Webpage UI Discrepancy Detection via DOM Simulation and ML
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Solution Overview
Problem
Online shopping systems often present discrepancies in shopping-related information across different webpages, causing user frustration due to the use of different data sources and backend APIs, leading to inconsistent information about items such as price, availability, and shipping details.
Innovation Solution
A system that uses simulated user interactions to generate and compare Document Object Models (DOMs) of webpages, identifies discrepancies, and employs machine learning to extract and normalize information, while also analyzing source code to detect and correct defects that cause these inconsistencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different data sources and backend APIs are used to populate shopping information on different webpages, then each page can be independently maintained and updated, but discrepancies in information consistency occur across webpages
Solution Approach 1:
The system implements automated feedback loops where shopping information is extracted from multiple webpages and compared against a reference page. When discrepancies are detected, the system generates notifications and alerts developers to correct the inconsistencies, creating a continuous improvement cycle that maintains information reliability across independently maintained pages
Solution Approach 2:
The patent introduces an intermediary system that acts as a mediator between independently maintained webpages. This system extracts shopping information from various pages, normalizes the data, and compares it against a reference page, serving as an intermediate verification layer that ensures consistency without requiring direct integration between all pages
2Productivity
If multiple independent teams maintain different webpages, then development flexibility and parallel work are improved, but user interface discrepancies and information inconsistencies arise
Solution Approach 1:
The system enables self-service automation where the discrepancy detection and monitoring process operates autonomously without requiring manual intervention from development teams. The automated extraction, comparison, and notification system handles UI consistency verification independently, allowing teams to maintain their pages in parallel while the system automatically manages consistency checks
Solution Approach 2:
An intermediary automated system is introduced to manage the complexity of UI discrepancy detection across multiple independently maintained pages. This mediator system handles the complex task of extracting, comparing, and reporting inconsistencies, shielding development teams from the complexity while enabling parallel maintenance
3Measurement precision
If manual monitoring of shopping information across webpages is performed, then information accuracy can be verified, but time consumption and resource usage increase significantly
Solution Approach 1:
The patent replaces the mechanical manual monitoring process with an automated computational system. Machine learning models and automated extraction algorithms substitute human reviewers, continuously monitoring shopping information across webpages without consuming human time or resources while maintaining high measurement precision
Solution Approach 2:
The automated monitoring system enables continuous verification of shopping information accuracy without interruption. Unlike manual monitoring which occurs periodically, the automated system operates continuously, extracting and comparing data from webpages in real-time, eliminating the time loss associated with periodic manual checks
Data Source
AI summary
Systems and methods are provided for detecting discrepancies in information that is displayed to a user across different webpages of an online shopping system. User interaction with the online shopping system is simulated in one example to collect data relating to webpages of the online shopping system. The data is analyzed to identify relevant semantic blocks of data and compare the data in those blocks across webpages. In an additional example, source code for the webpages is analyzed using machine-learning systems, which are targeted based on the results of the user interaction simulation to identify problematic source code and correct associated issues.