Accenture Global Solutions Limited

Accenture Global Services Gmbh

Rajendra Tanniru Prasad

Bhaskar Ghosh

Vijayaraghavan Koushik

Mohan Sekhar

Koushik M Vijayaraghavan

Krupa Srivastava

Arpan Shukla

Priya Athreyee

Mark Lazarus

Koustuv Jana

Aditi Kulkarni

Rajesh Nagarajan

Luke Higgins

Purnima Jagannathan

Roopalaxmi Manjunath

Pradeep Senapati

Chandrashekhar Arun Deshpande

Senthil Jeyachandran

Sandeep Rathod

VijayChandar Natesan

Niju Prabha

Shankaranand Mallapur

Srikanth Prasad

Sarvesh Madhusudan Damle

Prashant Sawant

Balaji Venkateswaran

Rajesh Pappu

Senthil Kumar Venugopal

Madhusudhana Desai

Raanan Zion

Avikar Banik

Kamakshi Girish

Divya Sampathkumar

Sunder Sankararaman

Le G Dang

Sumit Kute

Tiarenla Jamir

Rohan Sharma

KrishnaKumar Dorai Rangarajan

Chetan Belgur

Chetan Kumar Baikady

Jenice J Mukkada

Pankaj Jetley

Suparna Moitra

Radhu Selvaraj

Gayathri Pallail

Srinivasan Rangaswamy

David Golding

Udayakumar Nagarajan

Parvatha Narayanan

Lakshmi Paramasivam

Leon Whine

Chandrasekhar Athinarayanan

Sanjoy Paul

Nataraj Kuntagod

Jayant Swamy

Gopali Raval Contractor

Lakshmi Narasimhan

Nitin Madhukar Sawant

Senthil Kumar Kumaresan

Parikshit Maniar

Vijeth Srinivas Hegde

Ravindra Kabbinale

Jeffson Felix Dsouza

Raghavendra Meharwade

Jayashri Sridevi

Sundar Prasad Jayaraman

Bhavin Prafulchandra Mehta

Amrutha Pervody Bhat

Anubhav F Gupta

Sanjay Singatalur

Suhas Nagaraju

Aruna Sivakumar

Xiwen Sun

Niyaz Shaffi

Syed Mohammed Yusuf

Srikanth Nr

Prashant Bhadre

Anandakrishnan Rajaram

Jenice James Mukkada

Amaresh Swain

Somen Roy

Arulmozhi Dharmar

M V Koushik

Senthilkumar Jeyachandran

Prabhu Rajan

Siddharth Mehta

Manisha Johar Dhiman

Rajilakshmi Krishna

Shankarnarayanan Sethuraman

Chaitra Hebbal

Reshma Abhijit Gurjar

Manish Vijaywargiya

Kanthy Prasad Kalepu

Sunil Rana

Vijeth S Hedge

Pratap Venkata Naga Poorna Bontha

Arun Vidyadharan

Muthalanghat Nisha

Janagi Madhankumar

A device may detect a trigger to perform a benchmarking task. The benchmarking task may include a first benchmarking of a first resource utilization associated with one or more tasks completed via an automated procedure. The benchmarking task may include a second benchmarking of a second resource utilization associated with the one or more tasks completed via a manual procedure. The device may determine project data relating to a project platform based on detecting the trigger to perform the benchmarking task. The device may process the project data relating to the project platform to benchmark the project. The device may generate a recommendation relating to completion of the one or more tasks using the automated procedure or the manual procedure. The device may communicate with one or more other devices to perform a response action based on the recommendation.

Benchmarking for automated task management

A device may obtain first data relating to a machine learning model. The device may pre-process the first data to alter the first data to generate second data. The device may process the second data to select a set of features from the second data. The device may analyze the set of features to evaluate a plurality of types of machine learning models with respect to the set of features. The device may select a particular type of machine learning model for the set of features based on analyzing the set of features to evaluate the plurality of types of machine learning models. The device may tune a set of parameters of the particular type of machine learning model to train the machine learning model. The device may receive third data for prediction. The device may provide a prediction using the particular type of machine learning model.

Automated data processing and machine learning model generation

A device may receive requirement data identifying a requirement for modification of software code, and may process the requirement data, with a machine learning model, to identify entities and intents in the software code and to generate a query. The device may process the query, with a code locator model, to encode text of the query into high-dimensional vectors and to identify a semantic similarity between the high-dimensional vectors and code text. The device may process the query, the semantic similarity, and the code text, with a code developer model, to generate metadata, and may utilize the metadata to identify an identifier associated with the software code. The device may determine, utilizing the identifier, a portion of the software code, and may modify the portion of the software code based on the query to generate modified software code. The device may perform actions based on the modified software code.

