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 structural reinforcement comprising a rigid polymeric structure having a longitudinal axis and including a first portion having an opening for connecting the structure to a secondary surface and a second portion including a stop device having a terminal end that extends beyond any other surface of the structure, a fastening device for locating into the opening an adhesive associated with one or more of the first and second portion, wherein at least a section of the second portion lies in a plane that is skew to a plane in which the first portion lies.

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 baffle assembly for use in a transportation vehicle, comprising a carrier including an interior portion that includes a plurality of walls extending upward, a rim portion that extends to substantially surround the interior portion and a fastener that extends upward from the rim portion in the same direction as the plurality of walls. The baffle also includes a resinous sealant that includes a polymer that is adapted for thermal expansion upon activation by heat that is located on at least a portion of the interior portion wherein the rim portion is substantially free of any sealant.

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: A system for recommending products and providing instructions for projects is disclosed, the system comprising a product database to store a plurality of product information and a project database to store a plurality of project information. The project information includes a plurality of steps and a plurality of products to complete the project. A user interface module displays the plurality of steps and the plurality of products on a display of a computing device. A recommendation engine recommends at least one of the plurality of products and an amount for each of the plurality of products to complete at least one of the plurality of projects. The recommendation engine is in operable communication with the product database and the project database to recommend the at least one product and the at least one project to the user based on an inventory of a store.

Abstract: A structural reinforcement comprising a rigid polymeric structure having a longitudinal axis and including a first portion having an opening for connecting the structure to a secondary surface and a second portion including a stop device having a terminal end that extends beyond any other surface of the structure, a fastening device for locating into the opening an adhesive associated with one or more of the first and second portion, wherein at least a section of the second portion lies in a plane that is skew to a plane in which the first portion lies.

Abstract: An example apparatus includes a housing including an inlet and an outlet. The example apparatus also includes a rotor defining a bore. The rotor is disposed in the housing. The example apparatus further includes a piston disposed in the bore. A motor is operatively coupled to the rotor to rotate the rotor from a first position to a second position. The bore is to be in fluid communication with the inlet and the outlet when the rotor is in the second position.

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 example apparatus includes a housing including an inlet and an outlet. The example apparatus also includes a rotor defining a bore. The rotor is disposed in the housing. The example apparatus further includes a piston disposed in the bore. A motor is operatively coupled to the rotor to rotate the rotor from a first position to a second position. The bore is to be in fluid communication with the inlet and the outlet when the rotor is in the second position.

Abstract: A baffle assembly for use in a transportation vehicle, comprising a carrier including an interior portion that includes a plurality of walls extending upward, a rim portion that extends to substantially surround the interior portion and a fastener that extends upward from the rim portion in the same direction as the plurality of walls. The baffle also includes a resinous sealant that includes a polymer that is adapted for thermal expansion upon activation by heat that is located on at least a portion of the interior portion wherein the rim portion is substantially free of any sealant.