Accenture Global Solutions Limited

Accenture Global Services Gmbh

Avanade Holdings LLC

Ramkumar Kothandaraman

Puneet Kalra

Raghavan Tinniyam Iyer

Sudeep Sharma

Kishore P Durg

Sunil Balugari

Bhavna Butani

Amit Bahree

Koduvayur Vaidyanathan Ramesh

Niraj Gupta

Amod Dattatray Deshpande

Ravi Kant Gaur

Suja Jain

Dinesh Jaibhagwan Mittal

Praveen Subbnanjappa

Sundharraman Karthik Narain

Laxmikant Sudhakar Bhole

Murli Krishnan Iyer

John Francis Walsh

Suman Sundaravaradan

Ankita Kaushal

Kiran Vinayak Khambete

Sachin Premnath

Apratim Ghosh

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, a using a bot management framework to provide bot management solutions for software bots deployed in a robotic process automation (RPA) system. In one aspect, a method includes the actions of receiving, from a RPA system, information regarding a software bot deployed within the RPA system to perform an assigned task, the information including incident data from an anomaly in the RPA system while the software bot performed the assigned task; mapping the received incident data according to a common data model; determining a resolution for the anomaly based an analysis through a trained artificial intelligence model of the mapped incident data; and executing the determined resolution.

Bot management framework for robotic process automation systems

In some implementations, a system may receive a submission associated with an innovation associated with an entity. The system may analyze, using a first machine learning model, the submission to identify a classification associated with the innovation. The system may analyze, using a second machine learning model in association with the classification, content of the submission to identify a characteristic of the innovation. The system may determine, using a third machine learning model and based on the characteristic, a ranking of the innovation relative to individual innovations in the subset of innovations. The system may determine, using a fourth machine learning model, an impact score associated with the innovation. The system may perform, based on the impact score satisfying a threshold, an action associated with a project involving the innovation.

System using artificial intelligence and machine learning to determine an impact of an innovation associated with an enterprise

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a win prediction for an opportunity. In some implementations, a server receives data representing an opportunity. The server obtains historical data from a data repository based on the received data representing the opportunity. The server generates standardized feature vectors based on non-standardized data, the non-standardized data comprising (i) the received data and (ii) the obtained historical data. The server filters the standardized feature vectors. The server generates a win percentage based on the filtered feature vectors using classifiers. In response to generating the win percentage for the opportunity, the server generates a plurality of data insights using the classifiers and the generated win percentage, wherein the plurality of data insights describe one or more data predictions for pursuing the opportunity. The server provides the win percentage and the plurality of data insights for output.

Enhanced data driven intelligent cloud advisor system

An AI-based process monitoring system access a plurality of data sources having different data formats to collect and analyze KPI data and shortlist KPIs that are to be used for determining the impact of automation of an automated process or sub-process. Information regarding an automated process is received and KPIs associated with the process and sub-processes of the process are identified. The identified KPIs are put through an approval process and the approved KPIs are presented to a user for selection. The user-selected KPIs are evaluated based on classification, ranking and sentiments associated therewith. The evaluations are again presented to the user along with a set of questionnaires wherein each of the questions has a dynamically controlled weight associated therewith. Based at least on the weights and user responses, a subset of the evaluated KPIs are shortlisted for use in evaluating the impact of process automation.

Evaluating impact of process automation on KPIs

A method for improving an operational efficiency of a target system includes receiving, by an application lifecycle analyzer (ALA), performance information from the target system. The ALA determines whether the target system is experiencing or is about to experience or may experience a problem based on the received performance information, user activity details and historical trends. The ALA communicates a diagnostic event to a source control system when the ALA determines that a problem exists or is going to occur on the horizon with the target system. The ALA receives, from the source control system, development information that specifies one or more time periods associated with operations for diagnosing and resolving the problem experienced by the target system. In response to receiving the development information, the ALA communicates a build event to a build system, receives an application built by the build system; and deploys the application to the target analyzer.

Method and system for improving operational efficiency of a target system

An integrated application management platform manages applications, such as mobile apps, during various stages in their lifecycle, such as development, testing, deployment, usage by end-users, maintenance, and/or retirement of the applications. The platform integrates the management of various aspects and elements of the lifecycle of multiple applications. The platform may include a dashboard with any suitable number of screens to enable monitoring of the portfolio of applications in the various stages of their lifecycles. The platform may also provide automation to manage the applications during the various stages of their lifecycle. The platform facilitates managing a portfolio of applications by managing and presenting information regarding application deployment status, deployment status, testing status, security aspects (e.g., device management, user authentication, rights management), analytics for user adoption and application usage, proactive monitoring for user feedback and/or application failures, user feedback, upgrades and hotfixes, and/or release management.

Application management platform

An intelligent information delivery system facilitates dynamic interaction with the user's environment, and in certain environments may provide or support digital governance. The intelligent delivery system may make use of a distributed beacon network to accurately determine the user's location within an environment, which may then be leveraged to deliver relevant content to the user. The intelligent delivery system may also facilitate complex interactions between a user of the system and the user's environment.

Location specific content delivery

Systems, methods, and devices for presenting and managing interrelated sets of skills are provided. In some embodiments, a map interface is provided that presents the interrelated skills to a user, and provides the user an opportunity to strategize regarding how best the skills may be acquired to advance on a career path. In some embodiments, an administrative user may use the interface to view an aggregate skill set of employees in an organization.

Systems and methods for organizing and presenting skill progressions

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Bengaluru, India

Ramkumar Kothandaraman