Patents by Inventor Mandar Shrikant KULKARNI
Mandar Shrikant KULKARNI 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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Patent number: 11989648Abstract: A training log is selected from a plurality of well logs. A log window of a plurality of log windows is selected from the training log. A positive window is generated from the log window. A negative window is selected from the training log. A siamese neural network (SNN) is trained that includes a first self attention neural network (ANN) and a duplicate self attention neural network with the log window, the positive window, and the negative window, to recognize a similarity between the log window and the positive window and to differentiate against the negative window.Type: GrantFiled: September 11, 2020Date of Patent: May 21, 2024Assignee: SCHLUMBERGER TECHNOLOGY CORPORATIONInventors: Mandar Shrikant Kulkarni, Hiren Maniar, Aria Abubakar
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Publication number: 20230409783Abstract: A method involves obtaining query pressure transient analysis (PTA) data from a well associated with a reservoir, and obtaining a selected class of physics models from a multitude of classes of physics models using a first machine learning model operating on the query PTA data. A physics model in at least one of the multitude of classes of physics models includes a well model and a reservoir model. The well model and the reservoir model are parameterized with model parameters having model parameter values. The method further involves obtaining a multitude of model parameter value estimates to form a parameterized query physics model of the selected class of physics models, using a second machine learning model operating on the query PTA data; and providing the parameterized query physics model to a user.Type: ApplicationFiled: November 17, 2021Publication date: December 21, 2023Inventors: Mandar Shrikant KULKARNI, Guru Prasad NAGARAJ, Prashanth PILLAI
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Publication number: 20230334074Abstract: A method and system for searching language-agnostic code-mixed queries are disclosed. The method includes receiving one or more code mixed vernacular queries, from one or more electronic devices. Further, the method includes obtaining one or more vector representations, which are similar to the one or more code mixed vernacular queries, from a database. Furthermore, the method includes retrieving one or more English queries corresponding to the obtained one or more vector representations. Thereafter, the method includes outputting one or more retrieved English queries corresponding to the one or more code mixed vernacular queries.Type: ApplicationFiled: December 27, 2022Publication date: October 19, 2023Inventors: Mandar Shrikant Kulkarni, Nikesh Garera
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Publication number: 20230273338Abstract: A method for correlating well logs includes receiving a well log as input to a first machine learning model that is configured to predict first markers in the well log based at least in part on a global factor of the well log, receiving the well log as input to a second machine learning model that is configured to predict second markers in the well log based at least in part on local factors of the well log, generating a set of predicted well markers by merging at least some of the first markers and at least some of the second markers, and aligning the well log with respect to one or more other well logs based at least in part on the set of predicted well markers.Type: ApplicationFiled: July 26, 2021Publication date: August 31, 2023Inventors: Mandar Shrikant KULKARNI, Purnaprajna Raghavendra MANGSULI, Hiren MANIAR, Aria ABUBAKAR
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Publication number: 20210089892Abstract: A training log is selected from a plurality of well logs. A log window of a plurality of log windows is selected from the training log. A positive window is generated from the log window. A negative window is selected from the training log. A siamese neural network (SNN) is trained that includes a first self attention neural network (ANN) and a duplicate self attention neural network with the log window, the positive window, and the negative window, to recognize a similarity between the log window and the positive window and to differentiate against the negative window.Type: ApplicationFiled: September 11, 2020Publication date: March 25, 2021Inventors: Mandar Shrikant KULKARNI, Hiren MANIAR, Aria ABUBAKAR
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Patent number: 10482176Abstract: This disclosure relates generally to quality evaluation of collaborative text input, and more particularly to system and method for quality evaluation of collaborative text inputs using Long Short Term Memory (LSTM) networks. In one embodiment, the method includes receiving an input data associated with a task to be accomplished collaboratively and sequentially by a plurality of contributors. The input data includes task-wise data sequence of contributor's post-edit submissions. A plurality of features are extracted from the input data. Based on the plurality of features, a plurality of input sequences are constructed. The input sequences include a plurality of concatenated feature vectors, where each of the concatenated feature vectors includes a post-edit feature vector and a contributor representation feature vector. The input sequences are modelled as a LSTM network, where the LSTM network is utilized to train a binary classifier for quality evaluation of the post-edit submission.Type: GrantFiled: March 13, 2018Date of Patent: November 19, 2019Assignee: Tata Consultancy Services LimitedInventors: Manasi Smarth Patwardhan, Kanika Kalra, Mandar Shrikant Kulkarni, Shirish Subhash Karande
