Generative Layout Model for Customizable Design Tokenization
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Solution Overview
Problem
Conventional content layout recommendation systems lack customization and creativity, often returning similar layouts that require manual adjustment by graphic designers, leading to a time-consuming and skill-dependent process for generating new layouts.
Innovation Solution
A vector graphics system that uses generative machine learning to tokenize design inputs, creating embeddings that represent design elements, which are then processed to generate new layouts with customizable similarity to existing designs, allowing for innovative and unique layout arrangements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional content layout recommendation systems are used, then existing layouts can be found in the library, but the layouts are similar and require manual adjustment by graphic designers
Solution Approach 1:
The system automatically generates new layout arrangements by processing design inputs through machine learning models, eliminating the need for manual layout creation. The layout generator creates multiple layout options that are ready for use without requiring graphic designers to manually adjust similar existing layouts.
Solution Approach 2:
The system transforms design inputs into embeddings and processes them through machine learning models to generate layouts with varied parameters such as element positions, sizes, and arrangements. This creates diverse layout options rather than returning similar existing layouts, enabling customization without manual intervention.
2Adaptability or versatility
If graphic designers manually create new layouts, then unique and creative layouts can be generated, but the process is time-consuming and skill-dependent
Solution Approach 1:
The system replaces the manual mechanical process of layout creation by graphic designers with an automated machine learning-based layout generator. The model processes design inputs and generates creative layout arrangements automatically, eliminating the need for manual design skills while maintaining productivity.
Solution Approach 2:
The layout generator autonomously creates new layout arrangements by processing design inputs through machine learning models, eliminating the need for manual layout creation. The system generates multiple creative layout options automatically without requiring graphic designers to manually adjust similar existing layouts.
3Reliability
If typical layout recommendation systems are used, then the search can be guided by user content, but the results lack novelty and require manual adjustment
Solution Approach 1:
The system transforms design inputs into embeddings and processes them through machine learning models to generate layouts with varied parameters such as element positions, sizes, and arrangements. This creates novel layout options while maintaining reliability through the structured embedding and generation process.
Solution Approach 2:
The system dynamically generates new layout arrangements by processing design inputs through machine learning models, creating layouts that adapt to the specific design inputs rather than returning static similar existing layouts. This ensures both reliability and novelty in the recommended layouts.
Data Source
AI summary
Embodiments are disclosed for machine learning-based generation of recommended layouts. The method includes receiving a set of design elements for performing generative layout recommendation. A number of each type of design element from the set of design elements is determined. A set of recommended layouts are generated using a trained generative layout model and the number and type of design elements. The set of recommended layouts are output.