Defining cycle protocol for optimization in textile decontamination or cleaning system using artificial intelligence

Abstract

The present invention is in the field of decontamination, for example of medical textile products. The invention lies in the system's ability to autonomously improve the decontamination and processing of contaminated textiles by capturing data related to the type of textile and contamination, assigning a specific protocol to most effectively and efficiently decontaminate and process the textile and then measuring the effectiveness and efficiency of the decontamination and through the utilization of deep learning algorithms continuously improve the process effectiveness and efficiency. Data may include decontamination process monitoring, textile contamination quantification, facility and environmental information. Over time the system uses process data, machine learning algorithms, and computer vision techniques to learn and adapt decontamination cycle protocols based on textile items data, processing outcomes, and environmental parameters to optimize processing quality and efficiency while handling a range of item types and decontamination processing requirements.

Dependencies are inferred from the claim text.