Abstract: Pieces of location history data corresponding to an identified place of interest are obtained. Each piece of the obtained location history data references one or more signals detected at the identified place of interest and also references temporal data that corresponds to the detected signal(s). Some of the obtained pieces of location history data that represent one or more short, pass-through visits at the identified place of interest are identified. Based on the identified pieces that represent the short visit(s), conditions for determining the intent of future detected location visits at the identified place of interest are generated. The generated conditions can then be employed to determine that a detected location visit of a particular computing device determined to be at the identified place of interest is an extended visit as opposed to a short visit.

Abstract: A system and method to provide computer support for a meeting of invitees comprises accessing one or more sensory data streams providing digitized sensory data responsive to an activity of one or more of the invitees during the meeting, the one or more sensory data streams including at least one audio stream. The method also comprises subjecting the at least one audio stream to phonetic and situational computer modeling to recognize a sequence of words in the audio stream and to assign each word to an invitee, subjecting the sequence of words to semantic computer modeling to recognize a sequence of directives in the sequence of words, and releasing one or more output data streams based on the sequence of directives, the one or more output data streams including one or more notifications.

Abstract: A system and method to provide computer support for a meeting of invitees comprises accessing one or more sensory data streams providing digitized sensory data responsive to an activity of one or more of the invitees during the meeting, the one or more sensory data streams including at least one audio stream. The method also comprises subjecting the at least one audio stream to phonetic and situational computer modeling to recognize a sequence of words in the audio stream and to assign each word to an invitee, subjecting the sequence of words to semantic computer modeling to recognize a sequence of directives in the sequence of words, and releasing one or more output data streams based on the sequence of directives, the one or more output data streams including one or more notifications.

Abstract: Computerized systems are provided for automatically determining and providing enriched action items of an event, such as a meeting. Action items first may be determined and then enhanced or clarified in order to be more understandable by automatically modifying or supplementing the action item based on contextual information, such as related meeting discussion. Action items may be further enhanced through clarification by determining boundaries of their occurrence(s) so that extraneous content not related to the action items or not helpful for user understanding is deemphasized or excluded from presentation to the user. Some embodiments comprise an intelligent graphical user interface with functionality for receiving user feedback. The feedback can be employed to reconfigure the logic, such as machine learning models, utilized to determine the enriched action items to improve accuracy.

Abstract: Computerized systems are provided for automatically determining action items of an event, such as a meeting. The determined action items may be personalized to a particular user, such as a meeting attendee, and may include contextual information enabling the user to understand the action item. In particular, a personalized action item may be determined based in part from determining and utilizing particular factors in combination with an event dialog, such as an event speaker's language style; user role in an organization; historical patterns in communication; event purpose, name, or location; event participants, or other contextual information. Particular statements are evaluated to determine whether the statement likely is or is not an action item. Contextual information may be determined for action items, which then may be provided to the particular user during or following the event.

Abstract: Computerized systems are provided for automatically determining and providing enriched action items of an event, such as a meeting. Action items first may be determined and then enhanced or clarified in order to be more understandable by automatically modifying or supplementing the action item based on contextual information, such as related meeting discussion. Action items may be further enhanced through clarification by determining boundaries of their occurrence(s) so that extraneous content not related to the action items or not helpful for user understanding is deemphasized or excluded from presentation to the user. Some embodiments comprise an intelligent graphical user interface with functionality for receiving user feedback. The feedback can be employed to reconfigure the logic, such as machine learning models, utilized to determine the enriched action items to improve accuracy.

Abstract: Pieces of location history data corresponding to an identified place of interest are obtained. Each piece of the obtained location history data references one or more signals detected at the identified place of interest and also references temporal data that corresponds to the detected signal(s). Some of the obtained pieces of location history data that represent one or more short, pass-through visits at the identified place of interest are identified. Based on the identified pieces that represent the short visit(s), conditions for determining the intent of future detected location visits at the identified place of interest are generated. The generated conditions can then be employed to determine that a detected location visit of a particular computing device determined to be at the identified place of interest is an extended visit as opposed to a short visit.

Abstract: Pieces of location history data corresponding to an identified place of interest are obtained. Each piece of the obtained location history data references one or more signals detected at the identified place of interest and also references temporal data that corresponds to the detected signal(s). Some of the obtained pieces of location history data that represent one or more short, pass-through visits at the identified place of interest are identified. Based on the identified pieces that represent the short visit(s), conditions for determining the intent of future detected location visits at the identified place of interest are generated. The generated conditions can then be employed to determine that a detected location visit of a particular computing device determined to be at the identified place of interest is an extended visit as opposed to a short visit.

