Docugami, Inc.

Other

Zubin Rustom Wadia

Marcin Sawicki

Michael Brian Palmer

Jean Paoli

Taqi Jaffri

Andrew Paul Begun

Michael Taron

Christina Pavlopoulou

Zhanlin Liu

Jerome George Paliakkara

Zhaofeng Wu

Andrew Minh Hoang

Swagatika Sarangi

Yue Zhang

Manar Shehadeh

Xiaoquan Zhou

Bhaven Toprani

Eric White

David Watson

Joshua Yongshin Fan

Elena Pricoiu

Steven DeRose

Kush Gupta

Luis Marti Orosa

Yitzhack Schwartz

Simon Richard Daniel

Timothy W Coleman

Justyna Zander

A disaster response system includes a communication infrastructure including a plurality of sensor assemblies configured to generate data indicative of at least one of environmental conditions, motion, position, chemical detection, and medical information; and wirelessly provide the generated data to the communication infrastructure. The system also includes an incident command infrastructure configured to exchange data with the communication infrastructure; and detect an incident based on the data from the sensor assemblies. The system also includes an unmanned aerial vehicle (UAV) configured to deliver a payload in response to the detected incident.

Systems, methods and devices for the rapid assessment and deployment of appropriate modular aid solutions in response to disasters

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

An embodiment of a disaster response system is disclosed that includes a communication and monitoring environment (CME). The CME includes an incident command infrastructure, and a communication infrastructure configured to exchange data with the incident command infrastructure. The communication infrastructure includes a network comprising a plurality of sensor assemblies that are configured to wirelessly communicate with the communication infrastructure. The sensor assemblies are configured to acquire data that includes at least one of environmental conditions, motion, position, chemical detection, and medical information. One or more of the sensors are configured to aggregate data from a subset of the plurality of sensors. The CME is configured to detect an incident based on at least the data acquired by the sensor assemblies.

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

Growing community of inventors

Chappaqua, NY, United States of America

Zubin Rustom Wadia