Abstract: Aspects of the disclosure relate to performing enhanced exception processing using cognitive automation tools. In some embodiments, a computing platform may receive interaction data identifying one or more actions performed by one or more users in resolving a plurality of exception items associated with an exception queue. Subsequently, the computing platform may train, using a learning engine, a machine learning model to resolve a first exception and a second exception of one or more exceptions based on the interaction data. Based on training the machine learning model, the computing platform may generate one or more configuration commands directing a processing module to implement the machine learning model to process additional exception items associated with the exception queue. The computing platform then may send the one or more configuration commands to the processing module.

Abstract: A system is provided for file capture and in-line error correction. The system comprises: a controller configured to capture and process a file during a file capture sequence comprising one or more capture events, the controller being configured to: capture a file using a file capture device associated with a user device, wherein capturing the file comprises receiving a file image and a user input data field associated with the file; identify, using an in-line file analysis module, an error with a capture event during the file capture sequence, wherein the error is a discrepancy identified between the user input data field and an image-derived data field; correct the error in the file before the file capture sequence is completed and the file is exported; and export the file, wherein the file capture sequence is completed.

Abstract: A system for spacing rafters can include an outer member, an inner member, jaws, and a handle. The handle can be movably connected to the inner member and the outer member. The jaws can be secured to the inner member or the outer member. The handle can be operable to move the jaws with respect to each other to equally space the rafters with respect to each other.

Abstract: An ATM exception handler automatically handles certain forms of exceptions. The exception handler receives messages indicating amounts of cash counted and amounts of cash shown at different ATMs. If exceptions are indicated by these messages, the exception handler attempts to reconcile exceptions with each other depending on several factors, such as, the servicer of the ATMs and day of servicing.

Abstract: Aspects of the disclosure relate to performing enhanced deposit item processing using cognitive automation tools. In some embodiments, a computing platform may receive, from a deposit item input support server, image data associated with a deposit item. Subsequently, the computing platform may apply a machine learning classification model to the image data associated with the deposit item. In doing so, the computing platform may produce one or more predicted values for one or more fields of the deposit item and one or more confidence scores for the one or more predicted values. Based on these predicted values and confidence scores, the computing platform may generate and send one or more commands directing the deposit item input support server to accept or reject the deposit item, which in turn may cause the deposit item input support server to accept or reject the deposit item.

Abstract: An analgesic formulation for relief of postpartum perineal pain and discomfort is disclosed. The formulation generally includes an analgesic compound in a solution with herbal ingredients. The formulation has been optimized to provide a stable solution, free from precipitation. A method for treating a perineal area of a woman after giving birth can include: spraying a solution from a pump sprayer onto the perineal area, the solution comprising: a topical anesthetic; and at least one herbal ingredient.

Abstract: A system is configured for detecting errors in an ATM. The system includes a processor that receives a withdrawal request from a user to receive cash from a cash dispenser of the ATM. Currency denominations and a number of bills for each currency denomination needed to fulfill the withdrawal request is determined. The system sends signal commands to cassettes of the ATM to dispense bills to fulfill the withdrawal request. A camera is positioned in a pathway of the bills being dispensed from the cassettes to the cash dispenser. The camera captures images of bills being dispensed from the cassettes and sends them to the processor. The processor generates an alert if a number of bills dispensed from at least one cassette does not match a corresponding number of bills expected to be dispensed from the at least one cassette.

Abstract: Disclosed is a dual-fuel heater having one or more thermocouples that operate a control valve that, in turn, controls whether fuel is distributed from a regulator to a burner based on the signal generated by the thermocouple(s). Further disclosed is a primary fuel selector valve that selects the fuel being used based on the operation of a user. The primary fuel selector valve provides fuel to the control valve upstream from the thermocouples, allowing for upstream control mechanisms such as a slave valve, to control the flow of fuel to the control valve.

Abstract: A device includes a memory configured to store prioritization thresholds. The prioritization thresholds include one or both of threshold values and threshold ranges associated with an age and an amount of exceptions. A processor of the device receives exceptions associated with operation of at least a portion of the plurality of ATMs. For each exception of the plurality of exceptions, the processor determines, based on a comparison of an age property of the exception to an age threshold of the prioritization thresholds, an age score for the exception. The processor determines, based on a comparison of an amount property of the exception to an amount threshold of the prioritization thresholds, an amount score for the exception. Based on the age and amount scores, a prioritization score is determined for the exception which corresponds to a measure of an order in which action should be taken to resolve the exception.

