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:
Dec. 21, 2021

Filed:

Jun. 21, 2019
Applicant:

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

Inventors:

Dan Liu, Santa Clara, CA (US);

Daniel Sairom Krishnan Hewlett, Sunnyvale, CA (US);

Qi Guo, Sunnyvale, CA (US);

Wei Lu, Sunnyvale, CA (US);

Xuhong Zhang, Sunnyvale, CA (US);

Wensheng Sun, Sunnyvale, CA (US);

Mingzhou Zhou, Sunnyvale, CA (US);

Anthony Hsu, Sunnyvale, CA (US);

Keqiu Hu, Sunnyvale, CA (US);

Yi Wu, Sunnyvale, CA (US);

Chenya Zhang, Santa Clara, CA (US);

Baolei Li, Santa Clara, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/9038 (2019.01); G06N 3/02 (2006.01); H04L 29/08 (2006.01);
U.S. Cl.
CPC ...
G06F 16/9038 (2019.01); G06N 3/02 (2013.01); H04L 67/22 (2013.01); H04L 67/306 (2013.01);
Abstract

In an example embodiment, a platform is provided that utilizes information available to a computer system to feed a neural network. The neural network is trained to determine both the probability that a searcher would select a given potential search result if it was presented to him or her and the probability that a subject of the potential search result would respond to a communication from the searcher. These probabilities are essentially combined to produce a single score that can be used to determine whether to present the searcher with the potential search result and, if so, how high to rank the potential search result among other search results. In a further example embodiment, embeddings used for the input features are modified during training to maximize an objective.


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