Patents by Inventor Sanjay Nair

Sanjay Nair 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: 20210308189
    Abstract: The present disclosure provides novel methods for increasing ?-cell viability in islets by delivering RLIP76 polypeptides or GSTA4 polypeptides, or a combination thereof; or RLIP76 polynucleotides or GSTA4 polynucleotides, or a combination thereof, to the islets. The disclosure also provides novel methods for treating a disease or condition in a subject, such as type 1 diabetes mellitus, by delivering RLIP76 polypeptides or GSTA4 polypeptides, or a combination thereof; or RLIP76 polynucleotides or GSTA4 polynucleotides, or a combination thereof, to islets and transplanting the islets into the subject to treat the disease or condition. Kits and compositions including RLIP76 polypeptides or GSTA4 polypeptides, or a combination thereof; or RLIP76 polynucleotides or GSTA4 polynucleotides, or a combination thereof, are also provided to increase ?-cell viability.
    Type: Application
    Filed: April 19, 2021
    Publication date: October 7, 2021
    Applicant: CITY OF HOPE
    Inventors: Sanjay AWASTHI, Sushma YADAV, Ismail AL ABDULLAH, Fouad KANDEEL, Brian MCFADDEN, Indu NAIR, Sharad S. SINGHAL
  • Patent number: 11113283
    Abstract: A query having a Union All view is identified. A logical join between Union AH view/derived table and other tables is broken down into multiple physical joins. The physical joins are pushed to the branches. Cost-based processing statistics are obtained for the branches. An optimal plan for the joins is selected based on the statistics; representing an optimal query execution for the query. The optimal query execution plan is provided to a database engine for executing the optimal query execution plan against a data warehouse.
    Type: Grant
    Filed: December 18, 2017
    Date of Patent: September 7, 2021
    Assignee: Teradata US, Inc.
    Inventors: Mohammed Al-Kateb, Grace Kwan-On Au, Rama Krishna Korlapati, Lu Ma, Sanjay Nair
  • Patent number: 11086870
    Abstract: A data store system includes an array of persistent storage devices configured to store a plurality of data store tables. The data store system includes a processor in communication with the storage device. The processor may receive a query comprising an aggregate function and identify structure of an argument of the aggregate function. The subset of data store tables may be associated with the argument. The processor may partially-execute the aggregate function on each data store table in the subset involved in the argument of the aggregate function to create partially-executed results for each data store table of the subset of data store tables. The processor may join the partially-executed results based on join conditions contained in the aggregate function. The processor may complete execution of the aggregate function on the partially-executed results to generate a final result of the aggregate function. A method and computer-readable medium are also disclosed.
    Type: Grant
    Filed: December 30, 2015
    Date of Patent: August 10, 2021
    Assignee: Teradata US, Inc.
    Inventors: Anantha B. Subramanian, Sanjay Nair, Yi Xia, Grace Kwan-On Au, Kuorong Chiang
  • Publication number: 20210140668
    Abstract: A plurality of triggers may be presented and a selection of a predefined trigger may be accepted. Corresponding actions for the selected trigger may then be presented and an assignment of building control components may be accepted for the corresponding actions. During subsequent operation of a building automation system, the selected trigger may be detected and the corresponding actions may then be performed on the assigned building control components.
    Type: Application
    Filed: July 5, 2017
    Publication date: May 13, 2021
    Inventors: Franklin Joseph, Nilesh Akode, Sanjay Nair, Venugopala Kilingar Nadumane, Manish K. Sharma, Sheeladitya Karmakar, Rajendra S. Kumar
  • Patent number: 10997168
    Abstract: One or a soft correlation of a database can be adjusted (e.g., modified, replaced, overwritten) for use with respect to one or more record(s) of the database associated with the soft correlation, by considering at least one or more violations of the soft correlations in the one or more of records database records associated with the soft correlation. In addition, an adjusted soft correlation can be stored and used for optimizations of database queries pertaining to one or more records associated with the adjusted soft correlation. Typically, the adjusted soft correlation is adjusted by at least considering the violations of an original soft correlation in the one or more records relating to the database queries.
    Type: Grant
    Filed: December 13, 2018
    Date of Patent: May 4, 2021
    Assignee: Teradata US, Inc.