Abstract: A system is configured for detecting errors in an ATM. The system includes a processor that receives a withdrawal request from a user to receive cash from a cash dispenser of the ATM. Currency denominations and a number of bills for each currency denomination needed to fulfill the withdrawal request is determined. The system sends signal commands to cassettes of the ATM to dispense bills to fulfill the withdrawal request. A camera is positioned in a pathway of the bills being dispensed from the cassettes to the cash dispenser. The camera captures images of bills being dispensed from the cassettes and sends them to the processor. The processor generates an alert if a number of bills dispensed from at least one cassette does not match a corresponding number of bills expected to be dispensed from the at least one cassette.

Abstract: Aspects of the disclosure relate to performing enhanced exception processing using cognitive automation tools. In some embodiments, a computing platform may receive interaction data identifying one or more actions performed by one or more users in resolving a plurality of exception items associated with an exception queue. Subsequently, the computing platform may train, using a learning engine, a machine learning model to resolve a first exception and a second exception of one or more exceptions based on the interaction data. Based on training the machine learning model, the computing platform may generate one or more configuration commands directing a processing module to implement the machine learning model to process additional exception items associated with the exception queue. The computing platform then may send the one or more configuration commands to the processing module.

Abstract: An ATM exception handler automatically handles certain forms of exceptions. The exception handler receives messages indicating amounts of cash counted and amounts of cash shown at different ATMs. If exceptions are indicated by these messages, the exception handler attempts to reconcile exceptions with each other depending on several factors, such as, the servicer of the ATMs and day of servicing.

Abstract: Aspects of the disclosure relate to performing enhanced deposit item processing using cognitive automation tools. In some embodiments, a computing platform may receive, from a deposit item input support server, image data associated with a deposit item. Subsequently, the computing platform may apply a machine learning classification model to the image data associated with the deposit item. In doing so, the computing platform may produce one or more predicted values for one or more fields of the deposit item and one or more confidence scores for the one or more predicted values. Based on these predicted values and confidence scores, the computing platform may generate and send one or more commands directing the deposit item input support server to accept or reject the deposit item, which in turn may cause the deposit item input support server to accept or reject the deposit item.

Abstract: An ATM exception handler automatically handles certain forms of exceptions. The exception handler receives messages indicating amounts of cash counted and amounts of cash shown at different ATMs. If exceptions are indicated by these messages, the exception handler attempts to reconcile exceptions with each other depending on several factors, such as, the servicer of the ATMs and day of servicing.

Abstract: A system is provided for file capture and in-line error correction. The system comprises: a controller configured to capture and process a file during a file capture sequence comprising one or more capture events, the controller being configured to: capture a file using a file capture device associated with a user device, wherein capturing the file comprises receiving a file image and a user input data field associated with the file; identify, using an in-line file analysis module, an error with a capture event during the file capture sequence, wherein the error is a discrepancy identified between the user input data field and an image-derived data field; correct the error in the file before the file capture sequence is completed and the file is exported; and export the file, wherein the file capture sequence is completed.

Abstract: A system is configured for detecting errors in an ATM. The system includes a processor that receives a withdrawal request from a user to receive cash from a cash dispenser of the ATM. Currency denominations and a number of bills for each currency denomination needed to fulfill the withdrawal request is determined. The expected cash to be dispensed from the ATM is a sum of multiplications of each currency denomination with its number of bills. The system sends signal commands to cassettes of the ATM to dispense bills to fulfill the withdrawal request. A camera is positioned in a pathway of the bills being dispensed from the cassettes to the cash dispenser. The camera captures images of bills being dispensed from the cassettes and sends them to the processor. The processor generates an alert if the actual dispensed cash from the cassettes does not match the expected cash.

Abstract: In some embodiments, one or more processors receive a financial instrument via an interface. The financial instrument indicates attributes of a financial transaction. The processors determine, based on one or more of the attributes, whether the financial transaction involves a cross border exchange. If the financial transaction involves a cross border exchange, the processors flag the financial instrument accordingly, perform a currency conversion, and communicate an electronic image associated with the financial instrument to computing resources configured to complete the financial transaction electronically without having to transport a paper copy of the financial instrument between financial institutions.

Abstract: In some embodiments, one or more processors receive a financial instrument via an interface. The financial instrument indicates attributes of a financial transaction. The processors determine, based on one or more of the attributes, whether the financial transaction involves a cross border exchange. If the financial transaction involves a cross border exchange, the processors flag the financial instrument accordingly, perform a currency conversion, and communicate an electronic image associated with the financial instrument to computing resources configured to complete the financial transaction electronically without having to transport a paper copy of the financial instrument between financial institutions.

Abstract: In some embodiments, one or more processors receive a financial instrument via an interface. The financial instrument indicates attributes of a financial transaction. The processors determine, based on one or more of the attributes, whether the financial transaction involves a cross border exchange. If the financial transaction involves a cross border exchange, the processors flag the financial instrument accordingly, perform a currency conversion, and communicate an electronic image associated with the financial instrument to computing resources configured to complete the financial transaction electronically without having to transport a paper copy of the financial instrument between financial institutions.