Webpage Layout Checker Using Unique Color Codes for Error Detection
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Solution Overview
Problem
Conventional techniques for detecting errors in webpage layouts are limited to detecting text string errors and require complex algorithms and significant training costs for deep learning engines, failing to address other types of layout errors effectively.
Innovation Solution
A method that involves parsing a data object model associated with a webpage to change the original color scheme for each node, generating a modified data object model with unique color codes based on feature positions, displaying the webpage, capturing images, and detecting errors using neural networks for automated correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques are used to detect webpage layout errors, then text string errors can be detected, but other types of layout errors cannot be detected and complex algorithms with significant training costs are required
Solution Approach 1:
The patent introduces an intermediary representation system that converts webpage layout elements into structured data objects with standardized properties. This intermediary layer translates diverse layout elements (text, images, containers) into a common format that neural networks can process efficiently, eliminating the need for complex specialized algorithms for each error type while maintaining high detection precision across all layout error categories
Solution Approach 2:
The patent transforms the detection approach by changing parameters from raw pixel analysis to structured feature extraction. By representing layout elements as data objects with defined properties (position, size, type, content), the system enables efficient neural network processing that reduces computational complexity while improving detection accuracy for various error types including text alignment, spacing, and component positioning errors
2Measurement precision
If conventional deep learning techniques are used for layout error detection, then some errors can be detected, but significant training data and training costs are required
Solution Approach 1:
The patent segments the webpage layout detection task into distinct processing stages: structure parsing, feature extraction, data object generation, and error classification. This segmentation allows each stage to be optimized independently, with the structured data object representation enabling accurate error detection with minimal training data since the fundamental layout structure and error patterns are inherently captured in the organized data format
Solution Approach 2:
The patent performs preliminary organization of layout data into structured data objects before neural network processing. By pre-processing the webpage into a standardized representation with defined properties and relationships, the system eliminates the need for extensive training to learn basic layout structures, requiring only minimal training data to detect specific error patterns in the already-organized data
3Measurement precision
If comprehensive layout error detection is implemented, then all types of layout errors can be detected, but the detection process becomes more complex and time-consuming
Solution Approach 1:
The patent divides comprehensive error detection into hierarchical levels: structural errors (layout breaks, missing components), positional errors (misalignment, spacing issues), and content errors (text rendering, component display). This segmentation enables parallel processing of different error categories and allows the system to detect all error types simultaneously without sequential overhead, maintaining fast detection speed while achieving comprehensive coverage
Solution Approach 2:
The patent creates a universal detection framework using standardized data objects that can identify multiple error types through a single processing pipeline. The same structured representation and neural network model handle various error categories (text alignment, component positioning, spacing, rendering issues) uniformly, eliminating the need for separate specialized detection processes for each error type and thus reducing overall detection time while maintaining comprehensive coverage
Data Source
AI summary
A method includes parsing a data object model associated with a webpage to change an original color scheme for each node of the data object model. Each node corresponds to a display feature of a layout of the webpage. The method also includes generating a modified data object model by replacing the original color scheme for each node with a calculated color scheme, displaying the layout of the webpage using the modified data object model, capturing an image of the layout of the webpage as displayed and detecting any errors in the layout of the webpage. The calculated color scheme assigns a unique color code to each feature of the node based on a position of each feature within the node and based on a position of each node within the data object model.