US 12,267,202 B2
Systems and methods for a framework for algorithmically identifying critical software systems and applications within an organization
Jonathan Underwood, Leighton Buzzard (GB); David L. Houck, Maidens, VA (US); and Naoum Anagnos, Rockville, MD (US)
Assigned to Capital One Services, LLC, McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Nov. 3, 2023, as Appl. No. 18/501,522.
Application 18/501,522 is a continuation of application No. 18/062,336, filed on Dec. 6, 2022, granted, now 11,811,583.
Prior Publication US 2024/0187300 A1, Jun. 6, 2024
Int. Cl. H04L 41/22 (2022.01); H04L 41/0604 (2022.01)
CPC H04L 41/0627 (2013.01) [H04L 41/0609 (2013.01); H04L 41/22 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system of generating real-time notifications of software application importance based on current processing requirements in a disparate computer network, the system comprising:
one or more processors; and
a non-transitory computer readable medium having instructions recorded thereon that when executed by the one or more processors cause operations comprising:
receiving a first dataset, wherein the first dataset comprises first amount of traffic, wherein the first amount of traffic comprise a respective amount of traffic for a plurality of processing requirements;
receiving a second dataset, wherein the second dataset comprises second amount of traffic, wherein the second amount of traffic comprise a respective amount of traffic for a plurality of applications;
receiving a third dataset, wherein the third dataset comprises dependencies between the plurality of processing requirements and the plurality of applications, wherein the dependencies are determined by querying each of the plurality of applications to determine one or more of the plurality of processing requirements served by a respective application of the plurality of applications and by querying each of the plurality of processing requirements to determine one or more of the plurality of applications relied on by a respective processing requirement of the plurality of processing requirements;
determining a set of many-to-many relationships between each of the plurality of processing requirements and each of the plurality of applications based on the dependencies;
inputting the set of many-to-many relationships into a model to identify respective importance metrics for each of the plurality of applications, wherein machine learning is trained to determine importance metrics based on a number of relationships, in the set of many-to-many relationships, for a given application and a amount of traffic for the given application; and
generating for display, on a user interface of a user device, a ranking of the plurality of applications based on the respective importance metrics.