Structure-Based Template Matching for Aesthetic Layout Generation
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Solution Overview
Problem
Conventional systems for identifying and modifying digital templates are inefficient and prone to user error, relying on keyword searches and manual browsing, which fail to provide aesthetically pleasing layouts for digital design elements.
Innovation Solution
A computing device uses a template system that represents input data as a sentence in a design structure language, employs a machine learning model like Bidirectional Encoder Representations from Transformers to generate embeddings, and performs minimum cost bipartite matching to identify and generate digital templates with visually pleasing layouts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional keyword search and manual browsing methods are used to identify digital templates, then users can search through template databases, but the process is inefficient and prone to user error, failing to provide aesthetically pleasing layouts
Solution Approach 1:
The patent replaces manual keyword search and browsing mechanisms with an automated machine learning system. The system uses a trained model that takes as input a set of digital design elements and automatically generates a digital template with aesthetically pleasing layout, substituting the mechanical interaction of manual search and selection with an intelligent automated process that consistently produces high-quality results
Solution Approach 2:
The system enables the template generation process to serve itself by using the digital design elements provided by the user as direct input to the machine learning model. The model autonomously determines the optimal layout and template structure without requiring user intervention in the search and selection process, allowing the system to automatically generate professionally designed templates
2Ease of operation
If manual modification of design elements is performed to replace example content in templates, then users can customize templates, but the process is time-consuming and inefficient
Solution Approach 1:
The machine learning model is pre-trained on extensive template data and design principles, so that when provided with digital design elements, it can immediately generate a fully laid out template without requiring the user to perform time-consuming manual adjustments. The preliminary training of the model encapsulates years of design expertise, making the actual template generation instant and effortless
Solution Approach 2:
The system creates a new template by copying and adapting proven layout structures from the training data. Instead of manually modifying design elements, the system generates a complete template structure that can be directly populated with the user's content, eliminating the need for iterative manual adjustments and significantly reducing the time required for template customization
3Measurement precision
If a template system uses machine learning models to generate embeddings and perform matching, then template identification accuracy improves, but system complexity increases
Solution Approach 1:
The patent employs a universal machine learning architecture that can handle multiple tasks: generating embeddings from digital design elements, comparing templates through embedding similarity, and generating new templates. This single multi-functional model replaces what would otherwise require separate systems for each task, achieving high accuracy while managing complexity through architectural unification
Solution Approach 2:
The embedding vector serves as an intermediary representation that bridges the gap between complex digital design elements and simple comparison operations. By converting visual layouts into numerical embeddings, the system enables accurate template matching through straightforward vector operations, avoiding the need for complex image processing or manual feature extraction while maintaining high precision
Data Source
AI summary
In implementations of systems for generating templates using structure-based matching, a computing device implements a template system to receive input data describing a set of digital design elements. The template system represents the input data as a sentence in a design structure language that describes structural relationships between design elements included in the set of digital design elements. An input template embedding is generated based on the sentence in the design structure language. The template system generates a digital template that includes the set of digital design elements for display in a user interface based on the input template embedding.