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Publication number: 20190114320Abstract: This disclosure relates generally to quality evaluation of collaborative text input, and more particularly to system and method for quality evaluation of collaborative text inputs using Long Short Term Memory (LSTM) networks. In one embodiment, the method includes receiving an input data associated with a task to be accomplished collaboratively and sequentially by a plurality of contributors. The input data includes task-wise data sequence of contributor's post-edit submissions. A plurality of features are extracted from the input data. Based on the plurality of features, a plurality of input sequences are constructed. The input sequences include a plurality of concatenated feature vectors, where each of the concatenated feature vectors includes a post-edit feature vector and a contributor representation feature vector. The input sequences are modelled as a LSTM network, where the LSTM network is utilized to train a binary classifier for quality evaluation of the post-edit submission.Type: ApplicationFiled: March 13, 2018Publication date: April 18, 2019Applicant: Tata Consultancy Services LimitedInventors: Manasi Smarth Patwardhan, Kanika Kalra, Mandar Shrikant Kulkarni, Shirish Subhash Karande
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Patent number: 10163197Abstract: System and method for layer-wise training of deep neural networks (DNNs) are disclosed. In an embodiment, multiple labelled images are received at a layer of multiple layers of a DNN. Further, the labelled images are pre-processed. The pre-processed images are then transformed based on a predetermined weight matrix to obtain feature representation of the pre-processed images at the layer, the feature representation comprise feature vectors and associated labels. Furthermore, kernel similarity between the feature vectors is determined based on a predefined kernel function. Moreover, a Gaussian kernel matrix is determined based on the kernel similarity. In addition, an error function is computed based on the predetermined weight matrix and the Gaussian kernel matrix. Also, a weight matrix associated with the layer is computed based on the error function and predetermined weight matrix, thereby training the layer of the multiple layers.Type: GrantFiled: March 30, 2017Date of Patent: December 25, 2018Assignee: Tata Consultancy Services LimitedInventors: Mandar Shrikant Kulkarni, Anand Sriraman, Rahul Kumar, Kanika Kalra, Shirish Subhash Karande, Purushotam Gopaldas Radadia
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Patent number: 10095957Abstract: The present application provides a method and system for unsupervised word image clustering, comprises capturing one or more image wherein the one or more image comprises at least one word images. Extracting at least one feature vector using an untrained convolution neural network architecture, wherein the convolution filters are initialized by random filter based deep learning techniques using Gaussian random variable with zero mean and unit standard deviation, and wherein the convolution filters are constrained to sum to zero. The extracted feature vectors are used for clustering, wherein clustering is performed in two stages. First stage includes clustering word images which are similar using a graph connected component. Second stage clustering includes clustering a remaining word images which are not clustered during the first stage by evaluating the remaining images against the clusters formed during the first stage and assigning them to clusters based on the evaluation.Type: GrantFiled: February 14, 2017Date of Patent: October 9, 2018Assignee: Tata Consultancy Services LimitedInventors: Mandar Shrikant Kulkarni, Anand Sriraman, Rahul Kumar, Kanika Kalra, Shirish Subhash Karande, Sachin Premsukh Lodha
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Publication number: 20180158181Abstract: System and method for layer-wise training of deep neural networks (DNNs) are disclosed. In an embodiment, multiple labelled images are received at a layer of multiple layers of a DNN. Further, the labelled images are pre-processed. The pre-processed images are then transformed based on a predetermined weight matrix to obtain feature representation of the pre-processed images at the layer, the feature representation comprise feature vectors and associated labels. Furthermore, kernel similarity between the feature vectors is determined based on a predefined kernel function. Moreover, a Gaussian kernel matrix is determined based on the kernel similarity. In addition, an error function is computed based on the predetermined weight matrix and the Gaussian kernel matrix. Also, a weight matrix associated with the layer is computed based on the error function and predetermined weight matrix, thereby training the layer of the multiple layers.Type: ApplicationFiled: March 30, 2017Publication date: June 7, 2018Applicant: Tata Consultancy Services LimitedInventors: Mandar Shrikant Kulkarni, Anand Sriraman, Rahul Kumar, Kanika Kalra, Shirish Subhash Karande, Purushotam Gopaldas Radadia
