Abstract: A system for providing automated and domain specific contextual processing for context based verification may classify a plurality of extracted parameters from a set of digitized training document to assign a document similarity score with respect to a set of reference documents. The system may automatically detect a domain for the set of digitized training documents based on the document similarity score. The system may load a domain based neural model for the detected domain to generate a plurality of pre-defined contextual parameters. The system may receive a set of input documents and perform a contextual processing of the received set of documents based on the pre-defined contextual parameters to obtain an output in form of a plurality of filtered snippets, each bearing a corresponding rank. The context based verification may be performed based on the plurality of filtered snippets and the corresponding rank.

Abstract: An Artificial Intelligence (AI)-based automated process is monitored via a process monitoring system that identifies components used in the execution of the sub-processes of the automated process. Various metrics are selected for collection prior to or during the execution of the AI-based automated process. The values of the metrics are collected as step outputs corresponding to the sub-processes. A final output generated upon the execution of the automated process is also collected. The step outputs can be used to determine the reason why the automated process produced a certain final output.

Abstract: An Artificial Intelligence (AI) based data transformation system receives a process document and automatically generates processor-executable code which enables automatic execution of a process as detailed within the process document. Various structural elements of the process documents are identified and the data from the document is clustered based on common parameters which can include the structural elements or textual data from the process document. The contextual information including conditional and non-conditional statements along with the entities and entity attributes are also obtained. The domain knowledge is superimposed on the contextual information to generate flows that represent procedures which make up the process to be automated. Platform specific code for the automatic execution of the process is automatically generated from the flows.

Abstract: An Artificial Intelligence (AI) based data transformation system receives a process document and automatically generates processor-executable code which enables automatic execution of a process as detailed within the process document. Various structural elements of the process documents are identified and the data from the document is clustered based on common parameters which can include the structural elements or textual data from the process document. The contextual information including conditional and non-conditional statements along with the entities and entity attributes are also obtained. The domain knowledge is superimposed on the contextual information to generate flows that represent procedures which make up the process to be automated. Platform specific code for the automatic execution of the process is automatically generated from the flows.

Abstract: An Artificial Intelligence (AI)-based automated process is monitored via a process monitoring system that identifies components used in the execution of the sub-processes of the automated process. Various metrics are selected for collection prior to or during the execution of the AI-based automated process. The values of the metrics are collected as step outputs corresponding to the sub-processes. A final output generated upon the execution of the automated process is also collected. The step outputs can be used to determine the reason why the automated process produced a certain final output.