Machine-Learned Payroll Script Generation for Spreadsheet Migration
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Solution Overview
Problem
The diversity in employee data formats across different entities poses challenges during data migration or consolidation, leading to labor-intensive and error-prone manual conversion processes that can result in inaccuracies such as incorrect payroll calculations and compliance violations.
Innovation Solution
Employing machine-learned models, particularly large language models (LLMs), to automatically generate executable scripts that transform payroll data from one format to another, utilizing historical employee spreadsheets and their modified versions to identify similar data entries and apply data modification operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual conversion scripts are used to transform employee data between different formats, then data format flexibility is maintained, but conversion accuracy and efficiency deteriorate due to labor intensity and human error
Solution Approach 1:
The patent replaces manual mechanical script writing with an automated machine learning system. The ML model automatically generates conversion scripts by learning from historical data transformations, eliminating human manual operations while maintaining high accuracy and reducing time consumption.
Solution Approach 2:
The system enables self-service automated conversion where the machine learning model independently analyzes source and target data formats, identifies transformation rules, and generates execution scripts without requiring manual intervention or expert knowledge from users.
2Reliability
If manual script writing is performed for data format conversion, then adaptability to different formats is maintained, but error rates increase due to manual examination and customization requirements
Solution Approach 1:
The patent substitutes manual script writing with automated machine learning-generated scripts. The ML model processes complex format variations automatically, reducing the complexity burden on users while improving reliability through consistent, error-free execution.
Solution Approach 2:
The machine learning model acts as an intermediary between the source data format and target data format. It automatically bridges format differences by learning transformation patterns from historical data, eliminating the need for users to directly handle complex conversion logic.
3Adaptability or versatility
If custom conversion scripts are manually created for each data migration task, then format-specific requirements are met, but labor intensity and operational complexity increase
Solution Approach 1:
The machine learning system provides self-service automated script generation that adapts to different data formats without requiring user expertise. Users simply provide source and target format samples, and the system automatically generates appropriate conversion scripts, making the process simple and accessible to non-experts.
Solution Approach 2:
The system learns from historical conversion examples and copies successful transformation patterns to generate new conversion scripts. By replicating proven conversion approaches from historical data, the system maintains adaptability to various formats while simplifying the creation process through automated pattern recognition and replication.
Data Source
AI summary
A central database system accesses historical employee spreadsheets along with their modified versions, which have undergone data modification operations. The system creates a training dataset from those spreadsheets to train a machine-learned model. The model is configured to analyze a target employee spreadsheet, identify a historical spreadsheet similar to it, and generate executable scripts based on data modifications performed on the identified historical spreadsheet. The set of scripts is applied to the target spreadsheet to produce a target modified spreadsheet, mimicking historical modification. The modified spreadsheet is sent to an employee spreadsheet processing system for further processing.