U.S. patent application number 12/769031 was filed with the patent office on 2012-02-23 for system and method for profile tailoring in an aggregate profiling system.
This patent application is currently assigned to Waldeck Technology, LLC. Invention is credited to Sean T. Purdy.
Application Number | 20120047152 12/769031 |
Document ID | / |
Family ID | 45594457 |
Filed Date | 2012-02-23 |
United States Patent
Application |
20120047152 |
Kind Code |
A1 |
Purdy; Sean T. |
February 23, 2012 |
SYSTEM AND METHOD FOR PROFILE TAILORING IN AN AGGREGATE PROFILING
SYSTEM
Abstract
Systems and methods are disclosed for tailoring user profiles of
users in a mobile aggregate profiling system. In one embodiment,
the user profiles of the users in the mobile aggregate profiling
system are tailored based on aggregate profile data relevant to
current locations of the users. More specifically, in order to
tailor the user profile of a user, aggregate profile data is
obtained for the current location of the user. The user profile of
the user is then filtered based on the aggregate profile data for
the current location of the user. In one embodiment, the user
profile is filtered at a server of the mobile aggregate profiling
system. In another embodiment, the user profile is filtered at a
mobile device of the user prior to sending the user profile to a
server of the mobile aggregate profiling system.
Inventors: |
Purdy; Sean T.; (Durham,
NC) |
Assignee: |
Waldeck Technology, LLC
Wilmington
DE
|
Family ID: |
45594457 |
Appl. No.: |
12/769031 |
Filed: |
April 28, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61173625 |
Apr 29, 2009 |
|
|
|
Current U.S.
Class: |
707/754 ;
707/E17.059 |
Current CPC
Class: |
H04W 12/02 20130101;
G06F 16/284 20190101; H04W 4/029 20180201; H04W 8/16 20130101; G06Q
30/0204 20130101; H04L 51/32 20130101; G06Q 50/01 20130101 |
Class at
Publication: |
707/754 ;
707/E17.059 |
International
Class: |
G06F 17/30 20060101
G06F017/30 |
Claims
1. A computer-implemented method comprising: obtaining aggregate
profile data for a current location of a user; and filtering a user
profile of the user based on the aggregate profile data for the
current location of the user to thereby provide a filtered user
profile for the user.
2. The method of claim 1 wherein the aggregate profile data
comprises historical aggregate profile data.
3. The method of claim 1 wherein the aggregate profile data
comprises current aggregate profile data.
4. The method of claim 1 wherein filtering the user profile of the
user comprises, for each interest of a plurality of interests in
the user profile of the user, filtering the interest if a
representation value for a corresponding interest in the aggregate
profile data is less than a cut-off value.
5. The method of claim 4 wherein for each interest of the plurality
of interests in the aggregate profile data, the aggregate profile
data comprises a representation value for the interest that defines
a degree to which the interest is expressed in a plurality of user
profiles of a plurality of users that contributed to the aggregate
profile data.
6. The method of claim 4 wherein the cut-off value is a
predetermined, static value.
7. The method of claim 4 wherein the cut-off value is a dynamic
value.
8. The method of claim 4 wherein the cut-off value is a function of
an amount of time that the user has been located at the same
location.
9. The method of claim 8 wherein the amount of time that the user
has been located at the same location is an amount of time that the
user has been located within a predefined tolerance from a
predefined reference location.
10. The method of claim 8 wherein the cut-off value decreases as
the amount of time that the user has been located at the same
location increases.
11. The method of claim 8 wherein the cut-off value decreases from
an initial cut-off value to a minimum cut-off value as the amount
of time that the user has been located at the same location
increases from a minimum value to a maximum value in an adjustment
period.
12. The method of claim 11 wherein the adjustment period is a
static adjustment period.
13. The method of claim 11 wherein the adjustment period is a
dynamic adjustment period.
14. The method of claim 11 wherein the adjustment period is a
randomly selected value from a defined range of values.
15. The method of claim 11 wherein filtering the user profile of
the user comprises randomly selecting a value from a defined range
of values as the adjustment period such that the value is used as
the adjustment period as long as the user remains at the same
location.
16. The method of claim 11 wherein the adjustment period is a
combination of a predefined initial adjustment period and a
randomly selected variance value from a defined range of variance
values.
17. The method of claim 16 wherein the randomly selected variance
value remains the same as long as the user remains at the same
location.
18. The method of claim 11 wherein the adjustment period is a
function of a frequency at which the user has previously visited
the same location.
19. The method of claim 11 wherein the adjustment period is a
function of a number of times that the user has previously visited
the same location.
20. The method of claim 11 wherein the minimum cut-off value is a
randomly selected value from a defined range of values.
21. The method of claim 11 wherein filtering the user profile of
the user comprises randomly selecting a value from a defined range
of values as the minimum cut-off value such that the value is used
as the minimum cut-off value as long as the user remains at the
same location.
22. The method of claim 11 wherein the minimum cut-off value is a
combination of a predefined initial minimum cut-off value and a
randomly selected variance value from a defined range of variance
values.
23. The method of claim 22 wherein the randomly selected variance
value remains the same as long as the user remains at the same
location.
24. The method of claim 11 wherein the minimum cut-off value is a
function of a frequency at which the user has previously visited
the same location.
25. The method of claim 11 wherein the minimum cut-off value is a
function of a number of times that the user has previously visited
the same location.
26. The method of claim 1 wherein filtering the user profile of the
user comprises filtering the user profile of the user based on the
aggregate profile data for the current location of the user and
additional information obtained for the current location of the
user.
27. The method of claim 1 wherein filtering the user profile of the
user comprises filtering the user profile of the user based on the
aggregate profile data for the current location of the user and one
or more situational awareness rules defined by a community of
users.
28. The method of claim 1 wherein the aggregate profile data is an
aggregation of a plurality of user profiles of a plurality of
users, and interests in the plurality of user profiles having ages
that are less than a predefined threshold age do not contribute to
the aggregate profile data.
29. The method of claim 1 wherein the aggregate profile data is an
aggregation of a plurality of user profiles of a plurality of
users, and interests in the plurality of user profiles that are
relevant to one or more filtering rules that define a manner in
which the user profile of the user is to be filtered based on the
aggregate profile data and that have ages that are less than a
predefined threshold age do not contribute to the aggregate profile
data.
30. The method of claim 1 wherein filtering the user profile of the
user comprises: filtering the user profile of the user based on the
aggregate profile data for the current location of the user without
regard to an amount of time that the user has been at the same
location if configured in a first mode of operation; and filtering
the user profile of the user based on the aggregate profile data
for the current location of the user and an amount of time that the
user has been at the same location if configured in a second mode
of operation.
31. The method of claim 1 further comprising utilizing the filtered
user profile for the user to provide aggregate profile data in a
mobile aggregate profiling system.
32. A computing device comprising: a communication interface; and a
controller associated with the communication interface and adapted
to: obtain aggregate profile data for a current location of a user;
and filter a user profile of the user based on the aggregate
profile data for the current location of the user to thereby
provide a filtered user profile for the user.
33. The computing device of claim 32 wherein the computing device
is a server in a mobile aggregate profiling system.
34. The computing device of claim 32 wherein the computing device
is a mobile device of the user.
35. A computer-readable medium storing software for instructing a
controller of a computing device to: obtain aggregate profile data
for a current location of a user; and filter a user profile of the
user based on the aggregate profile data for the current location
of the user to thereby provide a filtered user profile for the
user.
36. A computer-implemented method comprising: obtaining aggregate
profile data for a desired location; and filtering the aggregate
profile data based on the aggregate profile data itself.
37. The method of claim 36 wherein filtering the aggregate profile
data comprises, for each interest of a plurality of interests in
the aggregate profile data, filtering the interest if a
representation value for the interest in the aggregate profile data
is less than a cut-off value.
38. The method of claim 37 wherein the desired location is a
location of a user for which the aggregate profile data is obtained
and filtered, and the cut-off value is a function of an amount of
time that the user has been located at the same location.
39. The method of claim 37 wherein the cut-off value decreases from
an initial cut-off value to a minimum cut-off value as an amount of
time that the user has been located at the same location increases
from a minimum value to a maximum value in an adjustment
period.
40. The method of claim 39 wherein the adjustment period is a
dynamic adjustment period.
41. The method of claim 39 wherein the minimum cut-off value is a
dynamic value.
42. A mobile device comprising: a communication interface; and a
controller associated with the communication interface and adapted
to: obtain aggregate profile data for a desired location; and
filter the aggregate profile data based on the aggregate profile
data itself.
43. A computer-readable medium storing software for instructing a
controller of a computing device to: obtain aggregate profile data
for a desired location; and filter the aggregate profile data based
on the aggregate profile data itself.
Description
RELATED APPLICATIONS
[0001] This application claims the benefit of provisional patent
application Ser. No. 61/173,625, filed Apr. 29, 2009, the
disclosure of which is hereby incorporated herein by reference in
its entirety.
FIELD OF THE DISCLOSURE
[0002] The present disclosure relates to an aggregate profiling
system and more particularly relates to tailoring a user profile of
a user in an aggregate profiling system.
