Abstract: The disclosed embodiments include computer-implemented processes that determine, in real time, a likelihood of a targeted future engagement using trained artificial intelligence processes. For example, an apparatus may generate a first input dataset based on elements of first interaction data associated with a first temporal interval, and based on an application of a trained first artificial intelligence process to the first input dataset, generate output data representative of a predicted likelihood of an occurrence of each of a plurality of target events during a second temporal interval. The second temporal interval is subsequent to the first temporal interval and is separated from the first temporal interval by a corresponding buffer interval. Further, the apparatus may transmit at least a portion of the output data to a computing system, which may generate notification data associated with the predicted likelihood, and provision the notification data to a device.

Abstract: The disclosed embodiments include computer-implemented systems, apparatuses, and processes that automatically generate and provision for presenting actionable icons on a graphical user interface (GUI) of a computer device.

Abstract: The disclosed embodiments include computer-implemented apparatuses and processes that dynamically predict future occurrences of targeted events using adaptively trained machine-learning or artificial-intelligence processes. For example, an apparatus may generate an input dataset based on interaction data associated with a prior temporal interval, and may apply a trained, gradient-boosted, decision-tree process to the input dataset. Based on the application of the trained, gradient-boosted, decision-tree process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of each of a plurality of targeted events during a future temporal interval, which may be separated from the prior temporal interval by a corresponding buffer interval. The apparatus may also transmit the output data to a computing system, and the computing system may transmit digital content to a device based on at least a portion of the output data.

Abstract: The disclosed embodiments include computer-implemented processes that determine, in real time, a likelihood of a targeted future engagement using trained artificial intelligence processes. For example, an apparatus may generate a first input dataset based on elements of first interaction data associated with a first temporal interval, and based on an application of a trained first artificial intelligence process to the first input dataset, generate output data representative of a predicted likelihood of an occurrence of each of a plurality of target events during a second temporal interval. The second temporal interval is subsequent to the first temporal interval and is separated from the first temporal interval by a corresponding buffer interval. Further, the apparatus may transmit at least a portion of the output data to a computing system, which may generate notification data associated with the predicted likelihood, and provision the notification data to a device.