Pittsburgh, PA, United States of America

Apoorv Singh

This inventor holds 5 USPTO granted patents and 7 published patent applications. Top assignee: Motional Ad LLC. Active years: 2022-2026.

USPTO Granted Patents = 5 

% Patents Active = 80.0

Average Co-Inventor Count = 3.0

ph-index = 1

Forward Citations = 5(Granted Patents)


Company Filing History:


Years Active: 2022-2026

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5 patents (USPTO):Explore Patents

Title: Innovations of Inventor Apoorv Singh

Introduction

Apoorv Singh is an accomplished inventor based in Pittsburgh, PA (US). He has made significant contributions to the field of technology, particularly in the areas of perception systems and machine learning. With a total of 5 patents to his name, Singh continues to push the boundaries of innovation.

Latest Patents

One of his latest patents is titled "Using scene dependent object queries to generate bounding boxes." This invention involves a perception system that generates bounding boxes for objects in a vehicle scene. The system receives images and feature maps corresponding to these images, generating scene-dependent object queries to create bounding boxes for objects within the scene. Another notable patent is "Machine learning-based framework for drivable surface annotation." This framework utilizes a machine learning model to automatically annotate semantic masks of multimodal map data for geographic regions.

Career Highlights

Apoorv Singh is currently employed at Motional Ad LLC, where he applies his expertise in developing advanced technologies. His work focuses on enhancing the capabilities of perception systems and improving the efficiency of machine learning applications.

Collaborations

Singh collaborates with talented individuals such as Sergi Adipraja Widjaja and Venice Erin Baylon Liong, contributing to a dynamic and innovative work environment.

Conclusion

Apoorv Singh's contributions to technology through his patents and work at Motional Ad LLC highlight his role as a leading inventor in the field. His innovative approaches continue to shape the future of perception systems and machine learning applications.

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