Abstract
The technology described in this paper relates to a predictive network equipment decommissioning management system. The described technology automates the creation and management of decommissioning requests to improve accuracy and reduce cycle times. By utilizing multi-signal context heuristics, the system determines the specific decommissioning scenario for various assets. A hybrid inference engine is employed to predict the presence of unmonitored passive components. An ambiguity resolution engine identifies vague or untagged parts using computer vision and topological exclusion techniques. A dynamic granularity transformation engine re-aggregates discrete components into top-level assemblies, accommodating for configuration drift over time. Furthermore, a retrofit analysis engine evaluates equipment for potential upcycling and modernization.
Keywords: predictive decommissioning, network equipment, top-level assemblies, context heuristics, hybrid inference, upcycling, reverse logistics, data entropy.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
N/A, "System and Method for Predictive Network Equipment Decommissioning Management", Technical Disclosure Commons, (April 01, 2026)
https://www.tdcommons.org/dpubs_series/9681