AI-Based Responsive Website Generation Using Layout Graphs
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Solution Overview
Problem
Existing website building systems struggle to automatically generate fully responsive designs that adapt to various screen sizes and devices, requiring manual effort and domain-specific knowledge, especially for non-professional users.
Innovation Solution
A system that uses AI-based generation of responsive websites through a website building system, employing a layout graph data structure, structure optimization model, and responsive optimization model to automatically transform static designs into optimized layouts that adapt to different screen parameters, leveraging learning algorithms and user feedback for improved design decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual methods are used to create responsive website designs, then design precision and control are improved, but productivity and ease of operation deteriorate due to requiring domain-specific knowledge and significant manual effort
Solution Approach 1:
The system enables automatic generation of responsive website designs through AI models that self-adjust layouts and content presentation based on screen parameters, eliminating the need for manual intervention and domain-specific knowledge while maintaining design quality
Solution Approach 2:
The system automatically adjusts design parameters such as layout structure, content positioning, and visual elements based on detected screen characteristics, transforming static designs into adaptive responsive designs through parameter optimization rather than manual reconfiguration
2Adaptability or versatility
If manual methods are used to create responsive website designs, then design quality and adaptability are improved, but ease of operation deteriorates by requiring domain-specific knowledge
Solution Approach 1:
The AI-based system automatically performs adaptability adjustments by detecting screen parameters and self-modifying the website layout and content presentation, making the system self-sufficient in handling responsive design requirements without user intervention
Solution Approach 2:
The system pre-processes designs through multiple trained models (layout graph, structure optimization, responsive optimization) to automatically prepare adaptive layouts for various screen sizes before deployment, eliminating the need for users to perform manual adaptability adjustments
3Productivity
If AI-based automatic generation is used, then productivity and ease of operation are improved, but manufacturing precision may deteriorate due to automated processes
Solution Approach 1:
The AI system divides the complex design generation process into multiple specialized stages (layout graph generation, structure optimization, responsive optimization), with each stage handled by a dedicated trained model that focuses on specific aspects of design precision
Solution Approach 2:
The system introduces intermediate data structures (layout graph data structure, webpage layout data structure) that serve as mediators between the input design and final output, allowing multiple transformation steps that preserve and enhance design precision throughout the automated generation process
4Adaptability or versatility
If complex AI models are used for responsive optimization, then adaptability and design quality are improved, but device complexity increases
Solution Approach 1:
The complex AI system is segmented into three distinct trained models (layout model, structure optimization model, responsive optimization model), each handling a specific aspect of the design process, which manages overall system complexity by dividing functionality into modular components
Solution Approach 2:
The system transforms the design problem from a two-dimensional static layout into a multi-dimensional responsive design space by incorporating screen parameters (width, height, orientation) as additional dimensions, enabling automatic adaptation across diverse device configurations
Data Source
AI summary
Embodiments provide for responsive optimization of websites. In some examples, a layout graph data structure is generated based on applying a trained layout model to extracted website elements from an input object including one or more website building tools. A webpage layout data structure is generated based on applying a trained structure optimization model to the layout graph data structure. An optimized webpage that is configured to render according to a plurality of screen parameters is generated based on applying a trained responsive optimization model to the webpage layout data structure and a plurality of screen parameters.