BACKGROUND
[0003] Mobile aggregate profiling systems are becoming increasingly
popular. One such system is described in commonly owned and
assigned U.S. patent application Ser. No. 12/645,535 entitled
MAINTAINING A HISTORICAL RECORD OF ANONYMIZED USER PROFILE DATA BY
LOCATION FOR USERS IN A MOBILE ENVIRONMENT, U.S. patent application
Ser. No. 12/645,532 entitled FORMING CROWDS AND PROVIDING ACCESS TO
CROWD DATA IN A MOBILE ENVIRONMENT, U.S. patent application Ser.
No. 12/645,539 entitled ANONYMOUS CROWD TRACKING, U.S. patent
application Ser. No. 12/645,544 entitled MODIFYING A USER'S
CONTRIBUTION TO AN AGGREGATE PROFILE BASED ON TIME BETWEEN LOCATION
UPDATES AND EXTERNAL EVENTS, U.S. patent application Ser. No.
12/645,546 entitled CROWD FORMATION FOR MOBILE DEVICE USERS, U.S.
patent application Ser. No. 12/645,556 entitled SERVING A REQUEST
FOR DATA FROM A HISTORICAL RECORD OF ANONYMIZED USER PROFILE DATA
IN A MOBILE ENVIRONMENT, and U.S. patent application Ser. No.
12/645,560 entitled HANDLING CROWD REQUESTS FOR LARGE GEOGRAPHIC
AREAS, all of which were filed on Dec. 23, 2009 and are hereby
incorporated herein by reference in their entireties.
[0004] One issue with such systems is that a user may desire to
share different portions of his user profile depending on the
environment in which he is located. For instance, the user may
desire to share one portion of his user profile when in an
environment with business associates and share another portion of
his user profile when in an environment with other users sharing
similar personal interests. As such, there is a need for systems
and methods that tailor a user's profile in an aggregate profiling
system based on the environment in which the user is located.
SUMMARY
[0005] Systems and methods are disclosed for tailoring user
profiles of users in a mobile aggregate profiling system. In one
embodiment, the user profiles of the users in the mobile aggregate
profiling system are tailored based on aggregate profile data
relevant to current locations of the users. More specifically, in
order to tailor the user profile of a user, aggregate profile data
is obtained for the current location of the user. The user profile
of the user is then filtered based on the aggregate profile data
for the current location of the user. In one embodiment, the user
profile is filtered at a server of the mobile aggregate profiling
system. In another embodiment, the user profile is filtered at a
mobile device of the user prior to sending the user profile to a
server of the mobile aggregate profiling system.
[0006] In one embodiment, the aggregate profile data includes an
aggregate list of interests from user profiles of users
historically located at or near the current location of the user
and/or user profiles of users currently located at or near the
current location of the user. In addition, for each interest in the
aggregate list of interests, the aggregate profile data includes a
representation value indicative of a degree to which that interest
is included in the user profiles contributing to the aggregate
profile data. The user profile of the user is filtered to remove
interests from the user profile that correspond to interests in the
aggregate profile data having representation values that are less
than a cut-off value. In one embodiment, the cut-off value is a
static cut-off value. In another embodiment, the cut-off value is a
dynamic value. More specifically, in one embodiment, the cut-off
value is a function of an amount of time that the user has been
located at the user's current location, a number of times or a
frequency at which the user has visited the user's current
location, or a combination thereof.
[0007] Those skilled in the art will appreciate the scope of the
present invention and realize additional aspects thereof after
reading the following detailed description of the preferred
embodiments in association with the accompanying drawing
figures.
BRIEF DESCRIPTION OF THE DRAWING FIGURES
[0008] The accompanying drawing figures incorporated in and forming
a part of this specification illustrate several aspects of the
invention, and together with the description serve to explain the
principles of the invention.
[0009] FIG. 1 illustrates a Mobile Aggregate Profile (MAP) system
according to one embodiment of the present disclosure;
[0010] FIG. 2 is a block diagram of the MAP server of FIG. 1
according to one embodiment of the present disclosure;
[0011] FIG. 3 is a block diagram of the MAP client of one of the
mobile devices of FIG. 1 according to one embodiment of the present
disclosure;
[0012] FIG. 4 illustrates a process for filtering a user profile of
a user at the MAP server of FIG. 1 according to one embodiment of
the present disclosure;
[0013] FIG. 5 illustrates the operation of MAP system of FIG. 1 to
obtain user profiles of the users at the MAP server according to
one embodiment of the present disclosure;
[0014] FIG. 6 illustrates the operation of MAP system of FIG. 1 to
obtain user profiles of the users at the MAP server according to
another embodiment of the present disclosure;
[0015] FIG. 7 illustrates the operation of MAP system of FIG. 1 to
obtain location updates for the users according to one embodiment
of the present disclosure;
[0016] FIG. 8 illustrates a process for filtering a user profile of
a user based on aggregate profile data for a current location of
the user according to one embodiment of the present disclosure;
[0017] FIG. 9 illustrates a process for filtering a user profile of
a user at the mobile device of the user according to another
embodiment of the present disclosure;
[0018] FIGS. 10 and 11 graphically illustrate bucketization of
users according to location for purposes of maintaining a
historical record of anonymized user profile data by location
according to one embodiment of the present disclosure;
[0019] FIG. 12 is a flow chart illustrating the operation of a
foreground bucketization process performed by the MAP server to
maintain the lists of users for location buckets for purposes of
maintaining a historical record of anonymized user profile data by
location according to one embodiment of the present disclosure;
[0020] FIG. 13 is a flow chart illustrating the anonymization and
storage process performed by the MAP server for the location
buckets in order to maintain a historical record of anonymized user
profile data by location according to one embodiment of the present
disclosure;
[0021] FIG. 14 graphically illustrates anonymization of a user
record according to one embodiment of the present disclosure;
[0022] FIG. 15 is a flow chart for a quadtree based storage process
that may be used to store anonymized user profile data for location
buckets according to one embodiment of the present disclosure;
[0023] FIG. 16 is a flow chart illustrating a quadtree algorithm
that may be used to process the location buckets for storage of the
anonymized user profile data according to one embodiment of the
present disclosure;
[0024] FIGS. 17A through 17E graphically illustrate the process of
FIG. 16 for the generation of a quadtree data structure for one
exemplary base quadtree region;
[0025] FIG. 18 illustrates a process for generating historical
aggregate profile data for utilization in filtering a user profile
of a user according to the processes of FIGS. 4 and 9 according to
one embodiment of the present disclosure;
[0026] FIG. 19 is a flow chart for a spatial crowd formation
process according to one embodiment of the present disclosure;
[0027] FIGS. 20A through 20D graphically illustrate the crowd
formation process of FIG. 19 for an exemplary bounding box;
[0028] FIGS. 21A through 21D illustrate a flow chart for a spatial
crowd formation process according to another embodiment of the
present disclosure;
[0029] FIGS. 22A through 22D graphically illustrate the crowd
formation process of FIGS. 21A through 21D for a scenario where the
crowd formation process is triggered by a location update for a
user having no old location;
[0030] FIGS. 23A through 23F graphically illustrate the crowd
formation process of FIGS. 21A through 21D for a scenario where the
new and old bounding boxes overlap;
[0031] FIGS. 24A through 24E graphically illustrate the crowd
formation process of FIGS. 21A through 21D in a scenario where the
new and old bounding boxes do not overlap;
[0032] FIG. 25 illustrates a process for generating current
aggregate profile data for utilization in filtering a user profile
of a user according to the processes of FIGS. 4 and 9 according to
one embodiment of the present disclosure;
[0033] FIG. 26 illustrates a process for filtering aggregate
profile data received from the MAP server at a mobile device
according to another embodiment of the present disclosure;
[0034] FIG. 27 illustrates a process for filtering aggregate
profile data based on the aggregate profile data itself according
to one embodiment of the present disclosure;
[0035] FIG. 28 is a block diagram of the MAP server of FIG. 1
according to one embodiment of the present disclosure; and
[0036] FIG. 29 is a block diagram of one of the mobile devices of
FIG. 1 according to one embodiment of the present disclosure.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0037] The embodiments set forth below represent the necessary
information to enable those skilled in the art to practice the
embodiments and illustrate the best mode of practicing the
embodiments. Upon reading the following description in light of the
accompanying drawing figures, those skilled in the art will
understand the concepts of the disclosure and will recognize
applications of these concepts not particularly addressed herein.
It should be understood that these concepts and applications fall
within the scope of the disclosure and the accompanying claims.
[0038] FIG. 1 illustrates a Mobile Aggregate Profile (MAP) system
10 (hereinafter "system 10") according to one embodiment of the
present disclosure. In this embodiment, the system 10 includes a
MAP server 12, one or more profile servers 14, a location server
16, a number of mobile devices 18-1 through 18-N having associated
users 20-1 through 20-N, a subscriber device 22 having an
associated subscriber 24, and a third-party service 26
communicatively coupled via a network 28. The network 28 may be any
type of network or any combination of networks. Specifically, the
network 28 may include wired components, wireless components, or
both wired and wireless components. In one exemplary embodiment,
the network 28 is a distributed public network such as the
Internet, where the mobile devices 18-1 through 18-N are enabled to
connect to the network 28 via local wireless connections (e.g.,
WiFi or IEEE 802.11 connections) or wireless telecommunications
connections (e.g., 3G or 4G telecommunications connections such as
GSM, LTE, W-CDMA, or WiMAX connections).
