Patents by Inventor Kriti Kumar
Kriti Kumar 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).
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Publication number: 20250068892Abstract: Existing Convolutional Dictionary Learning (CDL) based machine fault classification do not utilize label information while learning the dictionary, hence the representation learned are not class-discriminative. Method and system disclosed herein provide a label-consistent convolutional dictionary learning approach for machine fault classification. The approach involves generating a training data for a classifier, wherein coefficients forming a plurality of class-discriminative features form the training data. The training data is then used to train a classifier, which is then used to perform machine fault classification for a given test data.Type: ApplicationFiled: August 2, 2024Publication date: February 27, 2025Applicant: Tata Consultancy Services LimitedInventors: SAURABH SAHU, KRITI KUMAR, ACHANNA ANIL KUMAR, MARISWAMY GIRISH CHANDRA, ANGSHUL MAJUMDAR
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Publication number: 20250060284Abstract: This disclosure provides a system and method for transform based subspace interpolation for unsupervised domain adaptation for machine inspection. Embodiments of the present disclosure present a deep transform-based subspace interpolation method to cater to challenging unsupervised adaptation scenario for machine inspection of different but related machines. In the present disclosure, source and target domain data are modeled as low-dimensional subspace using deep transforms. The intermediate domains connecting the two domains are then learned to generate domain invariant features for cross-domain classification. The requisite formulation employing deep transform learning and the closed-form updates for the transforms and their corresponding coefficients are presented. The method of the present disclosure demonstrates potential in learning reliable data representations, particularly in limited data scenario and real-life industrial applications requiring adaptation between different machines.Type: ApplicationFiled: July 26, 2024Publication date: February 20, 2025Applicant: Tata Consultancy Services LimitedInventors: Kriti KUMAR, Mariswamy Girish CHANDRA, Achanna Anil KUMAR, Angshul MAJUMDAR
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Publication number: 20250012675Abstract: The disclosure generally relates to methods and systems for graph assisted unsupervised domain adaptation for machine fault diagnosis. The present disclosure solves the technical problems in the art using a Graph Assisted Unsupervised Domain Adaptation (GA-UDA) technique for the machine fault diagnosis. The GA-UDA technique carries out the domain adaptation in two stages. In the first stage, a Class-wise maximum mean discrepancy (CMMD) loss is minimized to transform the data from both source and target domains to a shared feature space. In the second stage, the augmented transformed (projected) data from both the source and the target domains are utilized to construct a joint graph. Subsequently, the labels of target domain data are estimated through label propagation over the joint graph. The GA-UDA technique of the present disclosure helps in addressing significant distribution shift between the two domains.Type: ApplicationFiled: June 25, 2024Publication date: January 9, 2025Applicant: Tata Consultancy Services LimitedInventors: NAIBEDYA PATTNAIK, KRITI KUMAR, MARISWAMY GIRISH CHANDRA, ACHANNA ANIL KUMAR
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Publication number: 20240288340Abstract: This disclosure relates generally to a field of industrial machine inspection, and, more particularly, to method and system for acoustic based industrial machine inspection using Delay-and-Sum beamforming (DAS-BF) and dictionary learning (DL). The disclosed method presents a two-stage approach for anomaly detection using a multi-channel acoustic mixed signal. In the first stage, separation of a plurality of acoustic signals corresponding to the spatially distributed acoustic sources is performed at a coarser level by using the DAS-BF. Subsequently, dictionaries pre-trained using the plurality of acoustic signals of the individual source machines are utilized for generating a plurality of separated acoustic source signals. The generated plurality of separated acoustic source signals are analyzed for the anomaly detection by comparing them with a corresponding normal machine sound template.Type: ApplicationFiled: December 29, 2023Publication date: August 29, 2024Applicant: Tata Consultancy Services LimitedInventors: Saurabh SAHU, Achanna Anil KUMAR, Mariswamy Girish CHANDRA, Kriti KUMAR, Angshul MAJUMDAR
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Publication number: 20240151690Abstract: In industrial inspection scenarios, early detection of machine malfunction is extremely essential as it helps in preventing any significant damage and the associated economic losses. Embodiments herein provide a method and system for an acoustic based anomaly detection in industrial machines using a beamforming and a sequential transform learning. Herein, the system employs two-stage multi-channel source separation technique that uses the well-known delay and sum beamforming followed by a recent data-driven sequential transform learning (STL) approach to obtain clean sources. The STL is a solution to linear state-space model where operators/matrices are learnt from data and is used here to model the dynamics of time-varying source signals for source separation. Subsequently, a reference template matching is employed on each separated source to detect an anomaly.Type: ApplicationFiled: September 25, 2023Publication date: May 9, 2024Applicant: Tata Consultancy Services LimitedInventors: Saurabh SAHU, Mariswamy Girish CHANDRA, Kriti KUMAR, Achanna Anil KUMAR, Angshul MAJUMDAR
