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).
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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