[0039] As discussed below in detail, the MAP server 12 operates to
obtain current locations, including location updates, and user
profiles of the users 20-1 through 20-N of the mobile devices 18-1
through 18-N. The current locations of the users 20-1 through 20-N
can be expressed as positional geographic coordinates such as
latitude-longitude pairs, and a height vector (if applicable), or
any other similar information capable of identifying a given
physical point in space in a two-dimensional or three-dimensional
coordinate system. Using the current locations and user profiles of
the users 20-1 through 20-N, the MAP server 12 is enabled to
provide a number of features such as, but not limited to,
maintaining a historical record of anonymized user profile data by
location, generating aggregate profile data over time for a Point
of Interest (POI) or Area of Interest (AOI) using the historical
record of anonymized user profile data, identifying crowds of users
using current locations and/or user profiles of the users 20-1
through 20-N, generating aggregate profiles for crowds of users at
a POI or in an AOI using the user profiles of users in the crowds,
and crowd tracking. While not essential, for additional information
regarding the MAP server 12, the interested reader is directed to
U.S. patent application Ser. No. 12/645,535 entitled MAINTAINING A
HISTORICAL RECORD OF ANONYMIZED USER PROFILE DATA BY LOCATION FOR
USERS IN A MOBILE ENVIRONMENT, U.S. patent application Ser. No.
12/645,532 entitled FORMING CROWDS AND PROVIDING ACCESS TO CROWD
DATA IN A MOBILE ENVIRONMENT, U.S. patent application Ser. No.
12/645,539 entitled ANONYMOUS CROWD TRACKING, U.S. patent
application Ser. No. 12/645,544 entitled MODIFYING A USER'S
CONTRIBUTION TO AN AGGREGATE PROFILE BASED ON TIME BETWEEN LOCATION
UPDATES AND EXTERNAL EVENTS, U.S. patent application Ser. No.
12/645,546 entitled CROWD FORMATION FOR MOBILE DEVICE USERS, U.S.
patent application Ser. No. 12/645,556 entitled SERVING A REQUEST
FOR DATA FROM A HISTORICAL RECORD OF ANONYMIZED USER PROFILE DATA
IN A MOBILE ENVIRONMENT, and U.S. patent application Ser. No.
12/645,560 entitled HANDLING CROWD REQUESTS FOR LARGE GEOGRAPHIC
AREAS, all of which were filed on Dec. 23, 2009 and have been
incorporated herein by reference in their entireties. Note that
while the MAP server 12 is illustrated as a single server for
simplicity and ease of discussion, it should be appreciated that
the MAP server 12 may be implemented as a single physical server or
multiple physical servers operating in a collaborative manner for
purposes of redundancy and/or load sharing.
[0040] In general, in one embodiment, the one or more profile
servers 14 operate to store user profiles for a number of persons
including the users 20-1 through 20-N of the mobile devices 18-1
through 18-N. For example, the one or more profile servers 14 may
be servers providing social network services such as the
Facebook.RTM. social networking service, the MySpace.RTM. social
networking service, the LinkedIN.RTM. social networking service, or
the like. As discussed below, in one embodiment, the MAP server 12
is enabled to directly or indirectly obtain the user profiles of
the users 20-1 through 20-N of the mobile devices 18-1 through
18-N, or filtered versions of the user profiles, using the one or
more profile servers 14. The location server 16 generally operates
to receive location updates from the mobile devices 18-1 through
18-N and make the location updates available to entities such as,
for instance, the MAP server 12. In one exemplary embodiment, the
location server 16 is a server operating to provide Yahoo!'s
FireEagle service.
[0041] The mobile devices 18-1 through 18-N may be mobile smart
phones, portable media player devices, mobile gaming devices, or
the like. Some exemplary mobile devices that may be programmed or
otherwise configured to operate as the mobile devices 18-1 through
18-N are the Apple.RTM. iPhone.RTM., the Palm.RTM. Pre.TM., the
Samsung Rogue.TM., the Blackberry.RTM. Storm.TM., and the
Apple.RTM. iPod Touch.RTM. device. However, this list of exemplary
mobile devices is not exhaustive and is not intended to limit the
scope of the present disclosure.
[0042] The mobile devices 18-1 through 18-N include MAP clients
30-1 through 30-N, MAP applications 32-1 through 32-N, third-party
applications 34-1 through 34-N, and location functions 36-1 through
36-N, respectively. Using the mobile device 18-1 as an example, the
MAP client 30-1 is preferably implemented in software. In general,
in the preferred embodiment, the MAP client 30-1 is a middleware
layer operating to interface an application layer (i.e., the MAP
application 32-1 and the third-party applications 34-1) to the MAP
server 12. More specifically, the MAP client 30-1 enables the MAP
application 32-1 and the third-party applications 34-1 to request
and receive data from the MAP server 12. In addition, the MAP
client 30-1 enables applications, such as the MAP application 32-1
and the third-party applications 34-1, to access data from the MAP
server 12. Note that while the MAP client 30-1 is implemented
separately from the MAP application 32-1 and the third-party
applications 34-1 in this embodiment, one of ordinary skill in the
art will appreciate that the MAP client 30-1, or functions of the
MAP client 30-1, may alternatively be implemented in the MAP
application 32-1 and the third-party applications 34-1.
[0043] The MAP application 32-1 is also preferably implemented in
software. The MAP application 32-1 generally provides a user
interface component between the user 20-1 and the MAP server 12.
More specifically, among other things, the MAP application 32-1 may
enable the user 20-1 to initiate historical requests for historical
data or crowd requests for crowd data (e.g., aggregate profile data
and/or crowd characteristics data) from the MAP server 12 for a POI
or AOI. The MAP application 32-1 may also enable the user 20-1 to
configure various settings. For example, the MAP application 32-1
may enable the user 20-1 to select a desired social networking
service (e.g., Facebook.RTM., MySpace.RTM., LinkedIN.RTM., etc.)
from which to obtain the user profile of the user 20-1 and provide
any necessary credentials (e.g., username and password) needed to
access the user profile from the social networking service.
[0044] The third-party applications 34-1 are preferably implemented
in software. The third-party applications 34-1 operate to access
the MAP server 12 via the MAP client 30-1. The third-party
applications 34-1 may utilize data obtained from the MAP server 12
in any desired manner. As an example, one of the third-party
applications 34-1 may be a gaming application that utilizes
historical aggregate profile data to notify the user 20-1 of POIs
or AOIs where persons having an interest in the game have
historically congregated.
[0045] The location function 36-1 may be implemented in hardware,
software, or a combination thereof. In general, the location
function 36-1 operates to determine or otherwise obtain the
location of the mobile device 18-1. For example, the location
function 36-1 may be or include a Global Positioning System (GPS)
receiver.
[0046] The subscriber device 22 is a physical device such as a
personal computer, a mobile computer (e.g., a notebook computer, a
netbook computer, a tablet computer, etc.), a mobile smart phone,
or the like. The subscriber 24 associated with the subscriber
device 22 is a person or entity. In general, the subscriber device
22 enables the subscriber 24 to access the MAP server 12 via a web
browser 38 to obtain various types of data, preferably for a fee.
For example, the subscriber 24 may pay a fee to have access to
historical aggregate profile data for one or more POIs and/or one
or more AOIs, pay a fee to have access to crowd data such as
aggregate profiles for crowds located at one or more POIs and/or
located in one or more AOIs, pay a fee to track crowds, or the
like. Note that the web browser 38 is exemplary. In another
embodiment, the subscriber device 22 is enabled to access the MAP
server 12 via a custom application.
[0047] Lastly, the third-party service 26 is a service that has
access to data from the MAP server 12 such as a historical
aggregate profile data for one or more POIs or one or more AOIs,
crowd data such as aggregate profiles for one or more crowds at one
or more POIs or within one or more AOIs, or crowd tracking data.
Based on the data from the MAP server 12, the third-party service
26 operates to provide a service such as, for example, targeted
advertising. For example, the third-party service 26 may obtain
anonymous aggregate profile data for one or more crowds located at
a POI and then provide targeted advertising to known users located
at the POI based on the anonymous aggregate profile data. Note that
while targeted advertising is mentioned as an exemplary third-party
service 26, other types of third-party services 26 may additionally
or alternatively be provided. Other types of third-party services
26 that may be provided will be apparent to one of ordinary skill
in the art upon reading this disclosure.
[0048] Before proceeding, it should be noted that while the system
10 of FIG. 1 illustrates an embodiment where the one or more
profile servers 14 and the location server 16 are separate from the
MAP server 12, the present disclosure is not limited thereto. In an
alternative embodiment, the functionality of the one or more
profile servers 14 and/or the location server 16 may be implemented
within the MAP server 12.
[0049] FIG. 2 is a block diagram of the MAP server 12 of FIG. 1
according to one embodiment of the present disclosure. As
illustrated, the MAP server 12 includes an application layer 40, a
business logic layer 42, and a persistence layer 44. The
application layer 40 includes a user web application 46, a mobile
client/server protocol component 48, and one or more data
Application Programming Interfaces (APIs) 50. The user web
application 46 is preferably implemented in software and operates
to provide a web interface for users, such as the subscriber 24, to
access the MAP server 12 via a web browser. The mobile
client/server protocol component 48 is preferably implemented in
software and operates to provide an interface between the MAP
server 12 and the MAP clients 30-1 through 30-N hosted by the
mobile devices 18-1 through 18-N. The data APIs 50 enable
third-party services, such as the third-party service 26, to access
the MAP server 12.
