Patents by Inventor Kush GUPTA
Kush GUPTA 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: 11960832Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).Type: GrantFiled: April 20, 2022Date of Patent: April 16, 2024Assignee: Docugami, Inc.Inventors: Andrew Paul Begun, Steven DeRose, Taqi Jaffri, Luis Marti Orosa, Michael B. Palmer, Jean Paoli, Christina Pavlopoulou, Elena Pricoiu, Swagatika Sarangi, Marcin Sawicki, Manar Shehadeh, Michael Taron, Bhaven Toprani, Zubin Rustom Wadia, David Watson, Eric White, Joshua Yongshin Fan, Kush Gupta, Andrew Minh Hoang, Zhanlin Liu, Jerome George Paliakkara, Zhaofeng Wu, Yue Zhang, Xiaoquan Zhou
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Patent number: 11822880Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).Type: GrantFiled: August 5, 2020Date of Patent: November 21, 2023Assignee: Docugami, Inc.Inventors: Andrew Begun, Steven DeRose, Taqi Jaffri, Luis Marti Orosa, Michael Palmer, Jean Paoli, Christina Pavlopoulou, Elena Pricoiu, Swagatika Sarangi, Marcin Sawicki, Manar Shehadeh, Michael Taron, Bhaven Toprani, Zubin Rustom Wadia, David Watson, Eric White, Joshua Yongshin Fan, Kush Gupta, Andrew Minh Hoang, Zhanlin Liu, Jerome George Paliakkara, Zhaofeng Wu, Yue Zhang, Xiaoquan Zhou
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Patent number: 11816428Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).Type: GrantFiled: August 5, 2020Date of Patent: November 14, 2023Assignee: Docugami, Inc.Inventors: Andrew Begun, Steven DeRose, Taqi Jaffri, Luis Marti Orosa, Michael Palmer, Jean Paoli, Christina Pavlopoulou, Elena Pricoiu, Swagatika Sarangi, Marcin Sawicki, Manar Shehadeh, Michael Taron, Bhaven Toprani, Zubin Rustom Wadia, David Watson, Eric White, Joshua Yongshin Fan, Kush Gupta, Andrew Minh Hoang, Zhanlin Liu, Jerome George Paliakkara, Zhaofeng Wu, Yue Zhang, Xiaoquan Zhou
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Patent number: 11514238Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).Type: GrantFiled: August 5, 2020Date of Patent: November 29, 2022Assignee: Docugami, Inc.Inventors: Andrew Paul Begun, Steven DeRose, Taqi Jaffri, Luis Marti Orosa, Michael Palmer, Jean Paoli, Christina Pavlopoulou, Elena Pricoiu, Swagatika Sarangi, Marcin Sawicki, Manar Shehadeh, Michael Taron, Bhaven Toprani, Zubin Rustom Wadia, David Watson, Eric White, Joshua Yongshin Fan, Kush Gupta, Andrew Minh Hoang, Zhanlin Liu, Jerome George Paliakkara, Zhaofeng Wu, Yue Zhang, Xiaoquan Zhou
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Patent number: 11507740Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).Type: GrantFiled: August 5, 2020Date of Patent: November 22, 2022Assignee: Docugami, Inc.Inventors: Andrew Begun, Steven DeRose, Taqi Jaffri, Luis Marti Orosa, Michael Palmer, Jean Paoli, Christina Pavlopoulou, Elena Pricoiu, Swagatika Sarangi, Marcin Sawicki, Manar Shehadeh, Michael Taron, Bhaven Toprani, Zubin Rustom Wadia, David Watson, Eric White, Joshua Yongshin Fan, Kush Gupta, Andrew Minh Hoang, Zhanlin Liu, Jerome George Paliakkara, Zhaofeng Wu, Yue Zhang, Xiaoquan Zhou
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Publication number: 20220245335Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).Type: ApplicationFiled: April 20, 2022Publication date: August 4, 2022Inventors: Andrew Paul Begun, Steven DeRose, Taqi Jaffri, Luis Marti Orosa, Michael B. Palmer, Jean Paoli, Christina Pavlopoulou, Elena Pricoiu, Swagatika Sarangi, Marcin Sawicki, Manar Shehadeh, Michael Taron, Bhaven Toprani, Zubin Rustom Wadia, David Watson, Eric White, Joshua Yongshin Fan, Kush Gupta, Andrew Minh Hoang, Zhanlin Liu, Jerome George Paliakkara, Zhaofeng Wu, Yue Zhang, Xiaoquan Zhou
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Patent number: 11392763Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).Type: GrantFiled: August 5, 2020Date of Patent: July 19, 2022Assignee: DOCUGAMI, INC.Inventors: Andrew Paul Begun, Steven DeRose, Taqi Jaffri, Luis Marti Orosa, Michael Palmer, Jean Paoli, Christina Pavlopoulou, Elena Pricoiu, Swagatika Sarangi, Marcin Sawicki, Manar Shehadeh, Michael Taron, Bhaven Toprani, Zubin Rustom Wadia, David Watson, Eric White, Joshua Yongshin Fan, Kush Gupta, Andrew Minh Hoang, Zhanlin Liu, Jerome George Paliakkara, Zhaofeng Wu, Yue Zhang, Xiaoquan Zhou
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Patent number: 11229418Abstract: An aspect of the present disclosure is to provide a device or needle placement system including a needle having a proximal end and a distal end, and an ultrasound transducer element attached to the distal end of the needle. The system also includes a needle constraining assembly configured to receive and constrain the needle to only rotational degrees of freedom within a range of angular motion. The system further includes a needle sensor system incorporated into the constraining assembly to sense an angular orientation of the needle with the range of angular motion. The system also includes an ultrasound data processor configured to communicate with the transducer element to receive ultrasound detection signals and communicate with the needle sensor system to receive needle angular orientation signals. Based on the ultrasound detection and the needle angular orientation signals, the ultrasound data processor can calculate synthetic aperture ultrasound images.Type: GrantFiled: May 2, 2017Date of Patent: January 25, 2022Assignee: The Johns Hopkins UniversityInventors: Nisu Patel, Ernest Scalabrin, Karun Kannan, Kush Gupta, Larissa Chan, Mateo Paredes, Melissa Lin, Shayan Roychoudhury, Suraj Shah, Abhay Moghekar, Emad M. Boctor, Nicholas J. Durr, Younsu Kim, Haichong K. Zhang
