Patents by Inventor Prithwish CHAKRABORTY

Prithwish CHAKRABORTY 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: 20230196378
    Abstract: An approach for training a machine learning model within a carbon budgetary constraint may be provided. The approach may include receiving a carbon budget constraint, for training a machine learning model. The approach may also include generate a training plan for the machine learning model within the carbon budget constraint. Generating the training plan may include sampling the search space of the machine learning model and identifying hyperparameters that will have the greatest effect on the accuracy of the machine learning model. The approach may also include monitoring carbon emissions of the machine learning model training plan. Further, the approach may include updating the training plan of the machine learning model based on the monitored carbon emissions.
    Type: Application
    Filed: December 21, 2021
    Publication date: June 22, 2023
    Inventors: Prithwish Chakraborty, Mohamed Ghalwash, Daby Mousse Sow
  • Patent number: 11681726
    Abstract: Systems and methods that use multi-tasking and transfer learning with sparse gating mechanisms and domain knowledge to generate pheno-embeddings in a scalable manner that can improve the relevance of the patient embeddings from Electronic Health Records. A system, comprises at least one processor that executes the following computer executable components stored in memory: a structural pheno-embedding model that employs a hierarchical knowledge graph; a data augmentation component that expands on a sparse data set associated with the knowledge graph; and an embedding component that generates a specialized embedding for phenotypes using the structural pheno-embedding model and the augmented data set for a selected cohort.
    Type: Grant
    Filed: December 3, 2020
    Date of Patent: June 20, 2023
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Mohamed Ghalwash, Zijun Yao, Prithwish Chakraborty, James V Codella, Daby Mousse Sow
  • Publication number: 20220415524
    Abstract: In an approach for building a machine learning model with a flexible prediction horizon, a processor gathers statistical data related to a disease from one or more regional sources. A processor clusters the statistical data according to a plurality of localized regional source similarity criteria and a plurality of region criteria. A processor builds a plurality of training models based on the clustered statistical data. A processor builds a plurality of feature vectors based on the plurality of localized regional source similarity criteria and the plurality of region criteria. A processor trains the plurality of training models separately against the plurality of feature vectors. A processor selects a best performing training model for each of the plurality of localized regional source similarity criteria and the plurality of region criteria based on a performance criterion. A processor tests the best performing training model to predict one or more future outcomes.
    Type: Application
    Filed: June 29, 2021
    Publication date: December 29, 2022
    Inventors: Sarah Kefayati, PRITHWISH CHAKRABORTY, Ajay Ashok Deshpande, Vishrawas Gopalakrishnan, Jianying Hu, Hu Trombley Huang, Gretchen Jackson, Xuan Liu, SAYALI NAVALEKAR, Raman Srinivasan
  • Publication number: 20220179880
    Abstract: Systems and methods that use multi-tasking and transfer learning with sparse gating mechanisms and domain knowledge to generate pheno-embeddings in a scalable manner that can improve the relevance of the patient embeddings from Electronic Health Records. A system, comprises at least one processor that executes the following computer executable components stored in memory: a structural pheno-embedding model that employs a hierarchical knowledge graph; a data augmentation component that expands on a sparse data set associated with the knowledge graph; and an embedding component that generates a specialized embedding for phenotypes using the structural pheno-embedding model and the augmented data set for a selected cohort.
    Type: Application
    Filed: December 3, 2020
    Publication date: June 9, 2022
    Inventors: MOHAMED GHALWASH, Zijun Yao, PRITHWISH CHAKRABORTY, James V. Codella, Daby Mousse Sow
  • Patent number: 9053439
    Abstract: Systems and automated methods for predicting photovoltaic (PV) generation are provided. Weather forecast data and present-day and historical PV generation data are provided to respective predictors. The predictors derive weighted predictions that are used to calculate a Bayesian model average. Near-future generation by the PV system is predicted using the Bayesian model average. Production rates, worker scheduling, hours of operation and other planning decisions can be made in accordance with the predicted near-future generation.
    Type: Grant
    Filed: September 28, 2012
    Date of Patent: June 9, 2015
    Assignee: Hewlett-Packard Development Company, L.P.
    Inventors: Manish Marwah, Martin Arlitt, Prithwish Chakraborty, Naren Ramakrishnan
  • Publication number: 20140095076
    Abstract: Systems and automated methods for predicting photovoltaic (PV) generation are provided. Weather forecast data and present-day and historical PV generation data are provided to respective predictors. The predictors derive weighted predictions that are used to calculate a Bayesian model average. Near-future generation by the PV system is predicted using the Bayesian model average. Production rates, worker scheduling, hours of operation and other planning decisions can be made in accordance with the predicted near-future generation.
    Type: Application
    Filed: September 28, 2012
    Publication date: April 3, 2014
    Applicant: HEWLETT-PACKARD DEVELOPMENT COMPANY, L.P.
    Inventors: Manish MARWAH, Martin ARLITT, Prithwish CHAKRABORTY, Naren Ramakrishnan