Macomb, MI, United States of America

Mohammed H Al Qizwini

USPTO Granted Patents = 2 

Average Co-Inventor Count = 2.7

ph-index = 1


Company Filing History:


Years Active: 2023

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2 patents (USPTO):Explore Patents

Title: Innovations of Mohammed H Al Qizwini

Introduction

Mohammed H Al Qizwini is an accomplished inventor based in Macomb, MI (US). He has made significant contributions to the field of technology, particularly in the areas of roadway identification and drone surveillance. With a total of two patents to his name, Al Qizwini is recognized for his innovative approaches to solving complex problems.

Latest Patents

Al Qizwini's latest patents include "Identification and clustering of lane lines on roadways using reinforcement learning" and "Reinforcement learning based system for aerial imagery acquisition using drone following target vehicle." The first patent describes a system that utilizes a processor and memory to process images of roadways. It employs a reinforcement learning-based agent, which is a neural network trained with a reward function to identify lane lines effectively. The second patent outlines a method for surveying roads using a drone that follows a target vehicle. This method generates a dynamic flight plan for the drone, allowing it to capture images of the road while maintaining a line of sight with the vehicle.

Career Highlights

Al Qizwini is currently employed at GM Global Technology Operations LLC, where he continues to develop innovative solutions in technology. His work focuses on enhancing the capabilities of autonomous systems and improving road safety through advanced imaging techniques.

Collaborations

Some of his notable coworkers include David Hahn Clifford and Orhan Bulan, who contribute to the collaborative environment at GM Global Technology Operations LLC.

Conclusion

Mohammed H Al Qizwini is a notable inventor whose work in roadway identification and drone technology showcases his commitment to innovation. His patents reflect a deep understanding of reinforcement learning and its applications in real-world scenarios.

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