San Francisco, CA, United States of America

Abraham Botros

USPTO Granted Patents = 3 

Average Co-Inventor Count = 4.0

ph-index = 1

Forward Citations = 4(Granted Patents)


Company Filing History:


Years Active: 2022-2024

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3 patents (USPTO):Explore Patents

Title: Innovations of Abraham Botros in Machine Learning

Introduction

Abraham Botros is an innovative inventor based in San Francisco, CA. He has made significant contributions to the field of machine learning, particularly in object identification systems. With a total of three patents to his name, Botros is recognized for his cutting-edge work that integrates technology with practical applications.

Latest Patents

One of Botros's latest patents is focused on machine learning-based object identification using scaled diagrams and three-dimensional models. This system automatically identifies objects in an environment by utilizing a walkthrough video and an annotated floorplan. The annotated floorplan indicates the expected locations and types of objects within the environment. The system generates a 3D model of the environment from the walkthrough video and applies a machine learning model to identify regions within frames where objects are captured. After identifying these regions, the system modifies the corresponding areas of the 3D model to include the identified objects. Furthermore, for each identified object, the system assesses the likelihood of its presence at a specific location by comparing the modified 3D model with the annotated floorplan.

Career Highlights

Abraham Botros is currently employed at Open Space Labs, Inc., where he continues to develop innovative solutions in the realm of machine learning. His work is characterized by a commitment to enhancing the accuracy and efficiency of object identification systems.

Collaborations

Botros collaborates with talented individuals such as Michael Ben Fleischman and Gabriel F Hein, contributing to a dynamic and innovative work environment.

Conclusion

Abraham Botros is a notable inventor whose work in machine learning has the potential to revolutionize object identification systems. His contributions reflect a deep understanding of technology and its applications in real-world scenarios.

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