Patents by Inventor Jayavardhana Rama GUBBI LAKSHMINARASIMHA

Jayavardhana Rama GUBBI LAKSHMINARASIMHA 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: 20240020962
    Abstract: The disclosure generally relates to scene graph generation. Scene graph captures rich semantic information of an image by representing objects and their relationships as nodes and edges of a graph and has several applications including image retrieval, action recognition, visual question answering, autonomous driving, robotics. However, to leverage scene graphs, computationally efficient scene graph generation methods are required, which is very challenging to generate due presence of a quadratic number of potential edges and computationally intensive/non-scalable techniques for detecting the relationship between each object pair using the traditional approach. The disclosure proposes a combination of edge proposal neural network and the Graph neural network with spatial message passing (GNN-SMP) along with several techniques including a feature extraction technique, object detection technique, un-labelled graph generation technique and a scene graph generation technique to generate scene graphs.
    Type: Application
    Filed: June 29, 2023
    Publication date: January 18, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: Jayavardhana Rama GUBBI LAKSHMINARASIMHA, Vivek Bangalore SAMPATHKUMAR, Rajan Mindigal Alasingara BHATTACHAR, Balamuralidhar PURUSHOTHAMAN, Arpan PAL
  • Publication number: 20240013522
    Abstract: This disclosure relates generally to identification and mitigation of bias while training deep learning models. Conventional methods do not provide effective methods for bias identification, and they require pre-defined concepts and rules for bias mitigation. The embodiments of the present disclosure train an auto-encoder to produce a generalized representation of an input image by decomposing into a set of latent embedding. The set of latent embedding are used to learn the shape and color concepts of the input image. The feature specialization is done by training an auto-encoder to reconstruct the input image using the shape embedding modulated by color embedding. To identify the bias, permutation invariant neural network is trained for classification task and attribution scores corresponding to each concept embedding are computed. The method also performs de-biasing the classifier by training it with a set of counterfactual images generated by modifying the latent embedding learned by the auto-encoder.
    Type: Application
    Filed: June 13, 2023
    Publication date: January 11, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: Jayavardhana Rama GUBBI LAKSHMINARASIMHA, Vartika SENGAR, Vivek Bangalore SAMPATHKUMAR, Gaurab BHATTACHARYA, Balamuralidhar PURUSHOTHAMAN, Arpan PAL
  • Publication number: 20240005512
    Abstract: This disclosure relates generally to methods and systems for automated image segmentation of an anatomical structure such as heart. Most of the techniques in literature are using 2-D or slice by-slice data due to lightweight and need of less data for training. These networks lack 3-D contextual information. Further, the conventional techniques are inaccurate and inefficient in the 3-D image segmentation till the last slice of the image. The present disclosure solves automated 3-D image segmentation of the anatomical structure such as heart, by proposing a new Generative Adversarial Network (GAN) based architecture for the 3-D segmentation, with a patch-based extraction technique and a class-weighted generalized dice loss. The proposed 3-D GAN based architecture is capable of storing the 3-D contextual information for the image segmentation of the anatomical structure, with high accuracy.
    Type: Application
    Filed: June 26, 2023
    Publication date: January 4, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: Aparna KANAKATTE GURUMURTHY, Avik GHOSE, Divya Manoharlal BHATIA, Jayavardhana Rama GUBBI LAKSHMINARASIMHA
  • Publication number: 20230410390
    Abstract: State of art techniques for color regeneration are complex and fail to provide color control. Embodiments of the present disclosure provide a method and system for generating color variants for fashion apparels by providing a Fashion Apparel Regeneration-Generative Adversarial Network (FAR-GAN) to generate color variants of the fashion apparels. The FAR-GAN utilizes a two-step encoding process to encapsulate both an input image and an edge-map information along with a target color embedding branch to manipulate the color information present in the fashion apparel present in the input image that is to be changed to a desired target color. Furthermore, the color and structural information is disentangled by controlling them using a color consistency loss. The FAR-GAN can be trained end-to-end without incorporating complex multi-step process.
