Truist Bank

Kevin Green

Raphael Fitzgerald

Paul Gerard Mistor

Kenneth William Cluff

Brian Franklin Kramer

Rinku Saha

A system for monitoring quantized signals in a bidirectional data stream and alerting a networked device of metric divergence by georegion includes a computing system that automatically, for each of multiple user entities, receives input signals and stores corresponding records , associated with the user entity, each representing a quantized input event. The computing system increments a resource of the user entity by a respective input quantity. Georegion-specific attributes associated with a respective georegion are discriminated, and the input records are associated with georegion. The computing system measures user-specific georegion-specific metrics for each georegion and determines whether the metrics diverge from a georegion-specific index. An alert is sent across a network for display on a user device upon determining a user-specific georegion-specific metric diverges from a georegion-specific index.

Alerting networked devices of signal divergence by georegion

A monitoring system for detecting anomalous discharges stores input and output records, and fetches and discharges corresponding quantities to and from user resources. The output event records are discriminated for category attributes, and user-specific output category-specific metrics are aggregated for comparison to peer-representative output-category indexes. An alert is sent across a network connection for display on a user device upon determining the user-specific output category-specific metric diverges from the peer-representative output-category index. An alert, in some examples, indicates that the user-specific output category metric exceeds the peer-representative output-category index. Exposure of user-entity resources to outputs that are divergent from peer-representative levels is mitigated. The alert is advantageous to user entities and user devices, enabling early action to be taken by the user entity.

Time-based input and output monitoring and analysis to predict future inputs and outputs

A system and method is described for labelling data for trigger identification. The system and method comprising receiving data, transforming the data, extracting content from the data, processing data content through a classification machine learning model to receive a label, and trigger a secondary system based on the label. The system and method may further include maintaining a database of labeled data.

System and method for labelling data for trigger identification

A system for monitoring quantized signals in a bidirectional data stream and alerting a networked device of metric divergence by georegion includes a computing system that automatically, for each of multiple user entities, receives input signals and stores corresponding records, associated with the user entity, each representing a quantized input event. The computing system increments a resource of the user entity by a respective input quantity. Georegion-specific attributes associated with a respective georegion are discriminated, and the input records are associated with georegion. The computing system measures user-specific georegion-specific metrics for each georegion and determines whether the metrics diverge from a georegion-specific index. An alert is sent across a network for display on a user device upon determining a user-specific georegion-specific metric diverges from a georegion-specific index.

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Matthews, NC, United States of America

Kevin Green