ML Time-Off Rule Optimization for Schedule and Compliance Control
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Solution Overview
Problem
Existing time-off management systems face inefficiencies due to varying rules across different systems and jurisdictions, leading to understaffing, overscheduling, and compliance risks, exacerbated by diverse time-off policies and manual rule management.
Innovation Solution
A system utilizing a machine learning model, such as a generative artificial intelligence or large language model, optimizes time-off rules by calculating utilization indicators, merging similar rules, and automating time-off request management to ensure compliance and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual rule management is used to handle diverse time-off policies, then flexibility in managing different rules is maintained, but administrative burden and inefficiency increase
Solution Approach 1:
The system enables self-service automation where the machine learning model autonomously analyzes time-off requests, matches them against relevant rules, and manages approvals without manual intervention. The system serves itself by automatically updating schedules and notifying stakeholders, eliminating the need for administrative personnel to manually manage diverse time-off policies.
Solution Approach 2:
The patent replaces manual mechanical rule management with an intelligent machine learning system. The ML model automatically processes time-off requests, interprets complex policies, and makes decisions that previously required human administrators, thereby substituting manual operations with automated intelligent processing.
2Productivity
If diverse time-off policies are managed manually, then compliance with various rules can be maintained, but efficiency and productivity decrease
Solution Approach 1:
The machine learning model serves as a universal system that handles multiple diverse time-off policies simultaneously. It can process various rule types (vacation, sick leave, parental leave) with different accrual rates and carryover limits through a single automated platform, eliminating the need for separate manual management processes for each policy type.
Solution Approach 2:
The system dynamically adjusts parameters such as accrual rates, carryover limits, and approval thresholds based on the specific policy being applied. The ML model automatically modifies these parameters according to the matched rules, enabling efficient handling of diverse policies without requiring complex manual configuration for each scenario.
3Adaptability or versatility
If time-off quotas are set statically, then rule simplicity is maintained, but resource availability may not align with operational needs
Solution Approach 1:
The system transitions from static time-off quotas to dynamic, adaptive quotas. The machine learning model continuously monitors operational needs, utilization patterns, and resource availability to automatically adjust time-off allocations in real-time, ensuring that quotas remain flexible and aligned with current operational requirements rather than being fixed in advance.
Solution Approach 2:
The patent implements feedback loops where the system monitors time-off utilization, operational performance, and resource availability, then uses this feedback to continuously optimize and adjust quotas. The ML model learns from historical data and current patterns to refine quota allocations, creating a self-improving system that adapts to changing conditions.
Data Source
AI summary
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.