Accenture Global Solutions Limited

Luke Higgins

Koushik M Vijayaraghavan

Rajendra Tanniru Prasad

Aditi Kulkarni

Vijayaraghavan Koushik

Krupa Srivastava

Rajendra Prasad Tanniru

VijayChandar Natesan

Rajesh Pappu

Sunder Sankararaman

Xiwen Sun

Chetan Kumar Baikady

KrishnaKumar Dorai Rangarajan

Radhu Selvaraj

David Golding

Lakshmi Paramasivam

Srinivasan Rangaswamy

Udayakumar Nagarajan

Leon Whine

Parvatha Narayanan

Raanan Zion

Chandrasekhar Athinarayanan

Senthil Kumar Venugopal

Bhaskar Ghosh

Mohan Sekhar

Rajesh Nagarajan

Purnima Jagannathan

Koustuv Jana

Srikanth Prasad

Jayashri Sridevi

Niju Prabha

Pradeep Senapati

Balaji Venkateswaran

Gayathri Pallail

Pankaj Jetley

Badrinath Parameswar

Alastair Donnelley

Riley Green

Man Lok Ching

Jiayi Chen

Xiaolei Liu

Isabella Phoebe Groenewegen Moore

Reuben Lema

Charles Grenet

Yuqing Xi

Easwer Chinnadurai

Liang Han

Jérémy Aeck

Jean-Francois Depoitre

Anthony R Webb

Genevieve Elizabeth Kuai Ying Lee

Niyaz Shaffi

Syed Mohammed Yusuf

In some implementations, a scheduling platform may receive task information regarding a set of tasks for execution using a set of computing resources, wherein the task information includes, for the set of tasks, at least one of: a run time parameter, a priority parameter, or a success rate parameter. The scheduling platform may communicate with a computing resource management device to obtain first computing resource information regarding the set of computing resources. The scheduling platform may generate a first assignment of the set of tasks to the set of computing resources. The scheduling platform may transmit assignment information identifying the first assignment. The scheduling platform may receive second computing resource information. The scheduling platform may generate a second assignment of the set of tasks to the set of computing resources. The scheduling platform may transmit second assignment information identifying the second assignment.

Dynamic scheduling platform for automated computing tasks

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 image data that includes an image of a document and lexicon data identifying a lexicon, and may perform an extraction technique on the image data to identify at least one field in the document. The device may utilize form segmentation to automatically generate label data identifying labels for the image data, and may process the image data, the label data, and data identifying the at least one field, with a first model, to identify visual features. The device may process the image data and the visual features, with a second model, to identify sequences of characters, and may process the image data and the sequences of characters, with a third model, to identify strings of characters. The device may compare the lexicon data and the strings of characters to generate verified strings of characters that may be utilized to generate a digitized document.

Utilizing machine learning models, position based extraction, and automated data labeling to process image-based documents

A device may receive information identifying a communication framework for a mass communication task. The device may determine a success score for the communication framework using a mass communication model, wherein the success score represents a likelihood of a successful response in connection with using the communication framework for the mass communication task. The device may generate a recommendation for the communication framework based on the success score and using the mass communication model. The device may alter the communication framework to implement the recommendation and generate a modified communication framework. The device may perform the mass communication task using the modified communication framework.

Automated natural language communication analysis

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 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

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West Pymble, Australia

Luke Higgins