    Type: Application
    Filed: June 16, 2023
    Publication date: December 21, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: BAGYA LAKSHMI VASUDEVAN, KALYAN PRAKASH BAISHYA, KURUVILLA ABRAHAM, JAYAVARDHANA RAMA GUBBI LAKSHMINARASIMHA, GAURAB BHATTACHARYA, NIKHIL KILARI
  • Publication number: 20230408682
    Abstract: Optical images in remote sensing are contaminated by cloud cover and bad weather conditions and are only available during the daytime. Whereas SAR images are completely cloud free, independent of weather conditions and can be acquired both during the day and at night. However, due to the speckle effect and side looking imaging mechanism of SAR images, they are not easily interpretable by untrained people. To address this issue, the present disclosure provides a method and system for LULC guided SAR visualization, wherein a GAN is trained to translate SAR images to optical images for visualization. A given SAR image is fed into a first generator of the GAN to obtain LULC map which is then concatenated with the SAR image and fed into a second generator of the GAN to generate an optical image. The LULC map provides semantic information required for generation of more realistic optical image.
    Type: Application
    Filed: June 8, 2023
    Publication date: December 21, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: JAYAVARDHANA RAMA GUBBI LAKSHMINARASIMHA, RAM PRABHAKAR KATHIRVEL, VEERA HARIKRISHNA NUKALA, BALAMURALIDHAR PURUSHOTHAMAN, ARPAN PAL
  • Publication number: 20230376781
    Abstract: This disclosure relates generally to systems and methods for autonomous task composition of vision pipelines using an algorithm selection framework. The framework leverages transformer architecture along with deep reinforcement learning techniques to search an algorithmic space for unseen solution templates. In an embodiment, the present disclosure describes a two stage process of identifying the vision pipeline for a particular task. At first stage, a high-level sequence of the vision pipeline is provided by a symbolic planner to create the vision workflow. At second stage, suitable algorithms for each high-level task are selected. This is achieved by performing a graph search using a transformer architecture over an algorithmic space on each component of generated workflow. In order to make the system more robust, weights of embedding, key and query networks of a visual transformer are updated with a Deep Reinforcement Learning framework that uses Proximal Policy Optimization (PPO) as underlying algorithm.
    Type: Application
    Filed: May 19, 2023
    Publication date: November 23, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: Abhishek Roy Choudhury, Vighnesh Vatsal, Mehesh Rangarajan, Naveen Kumar Basa Anitha, Aditya Kapoor, Jayavardhana Rama Gubbi Lakshminarasimha, Aravindhan Saravanan, Vartika Sengar, Balamuralidhar Purushothaman, Arpan Pal, Nijil George
  • Publication number: 20230373096
    Abstract: Conventional task planners assume that the task-plans provided are executable, hence these are not task-aware. Present disclosure alleviates the downward refinability assumption, that is, planning can be decomposed separate symbolic and continuous planning steps by introducing bi-level planning, a plan which is a series of actions that the robot needs to take to achieve the goal task is curated. Firstly, abstract symbolic actions are converted to continuous vectors and used therein to enable interaction with an environment. Images of objects placed in the environment are captured and concepts are learnt from the captured images and attributes of objects are detected. A hierarchical scene graph is generated from the concepts and attributes wherein the graph includes interpretable sub-symbolic representations and from these interpretable symbolic representations are obtained for identifying goal task.
    Type: Application
    Filed: May 17, 2023
    Publication date: November 23, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: Jayavardhana Rama GUBBI LAKSHMINARASIMHA, Vartika SENGAR, Vighnesh VATSAL, Balamuralidhar PURUSHOTHAMAN, Arpan PAL, Nijil GEORGE, Aditya KAPOOR
  • Patent number: 11790518
    Abstract: Current inspection processes employed for pipeline networks data acquisition aided with manually locating and recording defects/observations, thus leading labor intensive, prone to error and a time-consuming task thereby resulting in process inefficiencies. Embodiments of the present disclosure provide systems and methods for that leverage artificial intelligence/machine learning models and image processing techniques to automate log and data processing, reports and insights generation thereby reduce dependency on manual analysis, improve annual productivity of survey meterage and bring in process and cost efficiencies into overall asset health management for utilities, thereby enhancing accuracy in defect identification, analysis, classification thereof.
