Intel Corporation

Noam Sagi

Ronen Aharon Soffer

Oded Vainas

Omer Shmuel Peri

Gili Ilan

Gil Sharon

Amir Yissachar

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.

Contextual model-based event scheduling

A method for navigation includes receiving in a mobile computing device () a designation of an origin () and a destination () of an itinerary of a user of the device. An optimal primary route () is computed from the origin to the destination. Multiple points of interest () are identified in proximity to the optimal primary route. A respective first optimal sub-route () is computed between the origin and each of the points of interest, and a respective second optimal sub-route () is computed between the destination and each of the points of interest. One or more alternative routes () 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.

Route optimization including points of interest

Growing community of inventors

Kibbutz Nahsholim, Israel

Noam Sagi