US 2026/0300474 A1 · GENERATING ENHANCED ANOMALOUS SIGNAL GROUPS IN A CLOUD COMPUTING SYSTEM USING GROUPING LOGIC MODELS

Abstract

This disclosure describes utilizing an anomaly-based mitigation system to accurately, flexibly, and efficiently determine groupings of anomalous signals from across a computing system by identifying and combining different logical groups of anomalous signals. For example, the anomaly-based mitigation system utilizes different grouping logic models, which identify various connection strategies to determine initial groupings of anomalous signals. The anomaly-based mitigation system then determines commonalities between the initial anomalous signal groups using graph models to identify connected groups of anomalous signals. Furthermore, the anomaly-based mitigation system refines the groups to enhance their effectiveness in identifying and resolving service incidents in the computing system. In some instances, the anomaly-based mitigation system presents the anomalous signal groups in an incident report interface that includes actions to take or actions that were automatically taken to resolve a service incident.

Claims20

Dependent claims

A computer-implemented method for generating anomaly groupings for incidents in a computing system using one or more generative artificial intelligence (AI) models, the computer-implemented method comprising:

obtaining a set of detected anomalous signals corresponding to a cloud computing system;
generating, using multiple different grouping logic models, multiple data graphs that each include groups of related anomalous signals from the set of detected anomalous signals;
generating an aggregated data graph by combining the multiple data graphs to form multiple groups of connected anomalous signals;
determining a root cause for a first group of the multiple groups of connected anomalous signals based on a first set of anomalous signals in the first group using a generative AI model;
and
providing the multiple groups of connected anomalous signals within an incident report interface, the incident report interface including the first group and the root cause for the first set of anomalous signals.

3 more elements

A system comprising:

a processing system having a processor;
and
a computer memory including instructions that, when executed by the processing system, cause the system to carry out operations comprising:
generating, using multiple different grouping logic models, multiple data graphs that each include groups of related anomalous signals from a set of detected anomalous signals, wherein the groups of related anomalous signals from the multiple different grouping logic models differ from each other;
generating an aggregated data graph by combining the multiple data graphs to form multiple groups of connected anomalous signals;
determining a root cause for a first group of the multiple groups of connected anomalous signals based on a first set of anomalous signals in the first group using a generative AI model;
and
providing the multiple groups of connected anomalous signals within an incident report interface, the incident report interface including the first group and the root cause for the first set of anomalous signals.

5 more elements

Dependencies are inferred from the claim text.

Generating enhanced anomalous signal groups in a cloud computing system using grouping logic models

Life of this patent

Generating enhanced anomalous signal groups in a cloud computing system using grouping logic models

Life of this patent

Abstract

Claims20