Abstract: A computerized system for recursively detecting anomalies in monitored behavior of entities. The system comprises a storage unit to store monitored events, event deviations and parameters related to each event and to each event deviation. The system comprises a processing unit configured to receive a plurality of input events, construct a plurality of baseline models, receive an input event that occurred during an analyzed timeframe, compare parameters of the received input event to a corresponding baseline model in order to detect an event deviation, and associate an event deviation score to the detected event deviation. Using the detected event deviation as an input event, said operations are repeated until a predetermined condition is satisfied, and an alert is generated, indicating suspicious activity has been detected. A viewer application configured to receive and display alerts relating to the detected event deviation is provided.

Abstract: A system and method to provide computer support for a meeting of invitees comprises accessing one or more sensory data streams providing digitized sensory data responsive to an activity of one or more of the invitees during the meeting, the one or more sensory data streams including at least one audio stream. The method also comprises subjecting the at least one audio stream to phonetic and situational computer modeling to recognize a sequence of words in the audio stream and to assign each word to an invitee, subjecting the sequence of words to semantic computer modeling to recognize a sequence of directives in the sequence of words, and releasing one or more output data streams based on the sequence of directives, the one or more output data streams including one or more notifications.

Abstract: Aspects of the technology described herein provide improved battery life for a user device based on the use of an inferred location of the user that obviates the need for conventional location services like GPS. In particular, an inferred location for a user may be determined, including contextual information about the user location. Using information from the user's current context, with historical observations about the user and expected user events, out-of-routine events, or other lasting or ephemeral information, an inference of one or more user locations and corresponding confidences may be determined. The inferred user location may be provided to an application or service such as a personal assistant service associated with the user, or may be provided as an API to facilitate consumption of the inferred location information by an application or service.

Abstract: Aspects of the technology described herein identify a trusted group of devices that collaborate to minimize device limitations (e.g., data use, bandwidth, battery life, and the like). Personal assistant services or cloud-based services utilize user data (e.g., web browsing, calendar entries, communication data, social networks, and the like) and device data (e.g., location data, Bluetooth beacons, Wi-Fi, and the like) provided by user devices to identify devices in the trusted group of devices. A handshake between the devices establishes a means of communication and a selected topology. A hub device is selected from the trusted group of devices to upload or download relevant data based on the selected topology. The hub device shares the relevant data with the member devices via the established means of communication.

Abstract: Aspects of the technology described herein identify a trusted group of devices that collaborate to minimize device limitations (e.g., data use, bandwidth, battery life, and the like). Personal assistant services or cloud-based services utilize user data (e.g., web browsing, calendar entries, communication data, social networks, and the like) and device data (e.g., location data, Bluetooth beacons, Wi-Fi, and the like) provided by user devices to identify devices in the trusted group of devices. A handshake between the devices establishes a means of communication and a selected topology. A hub device is selected from the trusted group of devices to upload or download relevant data based on the selected topology. The hub device shares the relevant data with the member devices via the established means of communication.

Abstract: Pieces of location history data corresponding to an identified place of interest are obtained. Each piece of the obtained location history data references one or more signals detected at the identified place of interest and also references temporal data that corresponds to the detected signal(s). Some of the obtained pieces of location history data that represent one or more short, pass-through visits at the identified place of interest are identified. Based on the identified pieces that represent the short visit(s), conditions for determining the intent of future detected location visits at the identified place of interest are generated. The generated conditions can then be employed to determine that a detected location visit of a particular computing device determined to be at the identified place of interest is an extended visit as opposed to a short visit.

Abstract: A computerized system for recursively detecting anomalies in monitored behavior of entities. The system comprises a storage unit to store monitored events, event deviations and parameters related to each event and to each event deviation. The system comprises a processing unit configured to receive a plurality of input events, construct a plurality of baseline models, receive an input event that occurred during an analyzed timeframe, compare parameters of the received input event to a corresponding baseline model in order to detect an event deviation, and associate an event deviation score to the detected event deviation. Using the detected event deviation as an input event, said operations are repeated until a predetermined condition is satisfied, and an alert is generated, indicating suspicious activity has been detected. A viewer application configured to receive and display alerts relating to the detected event deviation is provided.