Patents by Inventor Lovekesh Vig

Lovekesh Vig has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20210103812
    Abstract: Neural networks can be used for time series data classification. However, in a K-shot scenario in which sufficient training data is unavailable to train the neural network, the neural network may not produce desired results. Disclosed herein are a method and system for training a neural network for time series data classification. In this method, by processing a plurality of task specific data, a system generates a set of updated parameters, which is further used to train a neural network (network) till a triplet loss is below a threshold. The network is trained on a diverse set of few-shot tasks sampled from various domains (e.g. healthcare, activity recognition, and so on) such that it can solve a target task from another domain using only a small number of training samples from the target task.
    Type: Application
    Filed: August 27, 2020
    Publication date: April 8, 2021
    Applicant: Tata Consultancy Services Limited
    Inventors: Pankaj MALHOTRA, Jyoti NARWARIYA, Lovekesh VIG, Gautam SHROFF
  • Patent number: 10970531
    Abstract: This disclosure relates to digitization of industrial inspection sheets. Digital scanning of paper based inspection sheets is a common process in factory settings. The paper based scans have data pertaining to millions of faults detected over several decades of inspection. The technical challenge ranges from image preprocessing and layout analysis to word and graphic item recognition. This disclosure provides a visual pipeline that works in the presence of both static and dynamic background in the scans, variability in machine template diagrams, unstructured shape of graphical objects to be identified and variability in the strokes of handwritten text. The pipeline incorporates a capsule and spatial transformer network based classifier for accurate text reading and a customized Connectionist Text Proposal Network (CTPN) for text detection in addition to hybrid techniques for arrow detection and dialogue cloud removal.
    Type: Grant
    Filed: February 25, 2019
    Date of Patent: April 6, 2021
    Assignee: Tata Consultancy Services Limited
    Inventors: Rohit Rahul, Arindam Chowdhury, Lovekesh Vig, . Animesh, Samarth Mittal
  • Patent number: 10936897
    Abstract: Various methods are using SQL based data extraction for extracting relevant information from images. These are rule based methods of generating SQL-Query from NL, if any new English sentences are to be handled then manual intervention is required. Further becomes difficult for non-technical user. A system and method for extracting relevant from the images using a conversational interface and database querying have been provided. The system eliminates noisy effects, identifying the type of documents and detect various entities for diagrams. Further a schema is designed which allows an easy to understand abstraction of the entities detected by the deep vision models and the relationships between them. Relevant information and fields can then be extracted from the document by writing SQL queries on top of the relationship tables. A natural language based interface is added so that a non-technical user, specifying the queries in natural language, can fetch the information effortlessly.
    Type: Grant
    Filed: March 14, 2019
    Date of Patent: March 2, 2021
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Lovekesh Vig, Gautam Shroff, Arindam Chowdhury, Rohit Rahul, Gunjan Sehgal, Vishwanath Doreswamy, Monika Sharma, Ashwin Srinivasan
  • Patent number: 10853640
    Abstract: This disclosure relates generally to document processing, and more particularly to extracting information from hand-marked industrial inspection sheets. In an embodiment, the system performs localization of text as well as arrows in the inspection sheet, and identifies text that matches each arrow. Further by identifying machine zone each arrow is pointing to, the system assigns corresponding text to the appropriate machine zone; thus facilitating digitization of the inspection sheets.
    Type: Grant
    Filed: March 28, 2018
    Date of Patent: December 1, 2020
    Assignee: Tata Consultancy Services Limited
    Inventors: Gaurav Gupta, Swati, Monika Sharma, Lovekesh Vig
  • Patent number: 10839246
    Abstract: The present disclosure provides systems and methods for end-to-end handwritten text recognition using neural networks. Most existing hybrid architectures involve high memory consumption and large number of computations to convert an offline handwritten text into a machine readable text with respective variations in conversion accuracy. The method combine a deep Convolutional Neural Network (CNN) with a RNN (Recurrent Neural Network) based encoder unit and decoder unit to map a handwritten text image to a sequence of characters corresponding to text present in the scanned handwritten text input image. The deep CNN is used to extract features from handwritten text image whereas the RNN based encoder unit and decoder unit is used to generate converted text as a set of characters. The disclosed method requires less memory consumption and less number of computations with better conversion accuracy over the existing hybrid architectures.
