Accenture Global Solutions Limited

Accenture Global Services Gmbh

Roopalaxmi Manjunath

Rajesh Nagarajan

Koushik M Vijayaraghavan

Aditi Kulkarni

Purnima Jagannathan

Rajendra Tanniru Prasad

Bhaskar Ghosh

Mohan Sekhar

Vijayaraghavan Koushik

Rajendra Prasad Tanniru

Srikanth Prasad

Jayashri Sridevi

Balaji Venkateswaran

Parul Jagtap

Pareshkumar Ramchandbhai Gelot

Sudhir Hanumanthappa

Krupa Srivastava

Mark Lazarus

Shankaranand Mallapur

Raghavendra Meharwade

Jeffson Felix Dsouza

Anubhav F Gupta

Sandeep Rathod

Jenice J Mukkada

Aruna Sivakumar

Sangeetha Jayaram

Suparna Moitra

Pankaj Jetley

Gayathri Pallail

Kanthy Prasad Kalepu

Andrew J Cook

Pratap Venkata Naga Poorna Bontha

Muthuvel Nagalingam

Alok Jain

Naveen Kumar

Niyaz Shaffi Hameed Musthafa

Srinivasan Kootalai Sundaram

Joydeep Sarkar

Muthalanghat Nisha

Vaishali Pandit

Sudha Srinivasan

Nishanth Kumar

Janagi Madhankumar

Abhijit Avinash Kulkarni

Nevis Ravi Kumar Rodriguez

Vyshak Kannan Iyengar

Akshay Bakhru

Rajilakshmi Krishna

Shankarnarayanan Sethuraman

Chaitra Hebbal

In some implementations, a device may receive project management data including information related to project elements associated with one or more projects and associations among the project elements associated with the one or more projects. The device may generate one or more data structures representing the project elements associated with the one or more projects and the associations among the project elements associated with the one or more projects. The device may receive a request to visualize project data, wherein the request may indicate a view to use in order to visualize the project data and/or specify criteria related to the project data to be visualized in the view. The device may search the one or more data structures to obtain the project data to be visualized based on the criteria specified in the request. The device may render the project data using the view indicated in the request.

Project visualization system

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

A device may receive project management data associated with development of a software product and may process a first portion of the project management data, with first models, to generate timeliness scores and an overall timeliness score for the software product. The device may process a second portion of the project management data, with second models, to generate quality scores and an overall quality score for the software product and may process a third portion of the project management data, with third models, to generate product readiness scores and an overall product readiness score for the software product. The device may utilize a fourth machine learning model, with the overall timeliness score, the overall quality score, and the overall product readiness score, to generate a success probability for the software product and may perform one or more actions based on the success probability for the software product.

Utilizing a combination of machine learning models to determine a success probability for a software product

A device may receive audio-video content regarding a system; segment the audio-video content to generate audio content and video content; process the audio content based on generating the audio content; process the video content based on generating the video content; identify a hierarchy for the set of sections based on processing the audio content and the video content; generate a system understanding document based on the hierarchy of the set of sections and based on the audio content and the video content; and store the system understanding document in a knowledge base.

Information transition management platform

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 receive input data identifying user stories, test case documents, event logs, and application logs associated with an application, and may perform natural language processing on the user stories and the test case documents, identified in the input data, to generate a first state diagram associated with the application. The device may process the event logs identified in the input data, with a heuristic miner model, to generate a second state diagram associated with the application, and may process the application logs identified in the input data, with a clustering model, to generate a volumetric analysis associated with the application. The device may perform post processing of the first state diagram, the second state diagram, and the volumetric analysis, to remove duplicate data and unmeaningful data and to generate modified outputs, and may perform actions based on the modified outputs.

Utilizing artificial intelligence and machine learning models to reverse engineer an application from application artifacts

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

Growing community of inventors

Bengaluru, India

Roopalaxmi Manjunath