Automated Data Structuring System for AI Model Training
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Solution Overview
Problem
Conventional data structuring for artificial intelligence requires manual preparation, which is time-consuming, prone to human bias, and costly, and also faces challenges such as incomplete data, missing values, and data pollution with future information.
Innovation Solution
A system for automated data structuring that includes software modules for selecting features, training machine learning models, and performing data preprocessing, which provides a user interface for users to select prediction types, provide entity and target information, and select attributes, thereby automating the data structuring process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual data preparation is performed by experienced data scientists, then data structuring quality and accuracy are improved, but time consumption and cost increase significantly
Solution Approach 1:
The system enables self-service automated data structuring where the computer system automatically performs data preprocessing, feature engineering, and model training without requiring manual intervention by data scientists. The automated system handles data cleaning, transformation, and structuring tasks that previously required expert manual effort, thereby reducing time consumption while maintaining quality through algorithmic consistency.
Solution Approach 2:
The patent replaces the mechanical manual process of data preparation with an automated computational system. Instead of data scientists manually performing preprocessing and feature engineering, the system uses software modules and algorithms to automatically execute these tasks, substituting human mechanical effort with automated computational processes that are faster and more scalable.
2Manufacturing precision
If manual data preparation is performed, then data quality control is improved, but human bias is introduced into predictions
Solution Approach 1:
The system replaces human manual data preparation with automated computational processes that eliminate human bias from the data structuring pipeline. The automated algorithms consistently apply predefined rules and transformations without the subjective judgment, unconscious prejudices, or fatigue-related errors that can affect human data scientists, thereby maintaining quality control while removing harmful human bias from predictions.
3Loss of time
If automation of data structuring is implemented, then time consumption and cost are reduced, but handling incomplete and polluted data becomes more challenging
Solution Approach 1:
The system performs preliminary automated actions to detect and handle data quality issues before they affect the modeling process. The automated preprocessing modules proactively identify missing values, outliers, and polluted data points, and apply appropriate cleaning and imputation techniques automatically, preventing these issues from propagating through the pipeline and reducing the need for complex manual intervention later.
Solution Approach 2:
The system incorporates feedback mechanisms where the automated data structuring process continuously monitors data quality metrics and adjusts preprocessing parameters accordingly. The system evaluates the effectiveness of cleaning and transformation operations in real-time and iteratively refines its approach to handling incomplete and polluted data, making the complexity management adaptive rather than static.
4Measurement precision
If comprehensive feature engineering is performed manually, then model accuracy is improved, but the process becomes extremely time-consuming
Solution Approach 1:
The system replaces manual feature engineering with automated algorithms that can process and transform data features at high speed. The automated feature engineering modules apply multiple transformation operations, aggregations, and derivations simultaneously across large datasets, achieving comprehensive feature processing that would be prohibitively time-consuming if performed manually while maintaining or improving model accuracy through systematic exploration of feature relationships.
Data Source
AI summary
A data structuring system that provides a user interface to enable data wrangling and modeling, and methods for making and using the same.