US 12,141,051 B2
Machine-learning-based techniques for predictive monitoring of a software application framework
Vipul Gupta, Banglore (IN); and Akshar Prasad, Bengaluru (IN)
Assigned to Atlassian Pty Ltd, Sydney (AU); and Atlassian US, Inc., San Francisco, CA (US)
Filed by ATLASSIAN PTY LTD., Sydney (AU); and ATLASSIAN US, INC., San Francisco, CA (US)
Filed on May 31, 2022, as Appl. No. 17/804,702.
Prior Publication US 2023/0385182 A1, Nov. 30, 2023
Int. Cl. G06F 9/44 (2018.01); G06F 11/36 (2006.01)
CPC G06F 11/3672 (2013.01) 20 Claims
OG exemplary drawing
 
1. An apparatus for predictive monitoring of a software application framework, the apparatus comprising at least one processor and at least one non-transitory memory comprising a computer program code, the at least one non-transitory memory and the computer program code configured to, with the at least one processor, cause the apparatus to:
identify, by the processor, a software alert data object for the software application framework, wherein the software alert data object is associated with one or more alert attribute data fields;
generate, by the processor and using an alert classification neural network machine learning model, and based on the one or more alert attribute data fields, a deep-learning-based alert priority designation for the software alert data object;
generate, by the processor and using an alert classification decision tree machine learning model, and based on the one or more alert attribute data fields, a decision-tree-based alert priority designation for the software alert data object;
generate, by the processor and based on the deep-learning-based alert priority designation and the decision-tree-based alert priority designation, an alert signature for the software alert data object; and
cause, by the processor, performance of one or more incident management actions based on the alert signature.