Widget Load Failure Detection Using Annotated Web Screenshots

The patent introduces an intermediary machine learning model that acts as a mediator between the synthetic monitoring system and the website screenshots. This model processes the visual information from screenshots to specifically identify widget load failures, which traditional monitoring systems cannot detect. The intermediary translates visual widget states into detectable failure signals, resolving the contradiction between general monitoring capability and specific widget detection accuracy.

The patent replaces the traditional mechanical/script-based monitoring approach with a machine learning-based visual analysis system. Instead of using automated scripts to check widget loading, the system captures screenshots and uses trained machine learning models to visually identify widget failures. This substitution enables accurate detection of widget load failures that were previously undetectable by conventional monitoring methods.

The patent segments the monitoring system into distinct functional modules: a screenshot capture module, a machine learning model processing module, and a result analysis module. The machine learning model itself is segmented into multiple specialized models (e.g., one for detecting presence, another for detecting loading state). This segmentation manages system complexity by organizing the machine learning integration into manageable, independent components that can be developed and maintained separately.

The patent implements preliminary action by pre-training machine learning models on annotated widget data before deployment. The models are trained in advance to recognize various widget types and their loading states, so that when the monitoring system operates, the models are already prepared to accurately detect widget failures without requiring complex real-time decision logic. This pre-preparation reduces the operational complexity of the integrated system.