San Jose, CA, United States of America

Stan Vitvitskyy

This inventor holds 1 USPTO granted patent and 1 EPO patent. Top assignee: Google Inc.. Active years: 2025.


% Patents Active = 100.0


Average Co-Inventor Count = 5.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):Explore Patents

Title: Innovations by Stan Vitvitskyy

Introduction

Stan Vitvitskyy is an accomplished inventor based in San Jose, California. He has made significant contributions to the field of video encoding, particularly in enhancing the quality of text legibility in video files. His innovative approach utilizes machine learning to optimize compression techniques for different types of content within video frames.

Latest Patents

Stan holds a patent for a groundbreaking technology titled "Encoding a video frame using different compression ratios for text blocks and non-text blocks." This patent describes systems and techniques that determine which blocks of a video frame contain text and which do not. By applying different compression ratios or algorithms to text and non-text blocks, the technology improves the legibility of text in videos without significantly increasing bandwidth requirements. This advancement is crucial for transmitting high-quality video files to remote devices.

Career Highlights

Stan Vitvitskyy is currently employed at Google Inc., where he continues to innovate in the realm of video technology. His work has garnered attention for its practical applications in enhancing user experience in video content. With a focus on machine learning and video encoding, Stan is at the forefront of technological advancements in this area.

Collaborations

Some of Stan's notable coworkers include Hao Zhuang and Sean Purser-Haskell. Their collaborative efforts contribute to the innovative environment at Google Inc., fostering the development of cutting-edge technologies.

Conclusion

Stan Vitvitskyy's contributions to video encoding technology exemplify the impact of innovative thinking in enhancing digital content. His patent reflects a significant step forward in improving text legibility in videos, showcasing the potential of machine learning in practical applications.

Profile summary based on public USPTO records.
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