Dr. Nyangon awarded patent for invention of new methods in outlier detection and feature engineering for machine learning models

CARY, N.C., January 7, 2025 — Under the leadership of Dr. Joe Nyangon, SAS Institute Inc. today announced the grant of U.S. Patent No. 12,190,219, entitled Systems and methods for outlier detection and feature transformation in machine learning model training. This invention, co-developed with Dr. Ruth Akintunde, introduces an end-to-end data pre-processing framework that automatically identifies and removes anomalous data points prior to feature transformation, significantly improving model accuracy and stability.

Dr. Nyangon delivering a keynote address at the 2024 Utility Analytics Week in Chicago.

Key Features

• Adaptive Outlier Removal: Dynamically filters noise and anomalies based on configurable thresholds, ensuring cleaner training datasets.

• Seamless Feature Transformation: Converts sanitized data into machine-ready features using a streamlined pipeline that integrates directly with existing ML platforms.

• Enterprise-Scale Architecture: Deployable as a scalable software component capable of handling terabyte-scale datasets with minimal manual tuning.

• Enhanced Predictive Reliability: Empirical benchmarks demonstrate up to a 15% reduction in prediction error rates when compared to traditional preprocessing methods.

Industry Impact
Data quality remains a critical barrier for organizations adopting advanced analytics across finance, healthcare, energy and beyond. Machine learning practitioners in these sectors and beyond face persistent challenges from noisy and inconsistent data. This patented technology promises to lower the barrier to entry for organizations seeking to deploy high-fidelity predictive models by automating critical data-cleaning steps. Early implementations are expected to accelerate deployment timelines, reduce data-science resource requirements, and yield more trustworthy insights.

Areas of Application
This patented technology can be deployed across multiple sectors to improve data quality and model performance, including:

• Finance: Fraud detection and risk modeling

• Healthcare: Diagnostic analytics and patient-outcome prediction

• Manufacturing: Quality control and predictive maintenance

• Energy: Electricity demand forecasting and grid optimization

• Telecommunications: Network anomaly detection

• Internet of Things: Sensor-data preprocessing

Quotes

“Automating outlier detection and feature transformation empowers data scientists to focus on innovation rather than manual data cleanup,” said Dr. Nyangon, Lead Inventor. “This patent represents a major milestone in making machine learning more accessible and reliable by tackling data imperfections at the source. It embodies our vision of making advanced analytics accessible and reliable for all organizations.”

“Collaborating with Joe to architect a flexible, scalable framework was a highlight of our R&D efforts,” added Dr. Ruth Akintunde, Co-Inventor. “I’m proud that our work will help teams derive maximum value from their data.”

Future Outlook
Building on this milestone, Dr. Nyangon and his team plan to extend the methodology to support real-time data streams and automated anomaly alerts for application in electricity demand forecasting, fraud detection and financial risk modeling, and telecommunications network anomaly detection, among others.

Call to Action
Learn more about U.S. Patent No. 12,190,219 at uspto.gov.