Scraped Webpage Reliability Detection Using Branding Elements
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Solution Overview
Problem
Distinguishing reliable websites from unauthorized copies that mimic branding is challenging, and existing methods that train machine learning models on large labeled datasets are computationally intensive and prone to identifying irrelevant features, wasting resources.
Innovation Solution
Training a model on a set of guidelines associated with the entity, such as branding elements and coding styles, reduces the computational load and minimizes the identification of irrelevant features, allowing for efficient detection of unauthorized websites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained on large labeled datasets to distinguish reliable websites from unauthorized copies, then detection accuracy is improved, but computational intensity increases and processing resources are wasted
Solution Approach 1:
The patent extracts only the essential branding elements (logos, fonts, colors) from the full website content for model training and analysis. By focusing solely on these key visual identifiers rather than processing entire websites, the system achieves accurate detection while significantly reducing computational resources required for each analysis.
Solution Approach 2:
The patent segments the website analysis into distinct components: logo detection, font analysis, and color identification. Each component is analyzed separately using specialized models trained on specific branding guidelines, allowing for efficient processing of each element without requiring comprehensive analysis of the entire website structure.
2Reliability
If machine learning models are trained on large labeled datasets to identify unauthorized copies, then detection capability is improved, but processing time increases
Solution Approach 1:
The system extracts only the critical branding elements (logos, fonts, colors) that are most indicative of authorization status. By limiting the analysis scope to these essential elements rather than processing all website content, the system maintains high detection capability while reducing processing time for each website evaluation.
Solution Approach 2:
The patent implements preliminary action by pre-training models on comprehensive datasets of authorized and unauthorized websites, storing the learned patterns in the model parameters. During actual detection, the system only needs to compare current websites against these pre-learned patterns using the extracted branding elements, eliminating the need for time-consuming real-time analysis of entire websites.
3Measurement precision
If comprehensive analysis of all website content is performed to ensure accurate identification, then detection precision is improved, but resource consumption increases
Solution Approach 1:
The patent extracts only the essential branding elements (logos, fonts, colors) from the full website content for model training and analysis. By focusing solely on these key visual identifiers rather than processing entire websites, the system achieves accurate detection while significantly reducing computational resources required for each analysis.
Solution Approach 2:
The system applies different analysis methods tailored to each specific branding element type. Logo detection uses image recognition models trained on logo patterns, font analysis uses typographic feature extraction, and color analysis uses spectral signature matching. This localized quality approach ensures precise identification of each element's authenticity while avoiding the resource waste of applying comprehensive analysis methods to all website content uniformly.
Data Source
AI summary
In some implementations, a reliability modeler may receive a plurality of webpages associated with a first entity from an Internet scraping device. The reliability modeler may detect, within the plurality of webpages, at least one of a logo, a font, or a color. The reliability modeler may apply a model, trained on a set of guidelines associated with the first entity, to the logo, the font, or the color. Accordingly, the reliability modeler may determine, based on output from the model, that the plurality of webpages are unlikely to be authorized by the first entity. The reliability modeler may transmit, to a user device, an alert based on determining that the plurality of webpages are unlikely to be associated with the first entity.