Multivariate Empirical Algorithm for Compensation Sequencing
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Solution Overview
Problem
The complexity of modern compensation systems, involving various benefits from multiple sources, makes it difficult for employees and HR specialists to determine which benefits apply and in what order, especially during life events like adopting a child or caring for an aging parent, due to varying laws and regulations across different jurisdictions.
Innovation Solution
A processor-implemented method using a multivariate empirical algorithm (MEA) that models employee benefits and compliance requirements with mathematical formulas, updates based on user input, and sequences compensation factors for optimal priority, providing a visual demonstration of payment and duration estimates, along with instructions for application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple compensation factors from multiple sources are applied to the same employee situation, then the comprehensiveness of benefits coverage is improved, but the determination of which compensation programs to use and in what order becomes excessively complex
Solution Approach 1:
The patent introduces an intermediary system comprising a policy database, usage configuration models, and a multivariate empirical algorithm that acts as a mediator between multiple compensation factors and employees. This intermediary automatically processes and sequences compensation programs from multiple sources (employer benefits, union benefits, government benefits) according to predefined rules and empirical data, eliminating the need for employees and HR specialists to manually determine the complex sequencing of multiple benefits.
2Adaptability or versatility
If varying laws and regulations across different jurisdictions are incorporated into the compensation system, then the compliance coverage is improved, but the difficulty of determining applicable benefits increases
Solution Approach 1:
The patent applies preliminary action by pre-incorporating varying laws and regulations from different jurisdictions into the policy database before they are needed. The system pre-processes and structures compliance requirements from federal, state, and local laws into usable formats, so that when employees need compensation information, the applicable benefits are already identified and sequenced according to the relevant jurisdictional laws, eliminating the need for real-time legal analysis.
Solution Approach 2:
The system uses parameter changes by dynamically adjusting compensation factor selection and sequencing based on jurisdiction-specific parameters. The multivariate empirical algorithm modifies compensation program recommendations according to geographic location, employee type, and other jurisdictional parameters, automatically adapting the benefits package to comply with local laws and regulations without requiring manual intervention.
3Quantity of substance
If a comprehensive policy database including employee benefits information and compliance requirements is created, then the information completeness is improved, but the processing complexity increases
Solution Approach 1:
The patent segments the comprehensive policy database into distinct, manageable components: a policy database storing raw benefits and compliance information, usage configuration models organizing benefits by employment situation, and a multivariate empirical algorithm processing specific cases. This segmentation allows the system to handle large volumes of compensation information by dividing it into modular sections that can be independently processed and updated without overwhelming the entire system.
Data Source
AI summary
Disclosed embodiments provide a processor-implemented method for optimization. A policy database including employee benefits information and compliance requirements is accessed and modeled via a set of usage configuration models. A multivariate empirical algorithm (MEA) is created, which arithmetically links the mathematical formulas. The MEA is based on a usage configuration model. Personal information is gathered from a user to create a user configuration and a first priority. The MEA is updated based on the user configuration. Compensation factors for which the user qualifies are identified by the MEA. The compensation factors are sequenced, based on a first priority. The compensation factors can be presented, to the user, based on the sequencing. The presenting includes a payment and duration estimate of each compensation factor that was identified. Instructions can be displayed for the user to apply for the compensation factors.