US 11,914,459 B2
Automated identification of website errors
Jesus Alberto Leon Moctezuma, Phoenix, AZ (US); Amit Mondal, Phoenix, AZ (US); Karla D. Rosette, Phoenix, AZ (US); and Ayuna Tckachenko, Phoenix, AZ (US)
Assigned to AMERICAN EXPRESS TRAVEL RELATED SERVICES COMPANY, INC., New York, NY (US)
Filed by American Express Travel Related Services Company, Inc., New York, NY (US)
Filed on Jan. 27, 2022, as Appl. No. 17/586,430.
Claims priority of provisional application 63/266,200, filed on Dec. 30, 2021.
Prior Publication US 2023/0214288 A1, Jul. 6, 2023
Int. Cl. G06F 11/00 (2006.01); G06F 11/07 (2006.01); G06N 20/20 (2019.01); H04L 67/02 (2022.01); G06F 18/214 (2023.01)
CPC G06F 11/0772 (2013.01) [G06F 11/079 (2013.01); G06F 11/0793 (2013.01); G06F 18/214 (2023.01); G06N 20/20 (2019.01); H04L 67/02 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
at least one computing device comprising a processor and a memory; and
machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least:
receive a website navigation sequence of a series of web page interactions between a client device and a website;
determine an expected completion time for a next measurement of the client device executing the website navigation sequence based at least in part on a first machine learning model being trained with historical navigation sequence data;
determine an actual completion time for the next measurement of the client device executing the website navigation sequence;
determine an anomaly website event in the website navigation sequence based at least in part on the actual completion time failing to meet a boundary threshold associated with the expected completion time; and
determine the anomaly website event is a website error based at least in part on a second machine learning model being trained with a plurality of previous website errors identified from a plurality of previous website navigation sequences.