    Inventors: Mohamed Yassin Eltabakh, Grace Kwan-On Au, Sanjay Nair, Mohammed Al-Kateb, Paul Laurence Sinclair
  • Publication number: 20210056106
    Abstract: An apparatus, method and computer program product for query optimization in a Relational Database Management System (RDBMS), wherein an optimizer accesses a query expression repository (QER), so that the optimizer learns from previous versions of the queries to improve current and subsequent versions of the queries. The QER stores planning and execution information for QEs from the previous versions of the queries, wherein the QEs comprise table relations, intermediate results and/or final results of operations in the previous versions of the queries. The optimizer searches the QER for QEs from the query execution plans, and uses information from the QEs stored in the QER when optimizing the current and subsequent versions of the queries. The optimizer may also reuses results from the QEs stored in the QER.
    Type: Application
    Filed: December 27, 2019
    Publication date: February 25, 2021
    Applicant: Teradata US, Inc.
    Inventors: Grace Kwan-On Au, Nobul Reddy Goli, Vivek Kumar, Ming Zhang, Bin Cao, Sanjay Nair, Kanaka Durga Rajanala, Sanjib Mishra, Naveen Jaiswal, Lu Ma, Xiaorong Luo
  • Publication number: 20210034588
    Abstract: An apparatus, method and computer program product for physical database design and tuning in relational database management systems. A relational database management system executes in a computer system, wherein the relational database management system manages a relational database comprised of one or more tables storing data. A Deep Reinforcement Learning based feedback loop process also executes in the computer system for recommending one or more tuning actions for the physical database design and tuning of the relational database management system, wherein the Deep Reinforcement Learning based feedback loop process uses a neural network framework to select the tuning actions based on one or more query workloads performed by the relational database management system.
    Type: Application
    Filed: December 27, 2019
    Publication date: February 4, 2021
    Applicant: Teradata US, Inc.
    Inventors: Louis Martin Burger, Emiran Curtmola, Sanjay Nair, Frank Roderic Vandervort, Douglas P. Brown
  • Publication number: 20210034624
    Abstract: In some examples, a system learns properties of an analytical function based on information of queries invoking the analytical function that have been previously executed, creates a function descriptor for the analytical function based on the learning, and provides the function descriptor for use by an optimizer in generating an execution plan for a received database query that includes the analytical function.
    Type: Application
    Filed: December 23, 2019
    Publication date: February 4, 2021
    Inventors: Mohamed Ahmed Yassin Eltabakh, Mohammed Al-Kateb, Awny Kayed Al-Omari, Sanjay Nair
  • Patent number: 10860929
    Abstract: An encoder system trains a compression model that includes an autoencoder model and a frame extractor model. The encoding portion of the autoencoder is coupled to receive a set of target frames and a previous state tensor for the set of target frames and generate compressed code. The decoding portion of the autoencoder is coupled to receive the compressed code and the previous state tensor for the set of frames and generate a next state tensor for the set of target frames. The frame extractor model is coupled to receive the next state tensor and generate a set of reconstructed frames that correspond to the set of target frames by performing one or more operations on the state tensor. The state tensor for the set of frames includes information from frames of the video that can be used by the frame extractor to generate reconstructed frames.
    Type: Grant
    Filed: May 11, 2020
    Date of Patent: December 8, 2020
    Assignee: WaveOne Inc.
    Inventors: Oren Rippel, Sanjay Nair, Carissa Lew, Steve Branson, Alexander Anderson, Lubomir Bourdev
  • Publication number: 20200272903
    Abstract: An encoder system trains a compression model that includes an autoencoder model and a frame extractor model. The encoding portion of the autoencoder is coupled to receive a set of target frames and a previous state tensor for the set of target frames and generate compressed code. The decoding portion of the autoencoder is coupled to receive the compressed code and the previous state tensor for the set of frames and generate a next state tensor for the set of target frames. The frame extractor model is coupled to receive the next state tensor and generate a set of reconstructed frames that correspond to the set of target frames by performing one or more operations on the state tensor. The state tensor for the set of frames includes information from frames of the video that can be used by the frame extractor to generate reconstructed frames.
