Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system for identifying handwritten characters on an image using a classification model that employs a neural network. The system includes a computer having a processor and a memory device that stores data and executable code that, when executed, causes the processor to read and convert typed text on the image to machine encoded text to identify locations of the typed text on the image; identify a location on the image that includes handwritten text based on the location of predetermined typed text on the image; identify clusters of non-white pixels in the image at the location having the handwritten text, where constraints are employed to refine and limit the clusters; generate an individual and separate cluster image for each identified cluster; and classify each cluster image using machine learning and at least one neural network to determine the likelihood that the cluster is a certain character.

Abstract: A system for identifying handwritten characters on an image using a classification model that employs a neural network. The system includes a computer having a processor and a memory device that stores data and executable code that, when executed, causes the processor to read and convert typed text on the image to machine encoded text to identify locations of the typed text on the image; identify a location on the image that includes handwritten text based on the location of predetermined typed text on the image; identify dusters of non-white pixels in the image at the location having the handwritten text, where constraints are employed to refine and limit the clusters; generate an individual and separate cluster image for each identified duster; and classify each cluster image using machine learning and at least one neural network to determine the likelihood that the duster is a certain character.

Abstract: A system and method for identifying handwritten characters on an image using a classification model that employs a neural network. The system includes a computer having a processor and a memory device that stores data and executable code that, when executed, causes the processor to read and convert typed text on the image to machine encoded text to identify locations of the typed text on the image; identify a location on the image that includes handwritten text based on the location of predetermined typed text on the image; identify clusters of non-white pixels in the image at the location having the handwritten text; generate an individual and separate cluster image for each identified cluster; classify each cluster image using machine learning and at least one neural network to determine the likelihood that the cluster is a certain character; and determine what character each cluster image is based on the classification.

Abstract: A system for identifying handwritten characters on an image using a classification model that employs a neural network. The system includes a computer having a processor and a memory device that stores data and executable code that, when executed, causes the processor to read and convert typed text on the image to machine encoded text to identify locations of the typed text on the image; identify a location on the image that includes handwritten text based on the location of predetermined typed text on the image; identify clusters of non-white pixels in the image at the location having the handwritten text; generate an individual and separate cluster image for each identified cluster; classify each cluster image using machine learning and at least one neural network to determine the likelihood that the cluster is a certain character; and determine the accuracy of the characters by comparing to a secondary database.

Abstract: An alert generating system uses event monitoring to mitigate exposure of user resources to third party envelopment. A computing system stores event records associated with user entities, each of the event records representing a quantized output or input event, for which a respective quantity is discharged from a user resource or fetched to a user resource. The system calculates, using the stored event records, an exposure metric representing exposure of any user entity to third party envelopment, the third party at least periodically executing a determinative protocol upon at least some of the user entities to determine fulfillment or nonfulfillment of prescribed actions. The computing system triggers, upon the exposure metric exceeding or subceeding a threshold of fulfillment or nonfulfillment of the prescribed actions respective to the user entity, an alert sent across the network connection to a user device of the user entity of forecasted third party envelopment.

Abstract: A system for identifying handwritten characters on an image using a classification model that employs a neural network. The system includes a computer having a processor and a memory device that stores data and executable code that, when executed, causes the processor to read and convert typed text on the image to machine encoded text to identify locations of the typed text on the image; identify a location on the image that includes handwritten text based on the location of predetermined typed text on the image; identify clusters of non-white pixels in the image at the location having the handwritten text, where constraints are employed to refine and limit the clusters; generate an individual and separate cluster image for each identified cluster; and classify each cluster image using machine learning and at least one neural network to determine the likelihood that the cluster is a certain character.

Abstract: 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.

Abstract: A system and a method for providing mobile services uses an autonomous automated teller machine vehicle including a controller operating a drive unit to move the autonomous vehicle and execute a machine learning algorithm configured to guide the movement along a selected travel path according to travel path information. Sensors on the autonomous vehicle detect fixed objects, new objects and persons along the travel path. The machine learning algorithm performs steps for guiding the movement along the travel path including: determining whether each of the detected fixed objects is included in the travel path information for use in guiding the vehicle; determining whether each of the detected new objects is to be included in the travel path information; and determining whether each of the detected persons interferes with the travel path and requires the vehicle to stop moving until the interfering person moves away from the travel path.

Abstract: A system and a method for providing mobile services uses an autonomous automated teller machine vehicle including a controller operating a drive unit to move the autonomous vehicle and execute a machine learning algorithm configured to guide the movement along a selected travel path according to travel path information. Sensors on the autonomous vehicle detect fixed objects, new objects and persons along the travel path. The machine learning algorithm performs steps for guiding the movement along the travel path including: determining whether each of the detected fixed objects is included in the travel path information for use in guiding the vehicle; determining whether each of the detected new objects is to be included in the travel path information; and determining whether each of the detected persons interferes with the travel path and requires the vehicle to stop moving until the interfering person moves away from the travel path.