Document Layout Extraction for Automated Template Import
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Solution Overview
Problem
Generating templates for customer communication management systems is a time-consuming and error-prone task, especially for those unfamiliar with graphic design principles, as existing content management systems lack a convenient mechanism to integrate content across multiple channels.
Innovation Solution
A computer-implemented method for automated visual analysis of documents to generate digital templates by analyzing digital images, identifying content areas, and importing layouts into templates, utilizing machine learning to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual template layout design is performed by users unfamiliar with graphic design principles, then template generation can be completed, but the process is time-consuming and error-prone
Solution Approach 1:
The system performs self-service by automatically analyzing existing documents and extracting layout patterns to generate templates without requiring manual user intervention. The visual analysis system autonomously identifies content areas, determines layout structures, and creates reusable templates, eliminating the need for users to manually design layouts even if they lack graphic design expertise.
Solution Approach 2:
The patent replaces the manual mechanical process of template design with an automated visual analysis system. Instead of users manually creating templates, the system uses image processing and pattern recognition algorithms to automatically analyze documents and generate templates, substituting human manual work with computational automation.
2Adaptability or versatility
If existing content management systems are used, then content creation and versioning are supported, but there is no convenient mechanism to integrate content across multiple channels
Solution Approach 1:
The template generation system creates universal templates that can be used across multiple communication channels. By extracting layout patterns from existing documents and generating channel-agnostic templates, the system enables the same content to be deployed across email, web, mobile, and other channels without requiring separate designs for each channel, thus achieving multi-functionality.
Solution Approach 2:
The system performs preliminary action by pre-processing existing documents to extract layout patterns and generate reusable templates before actual content deployment. This advance preparation creates a library of templates that can be quickly instantiated across multiple channels, eliminating the need for complex integration work when content needs to be deployed to different channels.
Data Source
AI summary
Embodiments extract a layout from a digital image of a document, including performing an analysis of image data to identify areas of content and storing the identified areas as design elements of an electronic document template. Analyzing the image data to identify areas of interest includes testing a plurality of lines of pixels from the digital image against a background color definition to identify boundaries of a content area of interest. Content from the content area of interest is processed using a machine learning model to assign a content type for the content area of interest, where the machine learning model represents multiple types of content and is trained to assign content types to input content. The content area of interest is stored as a design element of a digital page template.