[0050] The business logic layer 42 includes a profile manager 52, a
location manager 54, a history manager 56, a crowd analyzer 58, and
an aggregation engine 60, each of which is preferably implemented
in software. In one embodiment, the profile manager 52 operates to
obtain the user profiles of the users 20-1 through 20-N from the
one or more profile servers 14, filter the user profiles of the
users 20-1 through 20-N, and store the filtered user profiles and,
optionally, the user profiles of the users 20-1 through 20-N in the
persistence layer 44. In another embodiment, the profile manager 52
operates to obtain the user profiles of the users 20-1 through 20-N
from the mobile devices 18-1 through 18-N of the users 20-1 through
20-N, filter the user profiles of the user 20-1 through 20-N, and
store the filtered user profiles and, optionally, the user profiles
of the users 20-1 through 20-N in the persistence layer 44. In yet
another embodiment, the profile manager 52 operates to obtain
filtered user profiles of the users 20-1 through 20-N from the
mobile devices 18-1 through 18-N of the users 20-1 through 20-N and
stored the filtered user profiles of the users 20-1 through 20-N in
the persistence layer 44. In yet another embodiment, the profile
manager 52 operates to obtain the user profiles of the users 20-1
through 20-N directly or indirectly from the one or more profile
servers 14 or directly from the mobile devices 18-1 through 18-N of
the users 20-1 through 20-N and store the user profiles of the
users 20-1 through 20-N in the persistence layer 44.
[0051] The location manager 54 operates to obtain the current
locations of the users 20-1 through 20-N including location
updates. The current locations of the users 20-1 through 20-N may
be obtained directly from the mobile devices 18-1 through 18-N
and/or obtained from the location server 16. The history manager 56
generally operates to maintain a historical record of anonymized
user profile data by location. The crowd analyzer 58 operates to
form crowds of users. In one embodiment, the crowd analyzer 58
utilizes a spatial crowd formation algorithm. However, the present
disclosure is not limited thereto. In addition, the crowd analyzer
58 may also operate to track crowds. The aggregation engine 60
generally operates to provide aggregate profile data in response to
requests from the mobile devices 18-1 through 18-N, the subscriber
device 22, and the third-party service 26. The aggregate profile
data may be historical aggregate profile data or current aggregate
profile data.
[0052] The persistence layer 44 includes an object mapping layer 62
and a datastore 64. The object mapping layer 62 is preferably
implemented in software. The datastore 64 is preferably a
relational database, which is implemented in a combination of
hardware (i.e., physical data storage hardware) and software (i.e.,
relational database software). In this embodiment, the business
logic layer 42 is implemented in an object-oriented programming
language such as, for example, Java. As such, the object mapping
layer 62 operates to map objects used in the business logic layer
42 to relational database entities stored in the datastore 64. Note
that, in one embodiment, data is stored in the datastore 64 in a
Resource Description Framework (RDF) compatible format.
[0053] In an alternative embodiment, rather than being a relational
database, the datastore 64 may be implemented as an RDF datastore.
More specifically, the RDF datastore may be compatible with RDF
technology adopted by Semantic Web activities. Namely, the RDF
datastore may use the Friend-Of-A-Friend (FOAF) vocabulary for
describing people, their social networks, and their interests. In
this embodiment, the MAP server 12 may be designed to accept raw
FOAF files describing persons, their friends, and their interests.
These FOAF files are currently output by some social networking
services such as LiveJournal and Facebook.RTM.. The MAP server 12
may then persist RDF descriptions of the users 20-1 through 20-N as
a proprietary extension of the FOAF vocabulary that includes
additional properties desired for the system 10.
[0054] FIG. 3 illustrates the MAP client 30-1 of FIG. 1 in more
detail according to one embodiment of the present disclosure. This
discussion is equally applicable to the other MAP clients 30-2
through 30-N. As illustrated, in this embodiment, the MAP client
30-1 includes a MAP access API 66, a MAP middleware component 68,
and a mobile client/server protocol component 70. The MAP access
API 66 is implemented in software and provides an interface by
which the MAP client 30-1 and the third-party applications 34-1 are
enabled to access the MAP client 30-1. The MAP middleware component
68 is implemented in software and performs the operations needed
for the MAP client 30-1 to operate as an interface between the MAP
application 32-1 and the third-party applications 34-1 at the
mobile device 18-1 and the MAP server 12. The mobile client/server
protocol component 70 enables communication between the MAP client
30-1 and the MAP server 12 via a defined protocol.
[0055] FIG. 4 illustrates a process performed by the MAP server 12
to tailor user profiles of one or more of the users 20-1 through
20-N according to one embodiment of the present disclosure. In
general, using the user 20-1 as an example, the user 20-1 may
desire to reveal different portions of his user profile depending
on his environment. For example, the user 20-1 may desire to reveal
interests in his user profile related to his work when he is in a
work or business environment and reveal interests in his user
profile related to his personal life when he is in an environment
around other users that have similar personal interests. Using the
process of FIG. 4, the profile manager 52 operates to provide such
tailoring of the user profile of the user 20-1.
[0056] First, the profile manager 52 of the MAP server 12 obtains a
user profile of a user (step 1000). For this discussion, the user
is the user 20-1. However, this discussion is equally applicable to
the other users 20-2 through 20-N. As discussed below in detail, in
one embodiment, the profile manager 52 obtains the user profile of
the user 20-1 directly or indirectly from the one or more profile
servers 14. In another embodiment, the user profile of the user
20-1 may be created and maintained at the mobile device 18-1, in
which case the profile manager 52 obtains the user profile of the
user 20-1 from the mobile device 18-1 of the user 20-1. As
discussed below in detail, the user profile of the user 20-1
includes a number of interests of the user 20-1. The interests are
preferably expressed as keywords.
[0057] Next, the location manager 54 receives a location update for
the user 20-1 of the mobile device 18-1 (step 1002). As described
below in detail, in one embodiment, the location manager 54
receives the location update for the user 20-1 directly from the
mobile device 18-1 of the user 20-1. In another embodiment, the
location manager 54 receives the location update for the user 20-1
from the location server 16. The location update provides the
current location of the user 20-1. Note that, in one exemplary
alternative embodiment, rather than obtaining the user profile
separately in step 1000, both the user profile and the current
location may be included in the update received in step 1002.
[0058] In this embodiment, after receiving the location update, the
profile manager 52 obtains aggregate profile data for the current
location of the user 20-1 (step 1004). The aggregate profile data
includes current aggregate profile data for users currently located
at or near the current location of the user 20-1, historical
aggregate profile data for users previously located at or near the
current location of the user 20-1, or both current aggregate
profile data and historical aggregate profile data. The current
aggregate profile data includes an aggregate list of interests from
filtered user profiles of users currently located at or near the
current location of the user 20-1. In one embodiment, the users
currently located at or near the current location of the user 20-1
are users in one or more crowds of users located within a defined
bounding region centered at or otherwise encompassing the current
location of the user 20-1. In addition, for each interest in the
aggregate list of interests, the aggregate profile data preferably
includes a representation value defining a degree to which the
interest is expressed in the filtered user profiles of the users
currently located at or near the current location of the user 20-1.
In one embodiment, the representation value for an interest is
expressed as a number of occurrences or user matches of the
interest in the filtered user profiles of the users located at or
near the current location of the user 20-1. In another embodiment,
the representation value for an interest is expressed as a ratio of
a number of occurrences or user matches of the interest in the
filtered user profiles of the users located at or near the current
location of the user 20-1 over a total number of users. Note that
if the number of users at or near the current location of the user
20-1 are users in multiple crowds of users, then the current
aggregate profile data may include a separate aggregate profile
(i.e., aggregate list of interests and, preferably, corresponding
representation values) for each crowd of users or a combined
aggregate profile for all of the crowds of users. The combined
aggregate profile for all of the crowds of users may include, for
example, an aggregate list of interests for all of the crowds and,
preferably, corresponding representation values for the interests
across all of the crowds.
[0059] In one embodiment, the historical profile data includes one
or more historical aggregate profiles. Each historical aggregate
profile is an aggregate profile for users located at or near the
current location of the user 20-1 during a corresponding time
period that occurred in the past. More specifically, in one
embodiment, the users located at or near the current location of
the user 20-1 during the corresponding time period are users for
which filtered user profiles are stored in history objects relevant
to a bounding region encompassing the location of the user 20-1
during the corresponding time period, as discussed below. Further,
each historical aggregate profile includes an aggregate list of
interests from the filtered user profiles of the users located at
or near the current location of the user 20-1 during the
corresponding time period. In addition, each historical aggregate
profile preferably includes corresponding representation values for
the interests in the historical aggregate profile, where the
representation values define degrees to which the interests are
represented in the filtered user profiles of the users located at
or near the current location of the user 20-1 during the
corresponding time period. In one embodiment, a representation
value for an interest is expressed as a number of occurrences or
user matches of the interest in the filtered user profiles of the
users located at or near the current location of the user 20-1
during the corresponding time period. In another embodiment, the
representation value for an interest is expressed as a ratio of a
number of occurrences or user matches of the interest in the
filtered user profiles of the users located at or near the current
location of the user 20-1 over a total number of users during the
corresponding time period.
[0060] Note that for the discussion herein, it is assumed that that
the user profiles of all of the users 20-1 through 20-N are
filtered as described herein and, as such, the filtered user
profiles are used to generate both the current and historical
aggregate profile data. However, in some embodiments, the user
profiles of some of the users 20-1 through 20-N may not be
filtered, in which case the user profiles of those users are used
to generate both current and historical aggregate profile data.
