Patents by Inventor Gautam Shroff

Gautam Shroff 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).

  • Patent number: 11568203
    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: Grant
    Filed: March 13, 2019
    Date of Patent: January 31, 2023
    Assignee: Tata Consultancy Services Limited
    Inventors: Pankaj Malhotra, Vishnu Tv, Lovekesh Vig, Gautam Shroff
  • Patent number: 11551142
    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: Grant
    Filed: October 15, 2019
    Date of Patent: January 10, 2023
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Puneet Agarwal, Mayur Patidar, Lovekesh Vig, Gautam Shroff
  • Patent number: 11521281
    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: Grant
    Filed: July 12, 2019
    Date of Patent: December 6, 2022
    Assignee: Tata Consultancy Services Limited
    Inventors: Vishal Sunder, Lovekesh Vig, Arnab Chatterjee, Gautam Shroff
  • Patent number: 11488032
    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: Grant
    Filed: March 22, 2019
    Date of Patent: November 1, 2022
    Assignee: Tata Consultancy Limited Services
    Inventors: Rekha Singhal, Gautam Shroff, Vartika Tewari, Sanket Kadarkar, Siddharth Verma, Sharod Roy Choudhury, Lovekesh Vig, Rupinder Virk
  • Publication number: 20220342919
    Abstract: For various applications (for example, a Virtual Assistant), mechanisms that are capable of collecting user queries and generating responses are being used. While such systems handle structured queries well, they struggle to or fail to interpret an unstructured Natural Language (NL) query. The disclosure herein generally relates to data processing, and, more particularly, to a method and a system for generating responses to unstructured Natural Language (NL) queries. The system collects at least one NL query as input at a time, and generates a sketch, where the sketch is a structured representation of the unstructured NL query. Further by processing the sketch, the system generates one or more database queries. The one or more database queries are then used to search in one or more associated databases and to retrieve matching results, which are then used to generate response to the at least one NL query.
    Type: Application
    Filed: March 4, 2020
    Publication date: October 27, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: AMIT SANGROYA, GAUTAM SHROFF, CHANDRASEKHAR ANANTARAM, MRINAL RAWAT, PRATIK SAINI
  • Patent number: 11449413
    Abstract: 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.
    Type: Grant
    Filed: June 11, 2021
    Date of Patent: September 20, 2022
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Rekha Singhal, Gautam Shroff, Dheeraj Chahal, Mayank Mishra, Shruti Kunde, Manoj Nambiar
  • Publication number: 20220284215
    Abstract: This disclosure relates to a method and system for extracting information from images of one or more templatized documents. A knowledge graph with a fixed schema based on background knowledge is used to capture spatial and semantic relationships of entities present in scanned document. An adaptive lattice-based approach based on formal concepts analysis (FCA) is used to determine a similarity metric that utilizes both spatial and semantic information to determine if the structure of the scanned document image adheres to any of the known document templates, If known document template whose structure is closely matching the structure of the scanned document is detected, then an inductive rule learning based approach is used to learn symbolic rules to extract information present in scanned document image. If a new document template is detected, then any future scanned document images belonging to new document template are automatically processed using the learnt rules.
    Type: Application
    Filed: May 27, 2021
    Publication date: September 8, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: Mouli RASTOGI, Syed Afshan ALI, Mrinal RAWAT, Lovekesh VIG, Puneet AGARWAL, Gautam SHROFF, Ashwin SRINIVASAN
  • Patent number: 11429837
    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: Grant
    Filed: March 14, 2019
    Date of Patent: August 30, 2022
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Pankaj Malhotra, Narendhar Gugulothu, Lovekesh Vig, Gautam Shroff
  • Patent number: 11379717
    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: Grant
    Filed: March 25, 2019
    Date of Patent: July 5, 2022
    Assignee: Tata Consultancy Services Limited
    Inventors: Pankaj Malhotra, Priyanka Gupta, Lovekesh Vig, Gautam Shroff
  • Patent number: 11373090
    Abstract: In automated assistant systems, a deep-learning model in form of a long short-term memory (LSTM) classifier is used for mapping questions to classes, with each class having a manually curated answer. A team of experts manually create the training data used to train this classifier. Relying on human curation often results in such linguistic training biases creeping into training data, since every individual has a specific style of writing natural language and uses some words in specific context only. Deep models end up learning these biases, instead of the core concept words of the target classes. In order to correct these biases, meaningful sentences are automatically generated using a generative model, and then used for training a classification model. For example, a variational autoencoder (VAE) is used as the generative model for generating novel sentences and a language model (LM) is utilized for selecting sentences based on likelihood.
