APPARATUS AND METHOD FOR PROFILING USERS

• US 20140195530A1
• Filed: 06/14/2013
• Published: 07/10/2014
• Est. Priority Date: 01/04/2013
• Status: Active Grant

• Current Assignee: PlaceIQ, Inc.
• Original Assignee: PlaceIQ, Inc.
• Inventors: Milton, Stephen, McCall, Duncan
• Granted Patent: US 9,275,114 B2
• Time in Patent Office: Days
• US Class Current: 707/734
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9537 Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0261 based on user location
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: H04W 4/029 Location-based management or tracking services

Abstract

Provided is a process of profiling a user of a mobile computing device, the process including: obtaining a location history of a user, the location history being based on signals from a mobile computing device of the user; obtaining a location-attribute score of a location identified in, or inferred from, the location history; determining, with a computer, a user-attribute score based on the location-attribute score; and storing the user-attribute score in a user-profile datastore.

Claims

1. A method of profiling a user of a mobile computing device, the method comprising:

obtaining a location history of a user, the location history being based on signals from a mobile computing device of the user;

obtaining a location-attribute score of a location identified in, or inferred from, the location history;

determining, with a computer, a user-attribute score based on the location-attribute score; and

storing the user-attribute score in a user-profile datastore.

2. The method of claim 1, wherein:

obtaining a location-attribute score of a location comprises;

mapping a location in the location history to a tile in a geographic information system (GIS), the tile being a quantization of a geographic area described by the GIS and including the location; and

retrieving a list of tile-attribute scores for the tile from the GIS, the tile-attribute scores each describing a potential activity of a user while in the tile and a statistical likelihood that a user is engaging in the activity based on behavior of other users while in the tile.

3. The method of claim 1, wherein determining a user-attribute score comprises:

determining a user-attribute score for a user-profile time-bin, the user profile being divided into a plurality of time-bins, with the same attribute having different values in different time-bins of the user profile, the different values corresponding to a propensity of the user to engage in an activity described by the attribute during a time within a given time-bin.

4. The method of claim 1, wherein determining a user-attribute score comprises:

identifying a time-bin of a profile of the user, the time-bin corresponding to a time at which the user was at the location, the user profile having a plurality of time-bins; and

determining a user-attribute score corresponding to the identified time-bin.

5. The method of claim 1, wherein obtaining a location-attribute score comprises:

identifying a time-bin of a record in a GIS, the record corresponding to the location, and the time-bin corresponding to a time at which the user was at the location; and

determining a user-attribute score based on an attribute score of the identified time-bin of the location.

6. The method of claim 1, wherein the location-attribute score is a time-bin specific score for the location, and wherein the user-attribute score is a time-bin specific score for the user based on the location history.

7. The method of claim 1, wherein each user is associated with a plurality of scores for the attribute, each score corresponding to a different duration of time during the week.

8. The method of claim 1, wherein obtaining a location-attribute score of a location identified in, or inferred from, the location history comprises:

identifying a first tile in a GIS, the first tile defining a geographic area including the location identified in the location history.

9. The method of claim 8, wherein identifying a tile comprises:

determining that the user is located in a second tile, adjacent the first tile, based on attribute scores of both the first tile and the second tile.

10. The method of claim 1, wherein obtaining a location-attribute score of a location comprises:

obtaining an initial location of the user at an initial time;

obtaining a subsequent location of the user at a subsequent time, the subsequent time being more than one minute after the initial time;

inferring an intermediate location of the user at an intermediate time based on attributes of locations between the initial location and the subsequent location; and

obtaining a location-attribute score of the intermediate location.

11. The method of claim 10, wherein the intermediate location is associated with a confidence score indicative of a likelihood that the user was at the intermediate location, and wherein the user attribute score is calculated by weighting the location-attribute score with the confidence score.

12. The method of claim 1, wherein determining a user-attribute score based on the location-attribute score comprises:

calculating a measure of central tendency using the location-attribute score and scores for the attribute from previous locations of the user.

