Accenture Global Solutions Limited

Accenture Global Services Gmbh

Purnima Jagannathan

Bhaskar Ghosh

Koushik M Vijayaraghavan

Rajendra Tanniru Prasad

Rajesh Nagarajan

Roopalaxmi Manjunath

Mohan Sekhar

Aditi Kulkarni

Vijayaraghavan Koushik

Rajendra Prasad Tanniru

Jeffson Felix Dsouza

Pradeep Senapati

Balaji Venkateswaran

Luke Higgins

Koustuv Jana

Raghavendra Meharwade

Srikanth Prasad

Jayashri Sridevi

Shankaranand Mallapur

Parul Jagtap

Anubhav F Gupta

Sandeep Rathod

Jenice J Mukkada

Sudhir Hanumanthappa

Aruna Sivakumar

Sangeetha Jayaram

Pareshkumar Ramchandbhai Gelot

Badrinath Parameswar

Suparna Moitra

Pankaj Jetley

Gayathri Pallail

Rajilakshmi Krishna

Shankarnarayanan Sethuraman

Chaitra Hebbal

Kanthy Prasad Kalepu

Andrew J Cook

Muthuvel Nagalingam

Pratap Venkata Naga Poorna Bontha

Gaurav Sood

Joydeep Sarkar

Soumala Sarkar

Manas Mishra

Niyaz Shaffi

Vaijayanthi Ramaswamy

Gaurav Goenka

Muthalanghat Nisha

Syed Mohammed Yusuf

Mahesh Zurale

Abhijit Avinash Kulkarni

Janagi Madhankumar

Ramanathan Venkataraman

Ranjith Tharayil

Julie Sweet

Vivek Krishnan

Nevis Ravi Kumar Rodriguez

A system may receive enterprise information associated with a client enterprise. The system may select, using an industry analysis model, a set of queries associated with obtaining status information that is associated with a technology profile of the client enterprise. The system may generate client data that is associated with the enterprise information and the status information. The system may convert, using a matrix factorization technique, the client data associated with the client enterprise to a client matrix. The system may convert, using the matrix factorization technique, reference data associated with reference enterprises to a reference matrix. The system may determine, based on a comparison of the client matrix and the reference matrix, a set of scores associated with technology metrics of the technology profile. The system may perform an action associated with the client enterprise based on the set of scores.

Enterprise management system using artificial intelligence and machine learning for technology analysis and integration

A device may receive, in near-real time, unstructured data associated with an application or a system, and may extract textual data from the unstructured data. The device may parse the textual data to generate parsed textual data, and may perform natural language processing on the parsed textual data to generate processed textual data. The device may process the processed textual data, with a clustering model, to identify topical data associated with the processed textual data, and may process the topical data, with a classification model, to group the topical data into categories. The device may generate a knowledge graph based on the categories, and may store the knowledge graph in a data structure. The knowledge graph may enable the device to provide answers to questions associated with the application or the system.

Utilizing artificial intelligence models to manage and extract knowledge for an application or a system

This document describes a computer-implemented method that includes receiving, over a network, at least one of text, audio, image, or video data associated with an entity of interest; identifying, based on the received data, a set of entity-specific candidate features; loading a feature library comprising a plurality of features that are each assigned to one or more feature spaces; and selecting, using a feature selection engine, one or more features from each of the feature spaces based on the set of entity-specific candidate features.

Feature selection system

In some examples, artificial intelligence and machine learning based product development may include ascertaining an inquiry, by a user, related to a product that is to be developed or that is under development, and ascertaining an attribute associated with the user. The inquiry may be analyzed to determine at least one virtual assistant from a set of virtual assistants to respond to the inquiry. The determined at least one virtual assistant may be invoked based on an authorization by the user. Further, development of the product may be controlled based on the invocation of the determined at least one virtual assistant.

Artificial intelligence and machine learning based product development

A device may obtain ticket data relating to a set of tickets, and process the ticket data to generate a ticket analysis model that is a clustering based natural language analysis model of natural language text associated with tickets of the set of tickets. The device may classify the set of tickets using the ticket analysis model, may determine an automation plan for at least one class of ticket determined based on classifying the set of tickets, and may implement the automation plan to configure an automatic ticket resolution or ticket generation mitigation procedure for the at least one class of ticket. The device may receive a ticket after configuring the automatic ticket resolution or ticket generation mitigation procedure, may classify, using the ticket analysis model, the ticket into the at least one class of ticket, and may automatically implement a response action for the ticket based on classifying the ticket and using the automatic ticket resolution or ticket generation mitigation procedure.

Automation plan generation and ticket classification for automated ticket resolution

A component analysis platform may communicate with one or more devices to obtain prediction data relating to a type of component. The component analysis platform may process the prediction data to determine a set of predictors for failure of an instance of the component, and may generate a model for failure of the instance of the component based on the set of predictors. The component analysis platform may monitor the instance of the component to obtain component data relating to the instance of the component. The component analysis platform may determine, using the model and based on the component data relating to the instance of the component, a predicted failure for the instance of the component. The component analysis platform may perform a response action related to the predicted failure.

Data processing for component failure determination

A device may provide a user interface for receiving project information for a software implementation project. The project information may be associated with a set of requirements defining the software implementation project. The project information may be associated with a set of deliverables describing results of the software implementation project. The device may generate an initial project plan based on the project information. The device may receive information regarding the initial project plan during fulfillment of the project plan. The device may selectively provide an alert associated with the initial project plan based on receiving the information regarding the initial project plan. The device may selectively generate a modified project plan based on receiving the information regarding the project plan.

End-to end project management

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Chennai, India

Purnima Jagannathan