The Toronto-Dominion Bank
PREDICTING OCCURRENCES OF TARGETED CLASSES OF EVENTS USING TRAINED ARTIFICIAL-INTELLIGENCE PROCESSES

Abstract:

The disclosed embodiments include computer-implemented apparatuses and processes that dynamically predict future occurrences of targeted classes of 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 an expected occurrence of a corresponding one 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 a portion of the generated output data to a computing system, and the computing system may transmit digital content to a device associated with the expected occurrence based on the portion of the output data.