Accenture Global Solutions Limited

Vinu Varghese

Nirav Sampat

Balaji Janarthanam

Anil Kv Kumar

Saran Prasad

Kumar Viswanathan

Prakash Ghatage

Selvakuberan Karuppasamy

Shikhar Srivastava

Hari Kumar Karnati

Abhishek Patni

Santhosh Kumar Rajavelu Balachandar

Sanjay Tiwari

Badarayan Panigrahi

Raghavan Tinniyam Iyer

Srinivasan Rajagopalan

Anil Kumar

Ravi Kant Gaur

Sivasubramanian Arumugam Jalajam

Manikandan Chandran

Anand Yesuraj Prakash

Mohit Mahajan

Venkatesan Kamalakannan

Dinesh Vijayakumar

Mohit Chawla

Rajesh Gala

Prakash Tamilarasan

Ashwini Purushothaman

Ashwin D'Silva

Arun Viswanath

Saju P George

Karthik Srikantamurthy

Ragav Devanathan

Anto Jose

Christina Catharina De Vries

Dheeraj Kharya

Saumyabrata Mitra

Bhanu Teja V

Basaveswara Kiran Dumpala

Amit Vithoba Patil

Deepam Biswas

Peter S Carpenter

Narayan Sethi

Vinay Verma

Ujjala Chattopadhyay

Kunal Jaiwant Kharsadia

Elaine T Bombal

Thangaraj Paramasivan

Peter Ashly

Loganathan Muthu

Bhavika Sehgal

Nirav Jagdish Sampat

A system for obtaining optimized regular expression may convert a received input data into a plurality of embeddings. The system may receive a generated regular expression corresponding to the plurality of embeddings, wherein the generated regular expression is at least one of an existing regular expression from a database and a newly generated regular expression. The system may parse the generated regular expression into a plurality of sub-blocks. The system may classify the plurality of sub-blocks to obtain a plurality of classified sub-blocks. The system may evaluate a quantifier class for each classified sub-block to identify a corresponding computationally expensive class. The system may perform an iterative analysis to obtain a plurality of optimized sub-blocks associated with a minimum computation time. The system may combine the plurality of optimized sub-blocks to obtain the optimized regular expression.

Automated generation and integration of an optimized regular expression

Systems and methods for detecting, classifying, and managing impediments are disclosed. For example, embodiments may be related to impediments in project management. The proposed systems and methods are configured to evaluate data harvested from multiple different sources (in different formats), identify potential impediments that may be described or present in the data, and classify said impediments based on whether the impediment is non-technical or technical. In addition, the proposed systems implement a technical solution of active learning combined with reinforcement learning to produce a feedback loop that, over each iteration, improves the accuracy of the impediment classification. The impediment management assistant is configured to identify impediments from various inputs sources across industries with an AI-based self-learning capability, providing a robust and accurate model even with only a limited training dataset.

Detection and classification of impediments

Systems and methods for managing supply chain of products and services are disclosed herein. A system generates supply chain data based on historical data received from data sources corresponding to supply chain of product or service. Further, system extracts data entity and set of attributes from supply chain data, to determine semantically related data entities. Furthermore, system determines use case corresponding to management of supply chain, based on semantically related data entities. Additionally, system predicts, risk or priority associated with product or service in the supply chain, to generate risks and alerts, based on prediction. Further, system assigns critical and high-priority use case to one or more agents based on a performance score of the one or more agents. Furthermore, system provides insights and suggestions for managing the supply chain of product or service at regional level and global level of supply chain.

System and method for assigning agents to critical/high priority supply chain risk use cases

A device may receive audio data identifying a plurality of speakers and may process the audio data, with a plurality of clustering models, to identify a plurality of speaker segments. The device may determine a plurality of diarization error rates for the plurality of speaker segments and may identify a plurality of errors in the plurality of speaker segments. The device may select rectification models to rectify the plurality of errors and may segment and/or re-segment the audio data with the rectification models to generate re-segmented audio data. The device may determine a plurality of modified diarization error rates for the plurality of speaker segments based on the re-segmented audio data and may select one of the plurality of speaker segments based on the plurality of modified diarization error rates. The device may calculate an empathy score based on the selected speaker segment and may perform actions based on the empathy score.

