Demographic Data Field Identification for Automated File Reformatting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The manual review and reformatting of demographic information from various data sources is time-consuming and costly, often resulting in inconsistent or mislabeled nomenclatures and spurious data, contributing significantly to administrative overhead costs.
Innovation Solution
A method utilizing a machine learning model trained on demographic information to analyze data files, employing a combination of machine learning algorithms and rules to identify and correct field types, generate scores, and produce revised data files with accurate labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review and reformatting of demographic information is performed, then data accuracy and consistency can be maintained, but time consumption and administrative overhead costs increase significantly
Solution Approach 1:
The patent replaces manual mechanical review and reformatting processes with an automated machine learning-based system. The system uses trained models to automatically identify, validate, and reformat demographic information from various data sources, eliminating the need for manual intervention while maintaining data accuracy and consistency.
Solution Approach 2:
The system enables self-service processing where the machine learning models autonomously perform data validation and reformatting tasks. The automated system independently identifies data types, validates information against predefined criteria, and transforms data into required formats without human intervention, significantly reducing time consumption while preserving accuracy.
2Manufacturing precision
If manual review and reformatting of demographic information is performed, then data consistency can be ensured, but processing cost increases significantly
Solution Approach 1:
The patent replaces costly manual review processes with an automated machine learning system that performs data validation and reformatting. The system uses trained models to consistently apply validation rules and transformation logic, ensuring data consistency while dramatically reducing processing costs associated with manual labor.
Solution Approach 2:
The system changes the operational parameters from manual human processing to automated algorithmic processing. By transitioning from manual review to machine-based validation and reformatting, the system maintains data consistency while reducing processing costs through efficient automated execution of validation and transformation rules.
3Productivity
If automated machine learning processing is implemented, then processing speed and efficiency improve, but system complexity increases
Solution Approach 1:
The patent segments the complex data processing task into distinct functional modules: data reception, machine learning-based identification, validation against predefined criteria, and reformatting. This segmentation allows the system to handle complex processing by breaking it down into manageable, specialized components that work together efficiently.
Solution Approach 2:
The system introduces machine learning models as intermediary components between data reception and final reformatting. These models act as mediators that automatically identify data types and validate information, simplifying the overall system architecture by using pre-trained algorithms to handle complex decision-making tasks.
4Productivity
If automated processing is used to reduce administrative overhead, then cost reduction is achieved, but data validation accuracy may decrease
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning system continuously validates data against predefined criteria and correction rules. The system receives feedback from validation results and adjusts its processing accordingly, ensuring that automated operations maintain high accuracy by learning from validation outcomes and correcting errors automatically.
Solution Approach 2:
The system applies beforehand cushioning by using pre-trained machine learning models and predefined validation criteria that are established in advance. These pre-established frameworks provide a safety net that ensures automated processing maintains accuracy, as the models are trained on high-quality data and validation rules are predetermined to catch potential errors before they affect output.
Data Source
AI summary
The present disclosure is directed to systems and methods for identifying demographic information in a data file. The method may include: receiving the data file containing a plurality of fields of demographic information from a third-party, the data file having inconsistent or mislabeled nomenclatures for one or more fields of the plurality of fields or spurious demographic information; analyzing the data file using a machine learning model trained according to other data files to distinguish between each of the plurality of fields of demographic information, the machine learning model being based on a plurality of machine learning algorithms to identify different types demographic information; generating a score indicating a probability that each of the plurality of fields of demographic information was identified correctly; and generating a revised data file labeling each of the plurality of fields of demographic information based on the identified type.