Patents by Inventor Kartik Chaudhary
Kartik Chaudhary 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: 11915505Abstract: Systems, methods, and computer program products may be configured to generate a set of related document objects for a predictive entity and/or to generate an optimal document sequence for a set of related document objects. In one embodiment, for example, a set of related document objects for a predictive entity is generated by processing entity metadata features associated with the predictive entity using an entity-document correlation machine learning model, and an optimal document sequence is generated for the set of related document objects by processing the set of related document objects using a document sequence optimization machine learning model.Type: GrantFiled: October 20, 2022Date of Patent: February 27, 2024Assignee: Optum Technology, Inc.Inventors: Swapna Sourav Rout, Sharlene L. Tan, Vamsi Bhandaru, Vineet Shukla, Akshaya D. N, Kartik Chaudhary
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Patent number: 11663790Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing augmented reality assistance mode functionalities. Certain embodiments utilize systems, methods, and computer program products that perform augmented reality assistance mode functionalities by using at least one of environment familiarity predictions, assistance mode triggering need determinations, and threat detection machine learning models.Type: GrantFiled: August 18, 2021Date of Patent: May 30, 2023Assignee: Optum, Inc.Inventors: Kartik Chaudhary, Sudeep Choudhary, Raghav Bali, Anurag Das, Subhadip Maji
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Publication number: 20230134348Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by machine learning models that are trained using one or more filtered training entries that are selected from a plurality of candidate training entries in accordance with one or more optimal imbalance adjustment conditions, where the one or more optimal imbalance adjustment conditions that are selected from a plurality of candidate imbalance adjustment conditions in a manner that is configured to maximize a cumulative target score for the one or more optimal imbalance adjustment conditions while a cumulative non-target score for the one or more optimal imbalance adjustment conditions satisfies an upper cumulative non-target score threshold.Type: ApplicationFiled: November 2, 2021Publication date: May 4, 2023Inventors: Kartik Chaudhary, Ankit Varshney, Rajat Gupta, Snigdha Sree Borra, Yogesh K. Dagar
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Publication number: 20230059399Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing augmented reality assistance mode functionalities. Certain embodiments utilize systems, methods, and computer program products that perform augmented reality assistance mode functionalities by using at least one of environment familiarity predictions, assistance mode triggering need determinations, and threat detection machine learning models.Type: ApplicationFiled: August 18, 2021Publication date: February 23, 2023Inventors: Kartik Chaudhary, Sudeep Choudhary, Raghav Bali, Anurag Das, Subhadip Maji
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Publication number: 20230054624Abstract: Systems, methods, and computer program products may be configured to generate a set of related document objects for a predictive entity and/or to generate an optimal document sequence for a set of related document objects. In one embodiment, for example, a set of related document objects for a predictive entity is generated by processing entity metadata features associated with the predictive entity using an entity-document correlation machine learning model, and an optimal document sequence is generated for the set of related document objects by processing the set of related document objects using a document sequence optimization machine learning model.Type: ApplicationFiled: October 20, 2022Publication date: February 23, 2023Inventors: Swapna Sourav Rout, Sharlene L. Tan, Vamsi Bhandaru, Vineet Shukla, Akshaya D. N, Kartik Chaudhary
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Publication number: 20220391451Abstract: Systems and methods are configured to generate a set of related document objects for a predictive entity and/or to generate an optimal document sequence for a set of related document objects. In one embodiment, a set of related document objects for a predictive entity is generated by processing entity metadata features associated with the predictive entity using an entity-document correlation machine learning model, and an optimal document sequence is generated for the set of related document objects by processing the set of related document objects using a document sequence optimization machine learning model.Type: ApplicationFiled: August 19, 2022Publication date: December 8, 2022Inventors: Swapna Sourav Rout, Sharlene L. Tan, Vamsi Bhandaru, Vineet Shukla, Akshaya D. N, Kartik Chaudhary
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Patent number: 11508171Abstract: Systems and methods are configured to generate a set of related document objects for a predictive entity and/or to generate an optimal document sequence for a set of related document objects. In one embodiment, a set of related document objects for a predictive entity is generated by processing entity metadata features associated with the predictive entity using an entity-document correlation machine learning model, and an optimal document sequence is generated for the set of related document objects by processing the set of related document objects using a document sequence optimization machine learning model.Type: GrantFiled: September 9, 2020Date of Patent: November 22, 2022Assignee: Optum Technology, Inc.Inventors: Swapna Sourav Rout, Sharlene L. Tan, Vamsi Bhandaru, Vineet Shukla, Akshaya D. N, Kartik Chaudhary
