Geofences from context and crowd-sourcing

• US 9,936,346 B2
• Filed: 11/28/2013
• Issued: 04/03/2018
• Est. Priority Date: 11/28/2013
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Koukoumidis, Emmanouil, Bryar, Norm, Alme, Christopher, Parab, Namita, Lawler, Stephen, Bice, Anthony, Avramova, Vanya
• Primary Examiner(s): Shen, Qun
• Application Number:
  US14/092,968
• Publication Number:
  US 20150148061A1
• Time in Patent Office: 1,587 Days
• Field of Search: 4554561
• CPC Class Codes: H04W 4/021 Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 4/022 with dynamic range variability

Abstract

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.

Claims

1. A system, comprising:

a memory for storing executable program code; and

a processor, connected with the memory, the processor being responsive to computer-executable instructions contained in the program code and operative to;

access user context data of a user and ambient conditions data as related to a geographical location;

analyze the accessed user context data;

determine an entity type to generate a geofence for, wherein the analysis of the accessed context data indicates that the entity type is associated with one or more product or service types offered by an entity at the geographical location;

analyze the user context data and the ambient conditions data to generate geofence properties that relate in part to size, placement, and shape of the geofence; and

automatically generate the geofence for the geographical location based on the geofence properties, wherein the geofence is sized to contact a next closest geofence of an entity that offers one or more product or service type that is similar to one or more product or service type offered by the entity at the geographic location.

2. The system of claim 1, wherein the processor is further operative to:

automatically inflate or deflate the geofence based on changes to at least one of the user context data or the ambient context data.

3. The system of claim 1, wherein the processor is further operative to:

automatically generate an auxiliary geofence and places the auxiliary geofence for activation prior to activation of the geofence associated with the geographical location based on the changes to the geofence properties.

4. The system of claim 1, wherein the user context data includes at least one of mode of transport of the user, type of the geographical location, incentive to visit the geographical location, likelihood the user will be in proximity to the geographical location at a future time, importance of timely performing a task at the geographical location, or flexibility in a schedule to visit the geographical location.

5. The system of claim 1, wherein the ambient conditions data is at least one of external conditions configured to impede user mobility to the geographical location, uniqueness of the geographical location, or proximity of a next-closest geofence of an entity of similar type as the geographical location.

6. The system of claim 1, wherein the processor is further operative to:

determine an approximated shape of the geographical location based on crowd-sourced geographically-tagged data as part of the geofence properties, and shapes the geofence according to the approximated shape of the geographical location.

7. The system of claim 1, wherein the processor is further operative to:

extract crowd-sourced time-tagged data related to the geographical location to derive a time-based activation pattern for the geofence to determine when to trigger the geofence.

8. The system of claim 1, wherein the geofence is sized based on a specific mode of transport, and the geofence component is configured to size the geofence based on an initial mode of transport, and is configured to resize of the geofence based on changes in the modes of transport.

9. A method, comprising acts of:

accessing user context data of a user that relates to a type of entity as a destination;

accessing ambient context data that relates to external conditions which influence travel to the entity;

analyzing the accessed user context data;

determining an entity type to generate a geofence for, wherein the analysis of the accessed user context data indicates that the entity type is associated with one or more product or service types offered by an entity at the destination;

automatically generating a geofence based on the user context data and ambient context data;

computing a size of the geofence based on the type of the entity and the external conditions which influence travel to the entity, wherein the geofence is sized to contact a next closest geofence of an entity that offers one or more product or service type that is similar to one or more product or service type offered by the entity at the destination; and

placing the geofence relative to the entity.

10. The method of claim 9, further comprising automatically inflating or deflating the geofence based on changes to at least one of the user context data or the ambient context data.

11. The method of claim 9, further comprising automatically creating and placing an auxiliary geofence relative to the entity based on the external conditions which influence travel to the entity.

12. The method of claim 9, further comprising automatically re-sizing the geofence according to changes in the user context data that relate to time criticality in completing a task at the entity or time flexibility in completing the task at the entity.

13. The method of claim 9, further comprising deriving virtual boundaries of the entity that approximate actual boundaries of the entity and generating the geofence according to the virtual boundaries.

14. The method of claim 9, further comprising generating the geofence based on the user context data, which is crowd-sourced geographically-tagged data that are identifiable as related to the entity.

15. The method of claim 9, further comprising computing the size based on analysis of context of user data and user communications and inferring user intent of the geofence for the entity.

16. A computer-readable hardware storage medium comprising computer-executable instructions that when executed by a microprocessor, cause the microprocessor to perform acts of:

accessing crowd-sourced context data that relates to a type of geographical location;

creating a virtual perimeter of the geographical location based on the crowd-sourced data, the virtual perimeter shaped to the geographical location;

automatically generating a geofence for the geographical location according to the shaped virtual perimeter and aligning the geofence to match an outline of the geographical location;

sizing the gaeofence to contact a next closest geofence of an entity that offers one or more product or service type that is similar to one or more product or service type offered by an entity at the geographical location; and

automatically re-sizing the geofence based on changes in at least one of user context data or ambient context data.

17. The computer-readable hardware storage medium of claim 16, further comprising automatically creating and placing an auxiliary geofence on a travel route to the entity based on the external conditions which influence travel to the entity.

18. The computer-readable hardware storage medium of claim 16, further comprising refining shape of the virtual perimeter based on changes in the crowd-sourced data.

19. The computer-readable hardware storage medium of claim 16, further comprising modifying the size of the geofence based on a mode of transport of a user traveling to the geographical location.