US 12,190,356 B2
Line item-based audience extension
Moussa Taifi, Jackson Heights, NY (US); Yana Volkovich, New York, NY (US); and Carlos Eduardo Rodriguez Castillo, Brooklyn, NY (US)
Assigned to MICROSOFT TECHNOLOGY LICENSING, LLC, Redmond, WA (US)
Filed by MICROSOFT TECHNOLOGY LICENSING, LLC, Redmond, WA (US)
Filed on Jul. 12, 2022, as Appl. No. 17/863,019.
Application 17/863,019 is a continuation of application No. 16/748,259, filed on Jan. 21, 2020, granted, now 11,430,018.
Prior Publication US 2022/0351254 A1, Nov. 3, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/00 (2023.01); G06F 18/23 (2023.01); G06N 20/00 (2019.01); G06Q 30/0241 (2023.01); G06Q 30/0251 (2023.01); G06Q 30/0273 (2023.01); G06V 10/764 (2022.01)
CPC G06Q 30/0277 (2013.01) [G06F 18/23 (2023.01); G06N 20/00 (2019.01); G06Q 30/0256 (2013.01); G06Q 30/0261 (2013.01); G06Q 30/0275 (2013.01); G06V 10/764 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A device, comprising:
a processing system including a processor; and
a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:
receiving, from a campaign manager device, information defining a line item and constraints for the line item in an online advertising system;
collecting browsing history information for targetable users;
ranking the targetable users based upon the browsing history information;
building a new segment based on the ranking of the targetable users;
forming a list of line item, segment identifier pairs, where the list includes a pair that comprises an identifier for the line item and an identifier for the new segment;
constructing a mapping that maps line items to segments, where the mapping is constructed based upon the list of line item, segment identifier pairs;
generating a line item to segment co-occurrence matrix based upon the mapping;
training a machine learning model to output an embedding of a segment identifier to add to the line item, where the machine-learning model is trained based upon the line item to segment co-occurrence matrix;
receiving, from the machine learning model, the embedding of the segment identifier;
receiving a second embedding of a second segment identifier that identifies a second segment;
selecting the second segment based upon a proximity between the embedding of the segment identifier output by the machine learning model and the second embedding of the second segment identifier;
providing advertisement content to targeted users according to the line item including the second segment.