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: Systems and methods for controlling ad delivery to mobile clients while maintaining user privacy are herein provided. One exemplary method involves a location broker service. The method includes receiving, at an ad delivery service, a location use token from a mobile client, which may be a single use token. The method includes sending, from the ad delivery service, the location use token to the location broker service for verification at the location broker service. The method includes receiving, at the ad delivery service, the mobile client location from the location broker service based on the verification. The method includes delivering, from the ad delivery service, a location-targeted ad to the mobile client at the mobile client location, where the delivering is further based on a geographic density of a plurality of mobile clients. An advertiser using the ad delivery service may be billed based on location use token history.

Abstract: An application may be integrated into an operating system component of a mobile computing platform. The application communicates registration information to an extension mechanism within the mobile computing platform. The extension mechanism registers the application and modifies the operating system component to provide interoperability with the application based on the registration information. The extension mechanism couples the operating system component with content from a counterpart application to the application, which corresponds to information presented on the operating system component.

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: Systems and methods for controlling ad delivery to mobile clients while maintaining user privacy are herein provided. One exemplary method involves a location broker service. The method includes receiving, at an ad delivery service, a location use token from a mobile client, which may be a single use token. The method includes sending, from the ad delivery service, the location use token to the location broker service for verification at the location broker service. The method includes receiving, at the ad delivery service, the mobile client location from the location broker service based on the verification. The method includes delivering, from the ad delivery service, a location-targeted ad to the mobile client at the mobile client location, where the delivering is further based on a geographic density of a plurality of mobile clients. An advertiser using the ad delivery service may be billed based on location use token history.

Abstract: Systems and methods for controlling ad delivery to mobile clients while maintaining user privacy are herein provided. One exemplary method involves a location broker service. The method includes receiving, at an ad delivery service, a location use token from a mobile client, which may be a single use token. The method includes sending, from the ad delivery service, the location use token to the location broker service for verification at the location broker service. The method includes receiving, at the ad delivery service, the mobile client location from the location broker service based on the verification. The method includes delivering, from the ad delivery service, a location-targeted ad to the mobile client at the mobile client location, where the delivering is further based on a geographic density of a plurality of mobile clients. An advertiser using the ad delivery service may be billed based on location use token history.

Abstract: The subject disclosure is directed towards integrating an application into an operating system component of a mobile computing platform. The application communicates registration information to an extension mechanism within the mobile computing platform. The extension mechanism registers the application and modifies the operating system component to provide interoperability with the application based on the registration information. The extension mechanism couples the operating system component with content from a counterpart application to the application, which corresponds to information presented on the operating system component.

Abstract: The subject disclosure is directed towards integrating an application into an operating system component of a mobile computing platform. The application communicates registration information to an extension mechanism within the mobile computing platform. The extension mechanism registers the application and modifies the operating system component to provide interoperability with the application based on the registration information. The extension mechanism couples the operating system component with content from a counterpart application to the application, which corresponds to information presented on the operating system component.

Abstract: The subject disclosure is directed towards integrating an application into an operating system component of a mobile computing platform. The application communicates registration information to an extension mechanism within the mobile computing platform. The extension mechanism registers the application and modifies the operating system component to provide interoperability with the application based on the registration information. The extension mechanism couples the operating system component with content from a counterpart application to the application, which corresponds to information presented on the operating system component.