Abstract: Systems and methods of training and deploying a machine learning neural network in digital advertising. The method comprises receiving a plurality of input datasets at respective ones of a plurality of input layers of the neural network, the neural network being instantiated in one or more processors. The neural network comprises an output layer interconnected to the plurality of input layers via a set of intermediate layers, each of the input datasets being associated with a respective digital ad input attribute, ones of the intermediate layers being configured in accordance with an initial matrix of weights; and training the neural network in accordance with the plurality of input layers based upon recursively adjusting the initial matrix of weights by backpropogation in generating, at the output layer, at least one digital ad output attribute in accordance with diminishment of an error matrix computed at the output layer of the neural network.

Abstract: The present invention relates to a system and method for interacting with patrons of a service provider. In particular, the present invention relates to a system and method for identifying, tracking, and assigning seating to walk-in patrons of an establishment without previously receiving information from the patron. Specifically, the system and method identifies, tracks, and assigns seating to the walk-in patrons without previously making a reservation or providing their name to be placed on a waitlist for the service provider. The walk-in patrons can enter the establishment, have the establishment be automatically alerted as to who the patron is, and be instantly enabled to assign a seating or table location for that patron in a manner that multiple devices associated with the establishment are provided with the patron information and their seating or table location.