Abstract: Systems, computer program products, and methods are described herein for intelligent exception handling within a distributed network environment. The present disclosure is configured to receive an incidence of a first exception associated with a network interaction; extract information associated with the first exception; determine, via a machine learning (ML) subsystem, a first exception category associated with the first exception based on at least the information associated with the exception; retrieve, from a repository, a first set of actions associated with the first exception category, wherein the first set of actions are configured to resolve the first exception; and in response, execute the first set of actions.

Abstract: Aspects of the disclosure relate to intelligently managing invoice processing using blockchain and mixed reality applications. The computing platform may receive invoices and may analyze the invoices to determine a priority order in which the invoices should be processed. The computing platform may use an auto-matching algorithm to identify errors within the invoice and to generate proposed solutions to fix the errors. The computing platform may transmit the invoice to an enterprise organization for modifications and may receive, from the enterprise organization, confidential data including a modified invoice. The computing platform may receive, from an entity associated with a transaction indicated on the invoice, a request to access the modified invoice using a mixed reality application. The computing platform may receive, from the entity and based on the entity's review of the modified invoice using the mixed reality application, approval to execute the modified invoice.

Abstract: Apparatus and methods for accelerating the remediation of broken transactions using parallel machine learning processing and optimization is provided. The methods may include a network access point receiving, from an application programming interface, a broken transaction. The methods may also include a network access point selecting a bionic element within a bio-mesh network for processing the broken transaction and routing the broken transaction to the bionic element. The bio-mesh network may include a plurality of bionic elements. The methods may further include the bionic element receiving the broken transaction and extracting, from the broken transaction, a set of features, transmitting the set of features to a policy bank, and, in response to the policy bank failing to identify a stored routine associated with the set of features for fixing the broken transaction, running in parallel two machine-learning algorithms to identify an optimal routine for fixing the broken transaction.

Abstract: A system for prioritizing a plurality of requests received from a plurality of clients is disclosed. The system receives the plurality of requests. For each request from the plurality of requests, the system extracts features of the request, where the extracted features provide information regarding a priority in performing the request. The extracted features correspond to a numerical representation of the request, such that if a priority level associated with the request is high the numerical representation comprises higher numerical values compared to another request that is associated with a low priority level. The system determines a prioritization in performing the plurality of requests by ranking a plurality of extracted features representing the plurality of requests based on ranking numerical values associated with the plurality of extracted features.

Abstract: Systems, computer program products, and methods are described herein for testing application resiliency via context aware auditing equilibrium. The present invention is configured to receive an indication to test application resiliency of an application; initiate a resiliency test engine on the application; determine, using the resiliency test engine, one or more artifacts associated with the application to be tested; determine a first subset of the one or more test scripts is able to capture a behavior of a first subset of the one or more artifacts; initiate a context-based classification engine on the first subset of the one or more test scripts and the first subset of the one or more artifacts; classify the first subset of the one or more test scripts into a supporting behavior class and an opposing behavior class; generate a graphical representation of the classification; and display the graphical representation of the classification.

Abstract: A system for prioritizing a plurality of requests received from a plurality of clients is disclosed. The system receives the plurality of requests. For each request from the plurality of requests, the system extracts features of the request, where the extracted features provide information regarding a priority in performing the request. The extracted features correspond to a numerical representation of the request, such that if a priority level associated with the request is high the numerical representation comprises higher numerical values compared to another request that is associated with a low priority level. The system determines a prioritization in performing the plurality of requests by ranking a plurality of extracted features representing the plurality of requests based on ranking numerical values associated with the plurality of extracted features.