AI GUI Code Generation via Neural Network DSL
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Solution Overview
Problem
The existing GUI design process is time-consuming and requires significant human input, as it involves multiple stages and iterations, limiting creativity and efficiency, as designers and developers must manually translate visual designs into functional code, with existing solutions often restricting design freedom and requiring significant modifications for minor changes.
Innovation Solution
An AI-driven system that automatically generates code for graphical user interfaces by processing user-provided sketches and textual descriptions using recurrent neural networks and domain-specific languages, enabling fully automated generation of wireframes, styles, and mockups, reducing the need for manual input and streamlining the design process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual translation of visual designs into functional code is performed, then design freedom and creativity are maintained, but time-to-prototype and development duration increase significantly
Solution Approach 1:
The patent introduces an intermediary system comprising a neural network model and a domain-specific language (DSL) that automatically translates visual designs into functional code. The neural network processes design inputs and generates corresponding code through the DSL, eliminating the need for manual translation while maintaining design freedom. This intermediary automation resolves the contradiction by significantly reducing time-to-prototype without requiring complex manual processes.
Solution Approach 2:
The patent replaces the mechanical manual process of translating visual designs into code with an automated neural network-based system. The neural network model learns from training data to automatically generate functional code from design inputs, substituting the manual mechanical translation process with an intelligent automated system. This substitution dramatically improves productivity while the automation handles the complexity, resolving the contradiction between speed and process complexity.
2Productivity
If multiple stages of development including wireframes, mockups, and prototypes are performed manually, then design quality and functionality are improved, but the number of iterations and time required increase
Solution Approach 1:
The patent enables continuous automatic generation of wireframes, mockups, and prototypes through the neural network system. Once trained, the system can continuously generate design iterations without manual intervention, maintaining design quality while dramatically increasing iteration speed. The automated system processes design inputs and generates functional code for multiple stages continuously, resolving the contradiction by eliminating idle time between iterations while preserving design quality.
Solution Approach 2:
The patent performs preliminary training of the neural network model on comprehensive design data before actual design generation. This preliminary action equips the system with the knowledge to automatically generate high-quality wireframes, mockups, and prototypes across multiple stages. By pre-training the model, the system can rapidly iterate through design stages without manual intervention, increasing iteration speed while maintaining quality standards established during training.
3Adaptability or versatility
If existing solutions are used to generate GUI code, then some automation is achieved, but design freedom is restricted and significant modifications are required for minor changes
Solution Approach 1:
The patent employs parameter changes in the neural network model to achieve both automation and design flexibility. The system learns from training data containing diverse design parameters and can adjust its code generation based on specific design inputs. By modifying network parameters and using a configurable DSL, the system adapts to different design requirements while maintaining high automation. This allows minor changes in design inputs to produce相应 code modifications without requiring significant rework, resolving the contradiction between automation and flexibility.
Data Source
AI summary
Methods and apparatus to automatically generate code for graphical user interfaces are disclosed. An example apparatus includes a textual description analyzer to encode a user-provided textual description of a GUI design using a first neural network. The example apparatus further includes a DSL statement generator to generate a DSL statement with a second neural network. The DSL statement is to define a visual element of the GUI design. The DSL statement is generated based on at least one of the encoded textual description or a user-provided image representative of the GUI design. The example apparatus further includes a rendering tool to render a mockup of the GUI design based on the DSL statement.