Abstract: Various techniques for performing contextual event scheduling with an event scheduling service are disclosed herein. In an example, data is processed at an event scheduling service, based on the use of a trained machine learning model that is specific to a user. This trained machine learning model is operated by the event scheduling service determine a proposed time and proposed scheduling parameters based on the contextual information, to identify a proposed event time and event scheduling parameters based on the model, the data indicating a user state, or external data. Further examples to evaluate user activity and identify schedule characteristics based on data inputs from a user's mobile computing device, wearable sensors, and external weather, traffic, or event data sources, are also disclosed.

Abstract: Various techniques for performing contextual event rescheduling with an event scheduling service are disclosed herein. In an example, data is processed at an event scheduling service, based on the use of a trained machine learning model that is specific to a user. This model is operated by the event scheduling service determine a contextual action option for rescheduling an electronic communication event at a proposed time with proposed scheduling parameters. The model may identify the proposed time and event scheduling parameters, from data indicating a user state, or external data, in addition to a semantic text option (such as “Call Back After Meeting”) corresponding to the proposed time and event scheduling parameters. Further examples to evaluate user activity and identify reschedule options based on data inputs from a user's mobile computing device, wearable sensors, and external weather, traffic, or event data sources, are also disclosed.

Abstract: Various techniques for performing contextual event scheduling with an event scheduling service are disclosed herein. In an example, data is processed at an event scheduling service, based on the use of a trained machine learning model that is specific to a user. This trained machine learning model is operated by the event scheduling service determine a proposed time and proposed scheduling parameters based on the contextual information, to identify a proposed event time and event scheduling parameters based on the model, the data indicating a user state, or external data. Further examples to evaluate user activity and identify schedule characteristics based on data inputs from a user's mobile computing device, wearable sensors, and external weather, traffic, or event data sources, are also disclosed.

Abstract: A method for navigation includes receiving in a mobile computing device (24) a designation of an origin (32, 92) and a destination (34, 94) of an itinerary of a user of the device. An optimal primary route (96) is computed from the origin to the destination. Multiple points of interest (40, 98, 100) are identified in proximity to the optimal primary route. A respective first optimal sub-route (42) is computed between the origin and each of the points of interest, and a respective second optimal sub-route (44) is computed between the destination and each of the points of interest. One or more alternative routes (102, 104) from the origin to the destination via one or more of the points of interest are presented to the user on the mobile computing device, by combining the respective first and second optimal sub-routes computed for the one or more of the points of interest.

Abstract: A method for navigation includes receiving in a mobile computing device (24) a designation of an origin (32, 92) and a destination (34, 94) of an itinerary of a user of the device. An optimal primary route (96) is computed from the origin to the destination. Multiple points of interest (40, 98, 100) are identified in proximity to the optimal primary route. A respective first optimal sub-route (42) is computed between the origin and each of the points of interest, and a respective second optimal sub-route (44) is computed between the destination and each of the points of interest. One or more alternative routes (102, 104) from the origin to the destination via one or more of the points of interest are presented to the user on the mobile computing device, by combining the respective first and second optimal sub-routes computed for the one or more of the points of interest.

Abstract: A navigation system computes an optimal route between an origin and a destination using a map server. The map server identifies likely points of deviation from the optimal route, and prior to transmitting map data to a client device, establishes alternative optimal routes from the deviations to the destination. A search algorithm is employed in which computation effort is reduced through improved heuristics and enhanced recognition of previously expanded nodes and alternative routes through nodes determined in prior iterations of the search. A route corridor map including the optimal route and alternative routes is download from the map server to the client device.