公开(公告)号：US20210081601A1

公开(公告)日：2021-03-18

申请号：US16986136

申请日：2020-08-05

申请人： Docugami, Inc.

发明人： 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

IPC分类号： G06F40/169 ,  G06F40/106 ,  G06F40/117

摘要： 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). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.

公开(公告)号：US20240232518A1

公开(公告)日：2024-07-11

申请号：US18609740

申请日：2024-03-19

申请人： Docugami, Inc.

发明人： 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

IPC分类号： G06F40/186 ,  G06F16/2457 ,  G06F16/248 ,  G06F16/93 ,  G06F40/106 ,  G06F40/117 ,  G06F40/169 ,  G06F40/289 ,  G06F40/295 ,  G06F40/30 ,  G06N20/00 ,  G06V30/414 ,  G06V30/416

CPC分类号： G06F40/186 ,  G06F16/2457 ,  G06F16/248 ,  G06F16/93 ,  G06F40/106 ,  G06F40/117 ,  G06F40/169 ,  G06F40/289 ,  G06F40/295 ,  G06F40/30 ,  G06N20/00 ,  G06V30/414 ,  G06V30/416

摘要： 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). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.

公开(公告)号：US11822880B2

公开(公告)日：2023-11-21

申请号：US16986151

申请日：2020-08-05

申请人： Docugami, Inc.

发明人： 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

IPC分类号： G06F40/186 ,  G06N20/00 ,  G06F40/30 ,  G06F40/169 ,  G06F40/117 ,  G06F40/106 ,  G06F40/289 ,  G06F40/295 ,  G06F16/93 ,  G06F16/2457 ,  G06F16/248 ,  G06V30/414 ,  G06V30/416

CPC分类号： G06F40/186 ,  G06F16/248 ,  G06F16/2457 ,  G06F16/93 ,  G06F40/106 ,  G06F40/117 ,  G06F40/169 ,  G06F40/289 ,  G06F40/295 ,  G06F40/30 ,  G06N20/00 ,  G06V30/414 ,  G06V30/416

摘要： 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). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.

公开(公告)号：US11816428B2

公开(公告)日：2023-11-14

申请号：US16986139

申请日：2020-08-05

申请人： Docugami, Inc.

发明人： 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

IPC分类号： G06F40/186 ,  G06N20/00 ,  G06F40/30 ,  G06F40/169 ,  G06F40/117 ,  G06F40/106 ,  G06F40/289 ,  G06F40/295 ,  G06F16/93 ,  G06F16/2457 ,  G06F16/248 ,  G06V30/414 ,  G06V30/416

CPC分类号： G06F40/186 ,  G06F16/248 ,  G06F16/2457 ,  G06F16/93 ,  G06F40/106 ,  G06F40/117 ,  G06F40/169 ,  G06F40/289 ,  G06F40/295 ,  G06F40/30 ,  G06N20/00 ,  G06V30/414 ,  G06V30/416

摘要： 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). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.

公开(公告)号：US11507740B2

公开(公告)日：2022-11-22

申请号：US16986146

申请日：2020-08-05

申请人： Docugami, Inc.

发明人： 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

IPC分类号： G06F7/00 ,  G06F40/186 ,  G06N20/00 ,  G06F40/30 ,  G06F40/169 ,  G06F40/117 ,  G06F40/106 ,  G06F40/289 ,  G06F40/295 ,  G06F16/93 ,  G06F16/2457 ,  G06F16/248 ,  G06V30/414 ,  G06V30/416

摘要： 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). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.

公开(公告)号：US20220245335A1

公开(公告)日：2022-08-04

申请号：US17724934

申请日：2022-04-20

申请人： Docugami, Inc.

发明人： 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

IPC分类号： G06F40/186 ,  G06N20/00 ,  G06F40/30 ,  G06F40/169 ,  G06F40/117 ,  G06F40/106 ,  G06F40/289 ,  G06F40/295 ,  G06F16/93 ,  G06F16/2457 ,  G06F16/248 ,  G06V30/414 ,  G06V30/416

摘要： 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). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.

公开(公告)号：US20210081608A1

公开(公告)日：2021-03-18

申请号：US16986151

申请日：2020-08-05

申请人： Docugami, Inc.

发明人： 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

IPC分类号： G06F40/186 ,  G06K9/00 ,  G06F40/30 ,  G06F40/169 ,  G06N20/00

摘要： 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). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.

公开(公告)号：US20210081411A1

公开(公告)日：2021-03-18

申请号：US16986146

申请日：2020-08-05

申请人： Docugami, Inc.

发明人： 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

IPC分类号： G06F16/2457 ,  G06F16/93 ,  G06F16/248 ,  G06N20/00 ,  G06F40/186 ,  G06F40/30

摘要： 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). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.

公开(公告)号：US11960832B2

公开(公告)日：2024-04-16

申请号：US17724934

申请日：2022-04-20

申请人： Docugami, Inc.

发明人： 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

IPC分类号： G06F40/186 ,  G06F16/2457 ,  G06F16/248 ,  G06F16/93 ,  G06F40/106 ,  G06F40/117 ,  G06F40/169 ,  G06F40/289 ,  G06F40/295 ,  G06F40/30 ,  G06N20/00 ,  G06V30/414 ,  G06V30/416

CPC分类号： G06F40/186 ,  G06F16/2457 ,  G06F16/248 ,  G06F16/93 ,  G06F40/106 ,  G06F40/117 ,  G06F40/169 ,  G06F40/289 ,  G06F40/295 ,  G06F40/30 ,  G06N20/00 ,  G06V30/414 ,  G06V30/416

摘要： 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). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.

公开(公告)号：US11514238B2

公开(公告)日：2022-11-29

申请号：US16986142

申请日：2020-08-05

申请人： Docugami, Inc.

发明人： 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

IPC分类号： G06F17/00 ,  G06F40/186 ,  G06N20/00 ,  G06F40/30 ,  G06F40/169 ,  G06F40/117 ,  G06F40/106 ,  G06F40/289 ,  G06F40/295 ,  G06F16/93 ,  G06F16/2457 ,  G06F16/248 ,  G06V30/414 ,  G06V30/416

摘要： 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). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.