Xerox Corporation

Conduent Business Services, LLC

Tata Consultancy Services Limited

Shruti Kunde

Tridib Mukherjee

Gueyoung Jung

Rekha Singhal

Koustuv Dasgupta

Dheeraj Chahal

Sujit Gujar

Gurulingesh Raravi

Mayank Mishra

Varun Sharma

Joydeep Banerjee

Sindhu Kiranmai Ernala

Aditya Hegde

Gautam Shroff

Manoj Gupta

Manoj Karunakaran Nambiar

Partha Dutta

Sakyajit Bhattacharya

Avinash Sharma

Manoj Karunakara Nambiar

Deepthi Chander

Sharanya Eswaran

Meeralakshmi Radhakrishnan

Chetan Dnyandeo Phalak

Nimmi Rangaswamy

Priyanka Sharma

Manju Ramesh

This disclosure relates generally to configuring/building of applications. Typically, a deep learning (DL) application having multiple models composed and interspersed with corresponding transformation functions has no mechanism of efficient deployment on underlying system resources. The disclosed system accelerates the development of application to compose multiple models where each model could be a primitive model or a composite model itself. In an embodiment, the disclosed system optimally deploys a composable model application and transformation functions on underlying resources using performance prediction models, thereby accelerating the development and deployment of the application.

Efficient deployment of machine learning and deep learning model's pipeline for serving service level agreement

The disclosure generally relates to system architectures, and, more particularly, to a method and system for system architecture recommendation. In existing scenario, a solution architect often gets minimum details about requirements, hence struggles to design a system architecture that matches the requirements. The method and system disclosed herein are to provide system recommendation in response to requirements provided as input to the system. The system generates an acyclic dependency graph based on parameters and values extracted from an obtained user input. The system then identifies a reference architectures that matches the requirements, and further selects components that match the architecture requirements. The system further selects technologies considering inter-operability of the technologies. Further, the system generates architecture recommendations for the user, based on the selected components, and technologies. The system can collect user feedback for to the recommendation provided, and can generate rankings to improve future recommendations.

Method and system for system architecture technology recommendation

This disclosure relates generally to accelerating development and deployment of enterprise applications where the applications involve both data driven and task driven components in data driven enterprise information technology (IT) systems. The disclosed system is capable of determining components of the application that may be task-driven and/or those components which may be data-driven using inputs such as business use case, data sources and requirements specifications. The system is capable of determining the components that may be developed using task-driven and data-drive paradigms and enables migration of components from the task driven paradigm to the data driven paradigm. Also, the system trains a reinforcement learning (RL) model for facilitating migration of the identified components from the task driven paradigm to the data driven paradigm. The system is further capable of integrating the migrated and existing components to accelerate development and deployment an integrated IT application.

Accelerating development and deployment of enterprise applications in data driven enterprise IT systems

The disclosed embodiments illustrate method and system for data processing to recommend a list of physical stores in real-time for user-specified products and/or services. The method includes receiving a request, that comprises one or more products and/or services and one or more user-defined parameters, from a user-computing device. The method further includes aggregating information associated with the received request, profile information of a user, real-time traffic information, and geographical locations of a plurality of physical stores. Further, the method includes generating a recommendation list based on the aggregated information and a similarity score of the user for each of the one or more products and/or services. The method further includes transmitting the generated recommendation list to the user-computing device. The user may select a recommendation from the recommendation list for purchasing and/or availing products and/or services based on the selected recommendation.

Method and system for data processing to recommend list of physical stores in real-time

A method and a system for recommendation of a succession of one or more services for a service workflow are disclosed. In an embodiment, a query is received to retrieve one or more services from a repository of a plurality of services stored in a hierarchical structure. The hierarchical structure comprises a plurality of hierarchical levels, each hierarchical level in the plurality of hierarchical levels comprising a set of nodes. Each node in the set of nodes in each hierarchical level, stores a first semantic information and a second semantic information. Further, the query is compared with the first semantic information and the second semantic information associated with a first node. The first node or the second node is selected based on the comparison. Further, the first set of services or the combination of services, associated with the selected node is recommended, as the service workflow, to the user.

Method and system for recommendation of a succession of one or more services for a service workflow

The present disclosure discloses methods and systems for enhancing shopping experience in physical stores. The method includes receiving at least one persona associated with a user based on one or more of: ethnographic data obtained from a user, demographic data associated with the user, buying behavioral data associated with the user, and social networking data associated with the user. After this, one or more historical activities of the user inside one or more physical stores are received. Also, one or more constraints associated with the user are received. Once received, the at least one persona, the one or more constraints, and the one or more historical activities are analyzed to generate a pre-defined number of personalized recommendations. Finally, the personalized recommendations are displayed to the user within a window of a user interface.