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Patent number: 11455347Abstract: Systems and methods are configured to generate a set of related document objects for a predictive entity and/or to generate an optimal document sequence for a set of related document objects. In one embodiment, a set of related document objects for a predictive entity is generated by processing entity metadata features associated with the predictive entity using an entity-document correlation machine learning model, and an optimal document sequence is generated for the set of related document objects by processing the set of related document objects using a document sequence optimization machine learning model.Type: GrantFiled: September 9, 2020Date of Patent: September 27, 2022Assignee: Optum Technology, Inc.Inventors: Swapna Sourav Rout, Sharlene L. Tan, Vamsi Bhandaru, Vineet Shukla, Akshaya D. N, Kartik Chaudhary
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Publication number: 20220075831Abstract: Systems and methods are configured to generate a set of related document objects for a predictive entity and/or to generate an optimal document sequence for a set of related document objects. In one embodiment, a set of related document objects for a predictive entity is generated by processing entity metadata features associated with the predictive entity using an entity-document correlation machine learning model, and an optimal document sequence is generated for the set of related document objects by processing the set of related document objects using a document sequence optimization machine learning model.Type: ApplicationFiled: September 9, 2020Publication date: March 10, 2022Inventors: Swapna Sourav Rout, Sharlene L. Tan, Vamsi Bhandaru, Vineet Shukla, Akshaya D. N, Kartik Chaudhary
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Publication number: 20220076007Abstract: Systems and methods are configured to generate a set of related document objects for a predictive entity and/or to generate an optimal document sequence for a set of related document objects. In one embodiment, a set of related document objects for a predictive entity is generated by processing entity metadata features associated with the predictive entity using an entity-document correlation machine learning model, and an optimal document sequence is generated for the set of related document objects by processing the set of related document objects using a document sequence optimization machine learning model.Type: ApplicationFiled: September 9, 2020Publication date: March 10, 2022Inventors: Swapna Sourav Rout, Sharlene L. Tan, Vamsi Bhandaru, Vineet Shukla, Akshaya D. N, Kartik Chaudhary
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Automated systems and methods for identifying fields and regions of interest within a document image
Patent number: 11227153Abstract: Systems and methods are configured to extract text from images (e.g., document images) utilizing a combination of optical character recognition processes and neural network-based analysis of various images to train a machine-learning object recognition model that is configured to identify text within images based on object-comparisons between known and unknown text within images. In certain embodiments, identified text within images may be utilized to identify corresponding regions-of-interest for extraction of image data encompassing a portion of an image that may be indexed based at least in part on text identified as corresponding to the particular region-of-interest.Type: GrantFiled: December 11, 2019Date of Patent: January 18, 2022Assignee: Optum Technology, Inc.Inventors: V Kishore Ayyadevara, Nilav Baran Ghosh, Yeshwanth Reddy, Vineet Shukla, Kartik Chaudhary -
Publication number: 20210358640Abstract: There is a need for more reliable and efficient disease spread forecasting. This need can be addressed by, for example, solutions for performing optimization-based disease spread forecasting using a multi-risk-level disease spread forecasting machine learning model.Type: ApplicationFiled: August 25, 2020Publication date: November 18, 2021Inventors: Kartik Chaudhary, Vineet Shukla, Pooja Mahesh Rajdev, V Kishore Ayyadevara, Shivam Mishra, Neelesh Bhushan, Sahil Jolly
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Publication number: 20210326631Abstract: There is a need for more effective and efficient predictive document conversion. This need can be addressed by, for example, solutions for performing document conversion using a trained convolutional neural document conversion machine learning. In one example, the trained convolutional neural document conversion machine learning model is associated with a preprocessing block having a plurality of preprocessing subblocks, one or more main processing blocks each having a plurality of main processing subblocks, and a plurality of postprocessing subblocks each having one or more postprocessing subblocks, and the trained convolutional neural document conversion machine learning model is further associated with a preprocessing subblock repetition count hyper-parameter that defines a preprocessing subblock count of the plurality of preprocessing subblocks.Type: ApplicationFiled: August 21, 2020Publication date: October 21, 2021Inventors: Kartik Chaudhary, Raghav Bali, V Kishore Ayyadevara, Yerraguntla Yeshwanth Reddy
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AUTOMATED SYSTEMS AND METHODS FOR IDENTIFYING FIELDS AND REGIONS OF INTEREST WITHIN A DOCUMENT IMAGE
Publication number: 20210182547Abstract: Systems and methods are configured to extract text from images (e.g., document images) utilizing a combination of optical character recognition processes and neural network-based analysis of various images to train a machine-learning object recognition model that is configured to identify text within images based on object-comparisons between known and unknown text within images. In certain embodiments, identified text within images may be utilized to identify corresponding regions-of-interest for extraction of image data encompassing a portion of an image that may be indexed based at least in part on text identified as corresponding to the particular region-of-interest.Type: ApplicationFiled: December 11, 2019Publication date: June 17, 2021Inventors: Kishore V. Ayyadevara, Nilav Baran Ghosh, Yeshwanth Reddy, Vineet Shukla, Kartik Chaudhary