[0061] In this embodiment, the profile manager 52 also obtains
additional information, if any, for the current location of the
user 20-1 (step 1006). Note that step 1006 is optional. The
additional information may be any type of information about the
current location of the user 20-1 that may assist the profile
manager 52 in determining what interests in the user profile of the
user 20-1 should or should not contribute to aggregate profile data
accessible from the MAP server 12. For example, if the current
location of the user 20-1 indicates that the user 20-1 is located
at a POI, the additional data may be data describing that POI. As a
more specific example, if the current location of the user 20-1
indicates that the user 20-1 is at an arena at which different
types of events are held (e.g., sporting events, music concerts,
etc.), the profile manager 52 may obtain additional information
such as, for example, information describing an event currently
being held at the arena. The additional information may be obtained
from an internal database or repository maintained by the MAP
server 12, where this internal database or repository is manually
populated by a community of users such as the users 20-1 through
20-N, employees of an owner of the MAP server 12, or the like.
Alternatively, the internal database or repository may be populated
via an automated process where, for example, websites associated
with a number of known POIs are analyzed to obtain the additional
information for the POIs. In another embodiment, the MAP server 12
may obtain the additional information for the current location of
the user 20-1 from one or more remote sources that operate to
maintain such information.
[0062] Next, the profile manager 52 filters the user profile of the
user 20-1 based on the aggregate profile data for the current
location of the user 20-1, the additional information obtained for
the current location of the user 20-1, and one or more defined
filtering rules (step 1008). In general, the profile manager 52
filters the user profile of the user 20-1 based on the aggregate
profile data and the additional information for the current
location of the user 20-1 to remove any interests from the user
profile of the user 20-1 that are not desired to be revealed in the
current environment of the user 20-1. More specifically, as
described below in detail, the one or more defined filtering rules
are rules that enable the profile manager 52 to determine, for each
interest in the user profile of the user 20-1, whether to filter
that interest based on the aggregate profile data and/or the
additional information for the current location of the user 20-1.
The one or more defined filtering rules may include system-defined
rules, rules defined by the user 20-1, or a combination
thereof.
[0063] Lastly, the filtered user profile of the user 20-1 is stored
in the datastore 64 of the MAP server 12 (step 1010). Thereafter,
the filtered user profile of the user 20-1, rather than the user
profile of the user 20-1, is utilized by the MAP server 12 to
provide aggregate profile data. At this point, the process returns
to step 1002 and is repeated for the next location update for the
user 20-1. As such, as the current location of the user 20-1
changes and/or as the aggregate profile data for the current
location of the user 20-1 changes, the filtered user profile of the
user 20-1 is updated. Further, as described below, in some
embodiments, more of the user profile of the user 20-1 may be
revealed as the user 20-1 spends more time at the same
location.
[0064] FIG. 5 illustrates step 1000 of FIG. 4 in more detail
according to one exemplary embodiment of the present disclosure.
More specifically, FIG. 5 illustrates a process by which user
profiles of the users 20-1 through 20-N are obtained by the MAP
server 12 according to one exemplary embodiment of the present
disclosure. In this example, the user profile of the user 20-1 is
obtained by the MAP server 12. However, this process may also be
used to obtain the user profiles of the other users 20-2 through
20-N. First, an authentication process is performed (step 1100).
For authentication, in this embodiment, the mobile device 18-1
authenticates with the profile server 14 (step 1100A) and the MAP
server 12 (step 1100B). In addition, the MAP server 12
authenticates with the profile server 14 (step 1100C). Preferably,
authentication is performed using OpenID or similar technology.
However, authentication may alternatively be performed using
separate credentials (e.g., username and password) of the user 20-1
for access to the MAP server 12 and the profile server 14. Assuming
that authentication is successful, the profile server 14 returns an
authentication succeeded message to the MAP server 12 (step 1100D),
and the profile server 14 returns an authentication succeeded
message to the MAP client 30-1 of the mobile device 18-1 (step
1100E).
[0065] At some point after authentication is complete, a user
profile process is performed such that a user profile of the user
20-1 is obtained from the profile server 14 and delivered to the
MAP server 12 (step 1102). In this embodiment, the MAP client 30-1
of the mobile device 18-1 sends a profile request to the profile
server 14 (step 1102A). In response, the profile server 14 returns
the user profile of the user 20-1 to the mobile device 18-1 (step
1102B). The MAP client 30-1 of the mobile device 18-1 then sends
the user profile of the user 20-1 to the MAP server 12 (step
1102C). Note that while in this embodiment the MAP client 30-1
sends the complete user profile of the user 20-1 to the MAP server
12, in an alternative embodiment, the MAP client 30-1 may filter
the user profile of the user 20-1 according to criteria specified
by the user 20-1. For example, the user profile of the user 20-1
may include demographic information, general interests, music
interests, and movie interests, and the user 20-1 may specify that
the demographic information or some subset thereof is to be
filtered, or removed, before sending the user profile to the MAP
server 12.
[0066] Upon receiving the user profile of the user 20-1 from the
MAP client 30-1 of the mobile device 18-1, the profile manager 52
of the MAP server 12 processes the user profile (step 1102D). More
specifically, in the preferred embodiment, the profile manager 52
includes social network handlers for the social network services
supported by the MAP server 12. Thus, for example, if the MAP
server 12 supports user profiles from the Facebook.RTM. social
networking service, the MySpace.RTM. social networking service, and
the LinkedIN.RTM. social networking service, the profile manager 52
may include a handler for the Facebook.RTM. social network, a
handler for the MySpace.RTM. social network, and a handler for the
LinkedIN.RTM. social network. The social network handlers process
user profiles to generate user profiles for the MAP server 12 that
include lists of keywords for each of a number of profile
categories. The profile categories may be the same for each of the
social network handlers or different for each of the social network
handlers. Thus, for this example assume that the user profile of
the user 20-1 is from the Facebook.RTM. social networking service.
The profile manager 52 uses the handler for the Facebook.RTM.
social network to process the user profile of the user 20-1 to map
the user profile of the user 20-1 from the Facebook.RTM. social
networking service to a user profile for the MAP server 12
including lists of keywords for a number of predefined profile
categories. For example, for the handler for the Facebook.RTM.
social network, the profile categories may be a demographic profile
category, a social interaction profile category, a general
interests profile category, a music interests profile category, and
a movie interests profile category. As such, the user profile of
the user 20-1 from the Facebook.RTM. social networking service may
be processed by the handler for the Facebook.RTM. social network to
create a list of keywords such as, for example, liberal, High
School Graduate, 35-44, College Graduate, etc. for the demographic
profile category; a list of keywords such as Seeking Friendship for
the social interaction profile category; a list of keywords such as
politics, technology, photography, books, etc. for the general
interests profile category; a list of keywords including music
genres, artist names, album names, or the like for the music
interests profile category; and a list of keywords including movie
titles, actor or actress names, director names, move genres, or the
like for the movie interests profile category. In one embodiment,
the profile manager 52 may use natural language processing or
semantic analysis. For example, if the Facebook.RTM. user profile
of the user 20-1 states that the user 20-1 is 20 years old,
semantic analysis may result in the keyword of 18-24 years old
being stored in the user profile of the user 20-1 for the MAP
server 12.
[0067] After processing the user profile of the user 20-1, the
profile manager 52 of the MAP server 12 stores the resulting user
profile for the user 20-1 (step 1102E). More specifically, in one
embodiment, the MAP server 12 stores user records for the users
20-1 through 20-N in the datastore 64 (FIG. 2). The user profile of
the user 20-1 is stored in the user record of the user 20-1. The
user record of the user 20-1 includes a unique identifier of the
user 20-1, the current location of the user 20-1, the user profile
of the user 20-1, and, once filtering is performed, the filtered
user profile of the user 20-1. Note that the user profile of the
user 20-1 may be updated as desired. For example, in one
embodiment, the user profile of the user 20-1 is updated by
repeating step 1102 each time the user 20-1 activates the MAP
application 32-1.
[0068] FIG. 6 illustrates step 1000 of FIG. 4 in more detail
according to another exemplary embodiment of the present
disclosure. More specifically, FIG. 6 illustrates a process by
which the MAP server 12 obtains the current location of the user
20-1 according to another exemplary embodiment of the present
disclosure. This example is directed to obtaining the user profile
of the user 20-1. However, this process may also be used to obtain
the user profiles of the other users 20-2 through 20-N. First, an
authentication process is performed (step 1200). For
authentication, in this embodiment, the mobile device 18-1
authenticates with the MAP server 12 (step 1200A), and the MAP
server 12 authenticates with the profile server 14 (step 1200B).
Preferably, authentication is performed using OpenID or similar
technology. However, authentication may alternatively be performed
using separate credentials (e.g., username and password) of the
user 20-1 for access to the MAP server 12 and the profile server
14. Assuming that authentication is successful, the profile server
14 returns an authentication succeeded message to the MAP server 12
(step 1200C), and the MAP server 12 returns an authentication
succeeded message to the MAP client 30-1 of the mobile device 18-1
(step 1200D).
