Neural Network Interface Translation for Cross-Platform Functionality
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Solution Overview
Problem
Existing graphical user interface (GUI) generation methods struggle to provide cohesive experiences across diverse devices and platforms while maintaining functionality, context, and flow, often resulting in visually similar but functionally deficient interfaces.
Innovation Solution
A multi-step process utilizing neural networks, including convolutional neural networks (CNNs), attention transformers, variational autoencoders (VAEs), and generative adversarial networks (GANs), to analyze and translate GUIs, ensuring consistency and adherence to platform-specific constraints such as form factor, resolution, and input methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Shape
If design-based translation is used to automate interface generation, then visual similarity is improved, but functionality, context, or flow is lost
Solution Approach 1:
The patent segments the interface translation process into multiple specialized neural networks, each handling specific aspects: CNNs for visual feature extraction, VAEs for design element encoding, attention transformers for contextual relationship preservation, and GANs for generating functionally equivalent interface variants. This segmentation allows each component to optimize for its specific function while collectively preserving both visual similarity and functionality.
Solution Approach 2:
The patent introduces an intermediary representation layer that encodes both visual features and functional semantics of the original interface. This intermediary representation serves as a bridge between the source interface and generated variants, ensuring that functional relationships and contextual meanings are preserved during translation while allowing visual adaptation to different platforms.
2Reliability
If manual generation of interface variants is performed, then functionality and context are maintained, but the process is long, complicated, and expensive
Solution Approach 1:
The patent implements a self-service automated system where neural networks independently analyze source interfaces, extract functional and visual features, and generate adapted interface variants without human intervention. The system self-optimizes by learning from training data and can automatically validate generated interfaces against platform constraints, dramatically improving productivity while maintaining functionality through intelligent algorithms.
Solution Approach 2:
The patent replaces the mechanical manual process of interface design and adaptation with an automated neural network-based system. Instead of designers manually recreating interfaces for different platforms, the system uses machine learning models to automatically translate interfaces, substituting human cognitive and manual labor with computational processes that are faster and more scalable.
3Productivity
If automated interface generation is implemented, then productivity is improved, but visual and functional consistency across platforms deteriorates
Solution Approach 1:
The patent incorporates feedback mechanisms where generated interface variants are evaluated against platform-specific constraints and design guidelines. The neural networks receive feedback on whether generated interfaces maintain visual fidelity and functional equivalence, allowing iterative refinement and adjustment of generation parameters to ensure consistency across different platform translations.
Solution Approach 2:
The patent creates a universal interface translation framework that can adapt to multiple different platform constraints and design systems simultaneously. The neural networks are trained on diverse platform-specific data and can generate interfaces that are universally consistent in functionality while being specifically adapted to each platform's visual language and interaction patterns.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to generate one or more interfaces. In at least one embodiment, one or more neural networks are used to generate one or more second graphical user interfaces based, at least in part, on one or more functional features of one or more first graphical user interfaces.