US 12,073,438 B2
Consumer sentiment analysis for selection of creative elements
Steven Gerber, New York, NY (US); Pavan Korada, San Mateo, CA (US); David Schey, New York, NY (US); Sunpreet Khanuja, San Mateo, CA (US); and Gayan De Silva, Prague (CZ)
Assigned to Zeta Global Corp., New York, NY (US)
Filed by Zeta Global Corp., New York, NY (US)
Filed on May 2, 2022, as Appl. No. 17/735,093.
Claims priority of provisional application 63/182,553, filed on Apr. 30, 2021.
Prior Publication US 2022/0351252 A1, Nov. 3, 2022
Int. Cl. G06Q 30/0251 (2023.01); G06Q 30/0201 (2023.01); G06Q 30/0204 (2023.01); G06Q 30/0241 (2023.01)
CPC G06Q 30/0269 (2013.01) [G06Q 30/0201 (2013.01); G06Q 30/0205 (2013.01); G06Q 30/0277 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A digital publication system configured to publish targeted content to online consumers, the digital publication system comprising:
one or more processors connected to one or more datasets associated with the online consumers via a publication network; and
a memory storing instructions that, when executed by at least one processor in the one or more processors, cause the at least one processor to perform operations comprising:
deliver one or more pieces of targeted content each comprising at least one universal tag to one or more internet enabled devices through one or more vendor servers over an exposure time period, the one or more pieces of targeted content associated with one or more sentiments, the at least one universal tag being configured to activate a script in response to the one or more pieces of targeted content being accessed by the one or more internet enabled devices;
receive a raw event, a raw conversion, and a cookie associated with each of the one or more internet enabled devices in response to the script being activated by the at least one universal tag;
determine a set of static features based on the received cookie and a set of dynamic features based on the received raw event and raw conversion;
train a sentiment prediction model based on the one or more sentiments, the set of static features, and the set of dynamic features,
the sentiment prediction model being configured to determine one or more engagement probabilities for the one or more sentiments;
determine a creative element associated with one of the one or more sentiments having an increased engagement probability based on the one or more engagement probabilities determined by the sentiment prediction model; and
cause a display of the creative element on an internet enabled device via the one or more vendor servers.