Automatic UI Component Generation via ML Design Rules
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Solution Overview
Problem
Traditional user interface design is time-consuming and expensive, often resulting in generic templates with limited customization and rapid obsolescence due to the lack of resources and expertise, especially in scenarios with insufficient training data for machine learning models.
Innovation Solution
The automatic generation of user interface (UI) component instances using machine learning techniques, which account for compatibility and desirable visual characteristics by defining allowed states and design rules, allowing for the creation of customized UI designs through a tree structure and algorithmic processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual design by skilled designers and developers is used, then customization and aesthetic quality are improved, but time consumption and cost increase
Solution Approach 1:
The system pre-generates a comprehensive library of UI component instances with various allowed states (colors, shapes, sizes, positions) before the actual design task. This preliminary preparation enables rapid assembly of customized interfaces without time-consuming manual design during the actual customization process.
Solution Approach 2:
The interface design is segmented into reusable component instances (buttons, text fields, images, etc.) with defined allowed states. These segmented components can be independently selected and combined, enabling rapid customization while reducing overall design time and complexity.
2Adaptability or versatility
If manual design by skilled designers and developers is used, then customization and aesthetic quality are improved, but cost increases
Solution Approach 1:
The system creates reusable component instances that can be copied and instantiated multiple times with different allowed states. Instead of manually designing each interface element from scratch, the system copies pre-defined components and adjusts their states, significantly reducing the expertise and resources required.
Solution Approach 2:
The component instances are designed to be universal and multi-functional, with configurable allowed states that can adapt to different design requirements. A single component type can serve multiple purposes by changing its state (color, shape, size, position), reducing the need for multiple specialized components and expert designers.
3Loss of time
If generic templates are used, then time and cost are reduced, but customization capability and aesthetic quality deteriorate
Solution Approach 1:
The system allows customization at the local level of individual component instances rather than requiring complete template redesign. Each component can have its allowed states (color, shape, size, position) independently adjusted to achieve local customization while maintaining overall design consistency and reducing time investment.
4Manufacturing precision
If traditional design methods are used, then design quality is maintained, but productivity is low
Solution Approach 1:
The system pre-defines allowed states for each UI element (colors, shapes, sizes, positions) and pre-generates component instances, so that during actual design, high-quality interfaces can be assembled rapidly by selecting and configuring pre-validated components rather than designing each element from scratch.
Solution Approach 2:
The system maintains design quality by controlling and validating changes to key parameters (color, shape, size, position) of UI elements through predefined allowed states. This parameter-based approach ensures design consistency and quality while enabling rapid exploration of different design variations through automated parameter adjustment.
Data Source
AI summary
Techniques are disclosed relating to automatically synthesizing user interface (UI) component instances. In disclosed techniques a computer system receives a set of existing UI elements and a set of design rules for the set of existing elements, where design rules in the set of design rules indicate one or more allowed states for respective UI elements in the set of existing UI elements. The one or more allowed states may correspond to one or more visual characteristics. Using the set of existing UI elements, the computer system may then automatically generate a plurality of UI component instances based on the set of design rules, where a respective UI component instance includes a first UI element in a first allowed state. The computer system may then train, using the plurality of UI component instances, a machine learning model operable to automatically generate UI designs.