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Publication number: 20240151846Abstract: Existing multistatic configurations of Radar systems requires a direct LoS signal and/or time synchronization among the Radar transmitter and the multistatic distributed Radar receivers. The present disclosure provides a phaseless frequency-modulated continuous-wave multistatic Radar (PFMR) imaging that relaxes requirement of the direct LoS signal and only requires a plurality of parameters of a FMCW signal comprising a chirp signal rate, a carrier frequency and, a period of chirp to be known. Further, it also removes condition of the time synchronization among a plurality of FMCW multistatic distributed Radar receivers. However, because of absence of the time synchronization among a plurality of FMCW multistatic distributed Radar receivers, an unknown random phase offset appears after deramping.Type: ApplicationFiled: August 29, 2023Publication date: May 9, 2024Applicant: Tata Consultancy Services LimitedInventors: ACHANNA ANIL KUMAR, KRISHNA KANTH ROKKAM, ADITI KUCHIBHOTLA, KRITI KUMAR, TAPAS CHAKRAVARTY, ARPAN PAL, ANGSHUL MAJUMDAR
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Publication number: 20240077606Abstract: The present invention relates to a method and system for Phaseless Passive Synthetic Aperture Radar (PPSAR) imaging. Existing method for image reconstruction requires large number of measurements for satisfactory PPSAR image reconstruction. However, this leads to provisioning of more on-board storage and/or a high-speed data link between a mobile platform and a ground station. These requirements are undesirable in practice as PPSAR image reconstruction systems are deployed on resource constrained platforms. The present disclosure uses a regularized Wirtinger Flow (rWF) based approach that uses appropriate regularizers to facilitate the PPSAR image reconstruction with fewer measurements. Further the PPSAR image reconstruction is achieved using Alternating Direction Method of Multipliers (ADMM) by employing standard denoisers such as Total Variation (TV), Block-matching and 3D filtering (BM3D) and, Deep Image Prior (DIP).Type: ApplicationFiled: August 2, 2023Publication date: March 7, 2024Applicant: Tata Consultancy Services LimitedInventors: Aditi KUCHIBHOTLA, Achanna Anil KUMAR, Tapas CHAKRAVARTY, Kriti KUMAR, Angshul MAJUMDAR
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Patent number: 11899636Abstract: Methods and apparatuses are described for capturing and maintaining a timeline of data changes in a relational database system. A server identifies changed records from relational database tables. The server analyzes the changed records to determine a maximum timestamp for each primary key and extracts the changed records associated with each primary key where a timestamp is equal to or greater than the maximum timestamp for the primary key. The server generates timestamp ranges for each primary key, each comprising an effective date and an expiration date. The server determines whether each key-date combination already exists in a historical record table. The server updates an expiration date of an existing record in the historical record table using the effective date and inserts a new record for the timestamp range using the captured records.Type: GrantFiled: July 13, 2023Date of Patent: February 13, 2024Assignee: FMR LLCInventors: Kriti Kumar Verma, Sunil Gurusiddappa
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Publication number: 20230401428Abstract: This disclosure relates to a method and system for multi-sensor fusion in the presence of missing and noisy labels. Prior methods for multi-sensor fusion do not estimate and correct labels for learning effective models in semi-supervised learning methods. Embodiments of the present disclosure provides a method for learning robust sensor-specific autoencoder based fusion model by utilizing a graph structure to perform label propagation and correction. In the disclosed Graph regularized AutoFuse (GAF) method latent representation for each sensor is learnt using the sensor-specific autoencoders. Further these latent representations are combined and fed to a classifier for multi-class classification. The disclosure presents a joint optimization formulation for multi-sensor fusion where label propagation and correction, sensor-specific learning and classification are executed together.Type: ApplicationFiled: June 8, 2023Publication date: December 14, 2023Applicant: Tata Consultancy Services LimitedInventors: KRITI KUMAR, SAURABH SAHU, ACHANNA ANIL KUMAR, MARISWAMY GIRISH CHANDRA, ANGSHUL MAJUMDAR
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Publication number: 20230013631Abstract: This disclosure relates generally to a method and system for multi-modal image super-resolution. Conventional methods for multi-modal image super-resolution are performed using joint image based filtering, deep learning and dictionary based approaches which require large datasets for training. Embodiments of the present disclosure provide a joint optimization based transform learning framework wherein a high-resolution (HR) image of target modality is reconstructed from a HR image of guidance modality and a low-resolution (LR) image of target modality. A set of parameters, transforms, coefficients and weight matrices are learnt jointly from a training data which includes a HR image of guidance modality, a LR image of target modality and a HR image of target modality. The learnt set of parameters are used for reconstructing a HR image of target modality. The disclosed joint optimization transform learning framework is used in remote sensing, environment monitoring and so on.Type: ApplicationFiled: May 26, 2022Publication date: January 19, 2023Applicant: Tata Consultancy Services LimitedInventors: Andrew GIGIE, Achanna Anil KUMAR, Kriti KUMAR, Mariswamy Girish CHANDRA, Angshul MAJUMDAR