13. The method of claim 1, wherein determining a user-attribute score based on the location-attribute score comprises:

determining that the location-attribute score is not an outlier relative to scores for the attribute from previous locations of the user; and

in response, calculating an average of the location-attribute score and scores for the attribute from previous locations of the user.

14. The method of claim 1, wherein determining a user-attribute score based on the location-attribute score comprises:

determining that adjacent locations have attribute scores consistent with the location-attribute score; and

in response, calculating the user-attribute score based on the location-attribute score.

15. The method of claim 14, wherein determining that adjacent locations have attribute scores consistent with the location-attribute score comprises:

determining more than a threshold amount of adjacent tiles have scores for the attribute within a threshold difference from the location-attribute score.

16. The method of claim 1, comprising:

determining user-attribute scores for a plurality of attributes of a plurality of users based on location histories of the plurality of users and attributes of locations identified by the location histories; and

storing the user-attribute scores in a plurality of corresponding user profiles in the user-profile datastore.

17. The method of claim 1, wherein:

the location history comprises an list of geolocation records, each geolocation record including geographic coordinates expressed as a latitude and longitude and a time at which the mobile computing device was at the respective coordinates, each geolocation record being obtained by an end-user portable device having access to a location identifying service;

obtaining a location-attribute score comprises;

inferring locations between locations identified in the location history;

for each identified or inferred location, retrieving respective tile records from a GIS, the tile records corresponding to a tile in which the respective location is disposed and adjacent tiles, each tile record corresponding to a geographic area of between 100 square meters and 100,000 square meters and being associated with one or more location-attribute scores, each location-attribute score corresponding to an activity of interest to advertisers and ordinal values indicative of a likelihood that a user is engaged in the respective activity in the tile during each of a plurality of time-bins, the time-bins defining different subsets of a week, including a weekday morning bin, a weekday lunch-time bin, and a weekend evening bin, and the attributes including the user being at work, the user engaging in sports, the user shopping, and the user engaging in tourism;

determining a user-attribute score comprises;

determining that location attribute scores for the tile records for the time-bin in which the user was at the location are consistent among the adjacent tiles; and

in response, determining a plurality of user-attribute scores corresponding the location-attribute scores, the respective user-attribute score being an average of the corresponding location-attribute score for a time-bin including the time at which the user was a the location and previous scores for the attribute from other locations; and

storing the user-attribute score comprises;

storing the averaged user-attribute scores in a user profile in the user-profile datastore, user profile being stored on a tangible, non-transitory, machine-readable medium, and the user-profile datastore being operative to respond to queries from advertisers for data relevant to the selection of advertisements.

18. The method of claim 1, comprising:

receiving a request from an advertiser for data about a user, the request including a user identifier;

determining that the advertiser is authorized to access the user-profile datastore;

retrieving from a user profile responsive to the user identifier one or more attributes of the responsive user; and

sending to the advertiser data indicative of the one or more attributes.

19. The method of claim 1, comprising:

obtaining user profiles of users at each of a plurality of locations corresponding to a business; and

aggregating attributes of the user profiles to characterize the business.

20. The method of claim 1, comprising:

obtaining a user profile of a user operating an application; and

customizing the application based on the user profile.

21. A tangible, machine-readable, non-transitory medium storing instructions that when executed by a data processing apparatus, cause the data processing apparatus to perform operations comprising:

obtaining a location history of a user, the location history being based on signals from a mobile computing device of the user;

obtaining a location-attribute score of a location identified in, or inferred from, the location history;

determining a user-attribute score based on the location-attribute score; and

storing the user-attribute score in a user-profile datastore.

22. A system, comprising:

one or more processors; and

memory storing instructions that when executed by the processors cause the processors to perform operations comprising;

obtaining a location history of a user, the location history being based on signals from a mobile computing device of the user;

obtaining a location-attribute score of a location identified in, or inferred from, the location history;

determining a user-attribute score based on the location-attribute score; and

storing the user-attribute score in a user-profile datastore.