US 11,934,284 B2
Method and system for synthetic application monitoring
Bhoopendra Chauhan, Thane (IN); Connor Basilici, Wayne, PA (US); Jassi Singh, Short Hills, NJ (US); Ravindra P Padma, Glen Mills, PA (US); and Rohan Reddy Alluri, Wilmington, DE (US)
Assigned to JPMORGAN CHASE BANK, N.A., New York, NY (US)
Filed by JPMorgan Chase Bank, N.A., New York, NY (US)
Filed on Nov. 17, 2021, as Appl. No. 17/455,302.
Claims priority of application No. 202111045238 (IN), filed on Oct. 5, 2021.
Prior Publication US 2023/0106381 A1, Apr. 6, 2023
Int. Cl. G06F 11/00 (2006.01); G06F 11/30 (2006.01); G06F 11/32 (2006.01); G06F 11/34 (2006.01); G06N 20/00 (2019.01)
CPC G06F 11/302 (2013.01) [G06F 11/3051 (2013.01); G06F 11/327 (2013.01); G06F 11/328 (2013.01); G06F 11/3457 (2013.01); G06F 11/3466 (2013.01); G06N 20/00 (2019.01)] 16 Claims
OG exemplary drawing
 
1. A method for providing end-to-end monitoring of an application, the method being implemented by at least one processor, the method comprising:
receiving, by the at least one processor via a graphical user interface, at least one request to monitor the application, the at least one request including information relating to the application;
generating, by the at least one processor, at least one service call based on the at least one request, the at least one service call relating to a synthetic transaction in a master configuration;
scheduling, by the at least one processor, the at least one service call in the master configuration;
generating, by the at least one processor, at least one synthetic workflow based on the at least one service call;
executing, by the at least one processor, the at least one synthetic workflow based on a result of the scheduling;
capturing, by the at least one processor, at least one metric from the executed at least one synthetic workflow;
determining, by the at least one processor, at least one performance metric for the application based on the at least one captured metric;
comparing, by the at least one processor, the at least one performance metric with at least one predetermined threshold that is associated with the application,
wherein the at least one predetermined threshold is dynamically determined for the application by using at least one model; and
wherein the at least one model includes at least one from among a machine learning model, a statistical model, a mathematical model, a process model, and a data model; and
generating, by the at least one processor, at least one service ticket for the application based on a result of the comparison.