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Selector receives eight U.S. patents from USPTO

Source Decision Insights Record Published January 20, 2026
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Selector said the United States Patent and Trademark Office granted eight U.S. patents to the company, a development the organization tied to work on causal reasoning, natural language interaction, and predictive analytics for complex digital infrastructures.

The company framed the grants as relevant to its observability and AI Operations (AIOps) technology and said the patents concerned the data, events, and dependencies that affect network behavior; telecommunications providers, cloud service providers, and global enterprises rely on Selector's platform.

The granted patents covered causal inference, Large Language Model (LLM) training using dashboard metadata, AI-powered correlation, predictive maintenance, network path intelligence, telemetry extraction, scalable storage and querying methods, and maintenance-window aware reporting.

Selector provided patent titles and summaries that listed root causation for network operations; dashboard metadata as training data for natural language querying; metrics, events, and alert extraction from system logs; methods for network tracing, forecasting, and capacity planning; reconstruction of packet paths at a time of interest; early identification of optical transceiver failures; efficient storage and querying of network parameters; and automated detection and exclusion of maintenance windows from performance analytics.

“These patents reflect years of focused innovation to bring AI and causal reasoning to the heart of network operations,” said Nitin Kumar, CTO and Co-founder of Selector. “Selector's platform doesn't just monitor data, but actually understands relationships, predicts failures, and explains why events occur. These innovations are foundational to how we're reimagining observability for the AI era.”

“Selector's patent portfolio represents a step forward in how AI reasons about network data,” said Surya Nimmagadda, Chief Data Scientist at Selector. “Our goal has been to move from statistical correlation to genuine causal understanding—teaching machines to think like engineers. This body of work is the result of rigorous experimentation in applied AI, graph analytics, and knowledge representation.” Selector said the patents advanced its stated aim to give organizations clarity, context, and control across every layer of their network.

Press release, provided by Cision on behalf of Selector. Read the original.

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