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. 09, 2020

Filed:

Nov. 29, 2017
Applicant:

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

Inventors:

Karan Ashok Ahuja, Palo Alto, CA (US);

Befekadu Ayenew Ejigou, San Leandro, CA (US);

Ningfeng Liang, Cupertino, CA (US);

Lokesh P. Bajaj, Fremont, CA (US);

Wei Wang, San Jose, CA (US);

Paul Fletcher, Sunnyvale, CA (US);

Wei Lu, San Jose, CA (US);

Shaunak Chatterjee, Sunnyvale, CA (US);

Souvik Ghosh, Saratoga, CA (US);

Yang Li, Sunnyvale, CA (US);

Wei Deng, San Francisco, CA (US);

Qiang Wu, Sunnyvale, CA (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 17/00 (2019.01); G06F 40/174 (2020.01); G06Q 50/00 (2012.01); G06N 20/00 (2019.01); H04L 29/08 (2006.01);
U.S. Cl.
CPC ...
G06F 40/174 (2020.01); G06N 20/00 (2019.01); G06Q 50/01 (2013.01); H04L 67/22 (2013.01); H04L 67/306 (2013.01);
Abstract

In an example, first and second machine learned models corresponding to a particular context of a social networking service are obtained, the first machine learned model trained via a first machine learning algorithm to output an indication of importance of a social networking profile field to obtaining results in the particular context, and the second machine learned model trained via a second machine learning algorithm to output a propensity of the user to edit a social networking profile field if requested. One or more missing fields in a social networking profile for the user are identified. For each of one or more of the one or more missing fields, the field and an identification of the user are passed through the first and second machine learned models, and outputs of the first and second machine learned models are combined to identify one or more top missing profile fields.


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