SYSTEM AND METHOD FOR OPTIMIZING RULES USING A MACHINE LEARNING MODEL

Abstract

A system and method for intelligent computerized task scheduling and execution, including: optimizing a time off rule including a quota of time off units for a time period—by changing the quota of time off units based on calculating a time off utilization indicator; updating a computerized task schedule based on the optimized time off rule; and executing tasks based on the updated schedule. In some embodiments, time off optimization may include identifying, by a machine learning model (such as, e.g., a generative artificial intelligence or large language model), rules matching a given time off rule, and deleting/merging rules based on similar rule names or activity codes. The machine learning model may generate rule names for merged rules. Optimized time off rules may be used to accept or reject time off requests transmitted and/or received, e.g. over a data or communication network.

CPC Classifications