US 12,271,741 B2
Critical event management using predictive models, and related methods and software
Urvish Saraiya, Westborough, MA (US); Eamon G. O Neill, Newton, MA (US); Cory Veilleux, Hardwick, MA (US); Prashant Desai, Lexington, MA (US); Diptesh Shah, Ashland, MA (US); Prashant Darisi, Nashua, NH (US); Christopher E. Seline, Washington, DC (US); and Anton Dam, Orinda, CA (US)
Assigned to Everbridge, Inc., Burlington, MA (US)
Filed by Everbridge, Inc., Burlington, MA (US)
Filed on Jul. 24, 2023, as Appl. No. 18/225,470.
Application 18/225,470 is a continuation of application No. 17/290,800, granted, now 11,775,323, previously published as PCT/US2019/059471, filed on Nov. 1, 2019.
Claims priority of provisional application 62/754,303, filed on Nov. 1, 2018.
Prior Publication US 2023/0367613 A1, Nov. 16, 2023
Int. Cl. G06F 9/451 (2018.01); G06F 3/04847 (2022.01); G06F 16/901 (2019.01); G06Q 10/0635 (2023.01); G06Q 10/0637 (2023.01)
CPC G06F 9/451 (2018.02) [G06F 3/04847 (2013.01); G06F 16/9017 (2019.01); G06Q 10/0635 (2013.01); G06Q 10/0637 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method of assisting a user with critical-event management, the method being performed by a computing system and comprising:
displaying, to a user via a graphical user interface (GUI) of the computing system, information concerning a first stored critical event;
soliciting, via the GUI, a user to provide one or more attribute annotations for one or more corresponding respective attributes of the first stored critical event;
receiving, from the user via the GUI, the one or more attribute annotations;
storing, in memory of the computing system, the one or more attribute annotations in an analytics table comprising values for a plurality of attributes of each of a plurality of stored critical events, including the first stored critical event;
executing at least one first predictive algorithm that operates on contents of the analytics table so as to build one or more predictive models representing at least some of the plurality of stored critical events; and
storing, in a memory of the computing system, the one or more predictive models;
wherein the first stored critical event is a closed critical event.