US 11,809,840 B2
Cognitive software application learner and enhancer
Namrata Kurmi, Gautham Buddha Nagar (IN); Samir Kiranbhai Desai, Mumbai (IN); Pragyan Paramita Hembram, Telangana (IN); and Srikanth Vemula, Telangana (IN)
Assigned to BANK OF AMERICA CORPORATION, Charlotte, NC (US)
Filed by BANK OF AMERICA CORPORATION, Charlotte, NC (US)
Filed on Feb. 23, 2022, as Appl. No. 17/678,098.
Prior Publication US 2023/0266949 A1, Aug. 24, 2023
Int. Cl. G06F 8/30 (2018.01); G06N 20/00 (2019.01)
CPC G06F 8/311 (2013.01) [G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A system for continuous cognitive code logic detection and prediction using machine learning techniques, the system comprising:
at least one non-transitory storage device; and
at least one processor coupled to the at least one non-transitory storage device, wherein the at least one processor is configured to:
electronically receive, from a user input device, source code scripts for functional code logic components of a full stack, wherein the source code scripts are associated with one or more tiers in the full stack;
electronically receive, from the user input device, target code scripts for the functional code logic components of the full stack, wherein the target code scripts are associated with the one or more tiers in the full stack;
generate a training dataset based on at least the source code scripts, the target code scripts, and the functional code logic components of the full stack;
train, using a machine learning algorithm, a machine learning model using the training dataset;
determine a prediction accuracy associated with the trained machine learning model;
determine that the prediction accuracy associated with the trained machine learning model is greater than a predetermined threshold; and
in response to determining that the prediction accuracy associated with the trained machine learning model is greater than the predetermined threshold, deploy the trained machine learning model on unseen source code scripts.