Docugami, Inc.

Amazon Technologies, Inc.

Christina Pavlopoulou

Marcin Sawicki

Michael Brian Palmer

Jean Paoli

Taqi Jaffri

Andrew Paul Begun

Michael Taron

Zubin Rustom Wadia

Jerome George Paliakkara

Zhaofeng Wu

Yue Zhang

Xiaoquan Zhou

Zhanlin Liu

Manar Shehadeh

Steven DeRose

Elena Pricoiu

David Watson

Bhaven Toprani

Eric White

Swagatika Sarangi

Joshua Yongshin Fan

Kush Gupta

Andrew Minh Hoang

Luis Marti Orosa

Russ Thompson

Manish Gupta

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

Systems and methods are disclosed for hierarchical categorical and sub-categorical topic modeling allowing, in response to a query in natural language, a set of results to be determined which are both semantically relevant to the user and diverse, by containing information complementary or adjacent to that of the query. Such a paradigm permits exploration and discovery of new topics and ideas in large collections of documents. In some embodiments, one or more non-negative matrix factorization ('NMF') algorithms are applied in determining a hierarchical topic model including the semantically-related categories and sub-categories. The dataset may include authorized social media data collection, and machine learning techniques can optimize the generation of the topic model and/or the search results.

Systems and methods for large-scale content exploration

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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Emeryville, CA, United States of America

Christina Pavlopoulou