[0069] At some point after authentication is complete, a user
profile process is performed such that a user profile of the user
20-1 is obtained from the profile server 14 and delivered to the
MAP server 12 (step 1202). In this embodiment, the profile manager
52 of the MAP server 12 sends a profile request to the profile
server 14 (step 1202A). In response, the profile server 14 returns
the user profile of the user 20-1 to the profile manager 52 of the
MAP server 12 (step 1202B). Note that while in this embodiment the
profile server 14 returns the complete user profile of the user
20-1 to the MAP server 12, in an alternative embodiment, the
profile server 14 may return a filtered version of the user profile
of the user 20-1 to the MAP server 12. The profile server 14 may
filter the user profile of the user 20-1 according to criteria
specified by the user 20-1. For example, the user profile of the
user 20-1 may include demographic information, general interests,
music interests, and movie interests, and the user 20-1 may specify
that the demographic information or some subset thereof is to be
filtered, or removed, before sending the user profile to the MAP
server 12.
[0070] Upon receiving the user profile of the user 20-1, the
profile manager 52 of the MAP server 12 processes to the user
profile (step 1202C). More specifically, as discussed above, in the
preferred embodiment, the profile manager 52 includes social
network handlers for the social network services supported by the
MAP server 12. The social network handlers process user profiles to
generate user profiles for the MAP server 12 that include lists of
keywords for each of a number of profile categories. The profile
categories may be the same for each of the social network handlers
or different for each of the social network handlers.
[0071] After processing the user profile of the user 20-1, the
profile manager 52 of the MAP server 12 stores the resulting user
profile for the user 20-1 (step 1202D). More specifically, in one
embodiment, the MAP server 12 stores user records for the users
20-1 through 20-N in the datastore 64 (FIG. 2). The user profile of
the user 20-1 is stored in the user record of the user 20-1. The
user record of the user 20-1 includes a unique identifier of the
user 20-1, the current location of the user 20-1, the user profile
of the user 20-1, and, once filtering is performed, the filtered
user profile of the user 20-1. Note that the user profile of the
user 20-1 may be updated as desired. For example, in one
embodiment, the user profile of the user 20-1 is updated by
repeating step 1202 each time the user 20-1 activates the MAP
application 32-1.
[0072] FIG. 7 illustrates a process by which location updates are
sent to the MAP server 12 according to one embodiment of the
present disclosure. In one embodiment, the current location of the
user 20-1 received by the MAP server 12 in step 1002 of FIG. 4 is
received via this process. This example illustrates location
updates for the user 20-1 of the mobile device 18-1. However, this
process may be used for location updates for the other users 20-2
through 20-N of the other mobile devices 18-2 through 18-N. First,
in this example, a process is performed such that a current
location of the mobile device 18-1 and thus a current location of
the user 20-1 is obtained by the MAP server 12 (step 1300). In this
embodiment, the MAP application 32-1 of the mobile device 18-1
obtains the current location of the mobile device 18-1 from the
location function 36-1 of the mobile device 18-1. The MAP
application 32-1 then provides the current location of the user
20-1 of the mobile device 18-1 to the location server 16 (step
1300A). Note that step 1300A may be repeated periodically or in
response to changes in the location of the mobile device 18-1 in
order to provide location updates for the user 20-1 to the MAP
server 12. The location server 16 then provides the current
location of the user 20-1 to the MAP server 12 (step 1300B). The
location server 16 may provide the current location of the user
20-1 to the MAP server 12 automatically in response to receiving
the current location of the user 20-1 from the mobile device 18-1
or in response to a request from the MAP server 12.
[0073] In response to receiving the current location of the mobile
device 18-1, the location manager 54 of the MAP server 12 stores
the current location of the mobile device 18-1 as the current
location of the user 20-1 (step 1300C). More specifically, in one
embodiment, the current location of the user 20-1 is stored in the
user record of the user 20-1 maintained in the datastore 64 of the
MAP server 12. Note that, preferably, only the current location of
the user 20-1 is stored in the user record of the user 20-1. In
this manner, the MAP server 12 maintains privacy for the user 20-1
since the MAP server 12 does not maintain a historical record of
the location of the user 20-1. As discussed below in detail,
historical data maintained by the MAP server 12 is anonymized in
order to maintain the privacy of the users 20-1 through 20-N.
[0074] As discussed above, the use of the location server 16 is
particularly beneficial when the mobile device 18-1 does not permit
background processes. As such, if the mobile device 18-1 does not
permit background processes, the MAP application 32-1 will not
provide location updates for the user 20-1 to the location server
16 unless the MAP application 32-1 is active. However, other
applications running on the mobile device 18-1 (or some other
device of the user 20-1) may provide location updates to the
location server 16 for the user 20-1 when the MAP application 32-1
is not active. This is illustrated in step 1302 where the location
server 16 receives a location update for the user 20-1 from another
application running on the mobile device 18-1 or an application
running on another device of the user 20-1 (step 1302A). The
location server 16 then provides the location update for the user
20-1 to the MAP server 12 (step 1302B). In response, the location
manager 54 updates and stores the current location of the user 20-1
in the user record of the user 20-1 (step 1302C). In this manner,
the MAP server 12 is enabled to obtain location updates for the
user 20-1 even when the MAP application 32-1 is not active at the
mobile device 18-1.
[0075] FIG. 8 illustrates step 1008 of FIG. 4 in more detail
according to one embodiment of the present disclosure. More
specifically, FIG. 8 illustrates a process for filtering the user
profile of the user 20-1 based on aggregate profile data for the
current location of the user 20-1 and, optionally, additional
information obtained for the current location of the user 20-1
according to one embodiment of the present disclosure. First, a
cut-off value is determined (step 1400). As discussed below, the
cut-off value is utilized to filter interests in the user profile
of the user 20-1 that correspond to interests in the aggregate
profile data having representation values that are less than the
cut-off value. In one embodiment, the cut-off value is a
predefined, static value.
[0076] In another embodiment, the cut-off value is a function of a
highest representation value of the representation values for the
interests in the aggregate profile data. More specifically, the
cut-off value may be a defined percentage of the highest
representation value in the aggregate profile data. The defined
percentage may be a system-defined percentage such as, for example,
50%, or 1/2. Alternatively, the defined percentage may be a
user-configurable percentage that is defined by the user 20-1 via,
for example, a Graphical User Interface (GUI) provided by the MAP
application 32-1. As such, the cut-off value may be determined by
first determining the highest representation value from the
representation values for the interests in the aggregate profile
data. Then, the cut-off value is computed as the defined percentage
of the highest representation value.
[0077] In another embodiment, the cut-off value is a function of a
highest representation value of the representation values for the
interests in the aggregate profile data and an amount of time that
the user 20-1 has been located at the same location. In one
embodiment, the amount of time that the user 20-1 has been located
at the same location is an amount of time that the user 20-1 has
been located within a defined tolerance distance from a
corresponding reference location. With regard to the reference
location, in one embodiment, when a first location update is
received for the user 20-1, the location of the user 20-1 at that
time is defined as the reference location for the user 20-1, and
the current time and optionally date are stored as a reference
timestamp for the user 20-1. As long as the user 20-1 remains
within a predefined tolerance distance from the reference location,
the reference location remains the same and the user 20-1 is
determined to be at the same location for purposes of revealing
more of the user profile of the user 20-1 over time. Once the user
20-1 moves outside of the tolerance distance from the reference
location, the location of the user 20-1 at that time is stored as
the reference location for the user 20-1, and the reference
timestamp is set to the time and optionally date at that time.
[0078] The amount of time that the user 20-1 has been at or near
the current location is determined by first determining if the
current location of the user 20-1 is within the predefined
tolerance distance from the reference location stored for the user
20-1. If so, the user 20-1 is determined to have remained at the
same location, and the amount of time that the user 20-1 has been
at that location is computed as a difference between the current
time or timestamp associated with the corresponding location update
received for the user 20-1 and the reference timestamp stored for
the user 20-1. Otherwise, the user 20-1 is determined not to be at
the same location and, as such, the reference location and
reference timestamp are updated and the amount of time that the
user 20-1 has been at the same location is set to zero. The cut-off
value is computed as a function of the aggregate profile data and
the amount of time that the user 20-1 has been at the same
location.
[0079] More specifically, in one embodiment, the cut-off value
decreases from an initial cut-off value to a minimum cut-off value
as the amount of time that the user 20-1 remains at the same
location increases from a minimum value (e.g., 0) to a maximum
value in an adjustment period. In this manner, as the user 20-1
remains at the same location, more of the user profile of the user
20-1 is revealed (i.e., included in the filtered user profile of
the user 20-1). In one embodiment, the initial cut-off value is a
predefined, system value. In another embodiment, the initial
cut-off value is computed as a predefined percentage of the highest
representation value in the aggregate profile data. The predefined
percentage may be system-defined or configurable by the user 20-1.
For example, the predefined percentage may be 50%, or 1/2, such
that the initial cut-off value is half of the highest
representation value in the aggregate profile data. Then, an
adjustment value is subtracted from the initial cut-off value to
provide the cut-off value, where the adjustment value is a function
of the amount of time that the user 20-1 has been at the
location.