    Type: Grant
    Filed: June 24, 2021
    Date of Patent: October 17, 2023
    Assignee: Tata Consultancy Services Limited
    Inventors: Jayavardhana Rama Gubbi Lakshminarasimha, Mahesh Rangarajan, Rishin Raj, Vishnu Hariharan Anand, Vishal Bajpai, Vishwa Chethan Dandenahalli Venkatappa, Pradeep Kumar Mishra, Gourav Singh Jat, Meghala Mani, Gangadhar Shankarappa, Dinesh Sasidharan Nair, Shashank Lipate, Vineet Lall, Kavita Sara Mathew, Karthik Seemakurthy, Balamuralidhar Purushothaman
  • Publication number: 20230326101
    Abstract: State of the art mechanisms being used for achieving diagnostic-quality images under low-dose settings for general CT imaging have the disadvantages that CT images are fixed during the optimization process to generate perfusion maps, which can lead to suboptimal CT images with respect to the perfusion maps generated, although they might appear spatially smooth or denoised. The disclosure herein generally relates to Computer Tomography (CT) scanning, and, more particularly, to a method and system for CT image reconstruction. The system performs modelling an optimization problem for joint estimation of a set of structural CT images and a perfusion map, and further solves the optimization problem for the reconstruction of the CT images of a subject.
    Type: Application
    Filed: February 27, 2023
    Publication date: October 12, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: JAYAVARDHANA Rama Gubbi LAKSHMINARASIMHA, VISWANATH PAMULAKANTY SUDARSHAN, VARTIKA SENGAR, ARPAN PAL, PAVAN KUMAR REDDY KANCHAM
  • Publication number: 20230308454
    Abstract: State of the art systems used for airport automation and data processing may be prone to data security related issues, as unauthorized personal may gain entry to sensitive data. The disclosure herein generally relates to airport management, and, more particularly, to a method and system for service authentication in an airport management network. The system uses a neural network to process a received service request and decides whether the service request is to be allowed or denied, based on a determined validity of the service request, role based access defined for a user requesting the service, a feature map data generated.
    Type: Application
    Filed: February 22, 2023
    Publication date: September 28, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: Jayavardhana Rama GUBBI LAKSHMINARASIMHA, Raj Anil CHAUDHARI, Meena SINGH DILIP THAKUR, Balamuralidhar PURUSHOTHAMAN, Rajan Mindigal ALASINGARA BHATTACHAR, Sivakumar Kuppusamy SANTHANAM
  • Publication number: 20230305089
    Abstract: State of the art systems being used for QSM reconstruction have explored prior information from both magnitude and phase data. However, the underlying assumption is that the susceptibility maps and the magnitude images have coinciding edges. Establishing the ground-truth susceptibility maps is difficult and leads to limited applicability of supervised methods. Further, with portable MRI machines becoming a reality, low-field imaging is getting more prominence, which brings in several associated challenges due to noise and external interference. The disclosure herein generally relates to magnetic resonance imaging (MRI) imaging systems, and, more particularly, to a method and system for quantitative susceptibility mapping (QSM) reconstruction in magnetic resonance imaging (MRI) systems. The system performs an iterative reconstruction of QSM, wherein in each iteration the reconstructed QSM from previous iteration is refined by comparing with a reference image generated using same subject's prior MRI data.
    Type: Application
    Filed: February 27, 2023
    Publication date: September 28, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: JAYAVARDHANA Rama Gubbi LAKSHMINARASIMHA, VISWANATH PAMULAKANTY SUDARSHAN, ARPAN PAL, PAVAN KUMAR REDDY KANCHAM
  • Patent number: 11696736
    Abstract: Conventionally, systems and methods have been provided for manual annotation of anatomical landmarks in digital radiography (DR) images. Embodiments of the present disclosure provides system and method for anatomical landmark detection and identification from DR images containing severe skeletal deformations. More specifically, motion artefacts and exposure are filtered from an input DR image to obtain a pre-processed DR image and probable/candidate anatomical landmarks comprised therein are identified. These probable candidate anatomical landmarks are assigned a score. A subset of the candidate anatomical landmarks (CALs) is selected as accurate anatomical landmarks based on comparison of the score with a pre-defined threshold performed by a trained classifier. Position of remaining CALs may be fine-tuned for classification thereof as accurate anatomical landmarks or missing anatomical landmarks.
