Webpage Element Position Detection Using DOM Tree Analysis
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Solution Overview
Problem
Existing methods are inefficient and costly for automatically detecting the positions of webpage elements, such as payment options, on merchant websites, as manual inspection is time-consuming and prone to errors due to varying webpage layouts and coding structures.
Innovation Solution
A system utilizing graph-based and natural language processing (NLP) techniques to analyze programming code and generate a DOM tree, identifying related webpage elements and their positions by traversing the DOM tree and applying prediction models to classify payment options, thereby determining their relative positions on the webpage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to detect webpage element positions, then detection accuracy can be maintained through human judgment, but the process becomes time-consuming and costly
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated computer-based system that uses machine learning models and image processing algorithms to detect webpage element positions, thereby eliminating time-consuming human labor while maintaining detection accuracy through automated analysis
Solution Approach 2:
The system enables self-service detection where the webpage elements automatically annotate themselves through the trained machine learning model that identifies and positions elements without requiring external manual verification, allowing the system to perform its own detection function autonomously
2Productivity
If automated detection systems are implemented to reduce manual inspection time, then productivity increases, but detection precision may deteriorate due to layout variations and coding structure differences
Solution Approach 1:
The patent employs a dynamic machine learning model that adapts to various webpage layouts and coding structures by training on diverse datasets, allowing the system to maintain high detection precision across different webpage configurations while operating automatically at high speed
Solution Approach 2:
The system changes detection parameters dynamically by adjusting the machine learning model's classification thresholds and feature extraction parameters based on the specific webpage being analyzed, enabling accurate detection across varying layouts and structures while maintaining automated efficiency
3Reliability
If comprehensive webpage analysis is performed to accurately identify element positions, then detection reliability improves, but system complexity increases due to varying webpage structures
Solution Approach 1:
The patent segments the webpage analysis into distinct processing stages including image capture, preprocessing, feature extraction, and classification, with each stage handled by specialized computational modules that work together to achieve reliable detection without requiring a single complex system
Data Source
AI summary
Methods and systems are presented for automatically detecting positions of various webpage elements within a webpage when the webpage is rendered, based on analyzing the programming code of the webpage using graph-based and NLP-based techniques. A position detection system obtains the programming code of the webpage and generates a DOM tree based on the programming code. A group of leaf tags may be identified based on traversing the DOM tree. The position detection system may use two different, but coordinated, prediction models to make the classification prediction for the leaf tags, where the first prediction model focuses on recall by identifying as many positive leaf tags as possible at the cost of low precision and the second prediction model refines the results from the first prediction model by improving the precision of the classification.