    Type: Application
    Filed: May 11, 2020
    Publication date: August 27, 2020
    Inventors: Oren Rippel, Sanjay Nair, Carissa Lew, Steve Branson, Alexander Anderson, Lubomir Bourdev
  • Publication number: 20200192893
    Abstract: One or a soft correlation of a database can be adjusted (e.g., modified, replaced, overwritten) for use with respect to one or more record(s) of the database associated with the soft correlation, by considering at least one or more violations of the soft correlations in the one or more of records database records associated with the soft correlation. In addition, an adjusted soft correlation can be stored and used for optimizations of database queries pertaining to one or more records associated with the adjusted soft correlation. Typically, the adjusted soft correlation is adjusted by at least considering the violations of an original soft correlation in the one or more records relating to the database queries.
    Type: Application
    Filed: December 13, 2018
    Publication date: June 18, 2020
    Inventors: Mohamed Yassin Eltabakh, Grace Kwan-On Au, Sanjay Nair, Mohammed Al-Kateb, Paul Laurence Sinclair
  • Patent number: 10685282
    Abstract: An encoder system trains a compression model that includes an autoencoder model and a frame extractor model. The encoding portion of the autoencoder is coupled to receive a set of target frames and a previous state tensor for the set of target frames and generate compressed code. The decoding portion of the autoencoder is coupled to receive the compressed code and the previous state tensor for the set of frames and generate a next state tensor for the set of target frames. The frame extractor model is coupled to receive the next state tensor and generate a set of reconstructed frames that correspond to the set of target frames by performing one or more operations on the state tensor. The state tensor for the set of frames includes information from frames of the video that can be used by the frame extractor to generate reconstructed frames.
    Type: Grant
    Filed: November 7, 2018
    Date of Patent: June 16, 2020
    Assignee: WaveOne Inc.
    Inventors: Oren Rippel, Sanjay Nair, Carissa Lew, Steve Branson, Alexander Anderson, Lubomir Bourdev
  • Publication number: 20200175016
    Abstract: Multiple cost models (e.g., a sub-operations costing model and logical-operations costing model) can be used to make cost estimations of execution of database queries in one and each one of the multiple heterogeneous database systems. As a result, a “hybrid” cost estimating mode can be used whereby two or more cost models can be used in a single database system in to order maximize the advantages and minimize the disadvantages of each of the cost models, thereby striving to achieve an optimal balance. In addition, cost estimation can be switched between a hybrid cost estimating mode and a single cost estimating mode. The switch can, for example, be made as a part of tuning phase, as more information about actual costs of execution of database queries becomes more available, or as a result of changes to the database system and/or it operations, and so on. As a result, a flexible cost estimating mechanism can also be provided.
    Type: Application
    Filed: December 19, 2018
    Publication date: June 4, 2020
    Applicant: Teradata US, Inc.
    Inventors: Sanjay Nair, Sreyas Srimath Tirumala, Nurjahan Begum, Chandana Prakash, Mohammed Al-Kateb, Conrad Kwok-Wai Tang, Mohamed Yassin Eltabakh, Kassem Awada, Grace Kwan-On Au
  • Publication number: 20200151575
    Abstract: An apparatus, method and computer program product for neural network training over very large distributed datasets, wherein a relational database management system (RDBMS) is executed in a computer system comprised of a plurality of compute units, and the RDBMS manages a relational database comprised of one or more tables storing data. One or more local neural network models are trained in the compute units using the data stored locally on the compute units. At least one global neural network model is generated in the compute units by aggregating the local neural network models after the local neural network models are trained.
    Type: Application
    Filed: November 12, 2019
    Publication date: May 14, 2020
    Applicant: Teradata US, Inc.
    Inventors: Wellington Marcos Cabrera Arevalo, Anandh Ravi Kumar, Mohammed Al-Kateb, Sanjay Nair, Sandeep Singh Sandha
  • Patent number: 10594338
    Abstract: A compression system includes an encoder and a decoder. The encoder can be deployed by a sender system to encode a tensor for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode and reconstruct the encoded tensor. The encoder receives a tensor for compression. The encoder also receives a quantization mask and probability data associated with the tensor. Each element of the tensor is quantized using an alphabet size allocated to that element by the quantization mask data. The encoder compresses the tensor by entropy coding each element using the probability data and alphabet size associated with the element. The decoder receives the quantization mask data, the probability data, and the compressed tensor data. The quantization mask and probabilities are used to entropy decode and subsequently reconstruct the tensor.
