The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 05, 2021

Filed:

Sep. 25, 2019
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

Landon Prentice Cox, Seattle, WA (US);

Paramvir Bahl, Seattle, WA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/04 (2006.01); H04N 21/2343 (2011.01); H04N 21/2187 (2011.01); H04N 21/231 (2011.01); H04N 21/25 (2011.01); H04N 21/262 (2011.01); H04N 21/2662 (2011.01); H04N 21/442 (2011.01);
U.S. Cl.
CPC ...
H04N 21/2187 (2013.01); G06N 3/04 (2013.01); H04N 21/231 (2013.01); H04N 21/234363 (2013.01); H04N 21/251 (2013.01); H04N 21/2662 (2013.01); H04N 21/26258 (2013.01); H04N 21/44209 (2013.01);
Abstract

A thin-cloud system for distributing content, for example, live streaming video content, from a broadcaster to a viewer is provided herein. The computing devices of the broadcaster can provide the multi-bitrate transcoding, of the two or more bitstreams, sent to a file server, which alleviates the need for the file server to encode the streams for a viewer. These multiple streams are received by a file server for provision to one or more viewers. The viewers can receive the streams at one of the two or more bitrates. If the viewer receives the content at a lower bitrate, the viewers can employ a machine learning (ML) co-processor that can operate as an accelerator to improve the inbound content, if that content is provided at a lower bitrate, and thus, a lower resolution. The file server can train and provide the ML models used for the acceleration.


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