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/or methods that enable an electronic form to provide data external to the electronic form in response to a query while offline with respect to a data source from which the data originated are described. These systems and methods may also, in one embodiment, receive data from a data source that is capable of being inaccessible and associate that data with an electronic form, a user, and a query. These systems and methods may, in another embodiment, provide data in response to a query made by a user to an electronic form that was previously associated with the electronic form, the user, and the query.

Abstract: Systems and methods enabling creation and/or use of an electronic form capable of allowing a user to select from mutually exclusive options without the electronic form being in an invalid state are described. One of the described electronic forms remains valid to its schema by atomic swapping of data substructures corresponding to the form's mutually exclusive options. A method and a user interface for creating some of these electronic forms are also described.

Abstract: A first data source has a plurality of nodes each corresponding to a respective piece of a form template. Each piece of the form template has one of more dependencies to the correspond node of the first data source. Dependencies can be bindings or validation of data. A second data source has a plurality of nodes. Differences are found between the first and second data sources by comparing each node in the first data source with a corresponding node in the second data source. The differences can be as to type, cardinality, name, or a movement, removal or addition of a node. The differences are used to update the dependencies of each piece of the form template to each node of the first data source. Each of the first and second data sources can be a document expressed in a markup language or in a web service definition language.

Abstract: A designer uses a forms designer application to build electronic forms from hierarchical data. Displays of hierarchical data, facilitation of selection of a portion of the hierarchical data, and displays of one or more suggested transformation-language components associated with a selected portion of hierarchical data are described. From the transformation-language components selected by a designer, generation of an electronic form is also described.