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InsightFinder has been granted its automation patent which completes its unique closed-loop reliability platform consisting of incident prediction, automatic root cause analysis, and automated preventive actions, reinforcing a simple truth our customers already know: reliability isn’t achieved by “more data” or “better dashboards”; it’s achieved by deep analysis in the face of massive monitoring data and system complexity.
This newly granted invention covers a system and method for machine-learning-driven automated incident prevention in distributed systems. The system goes beyond observability to help teams learn from prior incidents and apply proven remediation techniques earlier (and more consistently) to prevent future disasters.
Why this patent matters now
Today’s complex systems don’t fail politely. They fail with partial symptoms, cascading dependencies, messy telemetry, and a flood of alerts that almost never explain what to do next.
Most observability tools stop at detection: “something looks wrong.” The best of breed might even take it a step further to triaging the source. But service reliability teams (SRE, DevOps, etc) need more than awareness. They need a fast, repeatable path to go from signal → diagnosis → prevention.
That’s the gap this patent targets: turning incident knowledge (telemetry + incident reports + prior fixes) into an engine that helps classify incidents, infer likely root causes, and recommend or trigger remediations. Constant learning means that incident prevention becomes a compounding capability in your teams, rather than an exhausting human memory exercise.
What the new patent covers
At a high level, the patent covers an end-to-end technique that brings multiple data types and workflows together, so that the system can learn incident patterns and amass remediation knowledge over time.
The invention includes:
- Pattern extraction across heterogeneous machine data (including semi-structured/free-form text) to surface incident and root-cause patterns.
- Learning from previous incidents to discover, apply, and annotate remediation techniques so that successful fixes become “known good” prevention actions.
- Using prior remediation techniques to predict and prevent future issues instead of just waiting and reacting after the fact.
Cementing our Composite AI strategy
This third patent is also a hallmark of InsightFinder’s commitment to using Composite AI to achieve the best outcomes for different reliability concerns across your stack.
We believe in using the best AI techniques (beyond simply correlation or Generative AI) to create effective solutions. When it comes to building service reliability, operational concerns pose fundamentally different questions and challenges:
- “Is anything unusual happening?” (detection)
- “What’s the most likely cause?” (diagnosis)
- “What should we do right now?” (decision / action)
- “How do we prevent this class of incident from recurring?” (prevention)
A single AI technique rarely excels at all of these. InsightFinder AI brings together multiple approaches—unsupervised AI, predictive AI, causal AI, and generative AI—to create a multidisciplinary approach (Composite AI) where each reliability domain is solved by methods best suited to their inherent tasks.
InsightFinder’s patents map to the Composite AI reliability lifecycle
Taken together, InsightFinder’s patent collection represents an intentional blueprint: detect early, explain accurately, then prevent repeatedly.
1) Detect: Unsupervised behavior learning unguided anomaly prediction
Our Unsupervised Behavior Learning patent focuses on predicting problems from unlabeled training data, using a model of normal vs anomalous behavior (including a self-organizing map) and producing ranked contributors to failures.
2) Explain: Signal pattern extraction + holistic root cause analysis
Our event pattern extraction + holistic RCA patent covers extracting patterns from diverse sources (metrics, system calls, logs), then identifying correlation and causality relationships to pinpoint an accurate root cause and estimate impact scope.
3) Prevent: ML-driven learning for automated incident prevention
This newly granted patent extends beyond “detect and explain” into the remediation loop. It uses reported incident data to help discover and apply remediations. It then learns from those remediations to predict and prevent future issues.
This is our Composite AI strategy made real: not one model doing everything, but an integrated reliability platform where detection, diagnosis, and prevention reinforce each other—and get better with every incident your team resolves.
What this means for reliability teams
This patent is ultimately about reducing repeated incidents and reducing toil by leveraging your team’s operational knowledge:
- Less time triaging the “same incident in a different disguise”
- Faster path from weak signals to concrete prevention actions
- A reliability loop that improves as your environment evolves, rather than relying on static thresholds and brittle runbooks
Try it for yourself
Three granted patents. One consistent direction: unparalleled capabilities for creating system reliability.
If you’d like to see how InsightFinder is built to move your team beyond observability and into consistent reliability outcomes, request a demo.
