Generative Filling of Repeating Design Elements With Machine Learning
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Solution Overview
Problem
Existing software design tools face challenges in efficiently managing and automating the generation of content in repeating design elements within interactive graphic design systems, requiring significant manual effort and resources.
Innovation Solution
An interactive graphic design system utilizing generative machine learning techniques to automatically detect and populate repeating design elements with content, leveraging a large language model to streamline the process and reduce manual editing overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual content generation is used for repeating design elements, then design precision and control are maintained, but productivity and time efficiency deteriorate
Solution Approach 1:
The system enables self-service by allowing the design system to automatically generate content for repeating design elements using machine learning models. The system detects repeating elements and autonomously populates them with generated content, eliminating the need for manual intervention in each instance while maintaining design consistency.
Solution Approach 2:
The system uses copying by generating content based on patterns from existing design elements and applying them to repeating elements. The machine learning model learns from example content and reproduces it in a transformed manner across multiple elements, reducing manual replication effort.
2Productivity
If automated content generation is implemented, then productivity and time efficiency improve, but device complexity and resource requirements worsen
Solution Approach 1:
The system introduces an intermediary machine learning model that acts as a bridge between the design system and content generation. This intermediary component handles the complex task of generating and adapting content, allowing the core design system to remain relatively simple while benefiting from advanced AI capabilities.
Solution Approach 2:
The machine learning model serves multiple functions: detecting repeating design elements, generating appropriate content, adapting content to different contexts, and maintaining design consistency. This multi-functional approach reduces the need for separate specialized tools and simplifies the overall system architecture.
3Ease of operation
If manual editing is performed for each design element, then content accuracy and design consistency are maintained, but ease of operation and user effort worsen
Solution Approach 1:
The system implements feedback mechanisms where the machine learning model continuously learns from user corrections and design patterns. When users modify generated content or provide feedback on accuracy, the system incorporates this information to improve future content generation, maintaining both ease of operation and design consistency.
Solution Approach 2:
The design system provides self-service by automatically maintaining consistency across repeating elements through the machine learning model. The system autonomously ensures that generated content adheres to design guidelines and patterns, reducing the burden on users to manually maintain consistency while still achieving high-quality results.
Data Source
AI summary
A network computer system provides interactive graphic design system instructions for performing generative filling of design content. The network computer system determines a set of repeating design elements within a design interface. The network computer system also determines input into a machine learning model that includes (i) example content associated with the set of repeating design elements and (ii) one or more instructions associated with the example content. The network computer system populates at least a portion of the set of repeating design elements with additional content generated by the machine learning model in response to the input.