Boulder, CO, United States of America

Maroof Mohammed Farooq

This inventor holds 2 USPTO granted patents and 3 published patent applications. Top assignee: Nvidia Corporation. Active years: 2023-2026.

USPTO Granted Patents = 2 

% Patents Active = 50.0

Average Co-Inventor Count = 5.0

ph-index = 1

Forward Citations = 2(Granted Patents)


Company Filing History:


Years Active: 2023-2026

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2 patents (USPTO):Explore Patents

Title: Maroof Mohammed Farooq: Innovator in Autonomous Machine Path Detection

Introduction

Maroof Mohammed Farooq is an accomplished inventor based in Boulder, CO. He has made significant contributions to the field of autonomous machines, particularly in the area of path detection using advanced deep learning techniques. His innovative work aims to enhance the capabilities of autonomous vehicles in navigating complex environments.

Latest Patents

Maroof holds a patent for "Path detection for autonomous machines using deep neural networks." This patent describes a deep learning solution that generates a more abstract definition of a drivable path without relying on explicit lane markings. The technology is designed to identify drivable paths in environments where conventional methods may fail, such as areas lacking lane markings or where they are occluded. The outputs generated by this deep learning solution can be directly utilized by autonomous vehicle software with minimal post-processing.

Career Highlights

Maroof is currently employed at Nvidia Corporation, a leading company in the field of artificial intelligence and graphics processing. His work at Nvidia focuses on developing cutting-edge technologies that improve the functionality and reliability of autonomous systems.

Collaborations

Maroof collaborates with talented individuals such as Regan Blythe Towal and Vijay Chintalapudi. Their combined expertise contributes to the advancement of innovative solutions in the realm of autonomous technology.

Conclusion

Maroof Mohammed Farooq is a notable inventor whose work in path detection for autonomous machines showcases the potential of deep learning in enhancing vehicle navigation. His contributions are paving the way for more reliable autonomous systems in the future.

Profile summary based on public USPTO records.
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