US 11,886,276 B2
Automatically correlating phenomena detected in machine generated data to a tracked information technology change
Yaron Lehmann, Tel Aviv (IL); Dror Mann, New York, NY (US); and Gabby Menahem, Santa Clara, CA (US)
Assigned to ServiceNow, Inc., Santa Clara, CA (US)
Filed by ServiceNow, Inc., Santa Clara, CA (US)
Filed on Nov. 16, 2020, as Appl. No. 17/099,501.
Prior Publication US 2022/0156134 A1, May 19, 2022
Int. Cl. G06F 11/00 (2006.01); G06N 20/00 (2019.01); G06Q 10/0635 (2023.01)
CPC G06F 11/004 (2013.01) [G06N 20/00 (2019.01); G06Q 10/0635 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
training a machine learning model based on previously identified valid matches between different phenomena detected in different machine-generated data and corresponding different root cause information technology changes;
receiving via an information technology service management system, a specification of an information technology change detected in the information technology service management system;
analyzing the specification of the information technology change to determine features of the information technology change;
tracking the information technology change in a data structure of tracked information technology changes and their features;
analyzing machine-generated computer log data to identify a change detected in the machine-generated computer log data;
providing to the trained machine learning model the features of the information technology change and features of the identified change in the machine-generated computer log data; and
identifying a match between the information technology change tracked in the data structure among other information technology changes tracked in the data structure and the identified change in the machine-generated computer log data based on a result of the trained machine learning model.