Webpage Zone Classification via Machine Learning
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Solution Overview
Problem
Current webpage analysis technologies face challenges in automatically detecting zones of interest, as existing solutions are limited to detecting layout types and lack performance optimizations, failing to classify zones based on page structure and user experience functionality.
Innovation Solution
A method and system using a trained machine learning model to classify webpage zones of interest by identifying content elements and storing their categories, based on functionality, within a network system comprising user devices, an analytic engine, web servers, and a database, enabling efficient detection and classification of zones relevant to user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If classification solutions detect only layout zone types based on code-level descriptions, then the detection scope is limited, but the ability to detect zones critical to user experience is insufficient
Solution Approach 1:
The patent segments the classification task into multiple levels: first detecting layout zones based on code-level descriptions, then further classifying each zone based on page structure information and functionality. This multi-stage segmentation allows the system to handle both broad detection scope and precise user experience relevance assessment.
Solution Approach 2:
The patent transitions from single-dimension classification (code-level only) to multi-dimension classification by incorporating page structure information and functionality as additional dimensions. This dimensional expansion enables the system to detect zones that are both structurally identifiable and functionally relevant to user experience.
2Ease of manufacture
If independent binary classification models are used for each zone type, then the model training is simple, but the number of models compounds and performance is insufficient
Solution Approach 1:
The patent merges multiple independent binary classification models into a single multi-class classification model that can simultaneously detect and classify different zone types. This consolidation reduces the number of models from O(n) to 1, simplifying the system while improving detection performance through unified training on diverse zone examples.
Solution Approach 2:
The patent creates a universal classification model that can handle multiple zone types (headers, footers, navigation elements, and user experience-critical zones) within a single model framework. This multi-functional model eliminates the need for separate binary models for each zone type while maintaining training simplicity through unified architecture.
3Productivity
If automated methods are used to analyze webpage zones, then the analysis speed is fast, but the ability to understand content arrangement and execute improvements is reduced
Solution Approach 1:
The patent implements self-service by automatically classifying zones into meaningful categories that directly indicate their functional importance to user experience. The system autonomously identifies which zones require attention for improvements, eliminating the need for manual analysis while providing actionable insights that maintain ease of operation for site administrators.
Data Source
AI summary
A system and method for detecting webpage zones of interest. A method includes receiving at least one webpage analysis request, wherein the received at least one webpage analysis request includes at least one webpage in a website; identifying, in the at least one webpage, at least one zone, wherein the at least one zone is a content element of a webpage; classifying the at least one zone into a category of interest, wherein the classification is based on a trained machine learning model configured to classify DOM elements of the least one webpage, and wherein a category of interest is a category determined based on a functionality of the website; and storing the classification by indicating the category of interest for each zone.