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:
Apr. 21, 2020

Filed:

Jul. 09, 2018
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Robert Bradshaw, Seattle, WA (US);

Craig D. Chambers, Seattle, WA (US);

Ezra Cooper, Seattle, WA (US);

Ashish Raniwala, Bellevue, WA (US);

Frances J. Perry, Seattle, WA (US);

Assignee:

Google LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 9/44 (2018.01); G06F 17/30 (2006.01); G06F 9/46 (2006.01); G06F 9/455 (2018.01); G06F 9/38 (2018.01); G06F 8/41 (2018.01); G06F 9/50 (2006.01); G06F 11/30 (2006.01); G06F 16/25 (2019.01); G06F 16/2453 (2019.01); G06F 16/51 (2019.01); G06F 16/23 (2019.01);
U.S. Cl.
CPC ...
G06F 9/466 (2013.01); G06F 8/453 (2013.01); G06F 9/3867 (2013.01); G06F 9/3885 (2013.01); G06F 9/455 (2013.01); G06F 9/5066 (2013.01); G06F 11/3006 (2013.01); G06F 16/235 (2019.01); G06F 16/24534 (2019.01); G06F 16/24542 (2019.01); G06F 16/258 (2019.01); G06F 16/51 (2019.01);
Abstract

One example method includes identifying synchronous code including instructions specifying a computing operation to be performed on a set of data; transforming the synchronous code into a pipeline application including one or more pipeline objects; identifying a first input data set on which to execute the pipeline application; executing the pipeline application on a first input data set to produce a first output data set; after executing the pipeline application on the first input data set, identifying a second input data set on which to execute the pipeline application; determining a set of differences between the first input data set and second input data set; and executing the pipeline application on the set of differences to produce a second output data set.


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