US 12,288,045 B2
Methods and systems for self-optimizing library functions
Sandeep Verma, Gurugram (IN)
Assigned to Bank of America Corporation, Charlotte, NC (US)
Filed by Bank of America Corporation, Charlotte, NC (US)
Filed on Jul. 25, 2023, as Appl. No. 18/225,712.
Prior Publication US 2025/0036377 A1, Jan. 30, 2025
Int. Cl. G06F 8/76 (2018.01); G06F 8/30 (2018.01); G06F 8/36 (2018.01); G06F 8/70 (2018.01); G06F 8/75 (2018.01); G06F 8/77 (2018.01); G06F 9/448 (2018.01); G06Q 10/10 (2023.01); H04L 65/612 (2022.01)
CPC G06F 8/31 (2013.01) [G06F 8/36 (2013.01); G06F 8/70 (2013.01); G06F 8/75 (2013.01); G06F 8/77 (2013.01); G06F 9/449 (2018.02); G06Q 10/103 (2013.01); H04L 65/612 (2022.05)] 18 Claims
OG exemplary drawing
 
11. A machine learning engine for increasing efficiency of an application written in a computer code, the computer code associated with a programing library, wherein the programming library comprises a library function, wherein the library function is associated with a default algorithm and a plurality of alternate algorithms, wherein the machine learning engine is configured to:
scan the computer code to identify a plurality of occurrences of the library function;
instruct a processor to iteratively execute altered versions of the application, each altered version comprising an altered algorithm at one or more of the occurrences;
record an execution parameter associated with executing the library function at each of the plurality of occurrences; and
output an optimized algorithm associated with the library function for each of the plurality of occurrences, based at least in part on the execution parameter;
wherein the machine learning engine is further configured to instruct the processor to operate in either a training mode or a default mode, wherein:
in the training mode, at a predetermined fraction of occurrences, the incoming datasets are processed using the altered versions of the application; and
in the default mode, all incoming datasets are processed using the application.