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Patent number: 9886746Abstract: This disclosure relates generally to image processing, and more particularly to system and method for image inpainting. In one embodiment, a method for image inpainting includes aligning a plurality of multi-view images of a scene with respect to a reference image to obtain a plurality of aligned multi-view images. A region of interest (ROI) representing a region to be removed from the reference image for image inpainting is selected. A dictionary is created by selecting image-patches from the reference image and the plurality of aligned multi-view images, and 3D rotations thereof. A priority value of each of a plurality of pixels of the ROI is created. The ROI is systematically reconstructed in the reference image based at least on the priority values of the plurality of pixels and the dictionary by computing a linear combination of two or more image-patches selected from the plurality of image-patches of the dictionary.Type: GrantFiled: July 20, 2016Date of Patent: February 6, 2018Assignee: Tata Consultancy Services LimitedInventors: Shirish Subhash Karande, Sandhya Sree Thaskani, Sachin P Lodha, Purushotam Gopaldas Radadia, Mandar Shrikant Kulkarni
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Publication number: 20170270387Abstract: The present application provides a method and system for unsupervised word image clustering, comprises capturing one or more image wherein the one or more image comprises at least one word images. Extracting at least one feature vector using an untrained convolution neural network architecture, wherein the convolution filters are initialized by random filter based deep learning techniques using Gaussian random variable with zero mean and unit standard deviation, and wherein the convolution filters are constrained to sum to zero. The extracted feature vectors are used for clustering, wherein clustering is performed in two stages. First stage includes clustering word images which are similar using a graph connected component. Second stage clustering includes clustering a remaining word images which are not clustered during the first stage by evaluating the remaining images against the clusters formed during the first stage and assigning them to clusters based on the evaluation.Type: ApplicationFiled: February 14, 2017Publication date: September 21, 2017Applicant: Tata Consultancy Services LimitedInventors: Mandar Shrikant Kulkarni, Anand Sriraman, Rahul Kumar, Kanika KaIra, Shirish Subhash Karande, Sachin Premsukh Lodha
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Publication number: 20170200101Abstract: Optimizing task allocation requires taking into account cognitive load on workers and their response time to allocated tasks. The present disclosure provides for allocation of task by receiving data pertaining to current activity of workers; receiving data pertaining to at least one task to be allocated and determining activity-task pairs based on an activity feature vector corresponding to at least one human body part used during the current activity and a task feature vector corresponding to at least one human body part required for the at least one task to be performed by the workers. Cognitive load on the workers is then estimated for the determined activity-task pairs. An optimum activity-task pair based on the estimated cognitive load is determined and at least one task is allocated to the workers based on the determined optimum activity-task pair.Type: ApplicationFiled: January 6, 2017Publication date: July 13, 2017Applicant: Tata Consultancy Services LimitedInventors: Rahul KUMAR, Anand SRIRAMAN, Mandar Shrikant KULKARNI, Kanika KALRA, Shirish Subhash KARANDE, Sachin Premsukh LODHA
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Publication number: 20170024864Abstract: This disclosure relates generally to image processing, and more particularly to system and method for image inpainting. In one embodiment, a method for image inpainting includes aligning a plurality of multi-view images of a scene with respect to a reference image to obtain a plurality of aligned multi-view images. A region of interest (ROI) representing a region to be removed from the reference image for image inpainting is selected. A dictionary is created by selecting image-patches from the reference image and the plurality of aligned multi-view images, and 3D rotations thereof. A priority value of each of a plurality of pixels of the ROI is created. The ROI is systematically reconstructed in the reference image based at least on the priority values of the plurality of pixels and the dictionary by computing a linear combination of two or more image-patches selected from the plurality of image-patches of the dictionary.Type: ApplicationFiled: July 20, 2016Publication date: January 26, 2017Applicant: Tata Consultancy Services LimitedInventors: Shirish Subhash KARANDE, Sandhya Sree THASKANI, Sachin P. LODHA, Purushotam Gopaldas RADADIA, Mandar Shrikant KULKARNI