US 11,748,776 B2
Systems and methods of generating context specification for contextualized searches and content delivery
Nedialko Dimitrov, Toronto (CA)
Assigned to STACKADAPT INC., Toronto (CA)
Filed by STACKADAPT, INC., Toronto (CA)
Filed on Feb. 10, 2021, as Appl. No. 17/172,689.
Claims priority of provisional application 62/978,746, filed on Feb. 19, 2020.
Prior Publication US 2021/0256568 A1, Aug. 19, 2021
Int. Cl. G06Q 30/0251 (2023.01); G06Q 30/0241 (2023.01); G06F 16/9532 (2019.01); G06F 18/22 (2023.01); G06F 18/2113 (2023.01); G06N 7/01 (2023.01); G06V 10/74 (2022.01); G06V 10/771 (2022.01)
CPC G06Q 30/0255 (2013.01) [G06F 16/9532 (2019.01); G06F 18/2113 (2023.01); G06F 18/22 (2023.01); G06N 7/01 (2023.01); G06Q 30/0276 (2013.01); G06V 10/761 (2022.01); G06V 10/771 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
prior to a bid-time and after training a machine-learning model,
applying, by a computer, the machine-learning model to a first set of context terms received from a client device to output a set of beacon terms extracted from a plurality of corpus terms stored in a corpus database, wherein the machine-learning model is trained on the plurality of corpus terms stored in the corpus database, wherein the machine-learning model determines a plurality of co-occurrence probabilities corresponding to the plurality of corpus terms indicating a probability that one or more context terms co-occurs with one or more corpus terms, and wherein the machine-learning model extracts each beacon term from each corpus term satisfying a threshold co-occurrence probability;
calculating, by the computer, a plurality of page scores for a plurality of corpus webpages stored in the corpus database based upon the set of beacon terms satisfying the threshold co-occurrence probability and the first set of context terms;
identifying, by the computer, in the plurality of corpus webpages a set of contextual webpages having page scores satisfying a threshold;
applying, by the computer, the machine-learning model to the first set of context terms and a second set of context terms received from the client device to output an updated set of beacon terms;
calculating, by the computer, one or more updated page scores for one or more corpus webpages stored in the corpus database based upon the updated set of beacon terms, the first set of context terms, and the second set of context terms;
updating, by the computer, the set of contextual webpages based upon the one or more updated page scores; and
storing, by the computer into a campaign database, campaign data of a user comprising the set of beacon terms and the set of contextual webpages, the campaign data configured for executing at a future bid-time a real-time bidding selection operation for one or more available webpages during the real-time bidding selection operation.