Accenture Global Solutions Limited

Aditi Kulkarni

Koushik M Vijayaraghavan

Rajesh Nagarajan

Rajendra Prasad Tanniru

Roopalaxmi Manjunath

Rajendra Tanniru Prasad

Luke Higgins

Vijeth Srinivas Hegde

Jayashri Sridevi

Ravindra Kabbinale

Srikanth Prasad

Parul Jagtap

Amrutha Pervody Bhat

Purnima Jagannathan

Mark Lazarus

Niju Prabha

Jeffson Felix Dsouza

Raghavendra Meharwade

Pradeep Senapati

Santhosh Mv

Pareshkumar Ramchandbhai Gelot

Sangeetha Jayaram

Sreenath Kothavoor

Xiwen Sun

Sanjay Singatalur

Sudhir Hanumanthappa

Suhas Nagaraju

Badrinath Parameswar

Bhaskar Ghosh

Krupa Srivastava

Priya Athreyee

Sundar Prasad Jayaraman

Shankaranand Mallapur

Sarvesh Madhusudan Damle

Balaji Venkateswaran

Anubhav F Gupta

Pankaj Jetley

Kamakshi Girish

Gayathri Pallail

Ranjith Tharayil

Minal Baronia Gupta

Nevis Ravikumar Rodriguez

Sneha S Shekar

Haridas Kadaba Srinivasan

Bhavana V Gudi

Gaurav Sood

Joydeep Sarkar

Ravi Kiran Singh

Meghana B Srinath

Narasimhachar Goutham

Naveen Raj K P

Lakshmi Srinivasan

Sumanth Channegowda

Sunil Rana

Vaishali Pandit

Nishanth Kumar

Vijeth S Hedge

Nivedita Shah

Sakshi A Kapoor

Divya R Purushotham

Prajwal Patrick Dsilva

Soumya Swarup Jena

Dhiviya Dhanasekaran

Shruthi Dhivakaran

Meiyarasu Selvam

Alastair Donnelley

Manas Mishra

Sherin Varghese

A Akhila

Vinay Chamarthi

Vaijayanthi Ramaswamy

Man Lok Ching

Abhijit Avinash Kulkarni

Dilip Krishnan

Anthony R Webb

Niyaz Shaffi Hameed Musthafa

Xiaolei Liu

Sudha Srinivasan

Dibyendu Chattopadhyay

Isabella Phoebe Groenewegen Moore

Srinivasan Kootalai Sundaram

Reuben Lema

Charles Grenet

Yuqing Xi

Rashmi Jhawar

Naveen Kumar

Arun Vidyadharan

Easwer Chinnadurai

Liang Han

Jérémy Aeck

Jean-Francois Depoitre

Gaurav Goenka

Riley Green

Mahesh Zurale

Alok Jain

Jiayi Chen

Khaja Kamal Avaruman Mohammed

Rajalakshmy Iyer

Smitha Ka

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

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

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 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 system may receive enterprise information associated with a client enterprise. The system may select, using an industry analysis model, a set of queries associated with obtaining status information that is associated with a technology profile of the client enterprise. The system may generate client data that is associated with the enterprise information and the status information. The system may convert, using a matrix factorization technique, the client data associated with the client enterprise to a client matrix. The system may convert, using the matrix factorization technique, reference data associated with reference enterprises to a reference matrix. The system may determine, based on a comparison of the client matrix and the reference matrix, a set of scores associated with technology metrics of the technology profile. The system may perform an action associated with the client enterprise based on the set of scores.

Enterprise management system using artificial intelligence and machine learning for technology analysis and integration

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 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 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

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 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 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

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

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Bengaluru, India

Aditi Kulkarni