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Publication number: 20220269940Abstract: Multi-sensor fusion is a technology which effectively utilizes the data from multiple sensors so as to portray a unified picture with improved information and offers significant advantages over existing single sensor-based techniques. This disclosure relates to a method and system for a multi-label classification using a two-stage autoencoder. Herein, the system employs autoencoder based architectures, where either raw sensor data or hand-crafted features extracted from each sensor are used to learn sensor-specific autoencoders. The corresponding latent representations from a plurality of sensors are combined to learn a fusing autoencoder. The latent representation of the fusing autoencoder is used to learn a label consistent classifier for multi-class classification. Further, a joint optimization technique is presented for learning the autoencoders and classifier weights together.Type: ApplicationFiled: February 17, 2022Publication date: August 25, 2022Applicant: Tata Consultancy Services LimitedInventors: KRITI KUMAR, MARISWAMY GIRISH CHANDRA, SAURABH SAHU, ARUP KUMAR DAS, ANGSHUL MAJUMDAR
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Publication number: 20220101205Abstract: This disclosure relates to multi-sensor fusion using Transform Learning (TL) that provides a compact representation of data in many scenarios as compared to Dictionary Learning (DL) and Deep network models that may be computationally intensive and complex. A two-stage approach for better modeling of sensor data is provided, wherein in the first stage, representation of the individual sensor time series is learnt using dedicated transforms and their associated coefficients and in the second stage, all the representations are fused together using a fusing (common) transform and its associated coefficients to effectively capture correlation between the different sensor representations for deriving an inference. The method and system of the present disclosure can find application in areas employing multiple sensors that are mostly heterogeneous in nature.Type: ApplicationFiled: August 20, 2021Publication date: March 31, 2022Applicant: Tata Consultancy Services LimitedInventors: Mariswamy Girish CHANDRA, Achanna Anil KUMAR, Kriti KUMAR, Angshul MAJUMDAR, Debasish MISHRA, Surjya Kanta PAL
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Patent number: 11270429Abstract: The disclosure herein generally relates to image processing, and, more particularly, to a method and system for impurity detection using multi-modal image processing. This system uses a combination of polarization data, and at least one of a depth data and an RGB image data to perform the impurity material detection. The system uses a graph fusion based approach while processing the captured images to detect presence of the impurity material, and accordingly alert the user.Type: GrantFiled: June 12, 2020Date of Patent: March 8, 2022Assignee: Tata Consultancy Services LimitedInventors: Achanna Anil Kumar, Rishab Khawad, Riddhi Panse, Andrew Gigie, Tapas Chakravarty, Kriti Kumar, Saurabh Sahu, Mariswamy Girish Chandra
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Patent number: 11119132Abstract: This disclosure relates generally to method and system for low sampling rate electrical load disaggregation. At low sampling rates, disaggregation of energy load is challenging due to unavailability of events and signatures of the constituent loads. The disclosed energy disaggregation technique receives aggregated load data from a utility meter and sequentially obtains training data for determining disaggregated energy load at low sampling rate. Dictionaries are used to characterize the different loads in terms of power values and time of operation. The obtained dictionary coefficients are treated as graph signals and graph smoothness is used for propagating the coefficients from the training phase to the test phase by formulating an optimization model. The derivation of the optimization model identifies the load of interest and estimate their power consumption based on optimization model constraints. This method achieves accuracy greater than 70% for the loads of interest at low sampling rates.Type: GrantFiled: March 10, 2020Date of Patent: September 14, 2021Assignee: Tata Consultancy Services LimitedInventors: Kriti Kumar, Mariswamy Girish Chandra, Achanna Anil Kumar, Naveen Kumar Thokala
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Patent number: 11006874Abstract: The present subject matter relates to a computer implemented method for real time determination of stress levels of an individual. The method includes receiving at least one stream of physiological data from at least one primary sensor for a predetermined duration, and preprocessing the at least one stream of physiological data to extract physiological parameters, where the preprocessing includes performing a preliminary analysis on the at least one stream of physiological data. The method further includes determining a stress level of the individual based on at least the physiological parameters, wherein the determining comprises performing a statistical analysis on the physiological parameters.Type: GrantFiled: August 13, 2013Date of Patent: May 18, 2021Assignee: Tata Consultancy Services LimitedInventors: Srinivasan Jayaraman, Kriti Kumar, Balamuralidhar Purushothaman
