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:
Mar. 21, 2023

Filed:

Oct. 23, 2019
Applicant:

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

Inventors:

Gary Shon Katzenberger, Fall City, WA (US);

Thomas William Finley, Bellevue, WA (US);

Pete Luferenko, Redmond, WA (US);

Mohammad Zeeshan Siddiqui, Bellevue, WA (US);

Costin Eseanu, Redmond, WA (US);

Eric Anthony Erhardt, Bismark, ND (US);

Yael Dekel, Redmond, WA (US);

Ivan Matantsev, Bothell, WA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/00 (2019.01); G06F 8/30 (2018.01); G06N 20/00 (2019.01); G06F 16/2455 (2019.01); G06F 16/2453 (2019.01); G06F 9/30 (2018.01); G06F 9/50 (2006.01); G06F 9/54 (2006.01);
U.S. Cl.
CPC ...
G06F 8/31 (2013.01); G06F 9/30036 (2013.01); G06F 9/30079 (2013.01); G06F 9/5016 (2013.01); G06F 9/5022 (2013.01); G06F 9/544 (2013.01); G06F 16/24535 (2019.01); G06F 16/24568 (2019.01); G06N 20/00 (2019.01);
Abstract

Methods, systems, and computer products are herein provided for lazy evaluation of input data by a machine learning (ML) framework. An ML pipeline receives input data and compiles a chain of operators into a chain of dataviews configured for lazy evaluation of the input data. Each dataview in the chain represents a computation over data as a non-materialized view of the data. The ML pipeline receives a request for column data and selects a chain of delegates comprising one or more delegates for one or more dataviews in the chain to fulfill the request. The ML pipeline processes the input data with the selected chain of delegates. The ML pipeline performs delegate chaining on a dataview. A feature value for a feature column of the dataview is determined based on the delegate chaining and provided to an ML algorithm to predict column data.


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