Seattle, WA, United States of America

Phillip Pham

This inventor holds 1 USPTO granted patent. Top assignee: Google Inc.. Active years: 2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Phillip Pham. Retrieved from https://idiyas.com/inventor/phillip-pham-lauieiq4

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile


Average Co-Inventor Count = 1.0

ph-index = 1


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: The Innovations of Phillip Pham

Introduction

Phillip Pham is an accomplished inventor based in Seattle, WA. He has made significant contributions to the field of machine learning, particularly in the development of attention models. His innovative work has garnered attention in the tech industry, showcasing his expertise and creativity.

Latest Patents

Phillip Pham holds a patent for "Machine-learned attention models featuring omnidirectional processing." This patent describes machine-learned attention models that feature omnidirectional processing, with implementations referred to as Omnidirectional Representations from Transformers (OMNINET). In these models, each token is allowed to attend to all tokens across the entire network, rather than maintaining a strictly horizontal receptive field.

Career Highlights

Phillip Pham is currently employed at Google Inc., where he continues to push the boundaries of technology and innovation. His work at Google has allowed him to collaborate with some of the brightest minds in the industry.

Collaborations

Among his notable coworkers are Yi Tay and Da-Cheng Juan, who share a commitment to advancing machine learning technologies. Their collaborative efforts contribute to the ongoing development of cutting-edge solutions in the field.

Conclusion

Phillip Pham's contributions to machine learning and his innovative patent demonstrate his significant role in the tech industry. His work continues to inspire advancements in technology and showcases the potential of machine-learned attention models.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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