US 11,790,638 B2
Monitoring devices at enterprise locations using machine-learning models to protect enterprise-managed information and resources
Stephen T. Shannon, Charlotte, NC (US); James Alexander, Austin, TX (US); and Brian J. Smith, St. Augustine, FL (US)
Assigned to Bank of America Corporation, Charlotte, NC (US)
Filed by Bank of America Corporation, Charlotte, NC (US)
Filed on Feb. 18, 2022, as Appl. No. 17/674,920.
Application 17/674,920 is a continuation of application No. 16/775,801, filed on Jan. 29, 2020, granted, now 11,288,494.
Prior Publication US 2022/0172516 A1, Jun. 2, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06V 40/16 (2022.01); G06V 10/774 (2022.01); G08B 25/01 (2006.01); G06N 20/00 (2019.01); G06F 18/21 (2023.01); G06V 10/778 (2022.01)
CPC G06V 10/774 (2022.01) [G06F 18/217 (2023.01); G06N 20/00 (2019.01); G06V 10/7784 (2022.01); G06V 40/172 (2022.01); G08B 25/014 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computing platform, comprising:
at least one processor;
a communication interface communicatively coupled to the at least one processor; and
memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:
receive, via the communication interface, from one or more data source computer systems, passive monitoring data;
apply a machine-learning classification model to the passive monitoring data received from the one or more data source computer systems, wherein applying the machine-learning classification model to the passive monitoring data received from the one or more data source computer systems comprises applying the machine-learning classification model to device identification data received from a first enterprise center monitoring system deployed at a first enterprise center and wherein applying the machine-learning classification model to the passive monitoring data received from the one or more data source computer systems comprises applying the machine-learning classification model to facial recognition data received from the first enterprise center monitoring system deployed at the first enterprise center;
based on applying the machine-learning classification model to the passive monitoring data received from the one or more data source computer systems, determine to trigger a data capture process at a first enterprise center;
in response to determining to trigger the data capture process at the first enterprise center, initiate an active monitoring process to capture event data at the first enterprise center;
generate one or more alert messages based on the event data captured at the first enterprise center; and
send, via the communication interface, to one or more enterprise computer systems, the one or more alert messages generated based on the event data captured at the first enterprise center.