Accenture Global Solutions Limited

Emc IP Holding Company LLC

Zensar Technologies Limited

Ullas Balan Nambiar

Guanglei Xiong

Omar Razi

Prakash Ghatage

Emmanuel Munguia Tapia

Nirav Sampat

Naveen Kumar Thangaraj

Swati Tata

Thirumale N Niranjan

Neeru Narang

Kumar Viswanathan

Sandeep Kishore

Soujanya Soni

Ditty Mathew

Kaustubh Kurhekar

Kavita V V Ganeshan

Sivasubramanian Arumugam Jalajam

Venkatesan Kamalakannan

Srinivasan Krishnan Rajagopalan

Anjani Kumari

Udit Chandna

Prateek Sharma

Harbhajan Singh

David Alfonso Guerra

Thyagarajan Delli

Madhur Bhasini Chaini

Kapil Bharti

Karthik Nagesh

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 Artificial Intelligence (AI) based document digitization, transformation and validation system extracts fields from digital documents via different document digitization processes. A document packet with a plurality of documents is initially accessed and any non-digital documents in the document packet are digitized. The errors in the digitized documents are corrected and non-English documents are translated into English. Each of the documents is provided to a plurality of digitization services for the extraction of fields by a plurality of field extraction models. If a field has multiple field instances extracted by more than one digitization service, then a field instance with the highest confidence score is selected for inclusion into the consolidated results. The consolidated results produced in different JavaScript Object Notation (JSON) formats are converted into a common JSON format which may be further validated and provided to downstream processes.

Document digitization, transformation and validation

The validity of engineering drawings is automatically determined based on compliance of the specifications of the engineering drawings with automatically generated rules. A document package including images of the engineering drawings and related documents is received. Rules codifying the requirements to be fulfilled by the engineering drawings are automatically generated from the related documents. Data such as specifications of the various parts of the entities in the engineering drawings are automatically extracted. The extracted data is analyzed to determine compliance with the rules to validate the engineering drawings.

Intelligent engineering data digitization

The present disclosure relates to the field of a system that maps employees' sentiments, and envisages a system for mapping employees' sentiments comprising a database, an input unit, a parser, a filter unit, a lexical analyse), an identifier, a mapping unit, an aggregator, a sentiments analyser, and a computation unit. The employee inputs are received through the input unit. The parser parses the inputs and generates tokens that are filtered based on the filter unit. Keywords are extracted from pre-determined list of keywords based on filtered words which are mapped with pre-determined group of themes and quantitative score based on the weightage score is computed by pre-determined weightage scores. The sentiments analyser analyses employee sentiments, based on quantitative score and computation unit computes a relationship between employee sentiments, employee feedback and employee performance.

System for mapping employees sentiments and a method thereof

A first data set is obtained specifying configuration information associated with elements of a computing system. A second data set is also obtained specifying dependency information associated with the elements of the computing system. A third data set is also obtained specifying deployment constraint information associated with the elements of the computing system. A plan for migrating one or more of the elements of the computing system is automatically generated based on at least a portion of the first data set, at least a portion of the second data set, and at least a portion of the third data set. The automatic generation of the migration plan checks for one or more conflicts between configuration information, dependency information and deployment constraint information and generates the migration plan to at least substantially eliminate the one or more conflicts.

Management of computing system element migration

Growing community of inventors

Bengaluru, India

Ullas Balan Nambiar