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:
Nov. 13, 2018

Filed:

Aug. 08, 2014
Applicant:

Facebook, Inc., Menlo Park, CA (US);

Inventors:

Russell Lee-Goldman, Oakland, CA (US);

Lada Ariana Adamic, Mountain View, CA (US);

David M. Goldblatt, Redwood City, CA (US);

Yuval Kesten, San Francisco, CA (US);

Mark Andrew Rich, Redwood City, CA (US);

Nidhi Gupta, Mountain View, CA (US);

Amy Campbell, San Francisco, CA (US);

Andrew Rocco Tresolini Fiore, San Francisco, CA (US);

Assignee:

Facebook, Inc., Menlo Park, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/30 (2006.01); G06Q 50/00 (2012.01); G06F 17/27 (2006.01);
U.S. Cl.
CPC ...
G06F 17/30864 (2013.01); G06F 17/278 (2013.01); G06F 17/3043 (2013.01); G06Q 50/00 (2013.01);
Abstract

In one embodiment, a method includes receiving unstructured text from a user of a social-networking system, determining whether the unstructured text includes a request for a recommendation, identifying one or more first entity names in the unstructured text, generating a structured query based upon the one or more first entity names, identifying, in the social graph, one or more second entity names corresponding to the structured query, and presenting the one or more second entity names and the unstructured text in a social context of the user. The unstructured text may include text of a post or message generated by the user on a social-networking system. A score may be generated based on the unstructured text to determine whether the text includes a request for recommendation using a machine-learning model based on comparison of the unstructured text to the one or more predetermined words associated with requests for recommendation.


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