This inventor holds 1 USPTO granted patent. Top assignee: Ford Motor Company Limited. Active years: 2020.
Company Filing History:
Years Active: 2020
Title: Alberto Rivera: Innovator in Manufacturing Part Identification
Introduction
Alberto Rivera is a notable inventor based in Palo Alto, CA (US). He has made significant contributions to the field of manufacturing through his innovative work in computer vision and machine learning. His expertise has led to the development of advanced systems that enhance the identification of manufacturing parts.
Latest Patents
Alberto Rivera holds 1 patent for his invention titled "Manufacturing part identification using computer vision and machine learning." This patent discloses systems, methods, and devices for object detection, particularly focusing on manufacturing part detection. The method involves receiving an image of a manufacturing part from an augmented reality device and determining a bounding perimeter that encapsulates the part. Additionally, it includes receiving a prediction label and a confidence value for the part from a neural network. The process culminates in generating a message that comprises the image, prediction label, and confidence value, which is then provided to the augmented reality device.
Career Highlights
Alberto Rivera is currently employed at Ford Motor Company Limited, where he applies his innovative skills to enhance manufacturing processes. His work is pivotal in integrating advanced technologies into the automotive industry, thereby improving efficiency and accuracy in part identification.
Collaborations
Alberto has collaborated with talented individuals such as Kiril Spasovski and Shounak Athavale. These collaborations have fostered a creative environment that encourages the development of cutting-edge solutions in manufacturing.
Conclusion
Alberto Rivera's contributions to manufacturing part identification through his innovative patent demonstrate his commitment to advancing technology in the industry. His work not only enhances operational efficiency but also showcases the potential of integrating machine learning with manufacturing processes.
