Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: The present disclosure extends to methods, systems, and computer program products for providing collaboration among users within a group and to provide improved scheduling and reports based on group characteristics.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: The present invention relates to a method and apparatus for assembling a set of documents related to a triggering item. One embodiment of a method for assembling a set of electronic documents related to an electronic triggering item detected by a computing device being operated by a user includes automatically extracting by the computing device a set of features from the triggering item, without receiving a request by the user to assemble the set of electronic documents, and assembling as the set of electronic documents a plurality of documents that is relevant to the set of features, wherein the plurality of documents is retrieved from a plurality of different types of electronic sources.

Abstract: The present disclosure extends to methods, systems, and computer program products for providing collaboration among users within a group for improved communication and scheduling.

Abstract: A system may include a machine-implemented data extractor and correlator configured to retrieve data from at least one data source. The data extractor and correlator may extract information from unstructured data within the retrieved data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data.

Abstract: A machine-implemented method for supporting a natural language user request against a user's personal data cloud can include a machine receiving the natural language user request from the user, determining a semantic interpretation of the natural language user request, querying a semantically-indexed, integrated knowledge store based on the semantic interpretation, and responding to the natural language user request by displaying results of the querying, wherein the results correspond to an item within the user's personal data cloud.

Abstract: A machine-implemented method for supporting a natural language user request against a user's personal data cloud can include a machine receiving the natural language user request from the user, determining a semantic interpretation of the natural language user request, querying a semantically-indexed, integrated knowledge store based on the semantic interpretation, and responding to the natural language user request by displaying results of the querying, wherein the results correspond to an item within the user's personal data cloud.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A machine-implemented method for supporting a natural language user request against a user's personal data cloud can include a machine receiving the natural language user request from the user, determining a semantic interpretation of the natural language user request, querying a semantically-indexed, integrated knowledge store based on the semantic interpretation, and responding to the natural language user request by displaying results of the querying, wherein the results correspond to an item within the user's personal data cloud.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: The present invention relates to an electronic assistant. In one embodiment, a system for processing data on a computing device includes at least one extractor for extracting data from an email to produce extracted data, a user model for generating a high-level model of an email environment on the computing device, based on the extracted data, and at least one predictor for generating a prediction related to the email, based on the extracted data and on the high-level model. In another embodiment, a system for scheduling an event includes an extractor for extracting constraints from a request for the event, a user model for receiving and storing scheduling preferences from a user, and a predictor coupled to the extractor and to the user model, for generating a candidate schedule for the event in accordance with the constraints and the scheduling preferences.

Abstract: The present invention relates to a method and apparatus for assembling a set of documents related to a triggering item. One embodiment of a method for assembling a set of electronic documents related to an electronic triggering item detected by a computing device being operated by a user includes automatically extracting by the computing device a set of features from the triggering item, without receiving a request by the user to assemble the set of electronic documents, and assembling as the set of electronic documents a plurality of documents that is relevant to the set of features, wherein the plurality of documents is retrieved from a plurality of different types of electronic sources.

Abstract: A computer implemented method and apparatus for analyzing content of a plurality of documents. The method extracts semantic information from content of a plurality of documents; assigns weights to the semantic information; assigns links between documents containing similar semantic information; assigns a weight to each link; extracts information about the content of the plurality of documents by using the weighted links and weighted semantics to cluster the documents, perform inferential analysis, or both.

Abstract: A system may include a machine-implemented data extractor and correlator configured to retrieve data from at least one data source. The data extractor and correlator may extract information from unstructured data within the retrieved data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data.

Abstract: The present disclosure extends to methods, systems, and computer program products for providing collaboration among users within a group for improved communication and scheduling.

Abstract: The present disclosure extends to methods, systems, and computer program products for providing collaboration among users within a group and to provide improved scheduling and reports based on group characteristics.