural language processing (NLP) module for content analysis, using topic modeling techniques to extract the main themes of the slide content and conducting contextual analysis to understand the text's argument and key information.

[0921] Uniform design generation procedure

[0922] After the analysis is complete, the server selects an appropriate uniform design template from the template library. The most suitable template is automatically determined based on the emotional information detected by the user's emotion engine. For example, if the user is depressed, a calm color scheme and design will be applied.

[0923] The server applies the extracted text and images to a template, adjusting font style, font size, color scheme, and page layout. It may also adjust design elements based on emotion data.

[0924] Export and provisioning procedures

[0925] The server exports the newly generated slides as a PDF or image file, temporarily stores the file, and then generates a download link for the user, who then downloads the flattened slide file via this link.

[0926] Specific examples

[0927] For example, consider a case where a user uploads their presentation slides to a university evaluation system. During upload, an emotion engine analyzes the user's facial expressions and tone of voice to obtain emotional data indicating nervousness. The server receives the slides uploaded by the user and extracts the text within them using OCR. Next, the extracted text is analyzed using an NLP module to identify the main themes and arguments of the presentation. Taking the emotional data into account, a calming design template is applied to reduce tension. New slides are generated with a consistent font style and color scheme. Finally, the server exports the new slides in PDF format, which the user can download and submit to the evaluation committee.

[0928] This system not only allows for fair evaluation of the quality of ideas regardless of their design, but also allows for more effective presentations by taking into account the user's emotions.

[0929] The processing flow will be explained below.

[0930] Step 1:

[0931] Users select slide files (PDF or image format) from their device and initiate the upload through the web interface.

[0932] Step 2:

[0933] The server receives the uploaded slide files and temporarily stores them in a designated directory or database.

[0934] Step 3:

[0935] The server passes the saved slide files to an image processing module, which, in the case of PDF format, converts each individual page into an image so that each page can be processed independently.

[0936] Step 4:

[0937] The server starts an emotion engine while the user is uploading. The emotion engine analyzes the video captured from the user's webcam, the audio captured from the microphone, and the text input to recognize the user's emotional state (e.g., joy, sadness, surprise, tension).

[0938] Step 5:

[0939] The server uses OCR (Optical Character Recognition) technology to extract text from slides. It performs image analysis on each page and generates text data. It performs accurate text extraction, taking into account differences in font and character size.

[0940] Step 6:

[0941] The server passes the extracted text data to a natural language processing (NLP) module to perform content analysis, using topic modeling to extract the main themes of the slides and semantic analysis to understand the argument and key information of the text.

[0942] Step 7:

[0943] The server selects an appropriate uniform design template from the template library, taking into account the emotional information recognized by the user's emotion engine. For example, if the user is nervous, a template with a calm color scheme will be selected.

[0944] Step 8:

[0945] The server applies the extracted text and images to a template, adjusting the font style, font size, and color scheme, and adjusting design elements within the template based on the emotion data.

[0946] Step 9:

[0947] The server redraws graphs and charts as needed to maintain a consistent visual design, including adjusting image size and placement.

[0948] Step 10:

[0949] The server exports the newly generated slides as PDFs or images, creating a uniformly designed slide file.

[0950] Step 11:

[0951] The server temporarily stores the generated slide files, generates a download link, and notifies the user via email or a web interface.

[0952] Step 12:

[0953] Users can access the provided download link and download the uniformed slide file, which can then be used for evaluation or presentation.

[0954] Example 2

[0955] 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."

[0956] Conventional slide generation systems not only lack design consistency, but also have the problem of being unable to propose appropriate designs that take the user's emotional state into account. Furthermore, while they are capable of analyzing the content of text and images within slides, there are no systems that can understand the user's emotions and modify the design based on those emotions. Furthermore, there is a need to achieve more effective communication in presentations by using designs that reflect the user's emotions.

[0957] 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.

[0958] In this invention, the server includes means for receiving slides in image or PDF format, means for extracting text from the content of the received slides using optical character recognition (OCR), means for content analysis of the extracted text using natural language processing (NLP), means for recognizing and acquiring user emotion data, means for selecting a standardized design template based on the acquired emotion data, means for generating new slides using the selected template, and means for exporting the generated slides in PDF or image format and providing them to the user, thereby enabling effective slide design that reflects the user's emotions.

[0959] "Slides" are images or PDF documents used in presentations and information presentations.

[0960] Optical character recognition (OCR) is a technology that extracts character information from an image as digital text.

[0961] "Natural language processing (NLP)" is a technology that analyzes the content of text data and understands the subject matter and context.

[0962] "Emotion data" refers to emotional information recognized from the user's facial expression, tone of voice, text input, and the like.

[0963] A "template library" is a database that collects slide design templates.

[0964] A "standardized design template" is a slide template with a consistent overall design style and format.

[0965] "Export" means converting data or files into a specific format and outputting it to an external device.

[0966] A "user" is a person who uses the system to upload slides and receive generated slides.

[0967] A "server" is a computer system that manages the overall processing of the system and stores, analyzes, and generates various types of data.

[0968] This invention is a system that receives slides uploaded by users, reproduces them with a standardized design, and recognizes the user's emotions and reflects them in the slide design. The system works in cooperation with the server, terminal, and user, each of which has a specific role.

[0969] Overall structure

[0970] The hardware and software used to implement the invention are as follows: The server receives and temporarily stores slide files uploaded from the user's device. It also analyzes the slide content using OCR technology and a natural language processing (NLP) module. Furthermore, it acquires the user's emotional data using an emotion recognition module, selects an emotion-based design template from a template library, generates new slides, and exports them.

[0971] Upload procedure

[0972] Users select slide files (PDF or image format) from their own devices and upload them through the system's web interface. The server receives the uploaded files and temporarily stores them, checking the file format and size.

[0973] Emotion Recognition Procedure

[0974] The server starts the emotion engine when the user uploads a slide. The emotion engine used here is software that captures and analyzes the user's facial expressions and voice through a webcam and microphone. The captured data is sent to an emotion recognition module, which generates the user's emotion data, which includes emotional information such as joy, sadness, and surprise.

[0975] Content analysis procedure

[0976] The server passes the saved slide files to an image processing module, which uses OCR (Optical Character Recognition) technology to extract the text from the slides. The extracted text data, including numbers and special characters, is saved as text data. This data is then passed to a natural language processing (NLP) module for content analysis. Specifically, topic modeling technology is used to extract the main themes from the slides, and contextual analysis is used to understand the main points and key points of the text.

[0977] Uniform design generation procedure

[0978] After analysis, the server selects an appropriate uniform design template from its template library. The optimal template is automatically selected based on the emotional data acquired by the emotion recognition module. For example, if the user is nervous, a calming color scheme and design will be applied. The server then applies the extracted text and images to the template, unifying the font style, size, and color scheme, and adjusting the page layout. This generates a new uniform slide.

[0979] Export and provisioning procedures

[0980] The server exports the newly generated slides in PDF or image format, temporarily saves the file, and then generates a download link via which users can download and use the generated slides.

