Abstract: A creative is uniquely and optimally customized for every user impression, using the materials and tools available to those wishing to send image- and text-based messages in the market dominated by walled garden platforms, together with a creative e engine process that combines supervised machine learning, relational databases, and generative adversarial networks in a particular configuration that generates the creative. In a first phase, the engine uses machine learning to identify Creative visual features that are associated with high (or low) levels of Performance Metrics when included in Creatives served to Users with a given high-dimensional set of User Attributes. In the second phase, the engine automatically composes Creatives that are composed of visual features that are optimized to create high Performance Metrics when served to Users with a set of attributes that are determined in real time.

Abstract: Embodiments of the invention overcome the shortcomings of prior art by transforming the understanding of how different creative placements and location helps drive sales and other KPIs to a computerized methodology that may allow advertising planners and buyers to generate plans that meet these expectations on effectiveness of their advertising campaign. Aspects of the invention fusing the “probability of exposure” estimates with segment level data to provide layers of intelligence in determining the probability estimates for sales conversion or other KPIs. Moreover, aspects of the invention may predict based on various models the reach and frequency relationship tradeoff for different impression levels.

Abstract: A system and method for accurately matching corresponding DSP event data and Ad-Server event data with associated with a single real-world ad serve event by (a) pairing DSP event data and Ad-Server event data into data pairs, (b) comparing various field data in associated source fields from each of the DSP event data and Ad-Server event data to determine if the field data is a match or unmatch, and (c) based on the likelihood that a match of field data in a particular source field indicates an overall event match, which is determined using a Bayesian analysis, determining the probability that the DSP event data and Ad-Server event data in the data pair truly corresponding to the same single real-world ad serve event.

Abstract: Embodiments of the invention provide a technical solution by generating a value index score based on aggregation of a value from a combination of features as a unit. In one embodiment, instead of generating a value index score based on a collection of features with each feature being a discrete parameter, aspects of the invention generate the value index score while accounting for weights of a combination of features as a unit. Furthermore, embodiments of the invention generate a weight value for each feature and that the weight, not only will it be a factor in the calculation, but also be modifiable in response to other factors of the features.