Accenture Global Solutions Limited

Avanade Holdings LLC

Raghavan Tinniyam Iyer

Ramkumar Kothandaraman

Puneet Kalra

Bhavna Butani

Sudeep Sharma

Kiran Raghunath Sathvik

Suja Jain

Kishore P Durg

Bhaskar Ghosh

Prakash Ghatage

Mohan Sekhar

Jayant Swamy

Vinu Varghese

Kumar Viswanathan

Sriram Lakshminarasimhan

Chandrashekhar Arun Deshpande

Amod Dattatray Deshpande

Payal Panda

Ashok P Vira

Bhavin Prafulchandra Mehta

Sangita Agarwal

Ravi Kant Gaur

Santhosh Kumar Rajavelu Balachandar

Chellappan Murugappan

N R Srikanth

Amit Bahree

Murli Krishnan Iyer

Swetha Kommaraju

Sankara Narayanan

Achal Srivastava

Ashwin D'Silva

Dinesh Jaibhagwan Mittal

Praveen Subbnanjappa

Sundharraman Karthik Narain

John Francis Walsh

Ankita Kaushal

Juhi A Gupta

Abigail Hart

Sadanand Padmawar

The invention provides a platform for evaluating and distributing packaged software capabilities. The packaged software capabilities described herein may be discrete pieces of software designed to perform a specific task and multiple packaged software capabilities may be combined to create larger functionality, such as an application. Packaged software capabilities may be analyzed using a variety of techniques to evaluate interoperability based on standard(s) to determine whether the packaged software capabilities support desired hardware, authentication, authorization, and the like. A catalog of packaged software capabilities satisfying the standard(s) may be created and the platform may provide a variety of techniques for searching the catalog based on search parameters to identify best fit packaged software capabilities meeting a user's needs.

Systems and methods supporting composable system architectures

Systems and methods for deep technology innovation management by cross-pollinating innovations dataset are disclosed. A system extracts context-based keyword from an innovation dataset by transforming the innovation dataset to a vector. Further, the system searches semantically relevant keywords for the extracted context-based keyword, by extracting an entity and a key phrase from the extracted a context-based keyword. Furthermore, system clusters the vector, by identifying frequent keywords in the semantically relevant keywords to obtain cluster centroids of the frequent keywords. Thereafter, the system determines weighted keywords in each cluster using the obtained cluster centroids, and classifies the weighted keywords to identify emerging innovation trends relevant to the innovation in the innovation dataset. The system forms cohorts of innovators to explore the reuse of innovations, assets, code, and build focused monetization model.

Deep technology innovation management by cross-pollinating innovations dataset

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

A device may receive a query from a user device, and may process the query using a machine learning model to determine categories related to the query. The device may send a message, instructing a user to select a category, to the user device to cause the user device to display the message. The device may receive, from the user device, a selection related to the categories, and may call, based on the selection, a module to cause the module to process the query. The device may receive from the module a response to the query, and may send the response to the user device, to cause the user device to display the response. The device may send the query, the selection, and the response to one or more servers for storage, and may retrain the machine learning model based on information stored by the one or more servers.

Information platform for a virtual assistant

A device may provide, to user devices, a set of objects that are to be used by the user devices as part of a collaborative virtual environment. The collaborative virtual environment may support a virtual workshop. The set of objects may include objects capable of being used to solve a problem that is presented to users within the virtual workshop. The device may receive data describing a set of solutions. The device may curate the data describing the set of solutions using one or more natural language processing techniques. The device may rank the set of solutions that has been curated using a ranking technique or a machine learning technique. The device may provide data identifying a highest ranked solution or data identifying the set of solutions for display within the virtual workshop or to additional user devices that are accessible outside of the virtual workshop.

Collaborative virtual environment

A device may receive a feature submission identifying a candidate feature for a software application. The device may pre-process the feature submission to reformat data associated with the candidate feature to match a data format associated with stored information regarding past feature submissions. The device may perform natural language processing on the data associated with the candidate feature to determine a semantic meaning of the candidate feature. The device may compare the semantic meaning of the candidate feature with an index of past feature submissions. The device may provide a response indicating whether the candidate feature matches the past feature submissions wherein the response comprises an indication that the candidate feature does not match any past feature submission or information identifying one or more past feature submissions associated with a threshold semantic similarity to the candidate feature.

Feature submission de-duplication engine

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

Raghavan Tinniyam Iyer