    Type: Grant
    Filed: July 10, 2019
    Date of Patent: November 17, 2020
    Assignee: Tata Consultancy Services Limited
    Inventors: Arindam Chowdhury, Lovekesh Vig
  • Patent number: 10769408
    Abstract: Method and system for automatic chromosome classification is disclosed. The system, alternatively referred as a Residual Convolutional Recurrent Attention Neural Network (Res-CRANN), utilizes property of band sequence of chromosome bands for chromosome classification. The Res-CRANN is end-to-end trainable system, in which a sequence of feature vectors are extracted from the feature maps produced by convolutional layers of a Residual neural networks (ResNet), wherein the feature vectors correspond to visual features representing chromosome bands in an chromosome image. The sequence feature vectors are fed into Recurrent Neural Networks (RNN) augmented with an attention mechanism. The RNN learns the sequence of feature vectors and the attention module concentrates on a plurality of Regions-of-interest (ROIs) of the sequence of feature vectors, wherein the ROIs are specific to a class label of chromosomes.
    Type: Grant
    Filed: January 11, 2019
    Date of Patent: September 8, 2020
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Monika Sharma, Swati Jindal, Lovekesh Vig
  • Patent number: 10719774
    Abstract: This disclosure relates generally to health monitoring of systems, and more particularly to monitor health of a system for fault signature identification. The system estimates Health Index (HI) of the system as time series data. By analyzing data corresponding to the estimated HI, the system identifies one or more time windows in which majority of the estimated HI values are low as a low HI window, and one or more time windows in which majority of the estimated HI values are high as a high HI window. Upon identifying a low HI window, which indicates an abnormal behavior of the system being monitored, based on a local Bayesian Network generated for the system being monitored, an Explainability Index (EI) for each sensor is generated, wherein the EI quantifies contribution of the sensor to the low HI. Further, associated component(s) is identified as contributing to abnormal/faulty behavior of the system.
    Type: Grant
    Filed: February 20, 2018
    Date of Patent: July 21, 2020
    Assignee: Tata Consultancy Services Limited
    Inventors: Pankaj Malhotra, Vishnu T V, Narendhar Gugulothu, Lovekesh Vig, Puneet Agarwal, Gautam Shroff
  • Publication number: 20200175372
    Abstract: Systems and methods for automating information extraction from piping and instrumentation diagrams is provided. Traditional systems and methods do not provide for end-to-end and automated data extraction from the piping and instrumentation diagrams.
    Type: Application
    Filed: April 11, 2019
    Publication date: June 4, 2020
    Applicant: Tata Consultancy Services Limited
    Inventors: Monika SHARMA, Rohit RAHUL, Lovekesh VIG, Shubham PALIWAL
  • Publication number: 20200175304
    Abstract: Various methods are using SQL based data extraction for extracting relevant information from images. These are rule based methods of generating SQL-Query from NL, if any new English sentences are to be handled then manual intervention is required. Further becomes difficult for non-technical user. A system and method for extracting relevant from the images using a conversational interface and database querying have been provided. The system eliminates noisy effects, identifying the type of documents and detect various entities for diagrams. Further a schema is designed which allows an easy to understand abstraction of the entities detected by the deep vision models and the relationships between them. Relevant information and fields can then be extracted from the document by writing SQL queries on top of the relationship tables. A natural language based interface is added so that a non-technical user, specifying the queries in natural language, can fetch the information effortlessly.
    Type: Application
    Filed: March 14, 2019
    Publication date: June 4, 2020
    Applicant: Tata Consultancy Services Limited
    Inventors: Lovekesh VIG, Gautam SHROFF, Arindam CHOWDHURY, Rohit RAHUL, Gunjan SEHGAL, Vishwanath DORESWAMY, Monika SHARMA, Ashwin SRINIVASAN
  • Publication number: 20200167557
    Abstract: This disclosure relates to digitization of industrial inspection sheets. Digital scanning of paper based inspection sheets is a common process in factory settings. The paper based scans have data pertaining to millions of faults detected over several decades of inspection. The technical challenge ranges from image preprocessing and layout analysis to word and graphic item recognition. This disclosure provides a visual pipeline that works in the presence of both static and dynamic background in the scans, variability in machine template diagrams, unstructured shape of graphical objects to be identified and variability in the strokes of handwritten text. The pipeline incorporates a capsule and spatial transformer network based classifier for accurate text reading and a customized Connectionist Text Proposal Network (CTPN) for text detection in addition to hybrid techniques for arrow detection and dialogue cloud removal.
    Type: Application
    Filed: February 25, 2019
    Publication date: May 28, 2020
    Applicant: Tata Consultancy Services Limited
    Inventors: Rohit RAHUL, Arindam CHOWDHURY, Lovekesh VIG, . ANIMESH, Samarth MITTAL
  • Publication number: 20200125992
    Abstract: Users have to assign labels to a ticket to route to right domain expert for resolving issue(s). In practice, labels are large and organized in form of a tree. Lack in clarity in problem description has resulted in inconsistent and incorrect labeling of data, making it hard for one to learn/interpret. Embodiments of the present disclosure provide systems and methods that identify relevant queries to obtain user response, for identification of right category and ticket logging there. This is achieved by implementing attention based sequence to sequence (seq2seq) hierarchical classification model to assign the hierarchical categories to tickets, followed by a slot filling model to enable identifying/deciding right set of queries, if the top-k model predictions are not consistent. Further, training data for slot filling model is automatically generated based on attention weight in the hierarchical classification model.