Utilizing machine learning models for automated software code modification

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 group information, associated with performing application maintenance, that includes information corresponding to one or more resources, associated with performing the application maintenance, and information associated with one or more shifts of the one or more resources. The device may receive ticket information, associated with performing the application maintenance, that includes priority information associated with one or more ticket types associated with performing the application maintenance. The device may simulate, based on the group information and the ticket information, performing the application maintenance to determine a simulation result. The simulation result may include information associated with one or more predicted performance metrics associated with performing the application maintenance. The device may provide the information associated with the one or more predicted performance metrics.

Simulation queuing based system for analyzing client or application related changes to an application maintenance project

A computer-implemented method and a system parse and aggregate unstructured data objects. The method includes obtaining the unstructured data objects from description fields of records in a database comprising client tickets that are created for application maintenance, transforming the obtained unstructured data objects to create transformed data objects, identifying a number of criteria attributes of the client tickets, where the number of criteria attributes are determined according to at least part of the transformed data objects, word importance, word sentiment, a user input, or client ticket priorities, or any combinations, and generating a plurality of ticket reports by aggregating the client tickets according to ranking orders of numbers of tickets for the client tickets and the identified criteria attributes.

Method and system for parsing and aggregating unstructured data objects

A device may receive issue resolution information, associated with a cognitive model, including an item of issue resolution information that describes an issue and a resolution to the issue. The device may assign the item of issue resolution information to a domain hierarchy, where the assigning is associated with a first user. The device may generate a question and an answer corresponding to the item of issue resolution information, where the generating of the question and the answer is associated with a second user. The device may approve the question and the answer, where the approving is associated with a third user. The device may generate a question/answer (QA) pair for the question and the answer. The device may create a data corpus including the QA pair, and provide the data corpus to cause the cognitive model to be trained based on the data corpus.

Processing data for use in a cognitive insights platform

A device receives virtual network function (VNF) data identifying a VNF to be implemented in a network and configures the VNF data with hardware and software requirements for deploying the VNF in a non-production environment, wherein configuring the VNF data with the hardware and software requirements generates a configured VNF. The device deploys the configured VNF in the non-production environment and performs one or more tests on the configured VNF deployed in the non-production environment. The device receives one or more test results based on performing the one or more tests on the configured VNF, and updates the configured VNF, based on the one or more test results, to generate an updated VNF. The device causes the updated VNF to be deployed in the network.

Deploying, testing, monitoring, scaling, and healing virtual network functions in a software-defined network

A device may communicate with a server to obtain historical ticket data. The device may generate a data model, based on the historical ticket data. The device may communicate with a client device to obtain ticket data relating to an issue associated with a project. The device may classify, using the data model, the ticket data into a ticket type. The device may generate, using the data model and based on the ticket type, a set of recommended resolutions for resolving the issue associated with the project. The device may select, from the set of recommended resolutions, a particular resolution based on a set of selection criteria. The device may automatically perform one or more actions to implement the particular resolution to resolve the issue associated with the project.

Automated ticket resolution

A device may receive data associated with a software development platform, and may correlate the data to generate correlated data. The device may train a first model, with the correlated data, to generate a software impact analyzer model, and may train a second model, with the correlated data, to generate a software development behavior model. The device may receive data identifying a new software requirement associated with the software development platform, and may process the data identifying the new software requirement, with the software impact analyzer model, to identify a file or a module impacted by the new software requirement. The device may process data identifying the file or the module, with the software development behavior model, to identify a developer to handle the new software requirement, and may perform one or more actions based on the data identifying the file or the module and data identifying the developer.

Utilizing artificial intelligence to improve productivity of software development and information technology operations (DevOps)

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 determine a plurality of components for a development project. The device may determine a blueprint template based on the plurality of components of the development project. The device may generate a blueprint for the development project based on the blueprint template. The device may generate a package for the development project based on the blueprint for the development project. The device may provide output associated with the blueprint and the package based on generating the blueprint and generating the package.

Development project blueprint and package generation

A device receives a command to identify an automation evaluation for a role, determines tasks of the role based on data relating to the role, and determines activities for the tasks based on the data relating to the role. The device determines one or more automation scores, which correspond to a suitability for automation of the activities, based on a set of characteristics of the activities and based on the data relating to the role. The automation scores are determined using a machine learning model to parse natural language descriptions of the activities and score parsed portions of the natural language descriptions. The device generates, for the role, an aggregate automation score based on the automation scores, determines the automation evaluation for the role based on the aggregate automation score and characteristics of an entity associated with the role, and performs an action relating to the automation evaluation.

Data processing for role assessment and course recommendation

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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Bengaluru, India

Rajendra Tanniru Prasad