# US 11,030,080 B2 · System for optimization of data loading in a software code development platform > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,030,080 B2 - Document ID: US11030080B2 - Status: Granted - Kind: B2 - Application number: 16/578,824 - Filed: 2019-09-23 - Published: 2021-03-25 - Granted: 2021-06-08 - Earliest claimed: 2019-09-23 - Assignees: Bank of America Corporation - Applicants: Bank of America Corporation - Inventors: Basavaiah Chandra - Web page: https://patentgrep.com/patent/US11030080B2 ## CPC - G06F 11/3457: {Performance evaluation by simulation} - G06F 11/302: {where the computing system component is a software system} - G06F 11/3419: {by assessing time} - G06F 11/3698: Environments for analysis, debugging or testing of software - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06F 18/24: Classification techniques - G06N 20/00: Machine learning - G06F 2201/805: Real-time - G06F 2201/81: Threshold - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A system is typically configured for gathering one or more input parameters associated with data loading in a software application, simulating a production environment based on the one or more input parameters, executing a data loading code associated with the software application in the simulated production environment, and calculating a data loading time based at least on historical data and an output associated with executing the data loading code in the simulated production environment. ## Claims ### Claim 1 A system for optimization of data loading in a software code development platform, comprising: at least one processing device; at least one memory device; and a module stored in the at least one memory device comprising executable instructions that when executed by the at least one processing device, cause the at least one processing device to: gather one or more input parameters associated with data loading in a software application; simulate a production environment based on the one or more input parameters for executing the software application, wherein the production environment is a replica of a real-time production server; execute a data loading code associated with the software application in the simulated production environment; calculate a data loading time based at least on historical data and an output associated with executing the data loading code in the simulated production environment; identify that the data loading time is greater than a predetermined threshold; automatically optimize, via a machine learning model, the data loading code, wherein the machine learning model is trained based on historical optimization data; and display the optimized data loading code to a user. ### Claim 2 (depends on 1) The system according to claim 1, wherein the executable instructions cause the at least one processing device to gather the one or more input parameters based on: prompting user to provide the one or more input parameters; and receiving the one or more input parameters from the user. ### Claim 3 (depends on 1) The system according to claim 1, wherein the executable instructions cause the at least one processing device to gather at least one of the one or more input parameters based on crawling into the real-time production server and identifying hardware parameters and software parameters associated with the real-time production server. ### Claim 4 (depends on 1) The system according to claim 1, wherein the executable instructions cause the at least one processing device to generate the data loading code based on the one or more input parameters, wherein the data loading code is compatible with a data load tool utilized in the production environment. ### Claim 5 (depends on 4) The system according to claim 4, wherein the executable instructions cause the at least one processing device to generate the data loading code based on converting at least one parameter of the one or more input parameters into programmable code based on performing natural language processing. ### Claim 6 (depends on 1) The system according to claim 1, wherein the executable instructions cause the at least one processing device to re-train the machine learning model based on real-time data loading time. ### Claim 7 (depends on 1) The system according to claim 1, wherein the executable instructions cause the at least one processing device to calculate the data loading time using classification algorithm. ### Claim 8 (depends on 1) The system according to claim 1, wherein the executable instructions cause the at least one processing device to receive the data loading code from the user. ### Claim 9 A computer program product for optimization of data loading in a software code development platform, comprising a non-transitory computer-readable storage medium having computer-executable instructions for: gathering one or more input parameters associated with data loading in a software application; simulating a production environment based on the one or more input parameters for executing the software application, wherein the production environment is a replica of a real-time production server; executing a data loading code associated with the software application in the simulated production environment; calculating a data loading time based at least on historical data and an output associated with executing the data loading code in the simulated production environment; identifying that the data loading time is greater than a predetermined threshold; automatically optimizing, via a machine learning model, the data loading code, wherein the machine learning model is trained based on historical optimization data; and displaying the optimized data loading code to a user. ### Claim 10 (depends on 9) The computer program product according to claim 9, wherein the non-transitory computer-readable storage medium comprises computer-executable instructions for gathering at least one of the one or more input parameters based on crawling into the real-time production server and identifying hardware parameters and software parameters associated with the real-time production server. ### Claim 11 (depends on 10) The computer program product according to claim 10, wherein the non-transitory computer-readable storage medium comprises computer-executable instructions for generating the data loading code based on the one or more input parameters, wherein the data loading code is compatible with a data load tool utilized in the production environment. ### Claim 12 (depends on 11) The computer program product according to claim 11, wherein the non-transitory computer-readable storage medium comprises computer-executable instructions for re-training the machine learning model based on real-time data loading time. ### Claim 13 A computer implemented method for optimization of data loading in a software code development platform, comprising: gathering one or more input parameters associated with data loading in a software application; simulating a production environment based on the one or more input parameters for executing the software application, wherein the production environment is a replica of a real-time production server; executing a data loading code associated with the software application in the simulated production environment; calculating a data loading time based at least on historical data and an output associated with executing the data loading code in the simulated production environment identifying that the data loading time is greater than a predetermined threshold; automatically optimizing, via a machine learning model, the data loading code, wherein the machine learning model is trained based on historical optimization data; and displaying the optimized data loading code to a user. ### Claim 14 (depends on 13) The computer implemented method according to claim 13, wherein the method for gathering at least one of the one or more input parameters comprises crawling into the real-time production server and identifying the hardware parameters and software parameters associated with the real-time production server. ### Claim 15 (depends on 13) The computer implemented method according to claim 13, wherein the method further comprises generating the data loading code based on the one or more input parameters, wherein the data loading code is compatible with a data load tool utilized in the production environment. ### Claim 16 (depends on 13) The computer implemented method according to claim 13, wherein the method further comprises re-training the machine learning model based on real-time data loading time. ### Claim 17 (depends on 13) The computer implemented method according to claim 13, wherein the method comprises receiving the data loading code from the user.