US 12,425,313 B2
Impact predictions based on incident-related data
Steven Earhart, Kirkwood, MO (US); Leila Hassan, Ballwin, MO (US); James J. Arnott, Ballwin, MO (US); and Adam Suarez, Chesterfield, MO (US)
Assigned to MASTERCARD INTERNATIONAL INCORPORATED, Purchase, NY (US)
Filed by MASTERCARD INTERNATIONAL INCORPORATED, Purchase, NY (US)
Filed on Jul. 12, 2023, as Appl. No. 18/351,458.
Application 18/351,458 is a continuation of application No. 17/459,309, filed on Aug. 27, 2021, granted, now 11,711,275.
Claims priority of provisional application 63/071,480, filed on Aug. 28, 2020.
Prior Publication US 2023/0362071 A1, Nov. 9, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. H04L 41/5074 (2022.01); H04L 41/147 (2022.01); H04L 41/16 (2022.01); H04L 41/5025 (2022.01); H04L 41/507 (2022.01); H04L 43/04 (2022.01); H04L 43/08 (2022.01); H04L 43/50 (2022.01)
CPC H04L 41/5074 (2013.01) [H04L 41/147 (2013.01); H04L 41/16 (2013.01); H04L 41/5025 (2013.01); H04L 41/507 (2013.01); H04L 43/04 (2013.01); H04L 43/50 (2013.01); H04L 43/08 (2013.01)] 20 Claims
OG exemplary drawing
 
9. A system for predicting impact on infrastructure elements based on incident ticket data, the system comprising:
at least one processor; and
at least one memory comprising computer program code, the at least one memory and the computer program code configured to cause the at least one processor to:
receive an incident ticket, the incident ticket including data related to an incident occurred during an impact time period;
perform natural language processing of the data included in the incident ticket to identify key fields;
using the key fields, search metric data and event data associated with the incident ticket to identify anomalous events during an updated time period including the impact time period and additional time;
generate predicted impact data for the updated time period based on the identified anomalous events, the predicted impact data identifying one or more infrastructure elements unidentified in the incident ticket;
using the predicted impact data, generate one or more labeled incident tickets;
using the one or more labeled incident tickets train an impact model;
cause the trained impact model to predict a set of infrastructure elements likely to be impacted based on a newly received incident ticket; and
update the newly received incident ticket with the predicted set of infrastructure elements.