[0981] Examples of concrete examples and prompts

[0982] For example, when a user uploads presentation slides for a university evaluation system, an emotion recognition engine may detect emotional data indicating "nervousness" from the user's facial expressions and tone of voice. The server receives the slides uploaded by the user, extracts text using OCR technology, and analyzes the content using an NLP module. It then applies a design template to reduce tension and standardizes fonts and color schemes to generate new slides. The final slides are exported as PDFs for users to download.

[0983] An example prompt is:

[0984] "Create a presentation slide design template for when users are feeling down"

[0985] "Generate slide designs that reflect the emotion of joy"

[0986] By using such prompts, it becomes possible to design effective slides that take emotional data into account.

[0987] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0988] Step 1:

[0989] Users select slide files (PDF or image format) from their terminal and upload them through the system's web interface.

[0990] Specifically, the user opens a browser, accesses the system's web page, selects a file, and presses the upload button.

[0991] Input: Slide file selected from the user's device

[0992] Output: Uploaded slide files are transferred to the server.

[0993] Step 2:

[0994] The server receives the uploaded slide files and temporarily stores them.

[0995] Specifically, the server verifies the file format and size and stores it in the appropriate folder.

[0996] Input: Uploaded slide files

[0997] Output: Temporarily saved slide files

[0998] Step 3:

[0999] The server starts the emotion engine when the slides are uploaded.

[1000] Specifically, the server captures the user's facial expressions and voice via a webcam and microphone connected to the user's device.

[1001] Input: User's facial expression data and voice data

[1002] Output: Obtained user emotion data

[1003] Step 4:

[1004] The server passes the saved slide files to an image processing module, which uses OCR technology to extract the text in the slides.

[1005] Specifically, the server analyzes each page of the slides and performs character recognition.

[1006] Input: Temporarily saved slide file

[1007] Output: Extracted text data

[1008] Step 5:

[1009] The server passes the extracted text data to a natural language processing (NLP) module for content analysis.

[1010] Specifically, the server uses topic modeling technology to extract themes from the slide content and understands the arguments and key points through contextual analysis.

[1011] Input: Extracted text data

[1012] Output: Analyzed content data (theme, argument, key points)

[1013] Step 6:

[1014] The server considers the emotion data obtained by the emotion recognition module and selects an appropriate uniform design template from the template library.

[1015] Specifically, the server searches the template library and automatically selects the most suitable template based on the emotion information.

[1016] Input: Analyzed content data, emotion data

[1017] Output: Selected design template

[1018] Step 7:

[1019] The server generates a new slide using the selected design template.

[1020] Specifically, the server applies text and images to templates, unifies font styles, sizes, and colors, and adjusts page layouts.

[1021] Input: Selected design template, parsed content data

[1022] Output: The new slide file that is generated.

[1023] Step 8:

[1024] The server exports the newly generated slides in PDF or image format and provides them to the user.

[1025] Specifically, the server converts the generated slides into a specified format, temporarily stores them, and generates a download link.

[1026] Input: The new slide file that was generated

[1027] Output: Exported slide files, generated download link

[1028] Step 9:

[1029] The user downloads the uniformed slide file via the provided download link.

[1030] Specifically, the user clicks on the link provided by the system and saves the file to their device.

[1031] Input: Generated download link

[1032] Output: Slide files downloaded to the user's device

[1033] (Application example 2)

[1034] 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."

[1035] In virtual stores and online presentations, a system is needed to provide personalized content in real time that responds to user emotions. Conventional slide production systems and advertising display systems are not designed with user emotions in mind, and the challenge was how to utilize emotional data to improve the user experience.

[1036] 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 receiving slides in image or PDF format, means for extracting text from the content of the received slides using optical character recognition (OCR), means for content analysis of the extracted text using natural language processing (NLP), means for generating new slides by applying a standardized design template based on the analysis results and user emotion data, means for exporting the generated slides in PDF or image format and providing them to the user, means for analyzing the user's facial expressions and tone of voice to generate emotion data, and means for generating and displaying advertisements according to the user's emotions. This makes it possible to provide personalized content according to the user's emotions in real time.

[1037] "Slides" are images or PDF format data used as presentation materials.

[1038] Optical character recognition (OCR) is a technology that detects characters in an image and extracts them as text data.

[1039] "Natural language processing (NLP)" is a technology that analyzes text data and understands and interprets its content.

[1040] A "uniform design template" is a template that provides a uniform layout and style used to ensure design consistency.

[1041] "Emotion data" is emotional information analyzed from the user's facial expressions, tone of voice, and the like.

[1042] "Advertisement generation means" is a technology that generates optimal advertisements according to the user's interests and emotions.

[1043] "Server" means the computer system that has the central function of data processing, receiving, analyzing, applying designs, generating advertisements and exporting slides.

[1044] "User facial expression analysis" is a technology that uses a camera to capture a user's face and infer their emotions from their facial expressions.

[1045] "Voice tone analysis" is a technology that uses a microphone to collect a user's voice and analyzes emotions from the tone of the voice.

[1046] "Personalized content" is content that is custom-made to suit a user's individual needs and feelings.

[1047] A "virtual store" is a virtual sales location that offers products and services over the Internet.

[1048] To implement this invention, it is necessary to build a system that receives slides uploaded by users, regenerates them with a standardized design, and recognizes users' emotions and reflects them in designs and advertisements. A specific embodiment of this system is shown below.

[1049] Hardware and Software Configuration

[1050] First, users use a web interface to upload slide files (image or PDF format) from their device, which then sends the uploaded slides to the server.

[1051] The server has the following features:

[1052] 1. Receive and temporarily store the slides.

[1053] 2. Use OCR technology to extract text from the slide content (specifically, an OCR engine such as Tesseract).

[1054] 3. Analyze the extracted text with a natural language processing (NLP) module (for example, using a library such as SpaCy or NLTK).

[1055] 4. Launch an emotion recognition engine to analyze the user's facial expressions and voice tone to generate emotion data (using a facial recognition library such as OpenCV or dlib, and a voice analysis engine).

[1056] 5. Based on the analysis results and sentiment data, a uniform design template is applied to generate new slides.

[1057] 6. Generate and display ads based on user emotions (e.g., using ad templates that correspond to specific topics or emotions).

[1058] 7. Export the newly generated slides as PDF or images and provide them to the user.

[1059] Processing flow

[1060] When a user uploads a slide, the server receives and temporarily stores the slide. Then, OCR technology is used to extract the text from the slide. The extracted text is then analyzed by an NLP module to identify the main themes and arguments of the slide.

[1061] The server then activates an emotion recognition engine to analyze the user's facial expressions and tone of voice, generating emotional data for the user. Based on this emotional data and the analysis of the slides, an optimal design template is selected and a new slide is generated.

[1062] The generated slides have a uniform font style, size, color scheme, and layout, and personalized advertisements are generated based on the user's emotional data and displayed on the user's smart glasses or device.

[1063] Specific examples

[1064] For example, if a user visits a virtual store and wears smart glasses, the camera and microphone analyze the user's facial expressions and voice to obtain emotional information. If the user is smiling, the emotional data is analyzed as "joy." Next, the text of the slides uploaded by the user is analyzed using OCR and NLP technology, and the main theme is determined to be "Newly Released 4K TVs."

