Charlotte, NC, United States of America

Manoj Acharya


Average Co-Inventor Count = 3.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):Explore Patents

Title: Inventor Manoj Acharya: Innovating Data Anomaly Detection

Introduction

Manoj Acharya, based in Charlotte, NC, has made significant contributions to the field of data science through his innovative inventions. With a focus on improving data integrity and machine learning, Acharya has created a solution that addresses critical challenges in identifying anomalies in data environments.

Latest Patents

Acharya holds a patent for "Methods and Systems for Identifying and Correcting Anomalies in a Data Environment." This patent encompasses a computing device designed to enhance the performance of machine learning classifiers. The technology enables continuous refinement of classifier models, which helps in detecting data anomalies effectively. The device generates a comprehensive corpus of data based on architectural standards, collects relevant information from multiple data sources, and applies sophisticated feature vectors to deliver precise analyses.

Career Highlights

Currently, Manoj Acharya is associated with Charter Communications Operating, LLC, where he contributes his expertise to further develop innovative data solutions. His dedication to advancing technology is evident in his successful patent application, which positions him as a leading figure in his field.

Collaborations

Acharya collaborates with talented individuals such as Irina Niyazov and Michael Bender, further enriching the project landscape at Charter Communications. Their teamwork contributes to developing state-of-the-art solutions in data analysis and anomaly detection.

Conclusion

Manoj Acharya's work exemplifies the impact of innovative thinking in technology. His patented methods for identifying and correcting data anomalies represent a significant advancement in machine learning and data integrity. As he continues to collaborate and innovate, his contributions are sure to influence the future of data science.

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