Venue identification from wireless scan data

• US 9,860,704 B2
• Filed: 03/31/2015
• Issued: 01/02/2018
• Est. Priority Date: 03/31/2015
• Status: Active Grant

• Current Assignee: Foursquare Labs, Inc.
• Original Assignee: Foursquare Labs, Inc.
• Inventors: Kapicioglu, Cagatay Berk, Shaw, Blake
• Primary Examiner(s): RAMPURIA, SHARAD K
• Application Number:
  US14/674,288
• Publication Number:
  US 20160295372A1
• Time in Patent Office: 1,008 Days
• Field of Search: 4554563, 4554143
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 16/9537 Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
• CPC Class Codes: H04W 24/02 Arrangements for optimising operational condition
• 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 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

In non-limiting examples, wireless scan data collected from a processing device is directly mapped to venues. Wireless scan data is received from a processing device where the wireless scan data comprises at least a first identifier of a wireless connection. Venues associated with the first identifier are identified from a mapping of wireless identifiers to venues based on active validation data associated with particular venues including historical wireless scan data of the particular venues. The plurality of venues are ranked according to a likelihood that the processing device is located at a particular venue based on evaluating the active validation data including the historical wireless scan data from the mapping. A predicted venue that the processing device is located at is determined based on the ranking of the plurality of venues. Other examples are also described.

Claims

1. A computer-implemented method comprising:

receiving wireless scan data from a processing device, the wireless scan data comprising a first identifier of a wireless connection;

identifying a plurality of venues associated with the first identifier of the collected wireless scan data from a mapping index of wireless identifiers to venues,

wherein the mapping index is generated from wireless scan data collected from a set of devices and active validation data collected during visits to one or more venues,

wherein the mapping index associates the wireless scan data with the one or more venues;

ranking the plurality of venues according to a likelihood that the processing device is located at a particular venue based on evaluating

at least one of spatial signals collected from the processing device and social signals associated with the processing device the active validation data including the historical wireless scan data from the mapping index; and

determining a predicted venue that the processing device is located at based on the ranking of the plurality of venues; and

transmitting a notification of the predicted venue to the processing device.

2. The computer-implemented method according to claim 1, wherein wireless scan data comprises a plurality of wireless identifiers, and the wireless identifier is at least one of a MAC address, beacon data and Bluetooth data.

3. The computer-implemented method according to claim 1, wherein the ranking further comprises applying a first machine-learning model to determine the likelihood the processing device is located at the predicted venue, wherein the first machine-learning model assigns a probability to at least a first venue of the plurality of venues that the first venue is associated with the first identifier based on evaluation of a second identifier associated with the active validation data accumulated from previous visits at the first venue.

4. The computer-implemented method according to claim 1, further comprising transmitting a notification including the predicted venue to the processing device, wherein the notification indicates that the processing device is located at the predicted venue.

5. The computer-implemented method according to claim 3, wherein the ranking further comprises applying at least a second machine-learning process to determine the likelihood the processing device is located at the venue, the second machine-learning process is a filtering process that removes one or more venues from the plurality of venues based on application of one or more filter processes.

6. The computer-implemented method according to claim 1, wherein the ranking further comprises applying a machine-learning model to determine the likelihood the processing device is located at the predicted venue, the machine learning model comparing similarities between signal strength detected for a wireless connection associated with the first identifier and a predicted signal strength.

7. The computer-implemented method according to claim 1, wherein determining the predicted venue further comprises transmitting, to the processing device, a listing comprising one or more venues based on the ranking of the plurality of venues.

8. The computer-implemented method according to claim 1, wherein determining the predicted venue further comprises receiving telemetry data regarding correctness of the predicted venue, and updating the mapping index based on the received telemetry data.

9. The computer-implemented method according to claim 1, wherein the active validation data further comprises the social signals from a user profile associated with the processing device.

10. The computer-implemented method according to claim 1, wherein the mapping index comprises at least one of wireless scan data, venue identifiers, contact information, venue information, and venue reviews.

11. The computer-implemented method according to claim 1, wherein generating the mapping index comprises using the wireless scan data to calculate auxiliary statistics, wherein the auxiliary statistics comprise at least one of:

descriptive statistics associated with the plurality of venues;

inferential statistics associated with the plurality of venues, coordinates associated with the wireless scan data; and

percentiles corresponding to distances associated with the coordinates.

12. A system comprising:

a memory; and

at least one processor connected with the memory, configured to execute operations comprising;

receiving wireless scan data from a processing device, the wireless scan data comprising a plurality of MAC addresses;

identifying venues associated with the MAC addresses of the collected wireless scan data from a mapping index of MAC addresses to venues,

wherein the mapping index is generated from wireless scan data collected from a set of devices and active validation data collected during visits to one or more venues,

wherein the mapping index associates the wireless scan data with the one or more venues;

ranking the plurality of venues according to a likelihood that the processing device is located at a particular venue based on evaluating

at least one of spatial signals collected from the processing device and social signals associated with the processing device the active validation data including the historical wireless scan data from the mapping index; and

determining a predicted venue that the processing device is located at based on the ranking of the plurality of venues; and

transmitting a notification of the predicted venue to the processing device.

13. The system according to claim 12, wherein the ranking executed by the processor further comprises applying a first machine-learning model to determine the likelihood the processing device is located at the predicted venue, wherein the first machine-learning model assigns a probability to at least a first venue of the plurality of venues that the first venue is associated with a MAC address based on evaluation of a second MAC address associated with the active validation data accumulated for previous visits at the first venue.

14. The system according to claim 12, wherein the operations executed by the processor further comprising transmitting a notification including the predicted venue to the processing device, wherein the notification indicates that the processing device is located at the predicted venue.

15. The system according to claim 13, wherein the ranking executed by the processor further comprises applying at least a second machine-learning model to determine the likelihood the processing device is located at the predicted venue, and wherein the second machine-learning model is a filtering process that removes one or more venues from the plurality of venues based on application of one or more filtering processes.

16. The system according to claim 12, wherein the ranking executed by the processor further comprises applying a machine-learning model to determine the likelihood the processing device is located at the predicted venue, the machine learning model comparing similarities between signal strength detected for a wireless connection associated with a MAC address and a predicted signal strength for a wireless connection of a venue.

17. The system according to claim 12, wherein the determining of the predicted venue executed by the processor further comprises transmitting, to the processing device, a listing comprising one or more venues based on the ranking of the plurality of venues.

18. The system according to claim 12, wherein the determining of the predicted venue executed by the processor further comprises receiving telemetry data regarding correctness of the predicted venue, and updating the mapping index based on the received telemetry data.

19. The system according to claim 12, wherein the active validation data further comprises social signals associated with a user profile operating on the processing device, and the ranking of the identified venues evaluates at least one of spatial signals collected from the processing device and the social signals associated with the user profile to determine a probability that a venue is associated with a MAC address of the wireless scan data.

20. A computer-method comprising:

receiving wireless scan data from a first processing device;

identifying a plurality of venues associated with the wireless scan data using at least one stored mapping index to identify candidate venues,

wherein the mapping index is generated from wireless scan data collected from a set of devices and active validation data collected during visits to one or more venues,

wherein the mapping index associates the wireless scan data with the one or more venues;

ranking the plurality of venues according to a likelihood that the processing device is located at a particular venue using

at least one of spatial signals collected from the processing device and social signals associated with the processing device the active validation data including the historical wireless scan data from the mapping index; and

associating the wireless scan data with one or more candidate venues; and

transmitting a notification of the predicted venue to the processing device.