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:
Sep. 06, 2022

Filed:

Feb. 19, 2018
Applicant:

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

Inventors:

Qi Guo, Sunnyvale, CA (US);

Xianren Wu, San Jose, CA (US);

Bo Hu, Mountain View, CA (US);

Shan Zhou, San Jose, CA (US);

Lei Ni, Belmont, CA (US);

Erik Eugene Buchanan, Mountain View, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/04 (2006.01); G06N 5/04 (2006.01); G06N 20/00 (2019.01); H04L 67/306 (2022.01); G06F 16/248 (2019.01); G06F 16/901 (2019.01); G06F 16/2457 (2019.01); H04L 67/50 (2022.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 16/248 (2019.01); G06F 16/24578 (2019.01); G06F 16/9024 (2019.01); G06N 3/0454 (2013.01); G06N 5/04 (2013.01); H04L 67/306 (2013.01); H04L 67/535 (2022.05);
Abstract

An indication of a plurality of different entities in a social networking service is received, including at least two entities having a different entity type. A plurality of user profiles in the social networking service is accessed. A first machine-learned model is used to learn embeddings for the plurality of different entities in a d-dimensional space. A second machine-learned model is used to learn an embedding for each of one or more query terms that are not contained in the indication of the plurality of different entities in the social networking service, using the embeddings for the plurality of different entities learned using the first machine-learned model, the second-machine learned model being a deep structured semantic model (DSSM). A similarity score between a query term and an entity is calculated by computing distance between the embedding for the query term and the embedding for the entity in the d-dimensional space.


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