    Type: Application
    Filed: October 15, 2019
    Publication date: April 23, 2020
    Applicant: Tata Consultancy Services Limited
    Inventors: Puneet AGARWAL, Mayur PATIDAR, Lovekesh VIG, Gautam SHROFF
  • Patent number: 10621474
    Abstract: The most challenging problems in karyotyping are segmentation and classification of overlapping chromosomes in metaphase spread images. Often chromosomes are bent in different directions with varying degrees of bend. Tediousness and time consuming nature of the effort for ground truth creation makes it difficult to scale the ground truth for training phase. The present disclosure provides an end-to-end solution that reduces the cognitive burden of segmenting and karyotyping chromosomes. Dependency on experts is reduced by employing crowdsourcing while simultaneously addressing the issues associated with crowdsourcing. Identified segments through crowdsourcing are pre-processed to improve classification achieved by employing deep convolutional network (CNN).
    Type: Grant
    Filed: February 13, 2018
    Date of Patent: April 14, 2020
    Assignee: Tata Consultancy Services Limited
    Inventors: Monika Sharma, Lovekesh Vig, Shirish Subhash Karande, Anand Sriraman, Ramya Sugnana Murthy Hebbalaguppe
  • Publication number: 20200090056
    Abstract: Business to Consumer (B2C) systems face a challenge of engaging users since offers are created using static rules generated using clustering on large transactional data generated over a period of time. Moreover, the offer creation and assignment engine is disjoint to the transactional system which led to significant gap between history used to create offers and current activity of users. Systems and methods of the present disclosure provide a meta-model based configurable auto-tunable recommendation model generated by ensembling optimized machine learning and deep learning models to predict a user's likelihood to take an offer and deployed in real time. Furthermore, the offer given to the user is based on a current context derived from the user's recent behavior that makes the offer relevant and increases probability of conversion of the offer to a sale. The system achieves low recommendation latency and scalable high throughput by virtue of the architecture used.
    Type: Application
    Filed: March 22, 2019
    Publication date: March 19, 2020
    Applicant: Tata Consultancy Services Limited
    Inventors: Rekha SINGHAL, Gautam SHROFF, Vartika TEWARI, Sanket KADARKAR, Siddharth VERMA, Sharod Roy CHOUDHURY, Lovekesh VIG, Rupinder VIRK
  • Publication number: 20200026951
    Abstract: The present disclosure provides systems and methods for end-to-end handwritten text recognition using neural networks. Most existing hybrid architectures involve high memory consumption and large number of computations to convert an offline handwritten text into a machine readable text with respective variations in conversion accuracy. The method combine a deep Convolutional Neural Network (CNN) with a RNN (Recurrent Neural Network) based encoder unit and decoder unit to map a handwritten text image to a sequence of characters corresponding to text present in the scanned handwritten text input image. The deep CNN is used to extract features from handwritten text image whereas the RNN based encoder unit and decoder unit is used to generate converted text as a set of characters. The disclosed method requires less memory consumption and less number of computations with better conversion accuracy over the existing hybrid architectures.
    Type: Application
    Filed: July 10, 2019
    Publication date: January 23, 2020
    Applicant: Tata Consultancy Services Limited
    Inventors: Arindam CHOWDHURY, Lovekesh VIG
  • Publication number: 20200019610
    Abstract: Conversational systems are required to be capable of handling more sophisticated interactions than providing factual answers only. Such interactions are handled by resolving abstract anaphoric references in conversational systems which includes antecedent fact references and posterior fact references. The present disclosure resolves abstract anaphoric references in conversational systems using hierarchically stacked neural networks. In the present disclosure, a deep hierarchical maxpool network based model is used to obtain a representation of each utterance received from users and a representation of one or more generated sequences of utterances. The obtained representations are further used to identify contextual dependencies with in the one or more generated sequences which helps in resolving abstract anaphoric references in conversational systems.
    Type: Application
    Filed: July 9, 2019
    Publication date: January 16, 2020
    Applicant: Tata Consultancy Services Limited
    Inventors: Puneet AGARWAL, Prerna KHURANA, Gautam SHROFF, Lovekesh VIG
  • Publication number: 20200020061
    Abstract: This disclosure relates generally to method and system for performing negotiation task using reinforcement learning agents. Performing negotiation on a task is a complex decision making process and to arrive at consensus on contents of a negotiation task is often expensive and time consuming due to the negotiation terms and the negotiation parties involved. The proposed technique trains reinforcement learning agents such as negotiating agent and an opposition agent. These agents are capable of performing the negotiation task on a plurality of clauses to agree on common terms between the agents involved. The system provides modelling of a selector agent on a plurality of behavioral models of a negotiating agent and the opposition agent to negotiate against each other and provides a reward signal based on the performance. This selector agent emulate human behavior provides scalability on selecting an optimal contract proposal during the performance of the negotiation task.
