Abstract: 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).

Abstract: 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).

Abstract: 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).

Abstract: 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.

Abstract: 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).

Abstract: 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.

Abstract: 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).

Abstract: 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).

Abstract: 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).

Abstract: 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).

Abstract: 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).

Abstract: 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).

Abstract: 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).

Abstract: 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).

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The invention relates generally to systems, devices and methods for global disaster response, more particularly to the rapid detection, qualified assessment and monitoring of disasters and electronic triage of victims, communication, alert and evacuation systems, provision of suitable modular sensing or medical aid solutions, and their rapid deployment via delivery platforms such as disaster messaging formats and resources on client mobile phone applications or physically via remote operated vehicles (unmanned aerial sea or land systems) or targeted air delivery.