[1065] Example prompt sentence:

[1066] "Text extracted from slide: 'New 4K TVs'"

[1067] "User Emotion: 'Delight'"

[1068] "Generates ad: 'Special offer now! Check out our 4K TV!'"

[1069] Based on this information, the server generates advertising content that is likely to attract the user's interest and displays it on the smart glasses, improving the user experience.

[1070] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1071] Step 1:

[1072] Users upload slide files (image or PDF format) from their own devices through a web interface. The uploaded slide files are received by the server and temporarily stored.

[1073] Input: Slide files (image or PDF format) uploaded by the user

[1074] Output: Slide files saved on the server

[1075] Step 2:

[1076] The server uses OCR technology to extract the text from the uploaded slides. Specifically, it uses an OCR engine such as Tesseract to analyze the character information from each page of the slides and extract it as text data.

[1077] Input: Slide files stored on the server

[1078] Data processing: Analysis of character information using OCR technology

[1079] Output: Extracted text data

[1080] Step 3:

[1081] The server then analyzes the extracted text data using a natural language processing (NLP) module, which uses NLP techniques to identify the main themes and arguments of the text. Specifically, it uses libraries such as SpaCy and NLTK.

[1082] Input: Text data extracted by OCR technology

[1083] Data processing: Text analysis using NLP technology

[1084] Output: Data on the analyzed themes and arguments

[1085] Step 4:

[1086] The server starts an emotion recognition engine to analyze the user's facial expressions and voice tone. Libraries such as OpenCV and dlib are used for facial expression analysis, and a dedicated voice analysis engine is used for voice analysis. The user's emotional data is generated.

[1087] Input: User's facial expression (camera image) and voice (microphone audio)

[1088] Data processing: Emotion recognition by facial expression analysis and voice tone analysis

[1089] Output: Generated emotion data

[1090] Step 5:

[1091] Based on the analysis results and emotion data, the server selects a uniform design template and generates new slides, which have a uniform font style, size, color scheme, and layout.

[1092] Input: NLP analysis data and emotion data

[1093] Data processing: Template selection and application

[1094] Output: The newly generated slide

[1095] Step 6:

[1096] The server exports the new slides in PDF or image format and generates a download link to provide to the user, who can use this link to download the slides.

[1097] Input: The newly generated slide

[1098] Data processing: Export in PDF or image format

[1099] Output: Download link

[1100] Step 7:

[1101] The server generates personalized advertisements based on the user's emotions and displays them on the user's device or smart glasses. The advertisement generation engine generates optimal advertisement content based on the emotion data and slide theme data.

[1102] Input: Sentiment and Theme Data

[1103] Data processing: Ad content generation by the ad generation engine

[1104] Output: Generated personalized ads

[1105] 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.

[1106] 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.

[1107] 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.

[1108] [Fourth embodiment]

[1109] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1110] 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.

[1111] 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).

[1112] 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.

[1113] 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.

[1114] 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).

[1115] 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.

[1116] 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.

[1117] 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.

[1118] 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.

[1119] 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.

[1120] 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.

[1121] 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."

[1122] Overall system configuration

[1123] The system of this invention receives slides uploaded by users and executes a process to regenerate them with a standardized design. In this system, the server, terminals, and users each have specific roles and work together.

[1124] Upload procedure

[1125] Users select slide files (PDF or image format) from their own devices and upload them through the system's web interface. The server receives the uploaded files and temporarily stores them.

[1126] Content analysis procedure

[1127] The server passes the saved slide files to the image processing module, which uses OCR technology to extract the text from the slides, identifying the text on each slide page and extracting text data, including numbers and special characters.

[1128] The server then passes the extracted text data to a natural language processing (NLP) module for content analysis, using topic modeling techniques to extract the main themes of the slide content and conducting contextual analysis to understand the argument and key information in the text.

[1129] Uniform design generation procedure

[1130] After the analysis is complete, the server selects an appropriate uniform design template from the template library. When placing text and images into the template, the server uniforms the following design elements:

[1131] Font style and font size

[1132] Text color scheme

[1133] Image placement and size

[1134] Redrawing graphs and charts

[1135] A consistent design is applied to all slide pages, creating layouts that maximize visibility and readability.

[1136] Export and provisioning procedures

[1137] The server exports the newly generated slides as a PDF or image file, temporarily stores the file, and then generates a download link for the user, who then downloads the flattened slide file via this link.

[1138] Specific examples

[1139] For example, consider a case where a user uploads their presentation slides to a university evaluation system. The server receives the slides and uses OCR to extract the text from them. It then analyzes the extracted text with an NLP module to identify the main themes and arguments of the presentation. It then applies a uniform design template to generate new slides with consistent font styles, colors, and other elements. Finally, the server exports the new slides as PDFs, which the user can download and submit to the evaluation committee.

[1140] This system not only allows for fair evaluation of the quality of ideas regardless of the design, but also reduces the time required to create designs and supports efficient presentation creation.

[1141] The processing flow will be explained below.

[1142] Step 1:

[1143] The user selects the slide file (PDF or image format) from their device and starts uploading. The file is sent through the web interface and reaches the server.

[1144] Step 2:

[1145] The server receives the uploaded slide files and temporarily stores them in a designated directory or database.

[1146] Step 3:

[1147] The server passes the saved slide files to an image processing module, which checks the file format (PDF or image) and splits them into individual pages if necessary.

[1148] Step 4:

[1149] The server uses OCR (Optical Character Recognition) technology to extract the text from the slides, performs image analysis on each page, and generates text data.

[1150] Step 5:

[1151] The server passes the extracted text data to a natural language processing (NLP) module to perform content analysis, using topic modeling to extract the main themes of the slides and semantic analysis to understand the gist of the text.

[1152] Step 6:

[1153] The server selects an appropriate uniform design template from the template library based on analysis results and automatically determines the template that best suits the slide content.

[1154] Step 7:

[1155] The server applies the extracted text and images to a template, adjusting font style, font size, color scheme, and page layout.

[1156] Step 8:

[1157] The server redraws graphs and charts as needed to ensure a consistent visual design, and images are sized and positioned appropriately according to the template.

[1158] Step 9:

[1159] The server exports the newly generated slides as PDFs or images, resulting in a uniformly designed slide file.

[1160] Step 10:

[1161] The server temporarily stores the generated slide files and generates a download link, which is notified to the user.

[1162] Step 11:

[1163] Users can access the provided download link and download the uniformed slide file, which can then be used for evaluation or presentation.

[1164] Example 1

[1165] 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."

[1166] Maintaining consistency in design and layout when creating slides is a significant burden for users, and is particularly time-consuming and laborious for presentations that contain many slides. Furthermore, users without specialized design knowledge or tools have difficulty creating attractive slides. To address this issue, it is necessary to provide a system that automatically analyzes the content of slides and regenerates them into a consistent design.

[1167] 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.

[1168] In this invention, the server includes: means for a user to select a slide file from their own terminal and upload it to the system; means for the server to receive and temporarily store the uploaded slide file; means for the server to pass the saved slide file to an image processing module and extract text from the slide using optical character recognition; means for the server to pass the extracted text data to a natural language processing module and perform content analysis; means for the server to select an appropriate uniform design template from a template library based on the analysis result and generate a new slide; and means for the server to export the generated slide in PDF or image format and provide it to the user. This enables users to efficiently create consistent slides with excellent visibility and readability without much effort.

