Website Analysis Algorithm for Custom Ecommerce Identification
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Solution Overview
Problem
Current website analysis techniques are inefficient in identifying ecommerce websites built with customized or bespoke programming platforms, as they require exhaustive checks and are time-consuming, often missing large online vendors with complex setups.
Innovation Solution
A computer-implemented method and system using a website analysis algorithm that filters out non-ecommerce sites, performs deep analysis of HTML, cookies, and HTTP headers to identify customized ecommerce platforms, providing quick and accurate results, and classifies websites by annual turnover.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a web crawler performs exhaustive analysis of HTML, cookies, and HTTP headers to identify customized ecommerce platforms, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent segments the website identification process into multiple analysis stages: initial filtering based on URL patterns, intermediate analysis of HTML structure and metatags, and deep analysis of cookies and HTTP headers. This segmentation allows the system to process websites efficiently by applying increasingly sophisticated analysis only when needed, rather than performing exhaustive checks on all websites.
Solution Approach 2:
The patent implements preliminary filtering mechanisms that assess basic website characteristics before committing to time-consuming deep analysis. By pre-evaluating URL structures, domain patterns, and initial HTML features, the system can quickly eliminate non-ecommerce sites and focus computational resources only on promising candidates, thereby reducing overall search time while maintaining identification accuracy.
2Measurement precision
If manual searching or browsing is used to determine whether a website sells goods or services online, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent enables websites to effectively self-identify as ecommerce platforms through structured data elements in their HTML code, metatags, and protocol headers. The analysis system automatically detects these pre-configured identifiers without requiring human intervention to manually examine each site, thereby achieving both high accuracy and high productivity simultaneously.
3Measurement precision
If opening multiple individual sites and searching within them is performed to determine ecommerce functionality, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent extracts key identifying features from websites through automated analysis of external indicators such as URL patterns, HTML metatags, and protocol headers, rather than requiring full navigation and manual inspection of each site's content. This extraction approach captures essential ecommerce indicators without the time cost of comprehensive site exploration.
Solution Approach 2:
The patent applies partial analysis by focusing on specific, high-value indicators that strongly correlate with ecommerce functionality, rather than performing exhaustive analysis of all site features. By concentrating computational effort on the most discriminative elements (such as shopping cart links, product catalog structures, and payment gateway indicators), the system achieves accurate identification with reduced analysis time.
Data Source
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AI summary
A computer-implemented method for analyzing a plurality of websites identified by a web crawler or software agent is described, by analyzing how particular websites have been built and structured to identify target ecommerce websites with an online store that have been built using a customized or bespoke programming platform. Based on an analysis (4100) of a uniform resource locator and associated header data, cookie data, Javascript files, stylesheets and images identifying a particular website and any descriptor data not including website content, by loading any one or more of routing protocol headers (4500), cookies (4400), files and images associated with the website identifiers into memory, evidence of the existence of ecommerce functionality associated with an online store may be found. In a first pass, it is determined whether online store ecommerce functionality is likely to be supported by the website based on said analysis (4200), or if the website is to be excluded from further analysis (4300). In a second pass, markup language content extracted from one or more pages of the website is analyzed (4600) to collect evidence of one or more ecommerce functionalities associated with an online store (5000).