    Type: Grant
    Filed: March 18, 2019
    Date of Patent: March 17, 2020
    Assignee: WaveOne Inc.
    Inventors: Carissa Lew, Steven Branson, Oren Rippel, Sanjay Nair, Alexander Grant Anderson, Lubomir Bourdev
  • Patent number: 10565499
    Abstract: An enhanced encoder system generates residual bitstreams representing additional image information that can be used by an image enhancement system to improve a low quality image. The enhanced encoder system upsamples a low quality image and compares the upsampled image to a true high quality image to determine image inaccuracies that arise due to the upsampling process. The enhanced encoder system encodes the information describing the image inaccuracies using a trained encoder model as the residual bitstream. The image enhancement system upsamples the same low quality image to obtain a prediction of a high quality image that can include image inaccuracies. Given the residual bitstream, the image enhancement system decodes the residual bitstream using a trained decoder model and uses the additional image information to improve the predicted high quality image. The image enhancement system can provide an improved, high quality image for display.
    Type: Grant
    Filed: December 15, 2017
    Date of Patent: February 18, 2020
    Assignee: WaveOne Inc.
    Inventors: Lubomir Bourdev, Carissa Lew, Sanjay Nair, Oren Rippel
  • Publication number: 20200036995
    Abstract: An encoder system trains a compression model that includes an autoencoder model and a frame extractor model. The encoding portion of the autoencoder is coupled to receive a set of target frames and a previous state tensor for the set of target frames and generate compressed code. The decoding portion of the autoencoder is coupled to receive the compressed code and the previous state tensor for the set of frames and generate a next state tensor for the set of target frames. The frame extractor model is coupled to receive the next state tensor and generate a set of reconstructed frames that correspond to the set of target frames by performing one or more operations on the state tensor. The state tensor for the set of frames includes information from frames of the video that can be used by the frame extractor to generate reconstructed frames.
    Type: Application
    Filed: November 7, 2018
    Publication date: January 30, 2020
    Inventors: Oren Rippel, Sanjay Nair, Carissa Lew, Steve Branson, Alexander Anderson, Lubomir Bourdev
  • Patent number: 10402722
    Abstract: A compression system trains a machine-learned encoder and decoder. The encoder can be deployed by a sender system to encode content for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode the encoded content and reconstruct the original content. The encoder receives content and generates a tensor as a compact representation of the content. The content may be, for example, images, videos, or text. The decoder receives a tensor and generates a reconstructed version of the content. In one embodiment, the compression system trains one or more encoding components such that the encoder can adaptively encode different degrees of information for regions in the content that are associated with characteristic objects, such as human faces, texts, or buildings.
    Type: Grant
    Filed: December 15, 2017
    Date of Patent: September 3, 2019
    Assignee: WaveOne Inc.
    Inventors: Oren Rippel, Lubomir Bourdev, Carissa Lew, Sanjay Nair
  • Publication number: 20190188299
    Abstract: A query having a Union All view is identified. A logical join between Union AH view/derived table and other tables is broken down into multiple physical joins. The physical joins are pushed to the branches. Cost-based processing statistics are obtained for the branches. An optimal plan for the joins is selected based on the statistics; representing an optimal query execution for the query. The optimal query execution plan is provided to a database engine for executing the optimal query execution plan against a data warehouse.
    Type: Application
    Filed: December 18, 2017
    Publication date: June 20, 2019
    Inventors: Mohammed Al-Kateb, Grace Kwan-On Au, Rama Krishna Korlapati, Lu Ma, Sanjay Nair
  • Publication number: 20180174047
    Abstract: A machine learning (ML) task system trains a neural network model that learns a compressed representation of acquired data and performs a ML task using the compressed representation. The neural network model is trained to generate a compressed representation that balances the objectives of achieving a target codelength and achieving a high accuracy of the output of the performed ML task. During deployment, an encoder portion and a task portion of the neural network model are separately deployed. A first system acquires data, applies the encoder portion to generate a compressed representation, performs an encoding process to generate compressed codes, and transmits the compressed codes. A second system regenerates the compressed representation from the compressed codes and applies the task model to determine the output of a ML task.
    Type: Application
    Filed: December 15, 2017
    Publication date: June 21, 2018
    Inventors: Lubomir Bourdev, Carissa Lew, Sanjay Nair, Oren Rippel