[0080] The adjustment value subtracted from the initial cut-off
value goes from a defined minimum value (e.g., 0) to a defined
maximum value (e.g., half the highest representation value) over
the adjustment period (e.g., 1 hour). By increasing the subtracted
adjustment value over time, the cut-off value decreases from the
initial cut-off value to the minimum cut-off value over the
adjustment period. More specifically, a number of time thresholds
within the adjustment period and corresponding adjustment values
are defined. For example, the time thresholds and corresponding
adjustment values may be defined such that the adjustment value is
10% of the highest representation value in the aggregate profile
after the user 20-1 has remained at the same location for 20
minutes, 20% of the highest representation value in the aggregate
profile after the user 20-1 has remained at the same location for
30 minutes, 30% of the highest representation value in the
aggregate profile after the user 20-1 has remained at the same
location for 40 minutes, 40% of the highest representation value in
the aggregate profile after the user 20-1 has remained at the same
location for 50 minutes, and 50% of the highest representation
value in the aggregate profile after the user 20-1 has remained at
the same location for 60 minutes. Therefore, continuing this
example, if the amount of time that the user 20-1 has been at the
same location is 35 minutes, then the adjustment value is set to
20% of the highest representation value, and this adjustment value
is subtracted from the initial cut-off value to provide the cut-off
value.
[0081] In one embodiment, the adjustment period, the number of time
thresholds, and the corresponding adjustment values are static and
may be either be system-defined or configurable by the user 20-1.
In another embodiment, the adjustment period, the number of time
thresholds, and/or the adjustment values are dynamic. More
specifically, the adjustment period, the number of time thresholds,
and/or the adjustment values may change each time the user 20-1
changes locations (e.g., a new reference location is set in
response to the user 20-1 no longer being in the same location).
For example, the adjustment period may be defined as an initial
adjustment period plus or minus a variance value. Both the variance
value and whether the variance value is added or subtracted from
the initial adjustment period may be generated using corresponding
random number generators, which may be implemented in software or
hardware. Further, a possible maximum value for the variance value
may be increased as a frequency at which the user 20-1 has visited
the same location and/or a number of times that the user 20-1 has
visited the same location increases. Alternatively, the adjustment
period may be generated using a random number generator such that a
different adjustment period is used each time the user 20-1 changes
locations.
[0082] In addition to or as an alternative to varying the
adjustment period, the adjustment values for the time thresholds
within the adjustment period may be varied. For example, the
maximum adjustment value may be defined as an initial maximum
adjustment value (e.g., 50% of the highest representation value in
the aggregate profile data) plus or minus a variance value. The
variance value and whether the variance value is added or
subtracted from the initial maximum adjustment value may be
generated using random number generators each time the user 20-1
changes locations (e.g., each time a new reference location is set
in response to the user 20-1 no longer being in the same location).
The maximum value for the variance value may be a static value or
may increase as the frequency at which the user 20-1 visits the
same location or the number of times that the user 20-1 has visited
the same location. Alternatively, the maximum adjustment value may
be generated using a random number generator where minimum and
maximum values for the maximum adjustment value for purposes of
random number generation may be static values or may vary depending
on the frequency at which the user 20-1 has visited the same
location or the number of times that the user 20-1 has visited the
same location.
[0083] In addition or alternatively, while the discussion above
describes embodiments where the cut-off value is a function of the
amount of time that the user 20-1 has been at the same location, in
a similar manner, the cut-off value may be a function of an amount
of time that the user 20-1 has been in the same group of users. The
same group of users may be determined to be the same as long as a
predefined threshold number or percentage of users in the group of
users remains the same. Here, the group of users may be, for
example, a crowd of users formed by the MAP server 12. In a similar
manner, the adjustment period, the adjustment values for the time
thresholds within the adjustment period, and/or the number of time
thresholds within the adjustment period may additionally or
alternatively vary based whether the user 20-1 has previously been
in the same group of users, the frequency at which the user 20-1
has been in the same group of users, the number of times that the
user 20-1 has been in the same group of users, or the like.
Information regarding the groups of users that the user 20-1 has
been in may be maintained by the MAP server 12 or the mobile device
18-1 of the user 20-1.
[0084] Once the maximum adjustment value has been generated, the
maximum adjustment value is assigned to a last time threshold of
the time thresholds defined for the adjustment period. The
adjustment values for the other time thresholds in the adjustment
period may be defined as:
adjustment_value i = adjustment_value MAX - adjustment_value MIN
Num i , for i = 1 Num , ##EQU00001##
[0085] where adjustment_value.sub.i is the i-th adjustment value in
the adjustment period, adjustment_value.sub.MAX is the maximum
adjustment value, adjustment_value.sub.MIN is a defined minimum
adjustment value which may be zero, and Num is the number of time
thresholds in the adjustment period. The number of time thresholds
(Num) may be a static value (e.g., 5) or may vary depending on the
adjustment period. For example, the time thresholds may occur every
15 minutes such that the number of time thresholds (Num) varies
depending on the length of the adjustment period. For example, if
the adjustment period is 1 hour, the number of time thresholds
(Num) may be 4. In contrast, if the adjustment period is 1.5 hours,
the number of time thresholds (Num) is 6.
[0086] In addition to or as an alternative to varying the minimum
cut-off value, the adjustment period, and/or the adjustment values
for the time thresholds in the adjustment period, and the number of
time thresholds in the adjustment period may vary. The number of
time thresholds may change each time the user 20-1 changes
locations (e.g., a new reference location is set in response to the
user 20-1 no longer being in the same location). For example, the
number of time thresholds may be generated using a random number
generator and a defined minimum and maximum value each time the
user 20-1 changes locations.
[0087] Note that, in order to provide the aforementioned features
for varying the adjustment period, the number of time thresholds,
and/or the corresponding adjustment values based on the frequency
at which or number of times that the user 20-1 has visited the same
location or the number of times that the user 20-1 has visited the
same location, this information is maintained or otherwise
accessible. For example, a historical record of locations (e.g.,
POIs) visited by the user 20-1 and the frequency at which or number
of times that the user 20-1 has visited each location may be
maintained by the mobile device 18-1, the MAP server 12, or the
location server 16.
[0088] After determining the cut-off value, the filtered user
profile of the user 20-1 is initialized (step 1402). The filtered
user profile of the user 20-1 is initialized by creating the
filtered user profile if it does not already exist. If the filtered
user profile already exists, all interests are removed from the
filtered user profile. Then, the representation value from the
aggregate profile data for the current location of the user 20-1
for the next interest in the user profile of the user 20-1 is
obtained (step 1404). A determination is then made as to whether
the representation value is greater than or equal to the cut-off
value (step 1406). If not, the process proceeds to step 1410.
Otherwise, the interest is added to the filtered user profile of
the user 20-1 (step 1408). At this point, whether proceeding from
step 1406 or 1408, a determination is made as to whether the last
interest from the user profile of the user 20-1 has been processed
(step 1410). If not, the process returns to step 1404 and is
repeated.
[0089] Once all of the interests from the user profile of the user
20-1 have been processed, the filtered user profile of the user
20-1 may be further filtered based on the additional information
obtained for the current location of the user 20-1 (step 1412). For
example, the additional information may be expressed as one or more
keywords, or the additional information may be processed to extract
one or more keywords. The filtered user profile may then be further
filtered to remove any interests in the filtered user profile that
do not match the one or more keywords provided as or extracted from
the additional information for the current location to at least a
defined threshold degree. As an example, if the user 20-1 is
currently located at an arena at which a music concert is being
held, the additional information for the current location may
include keywords such as music, concert, a keyword reflecting a
music genre of the concert, a keyword corresponding to a name of
the performer for the concert, or the like. Interests in the
filtered user profile of the user 20-1 that do not match or, in
some embodiments, are not directly or indirectly related to at
least one of the keywords in or extracted from the additional
information may be removed from the filtered user profile of the
user 20-1. Note that step 1412 is optional.
[0090] In addition, in this embodiment, the filtered user profile
of the user 20-1 may be further filtered based on one or more
situational awareness rules (step 1414). More specifically, in one
embodiment, a community of users such as, but not limited to, the
users 20-1 through 20-N define and submit situational awareness
rules to the MAP server 12. For example, a University of North
Carolina (UNC) study may define and submit a rule stating that any
interests indicating that that a user is a fan, alumni, or student
of UNC is not to be revealed if that person is surrounded by more
users having user profiles having interests indicating that they
are fans, alumni, or students of Duke University than users having
user profiles having interests indicating that they are fans,
alumni, or students of UNC. The mobile device 18-1 may provide
information defining a situation of the user 20-1, where this
information is then compared to the one or more situational
awareness rules to further filter the user profile of the user
20-1. Note that like step 1412, step 1414 is also optional.
[0091] Before proceeding, an embodiment where the user profile
filtering process has multiple modes of operation is to be
described. In general, there may be two modes of operation, namely,
a Sharing mode and a Blend mode. In the Sharing mode, user profile
filtering is performed such that the cut-off value determined in
step 1400 is a function of time, frequency at which the user 20-1
visits the same location, and/or the number of times that the user
20-1 has visited the same location. In the Blend mode, the user
profile filtering is performed such that the cut-off value is
static (e.g., remains fixed at a static percentage of the highest
representation value in the aggregate profile data). In one
embodiment, the user 20-1 is enabled to manually select the desired
mode of operation via the MAP application 32-1 of the mobile device
18-1. In addition or alternatively, either the MAP server 12 or the
mobile device 18-1 may monitor the selected mode of operation with
respect to location and then recommend a mode of operation to the
user 20-1 when the user 20-1 enters a new location based on the
mode of operation that the user 20-1 previously selected when at
the same or a similar location.
