Abstract: Aspects of the disclosure relate to automatically detecting, analyzing, and correcting errors in an application. The computing platform may identify errors in an application, and subsequently correct the errors based on corresponding actions that have been determined to correct the errors. The computing platform may determine a confidence score that corresponds to a particular action, representing the level of confidence that the particular action will successfully correct the corresponding error. The computing platform may set a predetermined threshold range that is compared to the confidence score, in which a confidence score greater than the threshold range will cause the computing platform to automatically correct the error. The computing platform may input results and feedback into a machine learning model to further refine the accuracy and reliability of the computing platform over time.

Abstract: Aspects of the disclosure relate to automatically detecting, analyzing, and correcting errors in an application. The computing platform may identify errors in an application, and subsequently correct the errors based on corresponding actions that have been determined to correct the errors. The computing platform may determine a confidence score that corresponds to a particular action, representing the level of confidence that the particular action will successfully correct the corresponding error. The computing platform may set a predetermined threshold range that is compared to the confidence score, in which a confidence score greater than the threshold range will cause the computing platform to automatically correct the error. The computing platform may input results and feedback into a machine learning model to further refine the accuracy and reliability of the computing platform over time.

Abstract: Aspects of the disclosure relate to automatically detecting, analyzing, and correcting errors in an application. The computing platform may identify errors in an application, and subsequently correct the errors based on corresponding actions that have been determined to correct the errors. The computing platform may determine a confidence score that corresponds to a particular action, representing the level of confidence that the particular action will successfully correct the corresponding error. The computing platform may set a predetermined threshold range that is compared to the confidence score, in which a confidence score greater than the threshold range will cause the computing platform to automatically correct the error. The computing platform may input results and feedback into a machine learning model to further refine the accuracy and reliability of the computing platform over time.

Abstract: A system for condensing user communications relating to a topic is provided. The system may include a processor and a non-transitory memory. The processor may: designate a topic of user interest; retrieve legacy communications; and remove duplicative communications. The processor may form a topic-centric training set for a neural network. The topic-centric training set may be based on the legacy communications, legacy intelligence, and the plurality of outcomes and may be delimited by an analysis of the database. The processor may synthesize the neural network using the topic-centric training set in order to assign individual weights to each of a plurality of nodes in the neural network. In response to a selection of the topic of user interest, the processor may generate a plurality of user options based on the neural network. The system may include a display in order to prompt the user to select one of the options.

Abstract: A system for condensing user communications relating to a topic is provided. The system may include a processor and a non-transitory memory. The processor may: designate a topic of user interest; retrieve legacy communications; and remove duplicative communications. The processor may form a topic-centric training set for a neural network. The topic-centric training set may be based on the legacy communications, legacy intelligence, and the plurality of outcomes and may be delimited by an analysis of the database. The processor may synthesize the neural network using the topic-centric training set in order to assign individual weights to each of a plurality of nodes in the neural network. In response to a selection of the topic of user interest, the processor may generate a plurality of user options based on the neural network. The system may include a display in order to prompt the user to select one of the options.

Abstract: A system for condensing user communications relating to a topic is provided. The system may include a processor and a non-transitory memory. The processor may: designate a topic of user interest; retrieve legacy communications; and remove duplicative communications. The processor may form a topic-centric training set for a neural network. The topic-centric training set may be based on the legacy communications, legacy intelligence, and the plurality of outcomes and may be delimited by an analysis of the database. The processor may synthesize the neural network using the topic-centric training set in order to assign individual weights to each of a plurality of nodes in the neural network. In response to a selection of the topic of user interest, the processor may generate a plurality of user options based on the neural network. The system may include a display in order to prompt the user to select one of the options.