    Type: Grant
    Filed: September 18, 2018
    Date of Patent: June 28, 2022
    Assignee: Tata Consultancy Services Limited
    Inventors: Puneet Agarwal, Mayur Patidar, Lovekesh Vig, Gautam Shroff
  • Publication number: 20220188899
    Abstract: This disclosure relates generally to method and system for handling popularity bias in item recommendations. In an embodiment the method includes initializing an item embedding look-up matrix corresponding to items in a sequence of item-clicks with respect to a training data. L2 norm is applied to the item embedding look-up matrix to learn a normalized item embeddings. Using a neural network, a session embeddings corresponding to the sequences of item-clicks is modeled and L2 norm is applied to the session embeddings to obtain a normalized session embeddings. Relevance scores corresponding to each of the plurality of items arc obtained based on similarity between the normalized item embeddings and the normalized session embeddings. A multi-dimensional probability vector corresponding to the relevance scores for the items to be clicked in the sequence is obtained. A list of the items ordered based on the multi-dimensional probability vector is provided as recommendation.
    Type: Application
    Filed: August 25, 2020
    Publication date: June 16, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: PANKAJ MALHOTRA, PRIYANKA GUPTA, DIKSHA GARG, LOVEKESH VIG, GAUTAM SHROFF
  • Publication number: 20220156607
    Abstract: Session-based Recommendation (SR) is the task of recommending the next item based on previously recorded user interactions. However, most existing approaches for SR either rely on costly online interactions with real users (model-free approaches) or rely on potentially biased rule-based or data-driven user-behavior models (model-based approaches) for learning. This disclosure relates to a system and method for selecting session-based recommendation policies using historical recommendations and user feedback. Herein, the learning of recommendation policies given offline or batch data from old recommendation policies based on a Distributional Reinforcement Learning (DRL) based recommender system in the offline or batch-constrained setting without requiring access to a user-behavior model or real-interactions with the users.
    Type: Application
    Filed: March 8, 2021
    Publication date: May 19, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: Diksha Garg, Pankaj Malhotra, Priyanka Gupta, Lovekesh Vig, Gautam Shroff
  • Patent number: 11294946
    Abstract: This disclosure relates generally to methods and systems for generating a textual summary from a tabular data. During the textual summary generation using conventional end-to-end neural network-based techniques, a numeric data present in the tables is encoded via textual embeddings. However, the textual embeddings cannot reliably encode information about numeric concepts and relationships. The methods and systems generate the textual summary from the tabular data, by incorporating rank information for different records present in the tabular data. Then, a two-stage encoder-decoder network is used to learn correlations between the rank information and the probability of including the records based on the rank information, to obtain the textual summary generation model. The textual summary generation model identifies the content selection having the records present in the tables to be included in the textual summary and generates the textual summary from the identified content selection.
    Type: Grant
    Filed: May 13, 2021
    Date of Patent: April 5, 2022
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Mrinal Rawat, Lovekesh Vig, Amit Sangroya, Gautam Shroff
  • Publication number: 20220093249
    Abstract: In presence of high-cardinality treatment variables, number of counterfactual outcomes to be estimated is much larger than number of factual observations, rendering the problem to be ill-posed. Furthermore, lack of information regarding the confounders among large number of covariates pose challenges in handling confounding bias. Essential is to find lower-dimensional manifold where an equivalent problem of causal inference can be posed, and counterfactual outcomes can be computed.
    Type: Application
    Filed: July 13, 2021
    Publication date: March 24, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: ANKIT SHARMA, GARIMA GUPTA, RANJITHA PRASAD, ARNAB CHATTERJEE, LOVEKESH VIG, GAUTAM SHROFF
  • Publication number: 20220092354
    Abstract: This disclosure relates generally to a method and system for generating labelled dataset using a training data recommender technique. Recommender systems face major challenges in handling dynamic data on machine learning paradigms thereby rendering inaccurate unlabeled dataset. The method of the present disclosure is based on a training data recommender technique suitably constructed with a newly defined parameter such as the labelled data prediction threshold to determine the adequate amount of labelled training data required for training the one or more machine learning models. The method processes the received unlabeled dataset for labelling the unlabeled dataset based on a labelled data prediction threshold which is determined using a trained training data recommender technique.
