Structure-Based Digital Template Matching for Faster Layout Selection
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Solution Overview
Problem
Conventional systems for identifying digital templates are limited by keyword-based searches and manual browsing, leading to inefficient and error-prone processes for users to find templates with visually pleasing layouts, especially when replacing example design elements with their own content.
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 structure-based matching to identify and generate digital templates with aesthetically pleasing layouts, using techniques such as minimum cost bipartite matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If keyword-based search and manual browsing are used to identify digital templates, then users can find templates in the database, but the process is inefficient and error-prone
Solution Approach 1:
The patent replaces manual keyword-based search and browsing with an automated machine learning system. The system uses a trained model to compute template embeddings from user content and automatically ranks templates by compatibility, eliminating the need for users to manually search and evaluate numerous templates.
Solution Approach 2:
The system enables self-service template selection by automatically analyzing user content, computing compatibility scores with existing templates, and presenting ranked results. The machine learning model performs the evaluation work that would otherwise require manual user effort, allowing users to quickly identify suitable templates without extensive browsing.
2Ease of operation
If users manually modify design elements to replace example content in templates, then templates can be customized, but the process is inefficient and prone to user error
Solution Approach 1:
The system performs preliminary analysis of user content and pre-computes compatibility scores with templates before the user makes any selections. The machine learning model evaluates how well user content matches template structures in advance, so users receive pre-filtered, ranked results that require minimal further modification or evaluation.
Solution Approach 2:
The patent replaces manual design element modification with an automated system that computes compatibility metrics and ranks templates. The machine learning model automatically evaluates template-user content匹配度, eliminating the need for users to manually test and adjust multiple templates to find the best fit.
3Measurement precision
If conventional template search systems are used, then templates can be identified, but the systems cannot efficiently evaluate structural compatibility between user content and template layouts
Solution Approach 1:
The patent introduces template embeddings as an intermediary representation that captures the structural characteristics of templates. The machine learning model computes these embeddings and uses them as a mediator to efficiently compare user content with template structures, enabling precise compatibility evaluation without direct complex analysis of each template's full structure.
Solution Approach 2:
The system transforms template structures into numerical embedding vectors that capture essential structural properties. By converting complex template layouts into compact numerical representations, the system enables efficient and accurate compatibility computation through mathematical operations on these embeddings, rather than direct structural comparison.
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.