Abstract
The evaluation of automated, multi-step digital workflows may face challenges such as context loss in long sequences, visual detail degradation from image processing, and non-deterministic results. A described technology can utilize a hybrid system architecture that may decouple visual analysis from state aggregation. A perception component can process visual captures of a workflow, such as screenshots, independently and potentially in parallel. This component can employ a high-resolution tiling method to analyze large images as smaller, overlapping segments, which can help preserve fine details for extraction by a machine learning model. A separate, programmatic aggregation component can then process the structured outputs in a specified order, for example, chronologically, using state-tracking logic to generate an assessment. This separation of components may be a factor in the reliability, scalability, and detail of automated workflow evaluations.
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Recommended Citation
Patel, Shravan and Ge, Neil, "Hybrid Architecture for Digital Workflow Evaluation via Parallel Analysis and Deterministic Aggregation", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10544