How AI Learns to Fix IT Problems by Asking for Feedback

This patent describes an AI system that continuously learns to identify and prevent IT issues by building a map of cause-and-effect relationships, getting human feedback, and automatically updating its understanding.

What it covers

The system creates a "causal graph," which is like a map showing how different events in an IT system are connected. It then asks for feedback on this map, for example, by displaying the causal graph and asking for a simple "yes" or "no" (Claim 5) if the connections are correct. This feedback, along with information about when and where events happened ("spatiotemporal context"), is fed into a machine learning model (Claim 1). The model uses this to build a "knowledge graph," a deeper understanding of the IT system. The process repeats: the system generates a new causal graph, gets more feedback, and updates the knowledge graph, even at different levels of detail (Claim 1), ultimately allowing an "Information Technology (IT) landscape manager" to find the root cause of problems and predict future issues to stop them before they happen, all without needing a person to step in (Claim 1).

What it doesn't cover

• —Does not cover systems that require manual tuning to determine event cluster boundaries, as the abstract states it does this "without requiring manual tuning."
• —Does not cover systems that only generate a knowledge graph once without a continuous feedback loop and update mechanism.
• —Does not cover systems that determine root causes or predict events without using a machine learning model to process feedback and spatiotemporal context.
• —Does not cover systems where the IT landscape manager requires human intervention to determine root causes or predict future events.
• —Does not cover systems that only use one level of detail for updating the knowledge graph, as it specifies updating at a "second level" different from the "first level" of feedback.

The clever bit

The novelty lies in the continuous, self-improving loop where a machine learning model constantly refines its understanding of cause-and-effect in an IT system by requesting and processing human feedback on its causal graphs, then using that to update its knowledge graph and predict future problems automatically.

Why it matters

In complex IT environments, finding the real cause of a problem can be like finding a needle in a haystack. This patent aims to automate that process, making IT systems more reliable and efficient. By predicting and preventing issues, it could significantly reduce downtime and operational costs for businesses relying on large-scale IT infrastructure.

Real-world examples

• 1.Automated IT operations (AIOps) platforms
• 2.Network monitoring and diagnostics tools
• 3.Cloud infrastructure management systems
• 4.Predictive maintenance for software systems