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Publication number: 20210081613Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).Type: ApplicationFiled: August 5, 2020Publication date: March 18, 2021Inventors: Andrew Begun, Steven DeRose, Taqi Jaffri, Luis Marti Orosa, Michael Palmer, Jean Paoli, Christina Pavlopoulou, Elena Pricoiu, Swagatika Sarangi, Marcin Sawicki, Manar Shehadeh, Michael Taron, Bhaven Toprani, Zubin Rustom Wadia, David Watson, Eric White, Joshua Yongshin Fan, Kush Gupta, Andrew Minh Hoang, Zhanlin Liu, Jerome George Paliakkara, Zhaofeng Wu, Yue Zhang, Xiaoquan Zhou
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Publication number: 20210081411Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).Type: ApplicationFiled: August 5, 2020Publication date: March 18, 2021Inventors: Andrew Begun, Steven DeRose, Taqi Jaffri, Luis Marti Orosa, Michael Palmer, Jean Paoli, Christina Pavlopoulou, Elena Pricoiu, Swagatika Sarangi, Marcin Sawicki, Manar Shehadeh, Michael Taron, Bhaven Toprani, Zubin Rustom Wadia, David Watson, Eric White, Joshua Yongshin Fan, Kush Gupta, Andrew Minh Hoang, Zhanlin Liu, Jerome George Paliakkara, Zhaofeng Wu, Yue Zhang, Xiaoquan Zhou
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Publication number: 20210081602Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).Type: ApplicationFiled: August 5, 2020Publication date: March 18, 2021Inventors: Andrew Begun, Steven DeRose, Taqi Jaffri, Luis Marti Orosa, Michael Palmer, Jean Paoli, Christina Pavlopoulou, Elena Pricoiu, Swagatika Sarangi, Marcin Sawicki, Manar Shehadeh, Michael Taron, Bhaven Toprani, Zubin Rustom Wadia, David Watson, Eric White, Joshua Yongshin Fan, Kush Gupta, Andrew Minh Hoang, Zhanlin Liu, Jerome George Paliakkara, Zhaofeng Wu, Yue Zhang, Xiaoquan Zhou
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Publication number: 20210081601Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).Type: ApplicationFiled: August 5, 2020Publication date: March 18, 2021Inventors: Andrew Begun, Steven DeRose, Taqi Jaffri, Luis Marti Orosa, Michael Palmer, Jean Paoli, Christina Pavlopoulou, Elena Pricoiu, Swagatika Sarangi, Marcin Sawicki, Manar Shehadeh, Michael Taron, Bhaven Toprani, Zubin Rustom Wadia, David Watson, Eric White, Joshua Yongshin Fan, Kush Gupta, Andrew Minh Hoang, Zhanlin Liu, Jerome George Paliakkara, Zhaofeng Wu, Yue Zhang, Xiaoquan Zhou
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Publication number: 20210081608Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).Type: ApplicationFiled: August 5, 2020Publication date: March 18, 2021Inventors: Andrew Begun, Steven DeRose, Taqi Jaffri, Luis Marti Orosa, Michael Palmer, Jean Paoli, Christina Pavlopoulou, Elena Pricoiu, Swagatika Sarangi, Marcin Sawicki, Manar Shehadeh, Michael Taron, Bhaven Toprani, Zubin Rustom Wadia, David Watson, Eric White, Joshua Yongshin Fan, Kush Gupta, Andrew Minh Hoang, Zhanlin Liu, Jerome George Paliakkara, Zhaofeng Wu, Yue Zhang, Xiaoquan Zhou
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Publication number: 20190117187Abstract: An aspect of the present disclosure is to provide a device or needle placement system including a needle having a proximal end and a distal end, and an ultrasound transducer element attached to the distal end of the needle. The system also includes a needle constraining assembly configured to receive and constrain the needle to only rotational degrees of freedom within a range of angular motion. The system further includes a needle sensor system incorporated into the constraining assembly to sense an angular orientation of the needle with the range of angular motion. The system also includes an ultrasound data processor configured to communicate with the transducer element to receive ultrasound detection signals and communicate with the needle sensor system to receive needle angular orientation signals. Based on the ultrasound detection and the needle angular orientation signals, the ultrasound data processor can calculate synthetic aperture ultrasound images.Type: ApplicationFiled: May 2, 2017Publication date: April 25, 2019Inventors: Nisu PATEL, Ernest SCALABRIN, Karun Kannan, Kush GUPTA, Larissa CHAN, Mateo PAREDES, Melissa LIN, Shayan ROYCHOUDHURY, Suraj SHAH, Abhay MOGHEKAR, Emad M. BOCTOR, Nicholas J. DURR, Younsu KIM, Haichong K. ZHANG