    Type: Application
    Filed: September 10, 2021
    Publication date: March 24, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: Shruti Kunde, Mayank Mishra, Rekha Singhal, Amey Pandit, Manoj Nambiar, Gautam Shroff
  • Publication number: 20220036166
    Abstract: This disclosure relates to optimizing an operation of an equipment by a neural network based optimizer is provided. The method include receiving, information associated with at least one equipment instance (j) as an input at a predefined sequence of timestamps; training, a plurality of simulation models for each equipment instance to obtain a function (fj); processing, the external input parameters (et) to obtain a fixed-dimensional vector and passed as an input to obtain an vector (it); generating, a modified (it) from the output vector (it) based on a domain constraint value; computing, a reward (rt) based on (i) the function (fj), (ii) the modified (it), (iii) the external input parameters (et), and (iv) a reward function (Rj); and iteratively performing the steps of processing, generating, and computing reward (rt) for a series of subsequent equipment instances after expiry of the predefined sequence of timestamps associated with a first equipment instance.
    Type: Application
    Filed: November 28, 2019
    Publication date: February 3, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: Vishnu TANKASALA VEPARALA, Solomon Pushparaj MANUELRAJ, Ankit BANSAL, Pankaj MALHOTRA, Lovekesh VIG, Gautam SHROFF, Venkataramana RUNKANA, Sivakumar SUBRAMANIAN, Aditya PAREEK, Vishnu Swaroopji MASAMPALLY, Nishit RAJ
  • Publication number: 20210406603
    Abstract: Several applications capture data from sensors resulting in multi-sensor time series. Existing neural networks-based approaches for such multi-sensor/multivariate time series modeling assume fixed input-dimension/number of sensors. Such approaches can struggle in practical setting where different instances of same device/equipment come with different combinations of installed sensors. In the present disclosure, neural network models are trained from such multi-sensor time series having varying input dimensionality, owing to availability/installation of different sensors subset at each source of time series. Neural network (NN) architecture is provided for zero-shot transfer learning allowing robust inference for multivariate time series with previously unseen combination of available dimensions/sensors at test time.
    Type: Application
    Filed: February 22, 2021
    Publication date: December 30, 2021
    Applicant: Tata Consultancy Services Limited
    Inventors: Jyoti NARWARIYA, Pankaj Malhotra, Vibhor Gupta, Vishnu Tankasala Veparala, Lovekesh Vig, Gautam Shroff
  • Publication number: 20210390033
    Abstract: 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.
    Type: Application
    Filed: June 11, 2021
    Publication date: December 16, 2021
    Applicant: Tata Consultancy Services Limited
    Inventors: Rekha SINGHAL, Gautam SHROFF, Dheeraj CHAHAL, Mayank MISHRA, Shruti KUNDE, Manoj NAMBIAR
  • Publication number: 20210357443
    Abstract: This disclosure relates generally to methods and systems for generating a textual summary from a tabular data. During the textual summary generation using conventional end-to-end neural network-based techniques, a numeric data present in the tables is encoded via textual embeddings. However, the textual embeddings cannot reliably encode information about numeric concepts and relationships. The methods and systems generate the textual summary from the tabular data, by incorporating rank information for different records present in the tabular data. Then, a two-stage encoder-decoder network is used to learn correlations between the rank information and the probability of including the records based on the rank information, to obtain the textual summary generation model. The textual summary generation model identifies the content selection having the records present in the tables to be included in the textual summary and generates the textual summary from the identified content selection.
    Type: Application
    Filed: May 13, 2021
    Publication date: November 18, 2021
    Applicant: Tata Consultancy Services Limited
    Inventors: Mrinal RAWAT, Lovekesh VIG, Amit SANGROYA, Gautam SHROFF
  • Publication number: 20210326727
    Abstract: Causality is a crucial paradigm in several domains where observational data is available. Primary goal of Causal Inference (CI) is to uncover cause-effect relationship between entities. Conventional methods face challenges in providing an accurate CI framework due to cofounding and selection bias in multiple treatment scenario. The present disclosure computes a Propensity Score (PS) from a received CI data for the plurality of subjects under test for a treatment. A Generalized Propensity Score (GPS) is computed for a plurality of treatments corresponding to the plurality of subjects by using the PS. Further, a plurality of task batches are created using the GPS and given as input to the DNN for training. Errors in factual data and in balancing representation of the DNN are rectified using a novel loss function. The trained DNN is further used for predicting the counter factual treatment response corresponding to the factual treatment data.
    Type: Application
    Filed: March 2, 2021
    Publication date: October 21, 2021
    Applicant: Tata Consultancy Services Limited
    Inventors: Garima GUPTA, Ankit SHARMA, Ranjitha PRASAD, Arnab CHATTERJEE, Lovekesh VIG, Gautam SHROFF