[1169] "User" refers to a person who utilizes the system to upload slide files and receive regenerated slides.

[1170] "Device" means the device used by a User to access the System and upload Slide Files, including, but not limited to, a PC, tablet, or smartphone.

[1171] "Server" refers to a central computer system for slide generation that receives, stores, analyzes, and regenerates slide files.

[1172] "Slide file" means an electronic file containing elements of a presentation, either in PDF or image format.

[1173] "Web interface" refers to a web page or web application that allows a user to access and operate the system via a browser.

[1174] "Image Processing Module" refers to software or libraries for analyzing text and images within slide files.

[1175] "Optical Character Recognition (OCR)" refers to the technology that mechanically or electronically recognizes text in an image and converts it into digital text data.

[1176] "Text data" refers to information extracted by OCR and stored as a string of characters.

[1177] A "natural language processing (NLP) module" refers to software or libraries for analyzing text data and understanding its meaning and context.

[1178] "Template library" refers to a database or group of files that stores multiple slide design templates.

[1179] A "standardized design template" refers to a presentation slide template that is constructed with a consistent design style.

[1180] "Export" refers to outputting a file in a specific format after processing is complete.

[1181] "Download link" refers to the URL for obtaining a file from the server to the user's device.

[1182] The system of this invention receives slides uploaded by users and executes a process to regenerate them with a standardized design. In this system, the server, terminals, and users each have specific roles and work together.

[1183] First, a user selects a slide file (PDF or image format) from their device and uploads it through the system's web interface. The device used by the user can be a PC, tablet, smartphone, etc. Once the upload is complete, the server receives the slide file and temporarily stores it. This storage is performed in a specific folder in a temporary directory (e.g., / tmp), and the file name is renamed with a unique identifier (e.g., UUID) to avoid duplication.

[1184] The server then passes the saved slide files to an image processing module (e.g., Tesseract OCR) and uses OCR technology to extract the text from the slides. The OCR process identifies the text on each slide page and extracts the text data, including numbers and special characters. The OCR process uses PNG images of each page as input, and outputs the text data for each page in JSON format.

[1185] The server passes the extracted text data to a natural language processing (NLP) module (e.g., SpaCy or NLTK) for content analysis. Topic modeling techniques (e.g., Latent Dirichlet Allocation) are used to extract the main themes of the slide content, and contextual analysis (e.g., dependency analysis) is performed to understand the argument and key information of the text.

[1186] After the analysis is complete, the server selects an appropriate uniform design template (e.g., Canva or a home-grown template) from the template library. When placing text and images on the template, it standardizes the following design elements:

[1187] Font style and font size (e.g., Arial 12pt)

[1188] Text color scheme (e.g., black with blue highlight)

[1189] Image placement and size (e.g., centered, 80% width)

[1190] Redrawing graphs and charts (e.g., using Excel or D3.js)

[1191] The server loads the template file and automatically runs a script (e.g., Python-PowerPoint) to rearrange each element of the slide based on the analysis results. A consistent design is applied to all slide pages, and a layout is generated that maximizes visibility and readability.

[1192] Finally, the server exports the newly generated slides in PDF or image format. This file is saved in a temporary directory, and the server generates a download link and notifies the user. The user can download the new slides from the provided link and use them as a presentation.

[1193] Specific examples

[1194] For example, consider a user uploading their presentation slides to a university evaluation system. Here's the process:

[1195] 1. The user uploads the presentation slides (e.g., PDF format) for the evaluation system to the system.

[1196] 2. The server receives and stores the file.

[1197] 3. The server runs the saved file through Tesseract OCR to extract the text for each page.

[1198] 4. The server analyzes the extracted text using SpaCy to understand the main themes and arguments of the presentation.

[1199] 5. The server selects a uniform design for the evaluation presentation from its template library and regenerates it with consistent font styles, color schemes, etc.

[1200] 6. The server exports the newly generated slides in PDF format and provides the user with a download link.

[1201] 7. Users can download the new slides from this link and submit them to the evaluation committee.

[1202] Prompt Sentence Examples

[1203] The following prompts are examples of inputs to a generative AI model (e.g., GPT-4):

[1204] Please explain in detail the process steps of the system that regenerates presentation slides uploaded by users with a standardized design. Please specify the roles of the user, server, and device, and explain each process step in detail.

[1205] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1206] Step 1:

[1207] The user selects a slide file (PDF or image format) from their own device and uploads it through the system's web interface. Specifically, a file selection dialog appears in the browser, the user selects the appropriate file, and clicks the "Upload" button. The input is the slide file, and the output is a file upload request to the system.

[1208] Step 2:

[1209] The server receives the uploaded slide file and temporarily stores it. The file is saved in a specific folder in a temporary directory (e.g., / tmp), and the file name is renamed with a unique identifier such as a UUID to avoid duplication. The input is a file upload request from the user, and the output is the saved slide file.

[1210] Step 3:

[1211] The server passes the saved slide files to an image processing module (e.g., Tesseract OCR) and extracts the text from the slides using OCR technology. Specifically, the server invokes the Tesseract command and passes the PNG-formatted images for each page as input. The OCR process outputs the text data for each page in JSON format. The input is the saved slide files, and the output is the extracted text data.

[1212] Step 4:

[1213] The server passes the extracted text data to a natural language processing (NLP) module (e.g., SpaCy or NLTK) for content analysis. Specifically, the server loads the text data into the NLP module and uses topic modeling techniques (e.g., Latent Dirichlet Allocation) and context analysis (e.g., dependency analysis) to identify major themes, arguments, and important information. The input is the text data obtained by OCR, and the output is the analysis results.

[1214] Step 5:

[1215] The server selects an appropriate uniform design template from the template library based on the analysis results. The template file is loaded, and a script (e.g., Python-PowerPoint) is automatically executed to rearrange each element of the slide based on the analysis results. The input is the analysis results, and the output is new slide data with the template applied.

[1216] Step 6:

[1217] The server exports the regenerated slides in PDF or image format. It passes the generated slide data to a PDF generation library (e.g., ReportLab) to create a PDF file. This file is saved in a temporary directory. The input is the new slide data with the template applied, and the output is the exported PDF file.

[1218] Step 7:

[1219] The server generates a download link for the new slide file and notifies the user of the link. Specifically, it converts the file path into a URL and provides it to the user via a notification email or web notification. The input is the exported PDF file, and the output is the download link provided to the user.

[1220] Step 8:

[1221] The user downloads the uniformed slide file via this link. Specifically, they click the provided URL to download the slide file and save it on their device. The input is the download link, and the output is the downloaded slide file.

[1222] (Application example 1)

[1223] 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."

[1224] In today's brick-and-mortar stores, product explanation materials, advertising materials, menus, and other items have different designs for each store, creating a visual inconsistency problem. Furthermore, using specialized software to unify designs requires specialized knowledge, making it difficult for store staff to use. This increases the cost and effort required to achieve a unified design, and risks damaging the brand image of the entire store. The present invention aims to solve these problems by providing a system that allows store staff to easily regenerate slides and advertising materials into a unified design.

