Wipro Limited

American Express Travel Related Services Company, Inc.

Arindam Chatterjee

Nitish Sharma

Kartik Subodh Ballal

Pratap Singh Rathore

Brian C Rosenfield

Shourya Roy

Sastry Vsm Durvasula

Sandeep Bose

Balaji Balaraman

Swatee Singh

Mohammad Torkzahrani

Shubham Arora

Manoj Kumar Rana

Rahul Ghosh

Krishnaprasad Narayanan

Rahul Arya

Pratyush Kotturu

Manjunath Ramachandra Iyer

Vinutha Bangalore Narayanamurthy

Vijay Garg

Anasuya Devi Kompella

Pratap Singh Singh Rathore

Lakshman Chaitanya

Tapati Bandop Adhyay

Priya Narayana

Debanjan Chaudhuri

Pratik Kumar Pandey

Disclosed are various embodiments for garbage collection for object-based storage systems. A first set of objects stored by an object storage service that have been accessed within a previously defined date range is identified. Then, a second set of objects stored by the object storage service is identified based at least in part on a relationship to one or more objects in the first set of objects. Next, a third set of objects stored by the object storage service that have been created prior to a predefined date is identified. Then, a subset of objects which are members of the third set of objects and not members of the first set of objects or the second set of objects is identified. Finally, a retention action is performed on individual members of the subset of objects based at least in part on a retention policy.

Garbage collection for object-based storage systems

At least some embodiments are directed to a system that receives from an online portal loaded in a computing device, a user request to instantiate a server cluster in a hybrid computer network. The system authenticates and redirects the user request to a selected computer network configured in the hybrid computer network. The system instantiates the server cluster in the selected computer network causing the server cluster to initiate a microservice agent during bootup. The system sends a command to the server cluster to initiate the execution of a process and receives from the microservice agent event data associated with the process. The system inputs the event data into a trained machine learning model to determine a first execution state of the process and sends a command to change the first execution state of the process to a second execution state.

Computer-based systems for management of big data development platforms based on machine learning techniques and methods of use thereof

A system for data migration is disclosed. The system may receive a migration request comprising a source file path and a target file location. The system may capture source file metadata based on the source file path and the migration request. The system may transfer a source file from a first data environment to an intermediate data environment via a first transfer process. The system may transfer the source file from the intermediate data environment to a second data environment via a second transfer process.

Seamless data movement and metadata management in a hybrid cloud setting using a configurable micro services based architecture

A system may receive a query configured to access a column in a data table. The data table may be in a flat file in a big data storage format. The system may detect the language type of the query and validate the query against the syntax of the language type. The system may also validate an access permission for data in the data table. The access permission may be stored in a permissions database. The system may generate a temporary table including the column with the temporary table configured to support the language type. The system may execute the query against the temporary table to generate a query result based on the temporary table as though it were the underlying data table. The system may enforce column-level or row-level access permissions by excluding columns or rows from the temporary table.

Converting a language type of a query

Disclosed herein is method and system for detecting intent as an ordered sequence from user query. The system identifies word embedding feature for each word using word embedding model and identifies Part of Speech tag feature. Thereafter, system determines dependency label feature based on dependency role and POS tag feature. Further, system provides feature vector comprising POS tag feature of target word, POS tag feature of previous two words of target word, word embedding feature of target word, word embedding feature of head word for each target word and dependency label feature of target word to deep neural network for detecting intent as ordered sequence. The ordered sequence includes desired action in user query, focal point pertaining to which desired action must be performed and one or more descriptors associated with focal point. In this manner, in present disclosure, overall intent from user query is captured for accurately providing response.

Method and system for detecting intent as an ordered sequence from a user query

This disclosure relates to method and system for providing an explanation for a prediction generated by an artificial neural network (ANN) model for a given input data. The method may include receiving the given input data and the prediction generated by the ANN model. The ANN model may be built and trained for a target application. The method may further include determining a plurality of relevant portions of the given input data. For each of the plurality of relevant portions, the method may further include fetching a portional prediction and a portional prediction score generated by the ANN model, and determining a degree of influence score based on the portional prediction score and a comparison between the portional prediction and the prediction. The method may further include providing the explanation for the prediction based on the degree of influence score of each of the plurality of relevant portions.

Method and system for providing explanation of prediction generated by an artificial neural network model

At least some embodiments are directed to a system that receives from an online portal loaded in a computing device, a user request to instantiate a server cluster in a hybrid computer network. The system authenticates and redirects the user request via a proxy service to a selected computer network configured in the hybrid computer network. The system instantiates the server cluster in the selected computer network causing the server cluster to initiate a microservice agent during bootup. The system sends a command to the server cluster to initiate the execution of a process and receives from the microservice agent event data associated with the process. The system inputs the event data into a trained machine learning model to determine a first execution state of the process and sends a command to change the first execution state of the process to a second execution state.

