US 9,811,795 B1
Real-time adaptive operations performance management system using event clusters and trained models
Justin David Kearns, Seattle, WA (US); Ophir Ronen, Seattle, WA (US); and Laura Ann Zuchlewski, Seattle, WA (US)
Assigned to PagerDuty, Inc., San Francisco, CA (US)
Filed by PagerDuty, Inc., San Francisco, CA (US)
Filed on Feb. 27, 2017, as Appl. No. 15/443,961.
Application 15/443,961 is a continuation of application No. 15/254,996, filed on Sep. 1, 2016, granted, now 9,582,781.
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 17/00 (2006.01); G06Q 10/06 (2012.01); G06N 99/00 (2010.01); G06N 5/04 (2006.01)
CPC G06Q 10/0637 (2013.01) [G06N 5/04 (2013.01); G06N 99/005 (2013.01)] 30 Claims
OG exemplary drawing
 
1. A method for managing operations for organizations over a network using one or more network computers that include one or more processors that perform actions, comprising:
employing a plurality of provided Operations events to perform further actions, including:
providing, by the one or more processors, one or more event clusters that are associated with one or more Operations events;
associating, by the one or more processors, one or more resolution metrics with the one or more event clusters;
employing, by the one or more processors, a modeling engine to train one or more models based on the one or more Operations events, the one or more event clusters, and the one or more resolution metrics, wherein the one or more trained models are stored in a datastore; and
retrieving, by the one or more processors, the one or more trained models, from the datastore, that are used to identify the one or more resolution metrics that are associated with one or more real-time Operations events.