    Type: Grant
    Filed: September 30, 2020
    Date of Patent: July 11, 2023
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Aparna Kanakatte Gurumurthy, Pavan Kumar Reddy Kancham, Jayavardhana Rama Gubbi Lakshminarasimha, Avik Ghose, Murali Poduval, Balamuralidhar Purushothaman
  • Publication number: 20230215176
    Abstract: This relates generally to a method and a system for spatio-temporal polarization video analysis. The spatio-temporal polarization data is analyzed for a computer vision application such as object detection, image classification, image captioning, image reconstruction or image inpainting, face recognition and action recognition. Numerous classical and deep learning methods have been applied on polarimetric data for polarimetric imaging analysis, however, the available pre-trained models may not be directly suitable on polarization data, as polarimetric data is more complex. Further compared to analysis of the polarimetric images, a significant number of actions can be detected by polarimetric videos, hence analyzing polarimetric videos is more efficient. The disclosure is a spatio-temporal analysis of polarization video.
    Type: Application
    Filed: December 21, 2022
    Publication date: July 6, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: Rokkam Krishna KANTH, Akshaya Ramaswamy, Achanna Anil Kumar, Jayavardhana Rama Gubbi Lakshminarasimha, Balamuralidhar Purushothaman
  • Publication number: 20230204494
    Abstract: Existing Mueller Matrix polarization techniques that rely only on polarization properties are insufficient for accurate characterization of transparent objects. Embodiments of the present disclosure provide a method and system for Mueller Matrix polarimetric characterization of transparent object using optical properties along with the polarization properties to accurately characterize the transparent object. The polarization properties of are derived from a decomposed Mueller matrix element. Additionally, the method derives the optical properties by further subjecting the decomposed Mueller matrix element to Fresnel’s law-based analysis and a reverse Monte Carlo analysis to extract optical properties such as a material refractive index and a material attenuation index. Optical properties vary with changes in the material property caused due to several factors such as manufacturing defect, aberration, inclusion of an impurity such as bubble or dust etc.
    Type: Application
    Filed: December 15, 2022
    Publication date: June 29, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: ACHANNA ANIL KUMAR, TAPAS CHAKRAVARTY, SUBHASRI CHATTERJEE, ARPAN PAL, JAYAVARDHANA RAMA GUBBI LAKSHMINARASIMHA, ROKKAM KRISHNA KANTH
  • Patent number: 11657590
    Abstract: State of the art techniques in the domain of video analysis have limitations in terms of capability to extract the spatial and temporal information. This limitation in turn affects interpretation of the video data. The disclosure herein generally relates to video analysis, and, more particularly, to a method and system for video analysis to extract spatio-temporal information from a video being analyzed. The system uses a neural network architecture which has multiple layers to extract spatial and temporal information from the video being analyzed. The method of training the neural network that extracts a micro-scale information from a latent representation of the video is presented. This is generated using an attention network, which is then used to extract spatio-temporal information corresponding to the collected video, which is then used in multiple video analysis applications such as searching actions in videos, action detection and localization.
    Type: Grant
    Filed: March 1, 2021
    Date of Patent: May 23, 2023
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Jayavardhana Rama Gubbi Lakshminarasimha, Akshaya Ramaswamy, Karthik Seemakurthy, Balamuralidhar Purushothaman
  • Patent number: 11631247
    Abstract: State of the art techniques in the domain of video analysis have limitations in terms of capability to capture spatio-temporal representation. This limitation in turn affects interpretation of video data. The disclosure herein generally relates to video analysis, and, more particularly, to a method and system for video analysis to capture spatio-temporal representation for video reconstruction and analysis. The method presents different architecture variations using three main deep network components: 2D convolution units, 3D convolution units and long short-term memory (LSTM) units for video reconstruction and analysis. These variations are trained for learning the spatio-temporal representation of the videos in order to generate a pre-trained video analysis module. By understanding the advantages and disadvantages of different architectural configurations, a novel architecture is designed for video reconstruction.
