US 11,704,115 B2
Software pipeline configuration
Luis Mirantes, Chevy Chase, MD (US); and Ryan McEntee, Midlothian, VA (US)
Assigned to Capital One Services, LLC, McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Jul. 6, 2022, as Appl. No. 17/810,926.
Application 17/810,926 is a continuation of application No. 17/159,613, filed on Jan. 27, 2021, granted, now 11,403,094.
Claims priority of provisional application 62/966,442, filed on Jan. 27, 2020.
Prior Publication US 2022/0334832 A1, Oct. 20, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 9/44 (2018.01); G06F 8/71 (2018.01); G06F 8/60 (2018.01); G06F 11/36 (2006.01); G06F 8/41 (2018.01)
CPC G06F 8/71 (2013.01) [G06F 8/60 (2013.01); G06F 8/41 (2013.01); G06F 11/3668 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for executing gates of a pipeline for deploying an application, the system comprising:
a computer system including one or more processors programmed with computer program instructions that, when executed, cause operations comprising:
obtaining, from a storage system, a compliance configuration file associated with an application, the compliance configuration file comprising a machine learning model type related to the application;
obtaining, from the storage system, a compliance gate mapping file comprising mappings of gates to respective attribute values, the gates comprising code processing gates for processing specific software code of a specific application, the mappings comprising (i) a first mapping indicating a first gate to be invoked for a first attribute value set having a first machine learning model and a second mapping indicating a second gate to be invoked for a second attribute value set having a second machine learning model;
processing, via a state machine subsystem, the configuration file using (i) the compliance gate mapping file and (ii) the machine learning model type to determine a set of gates to be invoked for one or more portions of the application; and
executing, via the state machine subsystem, a software routine set corresponding to the set of gates, wherein executing the software routine set comprises:
(i) in response to the machine learning model type in the compliance configuration file being the first machine learning model, executing software routines corresponding to the first gate indicated in the gate mapping file to process the application; and
(ii) in response to the machine learning model type in the compliance configuration file being the second machine learning model, executing software routines corresponding to the second gate indicated in the gate mapping file to process the application.