Accenture Global Solutions Limited

Soujanya Soni

Madhura Shivaram

Kavita V V Ganeshan

Aishwarya Kaliki

Kuntal Dey

Guanglei Xiong

Swati Tata

Ullas Balan Nambiar

Omar Razi

Jhilam Bera

Sivasubramanian Arumugam Jalajam

Venkatesan Kamalakannan

Anjani Kumari

Srinivasan Krishnan Rajagopalan

Thyagarajan Delli

Madhur Bhasini Chaini

Harbhajan Singh

Namratha Suresh

Libin Varughese

Mandar Mohan Patil

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.

Automated contextual processing of unstructured data

An AI-based rule generation system generates an ontology from user-provided information and further enables generating rules that govern processes via drag-and-drop operations by automatically generating code in the backend. The rule generation system after generating the ontology, provides access to the entities of the ontology via a drag-and-drop GUI which also includes operators required to generate the rules. The user can drag-and-drop the entity elements and the operator elements as needed onto a whitespace in addition to providing the requisite values in order to generate a rule flow. The rule flow is validated and published to an execution server for use by downstream processes. The rule generation system further includes custom functions in addition to enabling distributed knowledge base processes for generating the rules.

Artificial intelligence (AI) based automatic rule generation

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.

Monitoring an artificial intelligence (AI) based process

A message analysis system controls the transmission of outgoing messages based on the contents of the outgoing messages so that the proliferation of restricted data or confidential content can be monitored or prevented. An outgoing message is detected via various user actions executed during the creation of the outgoing message. The contents of the outgoing message are extracted to determine if the outgoing message includes restricted data or content based on a comparison of the message contents with contents of an index which includes prior tagged messages and their message counts. If the contents of the outgoing message match a prior received message, an associated message count is updated and the outgoing message is transmitted or restricted based on the comparison of the updated message count with a corresponding count threshold.

Message analysis for information security

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.

Automatic data transformations for process automations

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Bengaluru, India

Soujanya Soni