    Type: Application
    Filed: July 12, 2019
    Publication date: January 16, 2020
    Applicant: Tata Consultancy Services Limited
    Inventors: Vishal SUNDER, Lovekesh VIG, Arnab CHATTERJEE, Gautam SHROFF
  • Publication number: 20200012941
    Abstract: The disclosure herein describes a method and a system for generating hybrid learning techniques. The hybrid learning technique refers to learning techniques that are a combination a plurality of techniques that include of deep learning, machine learning and signal processing to enable a rich feature space representation and classifier construction. The generation of the hybrid learning techniques also considers influence/impact of domain constraints that include business requirements and computational constraints, while generating hybrid learning techniques. Further from the plurality hybrid learning techniques a single hybrid learning technique is chosen based on performance matrix based on optimization techniques.
    Type: Application
    Filed: July 9, 2019
    Publication date: January 9, 2020
    Applicant: Tata Consultancy Services Limited
    Inventors: Arijit UKIL, Soma BANDYOPADHYAY, Pankaj MALHOTRA, Arpan PAL, Lovekesh VIG, Gautam SHROFF, Tulika BOSE, Ishan SAHU, Ayan MUKHERJEE
  • Publication number: 20200012938
    Abstract: Traditional systems and methods have implemented hand-crafted feature extraction from varying length time series that results in complexity and requires domain knowledge. Building classification models requires large labeled data and is computationally expensive. Embodiments of the present disclosure implement learning models for classification tasks in multi-dimensional time series by performing feature extraction from entity's parameters via unsupervised encoder and build a non-temporal linear classifier model. A fixed-dimensional feature vector is outputted using a pre-trained unsupervised encoder, which acts as off-the shelf feature extractor. Extracted features are concatenated to learn a non-temporal linear classification model and weight is assigned to each extracted feature during learning which helps to determine relevant parameters for each class. Mapping from parameters to target class is considered while constraining the linear model to use only subset of large number of features.
    Type: Application
    Filed: March 25, 2019
    Publication date: January 9, 2020
    Applicant: Tata Consultancy Services Limited
    Inventors: Pankaj MALHOTRA, Priyanka GUPTA, Lovekesh VIG, Gautam SHROFF
  • Publication number: 20200012918
    Abstract: Anomaly detection from time series is one of the key components in automated monitoring of one or more entities. Domain-driven sensor selection for anomaly detection is restricted by knowledge of important sensors to capture only a certain set of anomalies from the entire set of possible anomalies. Hence, existing anomaly detection approaches are not very effective for multi-dimensional time series. Embodiments of the present disclosure depict sparse neural network for anomaly detection in multi-dimensional time series (MDTS) corresponding to a plurality of parameters of entities. A reduced-dimensional time series is obtained from the MDTS via an at least one feedforward layer by using a dimensionality reduction model. The dimensionality reduction model and recurrent neural network (RNN) encoder-decoder model are simultaneously learned to obtain a multi-layered sparse neural network. A plurality of error vectors corresponding to at least one time instance of the MDTS is computed to obtain an anomaly score.
    Type: Application
    Filed: March 14, 2019
    Publication date: January 9, 2020
    Applicant: Tata Consultancy Services Limited
    Inventors: Pankaj MALHOTRA, Narendhar GUGULOTHU, Lovekesh VIG, Gautam SHROFF
  • Publication number: 20200012921
    Abstract: Estimating Remaining Useful Life (RUL) from multi-sensor time series data is difficult through manual inspection. Current machine learning and data analytics methods, for RUL estimation require large number of failed instances for training, which are rarely available in practice, and these methods cannot use information from currently operational censored instances since their failure time is unknown. Embodiments of the present disclosure provide systems and methods for estimating RUL using time series data by implementing an LSTM-RNN based ordinal regression technique, wherein during training RUL value of failed instance(s) is encoded into a vector which is given as a target to the model. Unlike a failed instance, the exact RUL for a censored instance is unknown. For using the censored instances, target vectors are generated and the objective function is modified for training wherein the trained LSTM-RNN based ordinal regression is applied on an input test time series for RUL estimation.
    Type: Application
    Filed: March 13, 2019
    Publication date: January 9, 2020
    Applicant: Tata Consultancy Services Limited
    Inventors: Pankaj MALHOTRA, Vishnu TV, Lovekesh VIG, Gautam SHROFF