Cloud-Based LLM Payroll Processing for Jurisdictional Compliance

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional payroll systems are time-consuming, prone to human error, and require significant manpower and resources, failing to efficiently manage payroll processes and comply with diverse employment laws across different jurisdictions.

Innovation Solution

A cloud-based system utilizing Large Language Models (LLMs) automates payroll processing, including time and attendance tracking, salary calculation, tax deductions, and benefits administration, while ensuring compliance with local employment laws and regulations through a variability engine that monitors and updates these laws in real-time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual data entry and spreadsheet-based payroll processing are used, then the system can handle payroll calculations, but the process becomes time-consuming and prone to human error

Engineering Contradiction:

Improveaccuracy of payroll calculationsVSAvoidtime required for payroll processing

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical data entry and spreadsheet-based processing with an automated LLM-based system that extracts payroll information from unstructured data sources and performs calculations automatically, eliminating human error and reducing processing time

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The LLM-based system autonomously performs payroll processing without requiring manual intervention for data entry or calculation, with the model independently extracting information, performing tax withholdings, and generating payroll outputs

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If traditional payroll systems are used, then basic payroll functions can be performed, but compliance with diverse employment laws across different jurisdictions becomes difficult

Engineering Contradiction:

Improvecompliance with diverse employment lawsVSAvoidcomplexity of managing multiple jurisdictional requirements

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The LLM-based payroll system is designed to handle multiple jurisdictions and employment laws through a single unified platform, with the language model capable of adapting to different legal requirements across countries, states, and localities without requiring separate systems

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Productivity

If manual payroll processing is used, then the system can operate with existing infrastructure, but significant manpower and resources are required leading to increased operational costs

Engineering Contradiction:

Improveefficiency of payroll processingVSAvoidmanpower and resources required

Core Design Contradiction:

ProductivityVSQuantity of substance

Solution Approach 1:

The patent substitutes human manpower and manual resources with an LLM-based automated system that processes payroll efficiently without requiring significant human intervention, thereby reducing operational costs while maintaining or improving productivity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250307951A1Systems and methods for a cloud-based payroll processing workflow utilizing Large Language Models (LLMs)

Publication Date: 2025.10.02 VELOCITY GLOBAL LLC

AI summary

Systems and methods for a cloud-based payroll processing workflow utilizing Large Language Models (LLMs) includes receiving a request to perform payroll for a plurality of employees associated with an employer; extracting payroll information associated with the plurality of employees; and performing one or more phases of a payroll process for each of the plurality of employees automatically via one or more trained Large Language Models (LLMs).