[1225] 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.

[1226] In this invention, the server includes a means for receiving slides in image or PDF format, a means for extracting text from the content of the received slides using optical character recognition (OCR), and a means for content analysis of the extracted text using natural language processing (NLP). This allows store staff to easily upload slides and advertising materials using smartphones or tablets and regenerate them with a unified and consistent design. The system also unifies the generated slides with a unique design and regenerates them into in-store materials and advertising materials, thereby maintaining visual consistency throughout the store.

[1227] "Slides" refers to images or PDF pages provided in the form of presentations, advertisements, handouts, etc.

[1228] "Image Format" refers to bitmap image file formats such as JPEG, PNG, and GIF.

[1229] "PDF Format" refers to a file format known as Portable Document Format (PDF) for storing and sharing documents electronically.

[1230] "Optical Character Recognition (OCR)" refers to the technology that recognizes characters in an image and converts them into digital text data.

[1231] "Natural Language Processing (NLP)" refers to the technology that enables computers to understand, interpret, and generate human language.

[1232] "Standardized design template" refers to a design template that is standardized with specific font styles, font sizes, color schemes, and layouts.

[1233] "Smartphone" and "tablet" refer to a mobile information terminal equipped with a mobile operating system, capable of connecting to the Internet and using applications.

[1234] A "server" refers to a computer system that stores files and processes information via a network.

[1235] "Export" refers to the operation of saving or outputting generated data in a specified file format.

[1236] The system of this invention aims to standardize the design of materials and advertising materials used in brick-and-mortar stores to create a professional impression. The system allows users to upload slides using a smartphone or tablet and regenerates them into a standardized design.

[1237] Hardware and Software Configuration

[1238] The system uses the following hardware and software:

[1239] Smartphones and tablets: Mobile devices that perform upload operations.

[1240] Server: The computer system that handles all processes such as file storage, OCR processing, NLP analysis, design template application, and exporting.

[1241] OCR engine (pytesseract): Optical character recognition technology for extracting text from images.

[1242] NLP module (spaCy): Natural language processing technology to analyze extracted text and understand key themes and arguments.

[1243] PDF processing library (pdf2image, PIL): A library for converting PDF files to images and performing OCR.

[1244] Overview of data processing and calculation

[1245] Users select slide files (PDF or image format) from their smartphones or tablets and upload them through the system's web interface, where the server receives and temporarily stores them.

[1246] Next, the server passes the saved slide files to the image processing module, which uses an OCR engine (pytesseract) to extract the text from the slides, identifying the text on each slide page and obtaining text data including numbers and special characters.

[1247] The server then passes the extracted text data to a natural language processing module (spaCy) for content analysis, using topic modeling techniques to extract the main themes of the slides and then conducting contextual analysis to understand the text's arguments and key information.

[1248] After the analysis is complete, the server selects an appropriate uniform design template from the template library. When placing text and images into the template, the server uniforms the following design elements:

[1249] Font style and font size

[1250] Text color scheme

[1251] Image placement and size

[1252] Redrawing graphs and charts

[1253] A consistent design is applied to all slide pages, creating layouts that maximize visibility and readability.

[1254] Specific examples

[1255] For example, consider the case where a user uploads a slide file called "New Product Information.pdf." The server receives the slides uploaded by the user and uses an OCR engine to extract the text within the slides. Next, the extracted text is analyzed using an NLP module to identify the main themes and arguments of the presentation. A standardized design template is then applied to generate new slides with consistent font styles, color schemes, etc. Finally, the server exports the new slides in PDF format, which the user can download and use for in-store advertising materials.

[1256] Prompt Sentence Examples

[1257] "Please upload the new product information document.pdf and convert it into a unified design. The file has 10 pages, and each page contains different product information. Please unify the font style and make the design easy to read."

[1258] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1259] Step 1:

[1260] The user selects a slide file (PDF or image format) on a smartphone or tablet and uploads it through the system's web interface. At this stage, the file's format and content are not important and it is sent to the server through the specified interface. The input is the slide file, and the output is the identifier (ID) of the uploaded file.

[1261] Step 2:

[1262] The server receives the uploaded slide file and temporarily stores it. The saved file is stored in the file system and used for subsequent processing. The input is the identifier of the uploaded file, and the output is the path of the saved file.

[1263] Step 3:

[1264] The server passes the saved slide files to the image processing module, which uses an OCR engine (pytesseract) to extract the text in the slides. This process converts the PDF file into an image and identifies the text on each page. The input is the path of the saved file, and the output is the extracted text data.

[1265] Step 4:

[1266] The server passes the extracted text data to a natural language processing (NLP) module (spaCy) for content analysis. This process uses topic modeling techniques to extract major themes and understand the text's argument and important information. The input is text data, and the output is analyzed topics and contextual information.

[1267] Step 5:

[1268] The server selects an appropriate uniform design template from the template library and places the extracted and analyzed text and images into the template. In this step, it standardizes the font style, font size, color scheme, image placement, etc. The input is the analysis result and the template, and the output is new slide data with a uniform design.

[1269] Step 6:

[1270] The server exports the generated slides in PDF or image format and provides them to the user. In this process, a download link for the exported file is generated and notified to the user. The input is the new slide data with a unified design, and the output is the download link.

[1271] Specific examples

[1272] For example, if a user uploads "New Product Presentation.pdf," the server processes the file with OCR, extracts text, performs NLP analysis, applies a uniform design template, and then exports the new slides in PDF format and provides a download link to the user.

[1273] 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.

[1274] Overall system configuration

[1275] This system receives slides uploaded by users, reproduces them with a standardized design, and recognizes the user's emotions and reflects them in the slide design. In this system, the server, terminal, and user each have specific roles and work together.

[1276] Upload procedure

[1277] Users select slide files (PDF or image format) from their own devices and upload them through the system's web interface. The server receives the uploaded files and temporarily stores them.

[1278] Emotion Recognition Procedure

[1279] The server activates an emotion engine to recognize the user's emotion when a slide is uploaded or when analyzing the slide's content. The emotion engine analyzes the user's facial expressions, voice tone, text input, etc., and generates the user's emotion data. This data includes emotion information such as joy, sadness, and surprise.

[1280] Content analysis procedure

[1281] The server passes the saved slide files to the image processing module, which uses OCR technology to extract the text from the slides, identifying the text on each slide page and extracting text data, including numbers and special characters.

[1282] The server then passes the extracted text data to a natural language processing (NLP) module for content analysis, using topic modeling techniques to extract the main themes of the slide content and conducting contextual analysis to understand the argument and key information in the text.

[1283] Uniform design generation procedure

[1284] After the analysis is complete, the server selects an appropriate uniform design template from the template library. The most suitable template is automatically determined based on the emotional information detected by the user's emotion engine. For example, if the user is depressed, a calm color scheme and design will be applied.

[1285] The server applies the extracted text and images to a template, adjusting font style, font size, color scheme, and page layout. It may also adjust design elements based on emotion data.

[1286] Export and provisioning procedures

[1287] The server exports the newly generated slides as a PDF or image file, temporarily stores the file, and then generates a download link for the user, who then downloads the flattened slide file via this link.