Utilizing machine learning models to provide cognitive speaker fractionalization with empathy recognition

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, are described that enable identifying an insurance information associated with a property, retrieving one or more compliance rules related to the insurance information, automatically determining, based on the one or more compliance rules, whether the insurance information has a compliant status or a non-compliant status, in response to determining that the insurance information has the non-compliant status, processing the insurance information, based on one or more exception rules, to generate an exception waiver including a plurality of waiver parameters, applying the plurality of waiver parameters to a waiver model to determine a waiver confidence score, and based on the waiver confidence score selectively generate an insurance certificate for the property.

Automated regulatory compliance for insurance

A system for solution architecture prediction may identify a previous solution from a data source and may create a historical solution evaluation matrix by mapping a plurality of concern categories with the previous solution. The system may identify a plurality of solution components preponderant to deriving a solution associated with the solution architecture prediction and create a potential solution evaluation matrix therefrom. The system may evaluate the historical solution evaluation matrix and the potential solution evaluation matrix to determine a credibility score for each solution comprised therein. Based on the evaluation, a solution prediction data may be generated including a previous solution, a potential solution, and the associated credibility score. A service solution may be selected from the solution prediction data to resolve the solution prediction requirement.

Solution architecture prediction

A device may receive input data identifying a technical architecture diagram, a design document, an interface specification document, and technical architecture icons, and may process the input data identifying the technical architecture diagram, with a model, to determine hierarchical objects from the technical architecture diagram. The device may perform OCR and NLP of the hierarchical objects to determine blocks of data, and may compare the blocks of data and the input data identifying the design document to identify functionalities of applications. The device may compare the blocks of data and the input data identifying the interface specification document to identify attributes, and may compare the blocks of data and the input data identifying the technical architecture icons to identify icons. The device may consolidate the blocks of data, the functionalities, the attributes, and the icons into a final document, and may perform actions based on the final document.

Utilizing deep learning and natural language processing to convert a technical architecture diagram into an interactive technical architecture diagram

Systems and methods for email validation are disclosed. The email validation includes transforming format of emails to a predefined format understandable the present system and application of text mining component on the transformed format. The email validation further includes obtaining details from a repository related to a historical pattern associated with an email validation requirement and a cognitive learning operation employed for the historical email validation to ascertain an outcome of the historical validation for similar emails. The email validation also includes predicting misdirection of the email and change in configuration of the email account based on the validation of the email.

Email validation

A device may provide, to user devices, a set of objects that are to be used by the user devices as part of a collaborative virtual environment. The collaborative virtual environment may support a virtual workshop. The set of objects may include objects capable of being used to solve a problem that is presented to users within the virtual workshop. The device may receive data describing a set of solutions. The device may curate the data describing the set of solutions using one or more natural language processing techniques. The device may rank the set of solutions that has been curated using a ranking technique or a machine learning technique. The device may provide data identifying a highest ranked solution or data identifying the set of solutions for display within the virtual workshop or to additional user devices that are accessible outside of the virtual workshop.

Collaborative virtual environment

A device may receive, from a user device, a request to access a virtual reality (VR) learning environment that includes an identifier associated with a program that supports the VR learning environment. The device may identify a set of objects to use within the VR learning environment by searching a data structure using the identifier associated with the program that supports the VR learning environment. The device may provide the VR learning environment to the user device. The device may receive, from the user device, information associated with interactions within the VR learning environment. The device may identify one or more additional objects to use within the VR learning environment by using one or more natural language processing techniques to analyze the information associated with the interactions within the VR learning environment. The device may provide the one or more additional objects to the user device.

Customized virtual reality learning environment

A document presentation interface generates documents or reports based on automatic mapping of the specified data fields to the columns to be included in the document. The data fields are selected from a plurality of data sources associated with different applications. A template is selected for the generation of the document and one or more of a rules-based processing, a historical lookup or similarity scoring techniques are implemented to automatically map the selected data fields to the columns. The document which is generated from the automatic mapping is transmitted to the users via the modalities specified during the document configuration.

Document presentation interface based on intelligent mapping

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Bengaluru, India

Vinu Varghese