London, United Kingdom

Daniel James Visentin

USPTO Granted Patents = 1 

Average Co-Inventor Count = 13.0

ph-index = 1

Forward Citations = 1(Granted Patents)


Company Filing History:


Years Active: 2024

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1 patent (USPTO):

Title: Innovator Spotlight: Daniel James Visentin

Introduction: Daniel James Visentin is an accomplished inventor based in London, GB, known for his innovative contributions to the field of video encoding. With a strong foundation in machine learning, he has developed a patented technology that enhances the efficiency of video encoding processes.

Latest Patents: Daniel holds a patent titled "Rate control machine learning models with feedback control for video encoding." This invention encompasses methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, aimed at encoding video that contains a sequence of video frames. The methodology involves obtaining a feature embedding for each video frame and processing it through a machine learning model to generate scores for various quantization parameter values. By selecting the most suitable quantization parameter, the invention ensures efficient data representation for video encoding.

Career Highlights: Throughout his career, Daniel has made significant strides in the tech industry. He is currently employed at DeepMind Technologies Limited, where he contributes his expertise in artificial intelligence and machine learning. His innovative ideas and practical applications have positioned him as a valuable asset in his organization.

Collaborations: Daniel has worked alongside talented peers such as Chenjie Gu and Hongzi Mao, further enriching the collaborative environment at DeepMind Technologies Limited. These partnerships have allowed him to refine his skills and expand the reach of his innovative work in video encoding technology.

Conclusion: Daniel James Visentin exemplifies the spirit of innovation in the technology landscape. With his patented method for rate control in video encoding, he addresses the challenges of efficiently processing video data. His ongoing contributions in machine learning not only enhance video technologies but also inspire future innovations in the field.

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