US 12,353,566 B2
Custom patching automation with machine learning integration
Syed Luqman Ahmed, New Delhi (IN); and Adi Narayana Rao Garaga, Telangana (IN)
Assigned to Bank of America Corporation, Charlotte, NC (US)
Filed by Bank of America Corporation, Charlotte, NC (US)
Filed on Apr. 30, 2024, as Appl. No. 18/651,009.
Application 18/651,009 is a continuation of application No. 18/139,455, filed on Apr. 26, 2023, granted, now 12,008,114.
Application 18/139,455 is a continuation of application No. 16/902,456, filed on Jun. 16, 2020, granted, now 11,669,621, issued on Jun. 6, 2023.
Prior Publication US 2024/0281542 A1, Aug. 22, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G08B 23/00 (2006.01); G06F 8/65 (2018.01); G06F 21/57 (2013.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01)
CPC G06F 21/577 (2013.01) [G06F 8/65 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06F 2221/034 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computing platform comprising:
a processor; and
memory storing computer-readable instructions that, when executed by the processor, cause the computing platform to:
identify a vulnerability associated with a server;
schedule, based on analysis of a knowledge base by a machine learning module, a time interval associated with the server, wherein the time interval comprises an outage period for the server and a tracking module identifies existing time interval information associated with the server;
generate, automatically and by a patch generator, a patch job comprising an update for server software, wherein the patch job specifies that sub-processes of the patch job occur in a specific sequence;
validate, after deployment of the patch job during the time interval and continuously during execution of the patch job, that the sub-processes are ordered correctly;
update, based on an indication that the sub-processes are ordered incorrectly, a sub-process sequence of the patch job;
analyze, by the machine learning module, an assessment report associated with an executed patch job; and
update the knowledge base of the machine learning module based on the analysis of the assessment report associated with the executed patch job.