AI Data Modeling for Automated Cleaning and Enrichment
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Solution Overview
Problem
Creating a data model or data schema in databases is a time-consuming process that requires significant manual effort and expertise, often involving multiple manual steps.
Innovation Solution
A computer system uses AI/ML models to automate data preparation and modeling, providing an interface for users to create or edit data models, with automated error detection and correction, data transformation, and enrichment, while maintaining version history and supporting chatbot integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual data modeling processes are used, then data model accuracy can be maintained through expert review, but the process becomes time-consuming and requires significant manual effort
Solution Approach 1:
The patent replaces manual mechanical data modeling processes with an AI/ML-based automated system. The system uses machine learning models to automatically generate, validate, and refine data models, substituting human expert manual work with automated intelligent processing while maintaining or improving accuracy through algorithmic validation and iterative optimization.
Solution Approach 2:
The system enables self-service data modeling where the AI/ML models autonomously perform data model creation, validation, and improvement without requiring continuous human intervention. The models learn from data patterns and automatically adjust and refine data models based on feedback and performance metrics, making the process self-sufficient while maintaining high accuracy.
2Reliability
If manual data preparation steps are performed, then data quality can be controlled, but the process requires significant expertise and manual input
Solution Approach 1:
The patent replaces manual data preparation operations with automated AI/ML-based processing. The system uses machine learning models to automatically clean, validate, transform, and enrich data, substituting manual expert operations with intelligent automated processes that maintain data quality through algorithmic validation and error detection.
Solution Approach 2:
The system implements feedback mechanisms where AI/ML models continuously monitor data quality metrics and automatically adjust preparation processes based on performance feedback. The models learn from validation results and iteratively improve data preparation operations, maintaining high data quality while reducing manual intervention through closed-loop automated control.
Data Source
AI summary
Methods, systems, and apparatus, including computer-readable media, for data preparation and modeling using artificial intelligence. In some implementations, a system receives user input that indicates one or more data sets. In response, the system generates a set of recommendations for data modeling, data preparation, or data enrichment for the one or more data sets, where at least one recommendation in the set of recommendations is generated using one or more artificial intelligence and/or machine learning (AI/ML) models. The system provides the set of recommendations for display in the user interface in association with one or more interactive controls to accept or dismiss the recommendations. In response to receiving user input the system updates the data model or the one or more data sets to apply an update corresponding to an accepted recommendation. The system provides the updated data set or updated data model to a chatbot or other application.