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: 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: Modular content framework and document format methods and systems are described. The described framework and format define a set of building blocks for composing, packaging, distributing, and rendering document-centered content. These building blocks define a platform-independent framework for document formats that enable software and hardware systems to generate, exchange, and display documents reliably and consistently. The framework and format have been designed in a flexible and extensible fashion. In addition to this general framework and format, a particular format, known as the reach package format, is defined using the general framework. The reach package format is a format for storing paginated documents. The contents of a reach package can be displayed or printed with full fidelity among devices and applications in a wide range of environments and across a wide range of scenarios.

Abstract: Modular content framework and document format methods and systems are described. The described framework and format define a set of building blocks for composing, packaging, distributing, and rendering document-centered content. These building blocks define a platform-independent framework for document formats that enable software and hardware systems to generate, exchange, and display documents reliably and consistently. The framework and format have been designed in a flexible and extensible fashion. In addition to this general framework and format, a particular format, known as the reach package format, is defined using the general framework. The reach package format is a format for storing paginated documents. The contents of a reach package can be displayed or printed with full fidelity among devices and applications in a wide range of environments and across a wide range of scenarios.

Abstract: Modular content framework and document format methods and systems are described. The described framework and format define a set of building blocks for composing, packaging, distributing, and rendering document-centered content. These building blocks define a platform-independent framework for document formats that enable software and hardware systems to generate, exchange, and display documents reliably and consistently. The framework and format have been designed in a flexible and extensible fashion. In addition to this general framework and format, a particular format, known as the reach package format, is defined using the general framework. The reach package format is a format for storing paginated documents. The contents of a reach package can be displayed or printed with full fidelity among devices and applications in a wide range of environments and across a wide range of scenarios.

Abstract: Modular content framework and document format methods and systems are described. The described framework and format define a set of building blocks for composing, packaging, distributing, and rendering document-centered content. These building blocks define a platform-independent framework for document formats that enable software and hardware systems to generate, exchange, and display documents reliably and consistently. The framework and format have been designed in a flexible and extensible fashion. In addition to this general framework and format, a particular format, known as the reach package format, is defined using the general framework. The reach package format is a format for storing paginated documents. The contents of a reach package can be displayed or printed with full fidelity among devices and applications in a wide range of environments and across a wide range of scenarios.

Abstract: Modular content framework and document format methods and systems are described. The described framework and format define a set of building blocks for composing, packaging, distributing, and rendering document-centered content. These building blocks define a platform-independent framework for document formats that enable software and hardware systems to generate, exchange, and display documents reliably and consistently. The framework and format have been designed in a flexible and extensible fashion. In addition to this general framework and format, a particular format, known as the reach package format, is defined using the general framework. The reach package format is a format for storing paginated documents. The contents of a reach package can be displayed or printed with full fidelity among devices and applications in a wide range of environments and across a wide range of scenarios.

Abstract: Modular content framework and document format methods and systems are describe. The described framework and format define a set of building blocks for composing, packaging, distributing, and rendering document-centered content. These building blocks define a platform-independent framework for document formats that enable software and hardware systems to generate, exchange, and display documents reliably and consistently. The framework and format have been designed in a flexible and extensible fashion. In addition to this general framework and format, a particular format, known as the reach package format, is defined using the general framework. The reach package format is a format for storing paginated documents. The contents of a reach package can be displayed or printed with full fidelity among devices and applications in a wide range of environments and across a wide range scenarios.

Abstract: Modular content framework and document format methods and systems are described. The described framework and format define a set of building blocks for composing, packaging, distributing, and rendering document-centered content. These building blocks define a platform-independent framework for document formats that enable software and hardware systems to generate, exchange, and display documents reliably and consistently. The framework and format have been designed in a flexible and extensible fashion. In addition to this general framework and format, a particular format, known as the reach package format, is defined using the general framework. The reach package format is a format for storing paginated documents. The contents of a reach package can be displayed or printed with full fidelity among devices and applications in a wide range of environments and across a wide range of scenarios.

Abstract: A system and method that enables a designer to build electronic forms and corresponding hierarchical schemas are described. Displays of hierarchical schemas, electronic forms, and components used to build the hierarchical schemas and electronic forms are provided to the designer. The designer selects components and arranges them on a display to visually build an electronic form. As the form is built, the corresponding hierarchical schema is incrementally updated to reflect changes made to the electronic form.