Patents by Inventor Vineet Shukla
Vineet Shukla 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: 11741381Abstract: There is a need for more effective and efficient prediction data analysis. This need can be addressed by, for example, solutions for performing first-occurrence multi-disease prediction. In one example, a method includes determining a per-event-type loss value for each event type of a group of event types; determining a cross-event-type loss value based at least in part on each per-event-type loss value; training a multi-event-type prediction model based at least in part on the cross-event type loss value; generating a first-occurrence prediction based at least in part on the multi-event-type prediction model, wherein the first occurrence-prediction comprises a first-occurrence prediction item for each event type of the group of event types; and performing one or more prediction-based actions based at least in part on the first-occurrence prediction.Type: GrantFiled: July 14, 2020Date of Patent: August 29, 2023Assignee: OPTUM TECHNOLOGY, INC.Inventors: V Kishore Ayyadevara, Sree Harsha Ankem, Raghav Bali, Rohan Khilnani, Vineet Shukla, Saikumar Chintareddy, Ranraj Rana Singh
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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: 20220222571Abstract: There is a need for more effective and efficient performing classification-based predictive data analysis on named data collections. This need can be addressed by, for example, solutions for performing classification-based predictive data analysis on named data collections that utilize at least one of techniques for generating column classification machine learning models to perform column classification, techniques for generating column classification machine learning models to perform anomaly detection, techniques for utilizing trained column classification machine learning models to perform column classification, and techniques for utilizing trained column classification machine learning models to perform anomaly detection.Type: ApplicationFiled: January 12, 2021Publication date: July 14, 2022Inventors: Swapna Sourav Rout, Sudeep Choudhary, Subhadip Maji, Vineet Shukla, Ravi Kumar Raju Gottumukkala, John Markson
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Publication number: 20220222570Abstract: There is a need for more effective and efficient performing classification-based predictive data analysis on named data collections. This need can be addressed by, for example, solutions for performing classification-based predictive data analysis on named data collections that utilize at least one of techniques for generating column classification machine learning models to perform column classification, techniques for generating column classification machine learning models to perform anomaly detection, techniques for utilizing trained column classification machine learning models to perform column classification, and techniques for utilizing trained column classification machine learning models to perform anomaly detection.Type: ApplicationFiled: January 12, 2021Publication date: July 14, 2022Inventors: Swapna Sourav Rout, Sudeep Choudhary, Subhadip Maji, Vineet Shukla, Ravi Kumar Raju Gottumukkala
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Publication number: 20220083898Abstract: There is a need for more effective and efficient anomalous text detection.Type: ApplicationFiled: September 11, 2020Publication date: March 17, 2022Inventors: Vineet Shukla, V Kishore Ayyadevara, Rohan Khilnani, Ravi Kumar Raju Gottumukkala, Ankit Varshney, Rajat Gupta
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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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Publication number: 20220019913Abstract: There is a need for more effective and efficient prediction data analysis. This need can be addressed by, for example, solutions for performing first-occurrence multi-disease prediction. In one example, a method includes determining a per-event-type loss value for each event type of a group of event types; determining a cross-event-type loss value based at least in part on each per-event-type loss value; training a multi-event-type prediction model based at least in part on the cross-event type loss value; generating a first-occurrence prediction based at least in part on the multi-event-type prediction model, wherein the first occurrence-prediction comprises a first-occurrence prediction item for each event type of the group of event types; and performing one or more prediction-based actions based at least in part on the first-occurrence prediction.Type: ApplicationFiled: July 14, 2020Publication date: January 20, 2022Inventors: V Kishore Ayyadevara, Sree Harsha Ankem, Raghav Bali, Rohan Khilnani, Vineet Shukla, Saikumar Chintareddy, Ranraj Rana Singh
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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 -
Automated systems and methods for identifying fields and regions of interest within a document image
Patent number: 11210507Abstract: 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: December 28, 2021Assignee: Optum Technology, Inc.Inventors: V Kishore Ayyadevara, Yeshwanth Reddy, Vineet Shukla, Santosh Kumar Jami, Snigdha Borra -
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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AUTOMATED SYSTEMS AND METHODS FOR IDENTIFYING FIELDS AND REGIONS OF INTEREST WITHIN A DOCUMENT IMAGE
Publication number: 20210182548Abstract: 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, Yeshwanth Reddy, Vineet Shukla, Jami Santosh Kumar, Snigdha Borra -
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 -
Patent number: 10142814Abstract: A system includes a server complex and one or more mobile communication devices in communication with the server complex. The server complex includes one or more processors to define one or more elevated safety risk regions within a communication network. The server complex can optionally detect when a mobile communication device enters an elevated safety risk region and place the mobile communication device into an emergency mode of operation.Type: GrantFiled: March 20, 2015Date of Patent: November 27, 2018Assignee: Google Technology Holdings LLCInventors: Nagavali Jatavallabhula, Prasanth Nvs, Vineet Shukla, Karthik Talloju
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Publication number: 20150271655Abstract: A system includes a server complex and one or more mobile communication devices in communication with the server complex. The server complex includes one or more processors to define one or more elevated safety risk regions within a communication network. The server complex can optionally detect when a mobile communication device enters an elevated safety risk region and place the mobile communication device into an emergency mode of operation.Type: ApplicationFiled: March 20, 2015Publication date: September 24, 2015Inventors: Nagavali Jatavallabhula, Prasanth Nvs, Vineet Shukla, Karthik Talloju