US 12,067,593 B2
Advertisement target determining device and advertisement target determining method
Sanghun Park, Suwon-si (KR); Kunhee Jo, Suwon-si (KR); Seongmin Joe, Suwon-si (KR); and Yeongjin Chi, Suwon-si (KR)
Assigned to SAMSUNG ELECTRONICS CO., LTD., Suwon-si (KR)
Filed by SAMSUNG ELECTRONICS CO., LTD., Suwon-si (KR)
Filed on Mar. 17, 2023, as Appl. No. 18/123,105.
Application 18/123,105 is a continuation of application No. PCT/KR2023/003164, filed on Mar. 8, 2023.
Claims priority of application No. 10-2022-0033585 (KR), filed on Mar. 17, 2022.
Prior Publication US 2023/0325876 A1, Oct. 12, 2023
Int. Cl. G06Q 30/00 (2023.01); G06N 5/04 (2023.01); G06Q 30/0251 (2023.01)
CPC G06Q 30/0255 (2013.01) [G06N 5/04 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A method of determining an advertisement target according to an advertisement request, the method comprising:
obtaining usage history information from a first plurality of devices via transmission from a communication interface of a device of the first plurality of devices;
obtaining a first plurality of features of the first plurality of devices, based on the usage history information;
generating a plurality of feature vectors for the first plurality of features;
determining a first plurality of labels for the first plurality of devices, based on the advertisement request and the first plurality of features by:
extracting a second plurality of devices that have achieved an advertisement purpose with respect to an advertisement object included in the advertisement request from among the first plurality of devices, based on the usage history information;
determining a second plurality of labels of the second plurality of devices as a first value;
extracting a third plurality of devices from among the first plurality of devices for which labels have not been determined with a third plurality of features that are similar to a second plurality of features the second plurality of devices;
training a neural network model comprising a plurality of lavers and a first plurality of weights by:
providing the neural network model the advertise request and the first plurality of features as a plurality of input values; and
determining a second plurality of weights by optimizing a cost value of the first plurality of weights;
outputting the second plurality of weights of the first plurality of features;
determining one or more third labels of the third plurality of devices as the first value based on the third plurality of features and the second plurality of weights; and
determining one or more fourth labels of the third plurality of devices for which the labels have not been determined as a second value;
generating an advertisement target inference model, based on the first plurality of labels and the plurality of feature vectors; and
determining at least one advertisement target device among the first plurality of devices by applying the generated advertisement target inference model to the first plurality of devices.