Microsoft Technology Licensing, LLC

Namita Parab

Emmanouil Koukoumidis

Norm Bryar

Stephen L Lawler

Anthony Nino Bice

Christopher Alme

Vanya Avramova

Architecture that enables geofence combinations and compositions where multiple correlated geofences are generated for an entity such as a point of interest. The geofences can have varying radii relative to a specific entity and represent distinct areas or aspects of the entity. The geofences can relate to correspondingly different categories to which the entity can belong. The geofences can be of differing shapes than circular, such as polygons (e.g., rectangles, squares, etc.). Moreover, these differently shaped geofences can be applied to a single entity. Each geofence of a geofence set associated with an entity can be assigned to represent different parts of an entity such as a part a shopping mall. Geofence composition is obtained by combining multiple primitive geofences to compose more complex geofence(s) for an entity and for embedding the relationship of the primitive geofences into such compositions.

Geofence compositions

Architecture that enables the capability to more effectively define and resize geofences to provide improved geofence utility based on rich context and crowd-sourced data. The architecture enables the intelligent placement of geofences based on rich context that includes both user context and ambient context such as the (predicted or implicitly/explicitly defined) user's travel path, mode of transport, the type of the entity to be visited by the user and geofenced, and the user incentive for visiting the entity to be geofenced. The ambient context includes non-user specific information such as external conditions that may limit or thwart user mobility such as traffic and weather conditions. The rich context and crowd-sourced data assist in improving the spatiotemporal accuracy of suggested/constructed geofences thereby creating a 'shaped' geofence that is sufficiently defined to approximate the shape of the entity being geofenced with some degree of accuracy.

Geofences from context and crowd-sourcing

Growing community of inventors

Redmond, WA, United States of America

Namita Parab