[1288] Specific examples

[1289] For example, consider a case where a user uploads their presentation slides to a university evaluation system. During upload, an emotion engine analyzes the user's facial expressions and tone of voice to obtain emotional data indicating nervousness. The server receives the slides uploaded by the user and extracts the text within them using OCR. Next, the extracted text is analyzed using an NLP module to identify the main themes and arguments of the presentation. Taking the emotional data into account, a calming design template is applied to reduce tension. New slides are generated with a consistent font style and color scheme. Finally, the server exports the new slides in PDF format, which the user can download and submit to the evaluation committee.

[1290] This system not only allows for fair evaluation of the quality of ideas regardless of their design, but also allows for more effective presentations by taking into account the user's emotions.

[1291] The processing flow will be explained below.

[1292] Step 1:

[1293] Users select slide files (PDF or image format) from their device and initiate the upload through the web interface.

[1294] Step 2:

[1295] The server receives the uploaded slide files and temporarily stores them in a designated directory or database.

[1296] Step 3:

[1297] The server passes the saved slide files to an image processing module, which, in the case of PDF format, converts each individual page into an image so that each page can be processed independently.

[1298] Step 4:

[1299] The server starts an emotion engine while the user is uploading. The emotion engine analyzes the video captured from the user's webcam, the audio captured from the microphone, and the text input to recognize the user's emotional state (e.g., joy, sadness, surprise, tension).

[1300] Step 5:

[1301] The server uses OCR (Optical Character Recognition) technology to extract text from slides. It performs image analysis on each page and generates text data. It performs accurate text extraction, taking into account differences in font and character size.

[1302] Step 6:

[1303] The server passes the extracted text data to a natural language processing (NLP) module to perform content analysis, using topic modeling to extract the main themes of the slides and semantic analysis to understand the argument and key information of the text.

[1304] Step 7:

[1305] The server selects an appropriate uniform design template from the template library, taking into account the emotional information recognized by the user's emotion engine. For example, if the user is nervous, a template with a calm color scheme will be selected.

[1306] Step 8:

[1307] The server applies the extracted text and images to a template, adjusting the font style, font size, and color scheme, and adjusting design elements within the template based on the emotion data.

[1308] Step 9:

[1309] The server redraws graphs and charts as needed to maintain a consistent visual design, including adjusting image size and placement.

[1310] Step 10:

[1311] The server exports the newly generated slides as PDFs or images, creating a uniformly designed slide file.

[1312] Step 11:

[1313] The server temporarily stores the generated slide files, generates a download link, and notifies the user via email or a web interface.

[1314] Step 12:

[1315] Users can access the provided download link and download the uniformed slide file, which can then be used for evaluation or presentation.

[1316] Example 2

[1317] 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."

[1318] Conventional slide generation systems not only lack design consistency, but also have the problem of being unable to propose appropriate designs that take the user's emotional state into account. Furthermore, while they are capable of analyzing the content of text and images within slides, there are no systems that can understand the user's emotions and modify the design based on those emotions. Furthermore, there is a need to achieve more effective communication in presentations by using designs that reflect the user's emotions.

[1319] 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.

[1320] In this invention, the server includes means for receiving slides in image or PDF format, means for extracting text from the content of the received slides using optical character recognition (OCR), means for content analysis of the extracted text using natural language processing (NLP), means for recognizing and acquiring user emotion data, means for selecting a standardized design template based on the acquired emotion data, means for generating new slides using the selected template, and means for exporting the generated slides in PDF or image format and providing them to the user, thereby enabling effective slide design that reflects the user's emotions.

[1321] "Slides" are images or PDF documents used in presentations and information presentations.

[1322] Optical character recognition (OCR) is a technology that extracts character information from an image as digital text.

[1323] "Natural language processing (NLP)" is a technology that analyzes the content of text data and understands the subject matter and context.

[1324] "Emotion data" refers to emotional information recognized from the user's facial expression, tone of voice, text input, and the like.

[1325] A "template library" is a database that collects slide design templates.

[1326] A "standardized design template" is a slide template with a consistent overall design style and format.

[1327] "Export" means converting data or files into a specific format and outputting it to an external device.

[1328] A "user" is a person who uses the system to upload slides and receive generated slides.

[1329] A "server" is a computer system that manages the overall processing of the system and stores, analyzes, and generates various types of data.

[1330] This invention is a system that receives slides uploaded by users, reproduces them with a standardized design, and recognizes the user's emotions and reflects them in the slide design. The system works in cooperation with the server, terminal, and user, each of which has a specific role.

[1331] Overall structure

[1332] The hardware and software used to implement the invention are as follows: The server receives and temporarily stores slide files uploaded from the user's device. It also analyzes the slide content using OCR technology and a natural language processing (NLP) module. Furthermore, it acquires the user's emotional data using an emotion recognition module, selects an emotion-based design template from a template library, generates new slides, and exports them.

[1333] Upload procedure

[1334] Users select slide files (PDF or image format) from their own devices and upload them through the system's web interface. The server receives the uploaded files and temporarily stores them, checking the file format and size.

[1335] Emotion Recognition Procedure

[1336] The server starts the emotion engine when the user uploads a slide. The emotion engine used here is software that captures and analyzes the user's facial expressions and voice through a webcam and microphone. The captured data is sent to an emotion recognition module, which generates the user's emotion data, which includes emotional information such as joy, sadness, and surprise.

[1337] Content analysis procedure

[1338] The server passes the saved slide files to an image processing module, which uses OCR (Optical Character Recognition) technology to extract the text from the slides. The extracted text data, including numbers and special characters, is saved as text data. This data is then passed to a natural language processing (NLP) module for content analysis. Specifically, topic modeling technology is used to extract the main themes from the slides, and contextual analysis is used to understand the main points and key points of the text.

[1339] Uniform design generation procedure

[1340] After analysis, the server selects an appropriate uniform design template from its template library. The optimal template is automatically selected based on the emotional data acquired by the emotion recognition module. For example, if the user is nervous, a calming color scheme and design will be applied. The server then applies the extracted text and images to the template, unifying the font style, size, and color scheme, and adjusting the page layout. This generates a new uniform slide.

[1341] Export and provisioning procedures

[1342] The server exports the newly generated slides in PDF or image format, temporarily saves the file, and then generates a download link via which users can download and use the generated slides.

[1343] Examples of concrete examples and prompts

[1344] For example, when a user uploads presentation slides for a university evaluation system, an emotion recognition engine may detect emotional data indicating "nervousness" from the user's facial expressions and tone of voice. The server receives the slides uploaded by the user, extracts text using OCR technology, and analyzes the content using an NLP module. It then applies a design template to reduce tension and standardizes fonts and color schemes to generate new slides. The final slides are exported as PDFs for users to download.

[1345] An example prompt is:

[1346] "Create a presentation slide design template for when users are feeling down"

[1347] "Generate slide designs that reflect the emotion of joy"

[1348] By using such prompts, it becomes possible to design effective slides that take emotional data into account.

[1349] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1350] Step 1:

[1351] Users select slide files (PDF or image format) from their terminal and upload them through the system's web interface.

