This inventor holds 3 USPTO granted patents and 3 published patent applications and 3 EPO patents. Top assignee: Abb Schweiz Ag. Active years: 2021-2026.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile
Company Filing History:
Years Active: 2021-2026
Title: Innovations by Nevroz Sen
Introduction
Nevroz Sen is an accomplished inventor based in San Jose, CA. She has made significant contributions to the field of defect detection through her innovative methods and systems. With a total of 3 patents, her work focuses on enhancing the accuracy and efficiency of surface inspections.
Latest Patents
One of her latest patents is titled "Systems and methods for paint defect detection using machine learning." This method involves projecting various patterns onto the surface of an object and capturing images to identify any paint defects. The images are analyzed using a machine learning model to determine the presence of defects, allowing for effective quality control.
Another notable patent is the "Surface defect detection system." This inspection system utilizes a pattern with varying colors or darkness that is illuminated on the surface. The system captures the reflected pattern and any defects for image analysis, enabling precise identification of defect locations.
Career Highlights
Nevroz Sen is currently employed at Abb Schweiz AG, where she continues to develop innovative solutions for defect detection. Her expertise in machine learning and image analysis has positioned her as a leader in her field.
Collaborations
Throughout her career, Nevroz has collaborated with talented individuals such as Arash Mahyari and Thomas R. Locher. These partnerships have fostered a creative environment that encourages the development of groundbreaking technologies.
Conclusion
Nevroz Sen's contributions to the field of defect detection through her innovative patents demonstrate her commitment to advancing technology. Her work not only enhances quality control processes but also showcases the potential of machine learning in practical applications.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile
