Accenture Global Solutions Limited

Rajendra Prasad Tanniru

Koushik M Vijayaraghavan

Aditi Kulkarni

Vijeth Srinivas Hegde

Ravindra Kabbinale

Rajesh Nagarajan

Roopalaxmi Manjunath

Luke Higgins

Purnima Jagannathan

Jayashri Sridevi

Amrutha Pervody Bhat

Sreenath Kothavoor

Santhosh Mv

Bhaskar Ghosh

Srikanth Prasad

Shankaranand Mallapur

Balaji Venkateswaran

Pareshkumar Ramchandbhai Gelot

Sudhir Hanumanthappa

Badrinath Parameswar

Sanjay Singatalur

Suhas Nagaraju

Xiwen Sun

Isabella Phoebe Groenewegen Moore

Haridas Kadaba Srinivasan

Shruthi Dhivakaran

Bhavana V Gudi

Xiaolei Liu

Vivek Krishnan

Narasimhachar Goutham

Sherin Varghese

Reuben Lema

Sneha S Shekar

A Akhila

Ravi Kiran Singh

Joydeep Sarkar

Alok Jain

Meghana B Srinath

Vinay Chamarthi

Niyaz Shaffi Hameed Musthafa

Naveen Raj K P

Lakshmi Srinivasan

Soumala Sarkar

Dilip Krishnan

Srinivasan Kootalai Sundaram

Sumanth Channegowda

Abhijit Avinash Kulkarni

Anthony R Webb

Genevieve Elizabeth Kuai Ying Lee

Riley Green

Julie Sweet

Man Lok Ching

Rajalakshmy Iyer

Jiayi Chen

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 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

In some implementations, a device may generate, based at least in part on a first set of inputs, a serverless software development environment associated with a set of cloud resources. The device may generate, based at least in part on a first machine learning model, a technology stack recommendation having a set of associated tools for performing a software development task. The device may instantiate the selected technology stack in the serverless software development environment and generate a set of applications based at least in part on executing the set of tools. The device may deploy the set of applications in one or more serverless application environments. The device may use machine learning to observe deployed applications, detect hidden anomalies, and perform root-cause analysis, thereby providing a lean and sustainable serverless environment.

Serverless environment-based provisioning and deployment system

A device may receive software data identifying current logs and events associated with software products utilized by an entity and may process the software data, with a machine learning model, to generate error severity scores for the software products. The machine learning model may be trained based on historical software data identifying events and logs associated with software products utilized by the entity and based on a combination of historical health scores, historical sentiment scores, and historical dissimilarity scores for the software products. The device may process the error severity scores, with a prioritization model, to generate prioritized error scores and may process the error severity scores and the prioritized error scores, with a root cause analysis model, to generate root cause data identifying root causes associated with the error severity scores. The device may perform one or more actions based on the root cause data.

Utilizing automatic labelling, prioritizing, and root cause analysis machine learning models and dependency graphs to determine recommendations for software products

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

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

A device may receive, from the client device, provisioning data selected from a user interface and identifying a cloud provider or on premise resources, an infrastructure, applications, tools, and artificial intelligence (AI) assets for provisioning a customized platform. The device may process the provisioning data, with a machine learning model, to identify conflicting or redundant applications, tools, or AI assets and to generate updated provisioning data, and may determine computing resource data identifying computing resources required for execution of the applications, tools, and AI assets. The device may obtain the applications, tools, and AI assets from a data structure, and may generate a customized template based on the computing resources and the applications, tools, and AI assets. The device may execute the customized template to provision the computing resources with the applications, tools, and AI assets and to create the customized platform.

Automatically generating and provisioning a customized platform for selected applications, tools, and artificial intelligence assets

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

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Basking Ridge, NJ, United States of America

Rajendra Prasad Tanniru