[1352] Specifically, the user opens a browser, accesses the system's web page, selects a file, and presses the upload button.

[1353] Input: Slide file selected from the user's device

[1354] Output: Uploaded slide files are transferred to the server.

[1355] Step 2:

[1356] The server receives the uploaded slide files and temporarily stores them.

[1357] Specifically, the server verifies the file format and size and stores it in the appropriate folder.

[1358] Input: Uploaded slide files

[1359] Output: Temporarily saved slide files

[1360] Step 3:

[1361] The server starts the emotion engine when the slides are uploaded.

[1362] Specifically, the server captures the user's facial expressions and voice via a webcam and microphone connected to the user's device.

[1363] Input: User's facial expression data and voice data

[1364] Output: Obtained user emotion data

[1365] Step 4:

[1366] The server passes the saved slide files to an image processing module, which uses OCR technology to extract the text in the slides.

[1367] Specifically, the server analyzes each page of the slides and performs character recognition.

[1368] Input: Temporarily saved slide file

[1369] Output: Extracted text data

[1370] Step 5:

[1371] The server passes the extracted text data to a natural language processing (NLP) module for content analysis.

[1372] Specifically, the server uses topic modeling technology to extract themes from the slide content and understands the arguments and key points through contextual analysis.

[1373] Input: Extracted text data

[1374] Output: Analyzed content data (theme, argument, key points)

[1375] Step 6:

[1376] The server considers the emotion data obtained by the emotion recognition module and selects an appropriate uniform design template from the template library.

[1377] Specifically, the server searches the template library and automatically selects the most suitable template based on the emotion information.

[1378] Input: Analyzed content data, emotion data

[1379] Output: Selected design template

[1380] Step 7:

[1381] The server generates a new slide using the selected design template.

[1382] Specifically, the server applies text and images to templates, unifies font styles, sizes, and colors, and adjusts page layouts.

[1383] Input: Selected design template, parsed content data

[1384] Output: The new slide file that is generated.

[1385] Step 8:

[1386] The server exports the newly generated slides in PDF or image format and provides them to the user.

[1387] Specifically, the server converts the generated slides into a specified format, temporarily stores them, and generates a download link.

[1388] Input: The new slide file that was generated

[1389] Output: Exported slide files, generated download link

[1390] Step 9:

[1391] The user downloads the uniformed slide file via the provided download link.

[1392] Specifically, the user clicks on the link provided by the system and saves the file to their device.

[1393] Input: Generated download link

[1394] Output: Slide files downloaded to the user's device

[1395] (Application example 2)

[1396] 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."

[1397] In virtual stores and online presentations, a system is needed to provide personalized content in real time that responds to user emotions. Conventional slide production systems and advertising display systems are not designed with user emotions in mind, and the challenge was how to utilize emotional data to improve the user experience.

[1398] 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 receiving slides in image or PDF format, means for extracting text from the content of the received slides using optical character recognition (OCR), means for content analysis of the extracted text using natural language processing (NLP), means for generating new slides by applying a standardized design template based on the analysis results and user emotion data, means for exporting the generated slides in PDF or image format and providing them to the user, means for analyzing the user's facial expressions and tone of voice to generate emotion data, and means for generating and displaying advertisements according to the user's emotions. This makes it possible to provide personalized content according to the user's emotions in real time.

[1399] "Slides" are images or PDF format data used as presentation materials.

[1400] Optical character recognition (OCR) is a technology that detects characters in an image and extracts them as text data.

[1401] "Natural language processing (NLP)" is a technology that analyzes text data and understands and interprets its content.

[1402] A "uniform design template" is a template that provides a uniform layout and style used to ensure design consistency.

[1403] "Emotion data" is emotional information analyzed from the user's facial expressions, tone of voice, and the like.

[1404] "Advertisement generation means" is a technology that generates optimal advertisements according to the user's interests and emotions.

[1405] "Server" means the computer system that has the central function of data processing, receiving, analyzing, applying designs, generating advertisements and exporting slides.

[1406] "User facial expression analysis" is a technology that uses a camera to capture a user's face and infer their emotions from their facial expressions.

[1407] "Voice tone analysis" is a technology that uses a microphone to collect a user's voice and analyzes emotions from the tone of the voice.

[1408] "Personalized content" is content that is custom-made to suit a user's individual needs and feelings.

[1409] A "virtual store" is a virtual sales location that offers products and services over the Internet.

[1410] To implement this invention, it is necessary to build a system that receives slides uploaded by users, regenerates them with a standardized design, and recognizes users' emotions and reflects them in designs and advertisements. A specific embodiment of this system is shown below.

[1411] Hardware and Software Configuration

[1412] First, users use a web interface to upload slide files (image or PDF format) from their device, which then sends the uploaded slides to the server.

[1413] The server has the following features:

[1414] 1. Receive and temporarily store the slides.

[1415] 2. Use OCR technology to extract text from the slide content (specifically, an OCR engine such as Tesseract).

[1416] 3. Analyze the extracted text with a natural language processing (NLP) module (for example, using a library such as SpaCy or NLTK).

[1417] 4. Launch an emotion recognition engine (using a facial recognition library such as OpenCV or dlib, and a voice analysis engine) to analyze the user's facial expressions and voice tone to generate emotion data.

[1418] 5. Based on the analysis results and sentiment data, a uniform design template is applied to generate new slides.

[1419] 6. Generate and display ads based on user emotions (e.g., using ad templates that correspond to specific topics or emotions).

[1420] 7. Export the newly generated slides as PDF or images and provide them to the user.

[1421] Processing flow

[1422] When a user uploads a slide, the server receives and temporarily stores the slide. Then, OCR technology is used to extract the text from the slide. The extracted text is then analyzed by an NLP module to identify the main themes and arguments of the slide.

[1423] The server then activates an emotion recognition engine to analyze the user's facial expressions and tone of voice, generating emotional data for the user. Based on this emotional data and the analysis of the slides, an optimal design template is selected and a new slide is generated.

[1424] The generated slides have a uniform font style, size, color scheme, and layout, and personalized advertisements are generated based on the user's emotional data and displayed on the user's smart glasses or device.

[1425] Specific examples

[1426] For example, if a user visits a virtual store and wears smart glasses, the camera and microphone analyze the user's facial expressions and voice to obtain emotional information. If the user is smiling, the emotional data is analyzed as "joy." Next, the text of the slides uploaded by the user is analyzed using OCR and NLP technology, and the main theme is determined to be "Newly Released 4K TVs."

[1427] Example prompt sentence:

[1428] "Text extracted from slide: 'New 4K TVs'"

[1429] "User Emotion: 'Delight'"

[1430] "Generates ad: 'Special offer now! Check out our 4K TV!'"

[1431] Based on this information, the server generates advertising content that is likely to attract the user's interest and displays it on the smart glasses, improving the user experience.

[1432] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1433] Step 1:

[1434] Users upload slide files (image or PDF format) from their own devices through a web interface. The uploaded slide files are received by the server and temporarily stored.

[1435] Input: Slide files (image or PDF format) uploaded by the user

[1436] Output: Slide files saved on the server

[1437] Step 2:

[1438] The server uses OCR technology to extract the text from the uploaded slides. Specifically, it uses an OCR engine such as Tesseract to analyze the character information from each page of the slides and extract it as text data.