    Type: Grant
    Filed: March 10, 2021
    Date of Patent: April 18, 2023
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Jayavardhana Rama Gubbi Lakshminarasimha, Akshaya Ramaswamy, Balamuralidhar Purushothaman, Aparna Kanakatte Gurumurthy, Avik Ghose
  • Publication number: 20230069442
    Abstract: Traditional systems used for fashion attribute detection struggle to generate accurate predictions due to presence of large intra-class and relatively small inter-class variations in data related to the fashion attributes. The disclosure herein generally relates to image processing, and, more particularly, to a method and system for fashion attribute detection. The method proposes F-AttNet, an attribute extraction network to leverage the performance of fine-grained localized fashion attribute recognition. F-AttNet comprises Attentive Multi-scale Feature Encoder (AMF) blocks that encapsulate multi-scale fine-grained attribute information upon adaptive recalibration of channel weights. F-AttNet is designed by hierarchically stacking the AMF encoders to extract deep fine-grained information across multiple scales.
    Type: Application
    Filed: July 1, 2022
    Publication date: March 2, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: JAYAVARDHANA RAMA GUBBI LAKSHMINARASIMHA, GAURAB BHATTACHARYA, NIKHIL KILARI, BAGYALAKSHMI VASUDEVAN, BALAMURALIDHAR PURUSHOTHAMAN
  • Publication number: 20230047937
    Abstract: The disclosure herein relates to methods and systems for generating an end-to-end de-smoking model for removing smoke present in a video. Conventional data-driven based de-smoking approaches are limited mainly due to lack of suitable training data. Further, the conventional data-driven based de-smoking approaches are not end-to-end for removing the smoke present in the video. The de-smoking model of the present disclosure is trained end-to-end with the use of synthesized smoky video frames that are obtained by source aware smoke synthesis approach. The end-to-end de-smoking model localize and remove the smoke present in the video, using dynamic properties of the smoke. Hence the end-to-end de-smoking model simultaneously identifies the regions affected with the smoke and performs the de-smoking with minimal artifacts. localized smoke removal and color restoration of a real-time video.
    Type: Application
    Filed: December 16, 2021
    Publication date: February 16, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: Jayavardhana Rama GUBBI LAKSHMINARASIMHA, Vartika Sengar, Vivek Bangalore Sampathkumar, Aparna Kanakatte Gurumurthy, Murali Poduval, Balamuralidhar Purushothaman, Karthik Seemakurthy, Avik Ghose, Srinivasan Jayaraman
  • Publication number: 20230016233
    Abstract: Automation is the key to build efficient workflows with minimum effort consumption. However, there is a large gap in workflow synthesis for automated AI application development. Computer vision workflow synthesis largely rely on domain expert due to lack of generalization over solution search space for given goal. This search space for creating suitable solution(s) using available algorithms is quite vast, which makes exploratory work of solution building a time-, effort- and intellect intensive endeavor. Embodiments of the present disclosure provide system and method for goal-driven algorithm selection approach for building computer vision workflows on the fly. The system generates one or more task workflows with associated success probability depending on initial conditions and input natural language goal query by combining various image processing algorithms. Symbolic AI planning is aided by Reinforcement Learning to recommend optimal workflows that are robust and adaptive to changes in the environment.
    Type: Application
    Filed: September 16, 2021
    Publication date: January 19, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: Jayavardhana Rama Gubbi Lakshminarasimha, Balamuralidhar Purushothaman, Vartika Sengar
  • Publication number: 20220366618
    Abstract: The disclosure herein relates to methods and systems for localized smoke removal and color restoration of a real-time video. Conventional techniques apply the de-smoking process only on a single image, by finding the regions having the smoke, based on manual air-light estimation. In addition, regaining original colors of de-smoked image is quite challenging. The present disclosure herein solves the technical problems. In the first stage, video frames having the smoky and smoke-free video frames are identified, from the video received in the real-time. In the second stage, an air-light is estimated automatically using a combined feature map. An intermediate de-smoked video frame for each smoky video frame is generated based on the air-light using a de-smoking algorithm. In the third and the last stage, a smoke-free video reference frame is used to compensate for color distortions introduced by the de-smoking algorithm in the second stage.
    Type: Application
    Filed: December 20, 2021
    Publication date: November 17, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: Jayavardhana Rama Gubbi Lakshminarasimha, KARTHIK SEEMAKURTHY, VARTIKA SENGAR, APARNA KANAKATTE GURUMURTHY, AVIK GHOSE, BALAMURALIDHAR PURUSHOTHAMAN, MURALI PODUVAL, JAYEETA SAHA, SRINIVASAN JAYARAMAN, VIVEK Bangalore Sampathkumar