Accenture Global Solutions Limited

Rajesh Nagarajan

Koushik M Vijayaraghavan

Aditi Kulkarni

Roopalaxmi Manjunath

Rajendra Tanniru Prasad

Mohan Sekhar

Purnima Jagannathan

Bhaskar Ghosh

Jayashri Sridevi

Raghavendra Meharwade

Balaji Venkateswaran

Rajendra Prasad Tanniru

Koustuv Jana

Pradeep Senapati

Jeffson Felix Dsouza

Parul Jagtap

Anubhav F Gupta

Sudhir Hanumanthappa

Vijayaraghavan Koushik

Lakshmi Narasimhan

Mark Lazarus

Luke Higgins

Priya Athreyee

Niju Prabha

Srikanth Prasad

Shankaranand Mallapur

Senthil Jeyachandran

Sarvesh Madhusudan Damle

Aruna Sivakumar

Pareshkumar Ramchandbhai Gelot

Sangeetha Jayaram

Kamakshi Girish

Rejeesh Katathanadan

Somen Roy

Girish Hulkoti

Pratap Venkata Naga Poorna Bontha

Arulmozhi Dharmar

Naveen Kumar

Alok Jain

Khaja Kamal Avaruman Mohammed

Muthalanghat Nisha

Niyaz Shaffi Hameed Musthafa

Ashwini C Surve

Sakshi A Kapoor

Srinivasan Kootalai Sundaram

Ranjith Tharayil

Janagi Madhankumar

Minal Baronia Gupta

Nevis Ravikumar Rodriguez

Joydeep Sarkar

Sudha Srinivasan

Nishanth Kumar

Rashmi Jhawar

Nevis Ravi Kumar Rodriguez

Niyaz Shaffi

Prajwal Patrick Dsilva

Soumya Swarup Jena

Syed Mohammed Yusuf

Dhiviya Dhanasekaran

Geetika Pant

Meiyarasu Selvam

Abhijit Avinash Kulkarni

Rajmohan Palanikumar

Anubhav Gupta

Aravind Nagaraj

Saikat Banerjee

Amaresh Swain

Ashwini Surve

In some implementations, a device may receive training data identifying a set of workflows. The device may generate a set of workflow templates based on the training data identifying the set of workflows, wherein a workflow template, of the set of workflow templates, is associated with a set of steps for completing a task associated with the workflow template. The device may receive, from a client device associated with an entity, a new task for automation using a new workflow. The device may parse the new task to identify one or more steps associated with the new task. The device may identify one or more workflow templates. The device may a workflow recommendation relating to the one or more workflow templates. The device may output workflow data associated with the workflow recommendation.

Automatic workflow generation and optimization

A device may receive work item data identifying work items associated with requirements from different tools of a project and may perform data cleansing to remove and/or modify particular words from the work item data and to generate cleansed work item data. The device may perform natural language processing on the cleansed work item data to identify synonyms for words in the cleansed work item data and may replace abbreviations in the cleansed work item data with full form text to generate final work item data. The device may identify keywords in the final work item data and may process the final work item data, the synonyms, and the keywords, with a machine learning model, to identify mappings between work items of the final work item data and to determine a confidence score for the mappings. The device may perform actions based on the mappings and the confidence score.

Utilizing machine learning and natural language processing to determine mappings between work items of various tools

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

In some examples, artificial intelligence based project implementation may include implementing, for a project team, self-evaluation of viability for utilizing a project implementation framework. The project team may be guided on utilization of the project implementation framework. A discussion by the project team may be documented in a digital format during envisioning associated with the project implementation framework, and documented information may be maintained for reference during execution, by the project team, of a journey associated with the project implementation framework. During execution of the journey, implementation of a social contract by the project team may be evaluated. A determination may be made as to whether the social contract is not implemented for at least one specified occurrence associated with the project implementation framework, and at least one impediment associated with the project implementation framework may be identified. The identified impediment associated with the project implementation framework may be remediated.

Artificial intelligence based project implementation

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 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 device may obtain process data relating to a set of processes. The device may process the process data to generate a process analysis model. The device may classify, using the process analysis model, a particular process into a particular class of a set of classes of the process analysis model. The device may automatically assess the particular process based on the particular class, wherein a set of assessment parameters for assessing the particular process is selected based on the particular class, and wherein an assessment score is assigned to the particular process based on values for the set of assessment parameters. The device may determine, based on the assessment score and the particular class, an automation recommendation for the particular process. The device may automatically complete the particular process using a particular tool based on determining the automation recommendation.

Automated process analysis and automation implementation

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

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Chennai, India

Rajesh Nagarajan