[1439] Input: Slide files stored on the server

[1440] Data processing: Analysis of character information using OCR technology

[1441] Output: Extracted text data

[1442] Step 3:

[1443] The server then analyzes the extracted text data using a natural language processing (NLP) module, which uses NLP techniques to identify the main themes and arguments of the text. Specifically, it uses libraries such as SpaCy and NLTK.

[1444] Input: Text data extracted by OCR technology

[1445] Data processing: Text analysis using NLP technology

[1446] Output: Data on the analyzed themes and arguments

[1447] Step 4:

[1448] The server starts an emotion recognition engine to analyze the user's facial expressions and voice tone. Libraries such as OpenCV and dlib are used for facial expression analysis, and a dedicated voice analysis engine is used for voice analysis. The user's emotional data is generated.

[1449] Input: User's facial expression (camera image) and voice (microphone audio)

[1450] Data processing: Emotion recognition by facial expression analysis and voice tone analysis

[1451] Output: Generated emotion data

[1452] Step 5:

[1453] Based on the analysis results and emotion data, the server selects a uniform design template and generates new slides, which have a uniform font style, size, color scheme, and layout.

[1454] Input: NLP analysis data and emotion data

[1455] Data processing: Template selection and application

[1456] Output: The newly generated slide

[1457] Step 6:

[1458] The server exports the new slides in PDF or image format and generates a download link to provide to the user, who can use this link to download the slides.

[1459] Input: The newly generated slide

[1460] Data processing: Export in PDF or image format

[1461] Output: Download link

[1462] Step 7:

[1463] The server generates personalized advertisements based on the user's emotions and displays them on the user's device or smart glasses. The advertisement generation engine generates optimal advertisement content based on the emotion data and slide theme data.

[1464] Input: Sentiment and Theme Data

[1465] Data processing: Ad content generation by the ad generation engine

[1466] Output: Generated personalized ads

[1467] 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.

[1468] 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.

[1469] 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.

[1470] 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.

[1471] 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.

[1472] 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.

[1473] 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).

[1474] 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.

[1475] 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."

[1476] 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.

[1477] 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).

[1478] 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.

[1479] 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.

[1480] 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.

[1481] 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.

[1482] 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.

[1483] 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.

[1484] 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.

[1485] 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.

[1486] 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.

[1487] 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.

[1488] The following is further disclosed regarding the above embodiment.

[1489] (Claim 1)

[1490] A means of receiving the slides as images or PDFs,

[1491] A means to extract text from the received slides using optical character recognition (OCR);

[1492] A means of content analysis of the extracted text using natural language processing (NLP);

[1493] a means for generating new slides by applying a uniformed design template based on the analysis results;

[1494] A means to export the generated slides in PDF or image format and provide them to users;

[1495] A system including:

[1496] (Claim 2)

[1497] 10. The system of claim 1, further comprising means for analyzing not only text within a slide, but also images and graphs.

[1498] (Claim 3)

[1499] 10. The system of claim 1, further comprising means for setting font, size, color, and layout of newly generated slides to a consistent style.

[1500] "Example 1"

[1501] (Claim 1)

[1502] A means for users to select and upload slide files from their own devices to the system;

[1503] a means for the server to receive and temporarily store the uploaded slide files;

[1504] means for the server to pass the stored slide files to an image processing module and extract text within the slides using optical character recognition;

[1505] a means for the server to pass the extracted text data to a natural language processing module for content analysis;

[1506] a means for the server to select an appropriate uniform design template from a template library based on the analysis result and generate a new slide;

[1507] A means for the server to export the generated slides in PDF or image format and provide them to the user;

[1508] A system including:

[1509] (Claim 2)

[1510] 10. The system of claim 1, wherein the server further comprises means for analyzing not only text within a slide, but also images and graphs.

[1511] (Claim 3)

[1512] 10. The system of claim 1, further comprising means for the server to set font style, font size, text color, and layout of newly generated slides to a consistent style.

[1513] "Application Example 1"

[1514] (Claim 1)

[1515] A means of receiving the slides as images or PDFs,

[1516] A means to extract text from the received slides using optical character recognition (OCR);

[1517] A means of content analysis of the extracted text using natural language processing (NLP);

[1518] a means for generating new slides by applying a uniformed design template based on the analysis results;

[1519] A means to export the generated slides in PDF or image format and provide them to users;

[1520] How to use your smartphone or tablet to upload slides and

[1521] A means to unify the generated slides into a unique design and reproduce them as in-store materials or advertising materials,

[1522] A system including:

[1523] (Claim 2)

[1524] 10. The system of claim 1, further comprising means for analyzing not only text within a slide, but also images and graphs.

[1525] (Claim 3)

[1526] 10. The system of claim 1, further comprising means for setting font, size, color, and layout of newly generated slides to a consistent style.

[1527] "Example 2: Combining Emotion Engines"

[1528] (Claim 1)

[1529] A means of receiving the slides as images or PDFs,

[1530] A means to extract text from the received slides using optical character recognition (OCR);

[1531] A means of content analysis of the extracted text using natural language processing (NLP);

[1532] means for recognizing and acquiring user emotion data;

[1533] A means for selecting a uniformed design template based on the acquired emotion data;

[1534] means for generating new slides using the selected template;

[1535] A means to export the generated slides in PDF or image format and provide them to users;

[1536] A system including:

[1537] (Claim 2)

[1538] 10. The system of claim 1, further comprising means for analyzing not only text within a slide, but also images and graphs.

[1539] (Claim 3)

[1540] 10. The system of claim 1, further comprising means for setting font, size, color, and layout of newly generated slides to a consistent style.

[1541] "Application example 2 when combining emotion engines"

[1542] (Claim 1)

[1543] A means of receiving the slides as images or PDFs,

[1544] A means to extract text from the received slides using optical character recognition (OCR);

[1545] A means of content analysis of the extracted text using natural language processing (NLP);

[1546] a means for generating new slides by applying a uniformed design template based on the analysis results and the user's emotional data;

[1547] A means to export the generated slides in PDF or image format and provide them to users;

[1548] A means for analyzing a user's facial expression and tone of voice to generate emotion data;

[1549] means for generating and displaying advertisements according to user emotions;

[1550] A system including:

[1551] (Claim 2)

[1552] 10. The system of claim 1, further comprising means for analyzing not only text within a slide, but also images and graphs.

[1553] (Claim 3)

[1554] 10. The system of claim 1, further comprising means for setting font, size, color, and layout of newly generated slides to a consistent style. [Explanation of symbols]

[1555] 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 receiving the slides as images or PDFs, A means to extract text from the received slides using optical character recognition (OCR); A means of content analysis of the extracted text using natural language processing (NLP); a means for generating new slides by applying a uniformed design template based on the analysis results; A means to export the generated slides in PDF or image format and provide them to users; A system including:

2. 10. The system of claim 1, further comprising means for analyzing not only text within a slide, but also images and graphs.

3. 10. The system of claim 1, further comprising means for setting font, size, color and layout of newly generated slides to a consistent style.

Images