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:
Jun. 10, 2025

Filed:

Nov. 01, 2022
Applicant:

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

Inventors:

Ryan M. Rogers, Los Gatos, CA (US);

Man Chun D. Leung, Daly City, CA (US);

David Pardoe, Mountain View, CA (US);

Bing Liu, San Jose, CA (US);

Shawn F. Ren, Knoxville, TN (US);

Rahul Tandra, Santa Clara, CA (US);

Parvez Ahammad, San Jose, CA (US);

Jing Wang, Los Altos, CA (US);

Ryan T. Tecco, Philadelphia, PA (US);

Yajun Wang, Sunnyvale, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 3/00 (2006.01); G06F 9/54 (2006.01); G06F 21/64 (2013.01);
U.S. Cl.
CPC ...
G06F 9/54 (2013.01); G06F 21/645 (2013.01);
Abstract

Embodiments of the disclosed technologies receive first event data associated with a first party application, receive second event data representing a click, in the first party application, on a link to a third party application, receive third event data from the third party application, convert the third event data to a label, map a compressed format of the labeled third event data to the first event data and the second event data to create multi-party attribution data, group multiple instances of the multi-party attribution data into a batch, add noise to the compressed format of the labeled third event data in the batch, and send the noisy batch to a second computing device. A debiasing algorithm can be applied to the noisy batch. The debiased noisy batch can be used to train at least one machine learning model.


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