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:
Jan. 17, 2023

Filed:

Jun. 20, 2019
Applicant:

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

Inventors:

Qing Duan, Santa Clara, CA (US);

Xiaowen Zhang, Santa Clara, CA (US);

Xiaoqing Wang, San Jose, CA (US);

Junrui Xu, Fremont, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 16/29 (2019.01); G06F 16/901 (2019.01); G06K 9/62 (2022.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 16/29 (2019.01); G06F 16/9024 (2019.01); G06K 9/6218 (2013.01);
Abstract

Technologies for generating a graph containing clusters of feature attribute values for training a machine learning model for content item selection and delivery are provided. The disclosed techniques include, for each entity, of a plurality of entities, a system identifies transitions from one geographic location to another geographic location. A graph is generated based on the transitions associated with each entity. The graph comprises nodes representing geographic locations and edges connecting the nodes. Each of the edges connects two nodes, represents a transition from one geographic location to another geographic location, and each edge represents an edge weight value that is based on frequencies of transitions between geographic locations represented by the two connected nodes. The system generates a plurality of clusters from the nodes based upon the edge weight value of each edge. The system includes the plurality of clusters as features in a machine learning model.


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