Distributed artificial intelligence software code optimizer

Patent metadata

Field	Value
Publication number	US-2025077290-A1
Application number	US-202318240450-A
Country	US
Kind code	A1
Filing date	Aug 31, 2023
Priority date	Aug 31, 2023
Publication date	Mar 6, 2025
Grant date	—

Abstract

Software code optimization for specific computerized tasks is achieved by utilizing task-specific ML models that are trained to determine optimal sets of software code by intelligently assessing available hardware resources (i.e., specifically, processing devices, such as CPUs and GPUs) and, in some instances, SDKs for a specified task and, as a result, determining an optimal set of software codes for the specified task that is tied to one or more of the available hardware resources.

First claim

What is claimed is:

1 . A system for intelligent software code optimization, the system comprising:
a computing platform including a memory and one or more computing processor devices in communication with the memory; and an Artificial Intelligence (AI) engine stored in the memory, executable by at least one of the one or more computing processing devices and including a plurality of Machine Learning (ML) models, each ML model (i) trained to generate software code for a specific task and (ii) configured to:
receive first inputs that define a plurality of currently available hardware resources for executing software code, and
in response to receiving the first inputs, generate at least one set of software codes for the specific task associated with the ML model, wherein each set of software codes are specific to at least one of the plurality of currently available hardware resources and one of the least one set of software codes is a most optimal set of software codes for completing the specific task, wherein the most optimal is defined by at least one of (i) a rate at which the specific task is completed, and (ii) an accuracy of completing the specific task.

2 . The system of claim 1 , wherein each ML model is further configured to:
receive the first inputs that define the plurality of currently available hardware resources for executing software code, wherein the plurality of currently available hardware resources include processing units comprising at least one of Central Processing Units (CPUs) and Graphics Processing Units (GPUs), and in response to receiving the first inputs, generate at least one set of software codes for the specific task associated with the ML model, wherein each set of software codes are specific to at least one of the processing units.

3 . The system of claim 1 , wherein each ML model is further configured to:
receive second inputs that define at least one Software Development Kit (SDK) version associated with the specific task, and in response to receiving the first and second inputs, generate at least one set of software codes for the specific task associated with the ML model, wherein each set of software codes are specific to the at least one of the plurality of currently available hardware resources and one of the at least one SDK versions.

4 . The system of claim 1 , wherein each ML model is further configured to:
receive second inputs that define user-specified constraints for determining the most optimal set of software codes, wherein the user-specified constraints define at least one of (i) required time constraints for completing the specific task, (ii) required accuracy constraints for completing the specific task, and (iii) resource acquisition constraints for acquiring at least one of the currently available hardware resources.

5 . The system of claim 4 , wherein each ML model is further configured to:
in response to receiving the first and second inputs, generate at least one set of software codes for the specific task associated with the ML model, wherein the at least one set of software codes is one specific set of software codes that is the most optimal set of software codes for completing the task, wherein the most optimal is further defined by the user-specified constraints.

6 . The system of claim 1 , wherein each ML model is further configured to:
receive first inputs that define the plurality of currently available hardware resources for executing software code, wherein the currently available hardware resources are further defined as chosen from the group consisting of (i) commercially available hardware resources, and (ii) inventoried hardware resources.

7 . The system of claim 1 , wherein each ML model is configured to:
generate at least one set of software codes for the specific task associated with the ML model, wherein the at least one set of software codes is a plurality of sets of software codes, and wherein the system further comprises: a regression test engine stored in the memory, executable by at least one of the one or more computing processor devices and configured to:
perform regression testing on each of the plurality of sets of software codes to determine which one of the plurality of sets of software codes is the most optimal.

8 . The system of claim 7 , wherein each ML model is further configured to:
learn, over time, by receiving results of regression of regression testing including which one of the plurality of sets of software codes is the most optimal.

9 . The system of claim 1 , wherein each ML model is further configured to:
receive second inputs that define one or more newly available hardware resources for executing software code, and in response to receiving the first and second inputs, generate at least one set of software codes for the specific task associated with the ML model, wherein each set of software codes are specific to at least one of the plurality of currently available hardware resources or at least one of the one or more newly available hardware resources and one of the least one set of software codes is a most optimal set of software codes for completing the specific task.

10 . A computer-implemented method for intelligently optimizing software code, the method executed by one or more computing processor devices and comprising:
training a Machine Learning (ML) model to generate software code for a specific task; receiving, at the ML model, first inputs that define a plurality of currently available hardware resources for executing software code; and in response to receiving the first inputs, executing the ML model to generate at least one set of software codes for the specific task, wherein each set of software codes are specific to at least one of the plurality of currently available hardware resources and one of the least one set of software codes is a most optimal set of software codes for completing the specific task, wherein the most optimal is defined by at least one of (i) a rate at which the specific task is completed, and (ii) an accuracy of completing the specific task.

11 . The computer-implemented method of claim 10 , wherein receiving further comprises:
receiving, at the ML model, the first inputs that define the plurality of currently available hardware resources for executing software code, wherein the plurality of currently available hardware resources include processing units comprising at least one of Central Processing Units (CPUs) and Graphics Processing Units (GPUs), and wherein executing further comprises: executing the ML model to generate at least one set of software codes for the specific task, wherein each set of software codes are specific to at least one of the processing units.

12 . The computer-implemented method of claim 10 , further comprising:
receiving, at the ML model, second inputs that define at least one Software Development Kit (SDK) version associated with the specific task, and wherein executing further comprises: in response to receiving the first and second inputs, executing the ML model to generate at least one set of software codes for the specific task, wherein each set of software codes are specific to the at least one of the plurality of currently available hardware resources and one of the at least one SDK versions.

13 . The computer-implemented method of claim 10 , further comprising:
receiving, at the ML model, second inputs that define user-specified constraints for determining the most optimal set of software codes, wherein the user-specified constraints define at least one of (i) required time constraints for completing the specific task, (ii) require

Assignees

• Bank Of America

Inventors

• Abraham Joshua
• Sinks Steven E

Classifications

• G06N20/00

  Machine learning · CPC title

• G06F8/30 Primary

  Creation or generation of source code · CPC title

• G06F9/5044

  considering hardware capabilities · CPC title

• G06F9/5055

  considering software capabilities, i.e. software resources associated or available to the machine · CPC title

• G06F9/5038 Primary

  considering the execution order of a plurality of tasks, e.g. taking priority or time dependency constraints into consideration (scheduling strategies G06F9/4881 and subgroups) · CPC title