Innamul Hassan Abdul Azeez

Title: Innamul Hassan Abdul Azeez and His Patent in Anomaly Detection

Introduction

Innamul Hassan Abdul Azeez is an inventor based in Chennai, India. His patent work concerns machine-learning systems for detecting anomalies in data, including methods designed to distinguish among different patterns of behavior.

Latest Patents

The provided information lists one patent for Abdul Azeez: “Multi-contextual anomaly detection.” The disclosure describes a system that uses multiple machine-learning models, each trained to identify a particular behavior in historical values for a metric. By evaluating different behaviors, the system aims to reduce both false-positive anomaly detections and false-negative reports.

The approach can also apply models to multiple types of metrics. Its pluggable architecture allows models to be added or removed, enabling anomaly detection with a configurable set of machine-learning models. The supplied information does not specify the patent’s filing or grant date.

Career Highlights

Abdul Azeez is associated with The Bank of New York Mellon Corporation. His listed patent reflects work on configurable, multi-model anomaly detection, an approach intended to support analysis across varied metrics and historical data patterns.

Collaborations

The provided information identifies Badri V Mangalam and Sridhar M Seetharaman as Abdul Azeez’s coworkers. It does not specify whether they collaborated with him on the patent.

Conclusion

Innamul Hassan Abdul Azeez’s listed invention applies configurable machine-learning models to anomaly detection across different behaviors and metrics, with the goal of improving detection reliability.