Systems and methods for enhancing shopping experience in physical stores

A method and a system for recommending services to a requestor over a communication network. A request comprising one or more keywords and one or more service level agreements (SLAs), is received from a requestor computing device over the communication network, to process one or more tasks. One or more first services from one or more available services are selected based on the request. For a first service from the one or more first services, a first score and a second score is determined. The one or more first services are ranked based on the first score and the second score. A recommendation of the one or more first services is transmitted to the requestor based on the ranking.

Method and system for recommending services to requestors

Methods and systems for determining prices of customized virtual machines required to process customer-specified workloads are disclosed. A count of instances of the customized virtual machines, required to process the customer-specified workloads is determined, based on a configuration of the customized virtual machines. The instances of the customized virtual machines are consolidated on virtual machine servers. Further, the prices of the customized virtual machines are determined based on a count of the virtual machine servers, unused resources in the virtual machine servers, and unused resources in the customized virtual machines. The determined prices are recommended to the customer. Further, at least one of the prices of the customized virtual machines or the configuration of at least one or more customized virtual machines is modified, based on a response to the recommendation received from the customer.

Methods and systems to price customized virtual machines

A method and system to determine a computational resource requirement is described. The method and system rank one or more computational resources for each of the plurality of tasks in an ascending order, based on a cost associated with the plurality of tasks for each of the computational resource. Based on the ranked one or more computational resources and a fairness metric, the method and system allocates the one or more capacity units associated with the computational resource to perform the plurality of tasks. The method and system determines the computational resource requirement to perform the plurality of tasks based on the allocated one or more capacity units. The fairness metric ensures that allocation of the one or more capacity units to the plurality of tasks is performed in a manner to maximize the fairness.

Methods and systems for determining computational resource requirement

Methods and systems for creating one or more statistical classifiers. A first set of performance parameters, corresponding to the one or more applications and the one or more computing infrastructures, is extracted from a historical data pertaining to the execution of the one or more applications on the one or more computing infrastructures. Further, a set of application-specific and a set of infrastructure-specific parameters are selected, from the first set of performance parameters, based on one or more statistical techniques. A similarity between each pair of the applications, each pair of the computing infrastructures, and each pair of possible combinations of an application and a computing infrastructure is determined. One or more statistical classifiers are created, based on the determined similarity.

Methods and systems for determining inter-dependenices between applications and computing infrastructures

Methods and systems for sharing computational resources. A request from a first node is received for the one or more computational resources. The request comprises a service level agreement (SLA) associated with the requested one or more computational resources. The request is compared with one or more advertisements sent by at least two second nodes, other than the first node. The one or more advertisements correspond to an availability of a set of computational resources associated with each of the at least two second nodes. A portion of computational resources from the set of computational resources associated with each of the at least two second nodes is allocated to the first node, based on the comparison, such that a combination of the portion of computational resources satisfy the SLA associated with the request.

Methods and systems for sharing computational resources

Embodiments of a method are disclosed for computing trust index among multiple entities associated with a resource marketplace. The method includes receiving multiple inputs including interaction attributes, attribute importance factors, references to databases, and multiple entities associated with the resource marketplace. The method also includes creating a weighted-interaction graph based on the received inputs. The weighted-interaction graph includes multiple vertices representing the entities. The method further includes performing a topology-specific analysis of the weighted-interaction graph. The method furthermore includes computing Euclidean distances for each pair of vertices in the weighted-interaction graph based on the performed analysis. The method also includes determining a trust index for a first entity in the received multiple entities based on the computed Euclidean distances. The trust index includes ranking of at least one of the multiple entities with respect to the first entity. The ranking is inversely proportional to the computed Euclidean distances.

Methods and apparatuses for trust computation

Methods and systems for recommending one or more computational resources. A portion of computational resources is determined from a set of computational resources associated with a datacenter based on a user-profile associated with a user, from one or more users, and the set of computational resources. The user-profile comprises at least one of a required performance level, a required load, or a cost constraint. The one or more computational resources are recommended from the portion of computational resources, in response to requests received from the one or more users.

Methods and systems for recommending computational resources

There is provided systems and methods for creating a repository of templates. The templates are deterministic of a configuration a virtual machine. The method includes creating one or more templates for each of one or more applications types based on a benchmark data. Each of the one or more templates is stored in a hierarchal structure having one or more hierarchal levels. Each of the one or more hierarchal levels is indicative of a parameter of the configuration of the virtual machine. Thereafter, one or more rules are defined to traverse through the one or more hierarchal levels to access the one or more templates.

Growing community of inventors

Mumbai, India

Shruti Kunde