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Publication number: 20210019876Abstract: The disclosure herein generally relates to image processing, and, more particularly, to a method and system for impurity detection using multi-modal image processing. This system uses a combination of polarization data, and at least one of a depth data and an RGB image data to perform the impurity material detection. The system uses a graph fusion based approach while processing the captured images to detect presence of the impurity material, and accordingly alert the user.Type: ApplicationFiled: June 12, 2020Publication date: January 21, 2021Applicant: Tata Consultancy Services LimitedInventors: Achanna Anil KUMAR, Rishab KHAWAD, Riddhi PANSE, Andrew GIGIE, Tapas CHAKRAVARTY, Kriti KUMAR, Saurabh SAHU, Mariswamy Girish CHANDRA
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Publication number: 20210011062Abstract: This disclosure relates generally to method and system for low sampling rate electrical load disaggregation. At low sampling rates, disaggregation of energy load is challenging due to unavailability of events and signatures of the constituent loads. The disclosed energy disaggregation technique receives aggregated load data from a utility meter and sequentially obtains training data for determining disaggregated energy load at low sampling rate. Dictionaries are used to characterize the different loads in terms of power values and time of operation. The obtained dictionary coefficients are treated as graph signals and graph smoothness is used for propagating the coefficients from the training phase to the test phase by formulating an optimization model. The derivation of the optimization model identifies the load of interest and estimate their power consumption based on optimization model constraints. This method achieves accuracy greater than 70% for the loads of interest at low sampling rates.Type: ApplicationFiled: March 10, 2020Publication date: January 14, 2021Applicant: Tata Consultancy Services LimitedInventors: Kriti Kumar, Mariswamy Girish CHANDRA, Achanna Anil KUMAR, Naveen Kumar THOKALA
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Patent number: 10467533Abstract: System and method for predicting enterprise system response time is disclosed. System pre-processes causal variables of historical output time series data to select subset of causal variables by applying regression techniques to obtain significant causal variables. Historical output time series data shows response time of enterprise system. System derives dummy variables from historical output time series data using threshold based method. Dummy variables are specific to peak detection and trough detection in historic output time series data. System trains predictive model using historical output time series data, significant causal variables, and dummy variables to generate trained predictive model and predictive model designed using machine learning technique selected based on forecast methodology used for forecasting input time series data.Type: GrantFiled: September 21, 2016Date of Patent: November 5, 2019Assignee: Tata Consultancy Services LimitedInventors: Kriti Kumar, Naveen Kumar Thokala, Ravikumar Karumanchi, Mariswamy Girish Chandra, Kalyan Prathap Kamakolanu Guru, Suresh Upparapalli, Madhusudhan Kamma Chavala Chowdary, Prasanna Madhavrao Kulkarni, Pareshkumar Bhawanishankar Sharda
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Publication number: 20170185902Abstract: System and method for predicting enterprise system response time is disclosed. System pre-processes causal variables of historical output time series data to select subset of causal variables by applying regression techniques to obtain significant causal variables. Historical output time series data shows response time of enterprise system. System derives dummy variables from historical output time series data using threshold based method. Dummy variables are specific to peak detection and trough detection in historic output time series data. System trains predictive model using historical output time series data, significant causal variables, and dummy variables to generate trained predictive model and predictive model designed using machine learning technique selected based on forecast methodology used for forecasting input time series data.Type: ApplicationFiled: September 21, 2016Publication date: June 29, 2017Applicant: TATA CONSULTANCY SERVICES LIMITEDInventors: Kriti KUMAR, Naveen Kumar Thokala, Ravikumar Karumanchi, Mariswamy Girish Chandra, Kalyan Prathap Kamakolanu Guru, Suresh Upparapalli, Madhusudhan Kamma Chavala Chowdary, Prasanna Madhavrao Kulkarni, Pareshkumar Bhawanishankar Sharda
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Publication number: 20140046144Abstract: The present subject matter relates to a computer implemented method for real time determination of stress levels of an individual. The method includes receiving at least one stream of physiological data from at least one primary sensor for a predetermined duration, and preprocessing the at least one stream of physiological data to extract physiological parameters, where the preprocessing includes performing a preliminary analysis on the at least one stream of physiological data. The method further includes determining a stress level of the individual based on at least the physiological parameters, wherein the determining comprises performing a statistical analysis on the physiological parameters.Type: ApplicationFiled: August 13, 2013Publication date: February 13, 2014Applicant: Tata Consultancy Services LimitedInventors: Srinivasan Jayaraman, Kriti Kumar, Balamuralidhar Purushothaman