The present disclosure discloses method and a user intent identification system for identifying user intent from user statements. The user intent identification system receives input statement provided by a user from a Natural Language Understanding (NLU) engine. The input statement is processed to remove one or more irrelevant content. A plurality of features for each word in the processed input statement is extracted. The plurality of features comprises Parts of Speech (POS) label, dependency parse tree and word embeddings. The user intent determination system predicts class for each word in the processed input statement from a plurality of predefined classes using a neural network model. The neural network model predicts class for each word based on input vector generated for the each word based on the plurality of features. Thereafter, the user intent is identified based on class predicted for each word in processed input statement.

Method and system for identifying user intent from user statements

The present disclosure relates to a method and a system for generating sentiment-based summaries for a user review. In an embodiment, a text analyzer receives a block of text indicating a user review. The text analyzer may generate one or more vectors for the plurality of words. Further, a relation is identified among the one or more vectors. A model is trained to identify a relation among the one or more vectors. Using the relation between the one or more vectors, a sentiment associated with the block of text is determined. Thereafter, one or more keywords from the block of text contributing to the determined sentiment is are identified and are classified into categories according to the sentiment contributed by the one or more words. Thereafter, the summary is generated for each category using the corresponding one or more words.

Method and system for generating review summaries

A method an system for extracting factoid associated words from natural language sentences is disclosed. The method includes creating an input vector that includes a plurality of parameters for each target word in a sentence. For a target word, the plurality of parameters includes a Part of Speech (POS) vector, a word embedding, a word embedding for a head word of the target word, a dependency label, and a semantic role label. The method includes processing for each target word, the input vector through a trained neural network and assigning one or more factoid tags to each target word in the sentence. The method includes extracting text associated with factoids from the sentence based on the one or more factoid tags. The method further includes providing a response to the sentence inputted by the user based on the text associated with the factoids.

Method and device for extracting factoid associated words from natural language sentences

A method and device for extracting Action of Interest (AOI) from natural language sentences is disclosed. The method includes creating an input vector comprising a plurality of parameters for each target word in a sentence inputted by a user. The method further includes processing for each target word, the input vector through a trained neural network with RELU activation, which is trained to identify AOI from a plurality of sentences. The method includes assigning AOI tags to each target word in the sentence based on processing of associated input vector through the trained neural network with RELU activation. The method further includes extracting AOI text from the sentence based on the AOI tags assigned to each target word in the sentence. The method further includes providing a response to the sentence inputted by the user based on the AOI text extracted from the sentence.

Method and device for extracting action of interest from natural language sentences

A method and device for extracting attributes associated with Center of Interest (COI) from natural language sentences is disclosed. The method includes creating an input vector comprising a plurality of parameters for each target word in a sentence inputted by a user. The method further includes processing for each target word, the input vector through a trained bidirectional GRU neural network, which is trained to identify attributes associated with COI from a plurality of sentences. The method includes associating COI attribute tags to each target word in the sentence based on processing of associated input vector through the trained bidirectional GRU neural network. The method further includes extracting attributes from the sentence based on the COI attribute tags associated with each target word in the sentence. The method further includes providing a response to the sentence inputted by the user based on the attributes extracted from the sentence.

Method and device for extracting attributes associated with centre of interest from natural language sentences

A method and device for extracting Point of Interest (POI) from natural language sentences is disclosed. The method includes creating an input vector comprising a plurality of parameters for each target word in a sentence inputted by a user. The method further includes processing for each target word, the input vector through a trained bidirectional LSTM neural network, which is trained to identify POI from a plurality of sentences. The method includes associating POI tags to each target word in the sentence based on processing of associated input vector through the trained bidirectional LSTM neural network. The method further includes extracting POI text from the sentence based on the POI tags associated with each target word in the sentence. The method further includes providing a response to the sentence inputted by the user based on the POI text extracted from the sentence.

Method and device for extracting point of interest from natural language sentences

Disclosed herein is method and system for identifying one or more Places of Interest (PoI) in a natural language system. Word embedding representation for each word in the natural language input are retrieved from a knowledge repository. Further, for each word, Part-of-Speech (POS) tags are tagged, and dependency labels are generated. Subsequently, a PoI tag is assigned to each word based on the word embedding representation, the POS and the dependency labels of each word. Finally, the one or more PoI are identified based on PoI tag assigned to each word. In an embodiment, the method of present disclosure helps in dynamically identifying one or more PoI from natural language text utterances in interactive systems, thereby enhancing usability of interaction based intelligent systems.

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Gondalpara, India

Arindam Chatterjee