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:
Jul. 18, 2023

Filed:

Jun. 20, 2019
Applicant:

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

Inventors:

Yiming Ma, Menlo Park, CA (US);

Menglin L. Brown, Redwood City, CA (US);

Bee-Chung Chen, San Jose, CA (US);

Sheng Wu, Belmont, CA (US);

Jun Jia, Sunnyvale, CA (US);

Bo Long, Palo Alto, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/00 (2023.01); G06N 3/082 (2023.01); G06N 20/20 (2019.01); G06F 11/34 (2006.01); G06F 18/214 (2023.01);
U.S. Cl.
CPC ...
G06N 3/082 (2013.01); G06F 11/3495 (2013.01); G06F 18/214 (2023.01); G06N 20/20 (2019.01);
Abstract

The disclosed embodiments provide a system for processing data. During operation, the system obtains a training dataset containing a first set of records associated with a first set of identifier (ID) values and an evaluation dataset containing a second set of records associated with a second set of ID values. Next, the system selects a random subset of ID values from the second set of ID values. The system then generates a sampled evaluation dataset comprising a first subset of records associated with the random subset of ID values in the second set of records. The system also generates a sampled training dataset comprising a second subset of records associated with the random subset of ID values in the first set of records. Finally, the system outputs the sampled training dataset and the sampled evaluation dataset for use in training and evaluating a machine learning model.


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