Abstract: An AI-enabled digital search optimization system is described. The system includes a clustering module configured to cluster a set of keywords into one or more clusters. The system further includes a targeting AI module configured to determine a target audience cohort for a corresponding one of the at least one cluster. The system further includes an advertisement (ad) copy generation module configured to generate a personalized ad copy to the determined target audience. Herein each personalized ad copy comprises a headline and a description, wherein the headline and the description have at least one keyword from the corresponding one of the at least one cluster.

Abstract: The present disclosure provides a method for optimizing ad campaigns of iOS users. The method comprises collecting (201) data input from data sources, by a data module, generating (202) audience cohorts based on the data input, by an audience cohort generation module, ranking (203) the audience cohorts based on parameters, by a cohort ranking module, generating (204) clusters of ad sets based on similar targeting, by ad publishing module, assigning (205) a confidence metric to the cluster based on performance of the ad set, by the ad publishing module, enabling and/or disabling (206) ad sets based on confidence metric, by the ad publishing module, analyzing (207) performance of the ad sets and transmitting feedback of the performance to an artificial intelligence module, by a social media platform and attribution platform and optimizing (208) performance of the ad sets based on the feedback of the performance, by the artificial intelligence module.

Abstract: Systems and methods for a zero-code approach for model version upgrades are described herein. For example, a system includes memory. The system further includes a processor that receives source data from data sources; applies an ingestion process to the source data for storing storage data in a data storage repository; generates models from the storage data; applies an output process to generate output schemas based on the generated models; and applies the output schemas for providing data. Also, the source data and the models changes over time leading to different data type collections. Further, at least one of applying the ingestion and the output process includes performing a dynamic merge on the different data type collections to create a merged data type collection; acquiring a user-defined configuration for the merged data type collection; and generating an output schema to the merged data type collection based on the user-defined configuration.

Abstract: A digital advertisement optimization system for digital advertisement optimization is described. The system includes a budget pacing module for selecting a budget pacing model from a group of budget pacing models, and a budget distributor module for selecting a budget distributor model from a group of budget distributor models. The system further includes a single platform budget optimization module for allocating a budget, within each advertisement platform of a group of advertisement platforms for the digital advertisement optimization, to advertisement sets based at least in part on the selected budget pacing model, the selected budget distributor model, and one or more single platform budget optimization models.

Abstract: A method to estimate and model all or at least two parameters of hydraulic fracture geometry in predominantly horizontal or nearly horizontal wells, by use of inter-well electromagnetic recordings. In aspects, resistivity of the fracturing fluid is used in the modeling process.

Abstract: A system, computer-readable medium, and method for determining a potential drilling location, of which the method includes obtaining data representing a subterranean domain. The data includes at least seismic data. The method also includes inverting the seismic data, creating a petroleum systems model of the subterranean domain based at least in part on a result of inverting the seismic data, simulating a dynamic reservoir model of the subterranean domain based at least in part on the petroleum systems model, and identifying the potential drilling location based on a combination of the inverting of the seismic data, creating the petroleum systems model, and simulating the dynamic reservoir model.

Abstract: A method for modeling a subterranean formation includes measuring or receiving cross-well electromagnetic data representing a subterranean formation. The method also includes producing a resistivity profile of the subterranean formation based at least partially upon the cross-well electromagnetic data. The method also includes determining a static model of the subterranean formation based at least partially upon the resistivity profile. The method also includes determining a dynamic model of the subterranean formation based at least partially upon the static model.

Abstract: Systems and methods are provided for insight automation from content data. The system generates insights from online and offline data at scale using unsupervised or supervised machine learning algorithms. The system can further generate insight at various levels for continuous evaluation and automated promotion.

Abstract: A system, computer-readable medium, and method for determining a potential drilling location, of which the method includes obtaining data representing a subterranean domain. The data includes at least seismic data. The method also includes inverting the seismic data, creating a petroleum systems model of the subterranean domain based at least in part on a result of inverting the seismic data, simulating a dynamic reservoir model of the subterranean domain based at least in part on the petroleum systems model, and identifying the potential drilling location based on a combination of the inverting of the seismic data, creating the petroleum systems model, and simulating the dynamic reservoir model.