Organizations managing complex digital infrastructures increasingly demand AI-driven tools that go beyond monitoring to deliver true understanding, prediction, and resolution of network issues. Selector, a leader in AI-powered network operations intelligence, has achieved a major milestone with the United States Patent and Trademark Office granting eight foundational patents.
Quick Intel
These patents underscore Selector's commitment to redefining observability by embedding advanced AI directly into network operations. The innovations address longstanding challenges in correlating vast telemetry data, understanding causal relationships, and anticipating failures before they disrupt services.
"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."
The granted patents cover critical advancements that power Selector's core capabilities:
Root Causation for Network Operations AI-driven causal reasoning identifies fault origins in multi-domain setups, significantly lowering Mean Time to Resolution.
Dashboard Metadata as Training Data for Natural Language Querying Visualization and interaction data fine-tune Selector's network-specific LLM for more accurate, context-rich natural language interactions.
Metrics, Events, Alert Extractions from System Logs Unstructured logs are converted into structured, correlated insights to accelerate anomaly detection and consistent analytics.
Methods and Apparatus for Network Tracing, Forecasting, and Capacity Planning Advanced modeling predicts capacity risks and growth patterns to prevent performance degradation proactively.
Methods and Apparatus for Determining a Path that a Data Packet Would Traverse Through a Communication Network at a Time of Interest Reconstructs exact packet paths historically, enhancing forensic analysis and root cause precision.
Early Identification of Optical Transceiver Failures Predictive models detect hardware degradation in optics early, enabling preemptive replacements to avoid outages.
Methods and Apparatus for Efficient Storage and Querying of Communication Network Parameters Scalable indexing and querying techniques handle massive network topology and routing data efficiently.
Maintenance Window Aware Reporting Automatically identifies and excludes maintenance periods from analytics for more reliable service metrics.
"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."
With this strengthened intellectual property foundation, Selector continues to lead in delivering clarity, context, and control for network teams navigating the demands of modern, AI-era infrastructures.
About Selector
Selector delivers an AI-powered observability and network intelligence platform that unifies data, correlation, and automation across domains. By combining large language models, knowledge graphs, and causal reasoning, Selector enables teams to detect, diagnose, and resolve issues faster. Leading telecommunications providers, cloud service providers, and global enterprises rely on Selector to reduce MTTR, prevent outages, and accelerate transformation. Selector is backed by leading investors, including Two Bear Capital, Atlantic Bridge Ventures, Sinewave Ventures, Ansa Capital, Singtel Innov8, Hyperlink Ventures, AT&T Ventures, Bell Ventures, and Comcast Ventures.
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