Docugami, Inc.

Bhaven Toprani

Marcin Sawicki

Michael Brian Palmer

Jean Paoli

Taqi Jaffri

Andrew Paul Begun

Michael Taron

Zubin Rustom Wadia

Christina Pavlopoulou

Jerome George Paliakkara

Manar Shehadeh

Yue Zhang

Zhanlin Liu

Steven DeRose

Xiaoquan Zhou

David Watson

Zhaofeng Wu

Eric White

Joshua Yongshin Fan

Kush Gupta

Elena Pricoiu

Swagatika Sarangi

Andrew Minh Hoang

Luis Marti Orosa

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.

Cross-document intelligent authoring and processing, with arbitration for semantically-annotated documents

Enabling flexible processing of semantically-annotated documents

Automatically identifying chunks in sets of documents

Automatically assigning semantic role labels to parts of documents

Assisting authors via semantically-annotated documents

Cross-document intelligent authoring and processing, including format for semantically-annotated documents

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Cupertino, CA, United States of America

Bhaven Toprani