US 12,443,979 B2
Artificial intelligence analytics platform for cross-media advertising
Matthew R Ferris, Thornton, CO (US); and Taras Bugir, Golden, CO (US)
Assigned to Decentrix, Inc., Denver, CO (US)
Filed by Decentrix, inc., Denver, CO (US)
Filed on Dec. 16, 2019, as Appl. No. 16/716,480.
Claims priority of provisional application 62/779,800, filed on Dec. 14, 2018.
Prior Publication US 2020/0364755 A1, Nov. 19, 2020
Int. Cl. G06Q 30/00 (2023.01); G06Q 30/0241 (2023.01); G06Q 30/0601 (2023.01)
CPC G06Q 30/0276 (2013.01) [G06Q 30/0609 (2013.01)] 13 Claims
OG exemplary drawing
 
1. An artificial intelligence analytics platform comprising:
a front end system comprising a processor and software, the front end system adapted to provide a plurality of buyer interfaces and seller interfaces to allow a buyer process, a planning and buying process, a seller process, and a selling and planning process to be consolidated into a single process, wherein the single process is adapted to aggregate bid data from the buyer interfaces; and
a backend analytics system comprising a processor and software, the backend analytics system in communication with the front end system via the plurality of buyer and seller interfaces, provide a workflow on a buy side in response to data received from the buyer interfaces to allow a buyer to implement a campaign across a plurality of different media distribution platforms comprising digital, linear, and addressable distribution platforms in a real-time bidding operation, constrain a seller inventory made available by a seller, allow a buyer to bid, randomize a plurality of bids, and provide feedback loops for measuring the effectiveness of the campaign based on actual delivery of insertions across the plurality of distribution platforms to provide the system a mechanism for course correction and stewardship through conversion modeling,
wherein the backend analytics platform is adapted to perform a utility forecast for each individual buyer based on historical data pricing data points and estimates of product value to the buyer and adjust the offered price from a floor value and to obfuscate buyer information via a randomization of the bid data within a threshold.