Patents by Inventor Manar Shehadeh
Manar Shehadeh 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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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: 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: 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: 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