Systems and methods for a framework for algorithmically identifying critical software systems and applications within an organization

Patent metadata

Field	Value
Publication number	US-12267202-B2
Application number	US-202318501522-A
Country	US
Kind code	B2
Filing date	Nov 3, 2023
Priority date	Dec 6, 2022
Publication date	Apr 1, 2025
Grant date	Apr 1, 2025

Abstract

Methods and systems generating real-time notifications of software application importance based on current processing requirements. The method includes receiving a first dataset, wherein the first dataset comprises recovery time estimates for processing requirements. The method includes receiving a second dataset, wherein the second dataset comprises second recovery time estimates for applications. The method includes receiving a third dataset, wherein the third dataset comprises dependencies between processing requirements and applications. The method determines many-to-many relationships between the processing requirements and applications based on the dependencies. The method inputs the many-to-many relationships into a machine learning model to identify importance metrics for each application. The method generates, for display on a user interface, a ranking of the applications in order of importance metric.

First claim

What is claimed is:

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.

2. A method of generating real-time notifications of software application importance based on current processing requirements in a disparate computer network, the method comprising:
receiving a first data set, wherein the first data set comprises a first amount of traffic attributed to computational processes executed by a plurality of applications, wherein the first amount of traffic comprises a respective amount of traffic amount of traffic for a plurality of processing requirements;
receiving a second data set, wherein the second data set 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;
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.

3. The method of claim 2 , wherein identifying respective importance metrics comprises:
determining a first importance metric for a first application of the plurality of applications by:
determining a first number of relationships for the first application in the set of many-to-many relationships;
determining a first amount of traffic for the first application; and
weighing the first number of relationships and the first amount of traffic to determine the first importance metric.

4. The method of claim 3 , wherein weighing the first number of relationships and the first amount of traffic to determine the first importance metric further comprises:
determining a first ranking of the first application among the plurality of applications based on comparing the first number of relationships to respective number of relationships for other applications of the plurality of applications; and
determining a second ranking of the first application among the plurality of applications based on comparing the first amount of traffic to respective amount of traffic for the other applications.

5. The method of claim 2 , wherein identifying respective importance metrics comprises:
determining a first importance metric for a first application of the plurality of applications by:
determining a first computer processing unit (“CPU”) workload attributed to computational processes executed by the first application when serving the plurality of processing requirements; and
weighing the first CPU workload to determine the first importance metric.

6. The method of claim 2 , wherein identifying respective importance metrics comprises:
determining a first importance metric for a first application of the plurality of applications by:
determining a recovery time estimates on a network attributed to computational processes executed by the first application when serving the plurality of processing requirements; and
weighing the recovery time estimates to determine the first importance metric.

7. The method of claim 2 , wherein identifying respective importance metrics comprises:
determining a first importance metric for a first application of the plurality of applications by:
determining first amount of traffic, wherein the first amount of traffic comprise a respective amount of traffic for the plurality of processing requirements; and
weighing the first amount of traffic to determine the first importance metric.

8. The method of claim 7 , wherein the first amount of traffic comprise a maximal time allotment that one or more of the plurality of processing requirements can remain non-operational due to network connectivity interruptions, and wherein the second amount of traffic comprises a maximal time allotment that one or more applications can remain non-operational due to the network connectivity interruptions.

9. The method of claim 7 , wherein the first amount of traffic comprise a maximal time allotment that one or more of the plurality of processing requirements can remain non-operational due to power interruptions, and wherein the second amount of traffic comprises a maximal time allotment that one or more applications can remain non-operational due to the power interruptions.

10. The method of claim 2 , wherein identifying respective importance metrics comprises:
determining a first importance metric for a first application of the plurality of applications by:
determining a first maximum allowable latency for the first application when serving the plurality of processing requirements; and
weighing the first maximum allowable latency to determine the first importance metric.

11. The method of claim 2 , wherein identifying respective importance metrics comprises:
determining a first importance metric for a first application of the plurality of applications by:
determining a first rate of read and write operations for the first application when serving the plurality of processing requirements; and
weighing the first rate of read and write operations to determine the first importance metric.

Assignees

• Capital One Services Llc

Inventors

• Underwood Jonathan
• Houck David L
• Anagnos Naoum

Classifications

• H04L41/0609

  based on severity or priority · CPC title

• H04L41/22 Primary

  comprising specially adapted graphical user interfaces [GUI] · CPC title

• H04L41/0823

  characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability (for optimising operational conditions of wireless networks H04W24/02) · CPC title

• H04L41/16

  using machine learning or artificial intelligence · CPC title

• H04L43/0811

  by checking connectivity · CPC title