ML Script Generation for Payroll Data Format Conversion
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Solution Overview
Problem
The challenge of migrating employee data between different database systems is labor-intensive and error-prone, leading to inaccuracies such as incorrect payroll calculations and compliance violations due to variations in data formats and formats-specific peculiarities.
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
1Adaptability or versatility
If manual script generation is used for data format conversion, then customization to specific format requirements is achieved, but labor intensity and error rate increase significantly
Solution Approach 1:
The system uses historical employee spreadsheets and their associated modification scripts as training data to teach the machine-learned model to generate conversion scripts automatically. The model copies patterns from historical examples to handle new format conversion tasks without manual intervention.
Solution Approach 2:
The machine-learned model autonomously analyzes source and target data formats, identifies transformation rules, and generates executable conversion scripts without human assistance. The system serves itself by automatically handling the entire script generation process.
2Reliability
If manual script generation is used for data format conversion, then attention to format-specific peculiarities is achieved, but time consumption increases
Solution Approach 1:
The system pre-processes historical employee spreadsheets and their modification scripts to create a training dataset. This preliminary preparation enables the machine-learned model to quickly generate accurate conversion scripts for new tasks without requiring manual analysis of each case.
Solution Approach 2:
The patent replaces manual mechanical script writing with an automated machine-learned system that uses pattern recognition and inference to generate conversion scripts, eliminating the time-consuming manual process while maintaining accuracy.
3Adaptability or versatility
If manual data conversion is performed, then handling of complex data transformations is achieved, but error rate increases
Solution Approach 1:
The system uses historical modified spreadsheets as feedback examples to train the machine-learned model. The model learns from these feedback examples what correct transformations look like, enabling it to generate accurate scripts for complex data transformations without manual errors.
Solution Approach 2:
The machine-learned model copies proven transformation patterns from historical examples to handle complex data transformations. By replicating successful past conversions, the model maintains high accuracy while handling sophisticated transformation scenarios.
Data Source
AI summary
A system trains a machine-learned model using historical employee spreadsheets and historical modification operations applied to those spreadsheets. The model determines similarity scores between a received employee spreadsheet and each historical employee spreadsheet, and identifies a set of historical employee spreadsheets having highest similarity scores. Based on data modification operations associated with the identified set, the model generates executable scripts configured to apply the data modification operations to the employee spreadsheet. Upon receiving a target employee spreadsheet, the system applies the machine-learned model to generate a target set of executable scripts and executes the scripts on the target employee spreadsheet to produce a target modified spreadsheet. The target modified spreadsheet is transmitted to an employee spreadsheet processing system for further processing.