US 12,148,010 B2
Multi-platform integration for classification of web content
David Rose, Atlanta, GA (US); and Danny Portman, Atlanta, GA (US)
Assigned to Zeta Global Corp., New York, NY (US)
Filed by Zeta Global Corp., New York, NY (US)
Filed on Aug. 23, 2023, as Appl. No. 18/454,557.
Application 18/454,557 is a continuation of application No. 17/538,576, filed on Nov. 30, 2021, granted, now 11,769,178.
Claims priority of provisional application 63/131,303, filed on Dec. 28, 2020.
Prior Publication US 2023/0394536 A1, Dec. 7, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/0241 (2023.01); G06F 16/958 (2019.01); G06Q 30/0273 (2023.01)
CPC G06Q 30/0277 (2013.01) [G06F 16/958 (2019.01); G06Q 30/0275 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
at least one programmable processor; and
a machine-readable medium having processor-executable instructions stored thereon which, when executed by the at least one programmable processor, cause the at least one programmable processor to:
transmit a graphical user interface (GUI) to at least one user device to be rendered on a display of the at least one user device;
receive a request, via the GUI, from the at least one user device, to deploy a digital advertisement, the request including a set of platforms of a plurality of platforms and a set of allocation data;
integrate a digital advertisement directly with each of the platforms in the set of platforms based on the set of allocation data; and
dynamically adjust the set of allocation data for each of the platforms,
wherein the dynamic adjustment of the set of allocation data for each of the platforms comprises:
running an in-browser script to crawl data associated with one or more sub-URL webpages of a website of each of the platforms and to create a cascading analysis of the website using one or more of a URL parsing, HTML metadata, and text classification;
the in-browser script executing in real-time a machine learning model to categorize the crawled data associated with the one or more sub-URL webpages to generate a webpage classification for each of the platforms, wherein machine-learning used by the in-browser script parses elements of the sub-URL webpages, the elements including one or more of a placement of images, a text, and comments on the sub-URL webpages, and wherein the machine-learning used by the in-browser script further learns or uses contextual queues to parse a meaning of the text or comments; and
dynamically adjusting the set of allocation data for each of the platforms based on the webpage classification.