Abstract: Systems, devices, and methods are described herein for improving inventory management. As used herein, “inventory” refers to digital space at an inventory providers webpage at which content can be delivered. The disclosed techniques utilize reinforced machine learning and an offline training process to train various models with which a content request corresponding to the inventory can be classified according to historical requests and a selection process identified for the request (e.g., a direct or an indirect selection process). If an indirect selection process is chosen, the content request may be optimized for that process utilizing additional machine learning models trained using reinforced machine learning and the offline training process. The disclosed techniques enable the inventory provider to optimize content selections according to a preferred objective. The training operations are performed offline, in a training system configured to simulate the run time environment.

Abstract: Devices, systems, and methods are provided for on-target rate optimization for video.

Abstract: Devices, systems, and methods are provided for on-target rate optimization for video.

Abstract: Devices, systems, and methods are provided for audience recognition. A method may include receiving over-the-top (OTT) advertisement impression data comprising metadata and content of advertisement bid requests, the metadata indicative of scheduled OTT media presentation; receiving user activity data indicative of day-part times when viewers watch content absent from the OTT advertisement impression data; generating, based on the OTT advertisement impression data, a first demographic probability vector; generating, based on the user activity data, a second demographic probability vector; generating, based on a combination of the first demographic probability vector and the second demographic probability vector, a third demographic probability vector, each entry of the third demographic probability vector indicative of a third probability that a viewer is in a respective age range; and generating an indication of the third demographic probability vector.