[0092] FIG. 9 illustrates the operation of a mobile device to
tailor a user profile of an associated user according to another
embodiment of the present disclosure. In general, in FIG. 9,
filtering of the user profile is performed at the mobile device
rather than the MAP server 12. Otherwise, this process is similar
to that described above with respect to FIG. 4. Again, using the
user 20-1 of the mobile device 18-1 as an example, first, the MAP
application 32-1 of the mobile device 18-1 obtains the user profile
of the user 20-1 (step 1500). In one embodiment, the MAP
application 32-1 obtains the user profile of the user 20-1 from one
of the profile servers 14. In another embodiment, the MAP
application 32-1 provides a GUI that enables the user 20-1 to
manually define the user profile of the user 20-1 at the mobile
device 18-1. In addition to the user profile of the user 20-1, the
MAP application 32-1 obtains the current location of the user 20-1
from the location function 36-1 (step 1502).
[0093] Next, the MAP application 32-1 sends a request to the MAP
server 12 for aggregate profile data for the current location of
the user 20-1 (step 1504). In response to the request, the MAP
application 32-1 receives aggregate profile data for the current
location from the MAP server 12 (step 1506). As discussed above,
the aggregate profile data includes current aggregate profile data
and/or historical aggregate profile data for the current location
of the user 20-1. In addition, the MAP application 32-1 may obtain
additional information for the current location of the user 20-1
(step 1508). In one embodiment, as discussed above, the MAP server
12 maintains a database or repository of additional information for
a number of locations, such as POIs. The MAP application 32-1 may
then query the MAP server 12 for any additional information for the
current location of the user 20-1. In another embodiment, the MAP
application 32-1 queries one or more remote sources, other than the
MAP server 12, for any additional information for the current
location of the user 20-1.
[0094] Next, the MAP application 32-1 filters the user profile of
the user 20-1 based on the aggregate profile data for the current
location of the user 20-1, the additional information for the
current location of the user 20-1, and one or more filtering rules
(step 1510). The filtering process is the same as that described
above with respect to FIG. 8. As such, the details will not be
repeated. Once the user profile has been filtered to provide the
filtered user profile of the user 20-1, the MAP application 32-1
sends the filtered user profile to the MAP server 12 (step 1512).
The MAP server 12 stores the filtered user profile as the user
profile of the user 20-1. At this point, the process returns to
step 1502 and is repeated such that the filtered user profile is
updated over time as the location and/or aggregate profile data for
the current location of the user 20-1 changes.
[0095] FIGS. 10 through 18 illustrate the operation of the MAP
server 12 to maintain a historical record of anonymized user
profile data and to provide historical aggregate profile data based
thereon according to one embodiment of the present disclosure.
Historical aggregate profile data provided by the MAP server 12
using the following processes may be used to filter user profile
data as described above. As illustrated in FIG. 10, in the
preferred embodiment, the history manager 56 maintains lists of
users located in a number of geographic regions, or "location
buckets." Preferably, the location buckets are defined by
floor(latitude, longitude) to a desired resolution. The higher the
resolution, the smaller the size of the location buckets. For
example, in one embodiment, the location buckets are defined by
floor(latitude, longitude) to a resolution of 1/10,000.sup.th of a
degree such that the lower left-hand corners of the squares
illustrated in FIG. 10 are defined by the floor(latitude,
longitude) values at a resolution of 1/10,000.sup.th of a degree.
In the example of FIG. 10, users are represented as dots, and
location buckets 72 through 88 have lists of 1, 3, 2, 1, 1, 2, 1,
2, and 3 users, respectively.
[0096] As discussed below in detail, at a predetermined time
interval such as, for example, 15 minutes, the history manager 56
makes a copy of the lists of users in the location buckets,
anonymizes the filtered user profiles of the users in the lists to
provide anonymized user profile data for the corresponding location
buckets, and stores the anonymized user profile data in a number of
history objects. Note that if filtered user profiles of any of the
users in the lists are not available, the user profiles of those
users may be anonymized and stored in the history objects. In one
embodiment, a history object is stored for each location bucket
having at least one user. In another embodiment, a quadtree
algorithm is used to efficiently create history objects for
geographic regions (i.e., groups of one or more adjoining location
buckets).
[0097] FIG. 11 graphically illustrates a scenario where a user
moves from one location bucket to another, namely, from the
location bucket 74 to the location bucket 76. As discussed below in
detail, assuming that the movement occurs during the time interval
between persistence of the historical data by the history manager
56, the user is included on both the list for the location bucket
74 and the list for the location bucket 76. However, the user is
flagged or otherwise marked as inactive for the location bucket 74
and active for the location bucket 76. As discussed below, after
making a copy of the lists for the location buckets to be used to
persist the historical data, users flagged as inactive are removed
from the lists of users for the location buckets. Thus, in sum,
once a user moves from the location bucket 74 to the location
bucket 76, the user remains in the list for the location bucket 74
until the predetermined time interval has expired and the
anonymized user profile data is persisted. The user is then removed
from the list for the location bucket 74.
[0098] FIG. 12 is a flow chart illustrating the operation of a
foreground "bucketization" process performed by the history manager
56 to maintain the lists of users for location buckets according to
one embodiment of the present disclosure. First, the history
manager 56 receives a location update for a user (step 1600). For
this discussion, assume that the location update is received for
the user 20-1. The history manager 56 then determines a location
bucket corresponding to the updated location (i.e., the current
location) of the user 20-1 (step 1602). In the preferred
embodiment, the location of the user 20-1 is expressed as latitude
and longitude coordinates, and the history manager 56 determines
the location bucket by determining floor values of the latitude and
longitude coordinates, which can be written as floor(latitude,
longitude) at a desired resolution. As an example, if the latitude
and longitude coordinates for the location of the user 20-1 are
32.24267381553987 and -111.9249213502935, respectively, and the
floor values are to be computed to a resolution of 1/10,000.sup.th
of a degree, then the floor values for the latitude and longitude
coordinates are 32.2426 and -111.9249. The floor values for the
latitude and longitude coordinates correspond to a particular
location bucket.
[0099] After determining the location bucket for the location of
the user 20-1, the history manager 56 determines whether the user
20-1 is new to the location bucket (step 1604). In other words, the
history manager 56 determines whether the user 20-1 is already on
the list of users for the location bucket. If the user 20-1 is new
to the location bucket, the history manager 56 creates an entry for
the user 20-1 in the list of users for the location bucket (step
1606). Returning to step 1604, if the user 20-1 is not new to the
location bucket, the history manager 56 updates the entry for the
user 20-1 in the list of users for the location bucket (step 1608).
At this point, whether proceeding from step 1606 or 1608, the user
20-1 is flagged as active in the list of users for the location
bucket (step 1610).
[0100] The history manager 56 then determines whether the user 20-1
has moved from another location bucket (step 1612). More
specifically, the history manager 56 determines whether the user
20-1 is included in the list of users for another location bucket
and is currently flagged as active in that list. If the user 20-1
has not moved from another location bucket, the process proceeds to
step 1616. If the user 20-1 has moved from another location bucket,
the history manager 56 flags the user 20-1 as inactive in the list
of users for the other location bucket from which the user 20-1 has
moved (step 1614).
[0101] At this point, whether proceeding from step 1612 or 1614,
the history manager 56 determines whether it is time to persist
(step 1616). More specifically, as mentioned above, the history
manager 56 operates to persist history objects at a predetermined
time interval such as, for example, every 15 minutes. Thus, the
history manager 56 determines that it is time to persist if the
predetermined time interval has expired. If it is not time to
persist, the process returns to step 1600 and is repeated for a
next received location update, which will typically be for another
user. If it is time to persist, the history manager 56 creates a
copy of the lists of users for the location buckets and passes the
copy of the lists to an anonymization and storage process (step
1618). In this embodiment, the anonymization and storage process is
a separate process performed by the history manager 56. The history
manager 56 then removes inactive users from the lists of users for
the location buckets (step 1620). The process then returns to step
1600 and is repeated for a next received location update, which
will typically be for another user.
[0102] FIG. 13 is a flow chart illustrating the anonymization and
storage process performed by the history manager 56 at the
predetermined time interval according to one embodiment of the
present disclosure. First, the anonymization and storage process
receives the copy of the lists of users for the location buckets
passed to the anonymization and storage process by the
bucketization process of FIG. 12 (step 1700). Next, anonymization
is performed for each of the location buckets having at least one
user in order to provide anonymized user profile data for the
location buckets (step 1702). Anonymization prevents connecting
information stored in the history objects stored by the history
manager 56 back to the users 20-1 through 20-N or at least
substantially increases a difficulty of connecting information
stored in the history objects stored by the history manager 56 back
to the users 20-1 through 20-N. Lastly, the anonymized user profile
data for the location buckets is stored in a number of history
objects (step 1704). In one embodiment, a separate history object
is stored for each of the location buckets, where the history
object of a location bucket includes the anonymized user profile
data for the location bucket. In another embodiment, as discussed
below, a quadtree algorithm is used to efficiently store the
anonymized user profile data in a number of history objects such
that each history object stores the anonymized user profile data
for one or more location buckets.
[0103] FIG. 14 graphically illustrates one embodiment of the
anonymization process of step 1702 of FIG. 13. In this embodiment,
anonymization is performed by creating anonymous user records for
the users in the lists of users for the location buckets. The
anonymous user records are not connected back to the users 20-1
through 20-N. More specifically, as illustrated in FIG. 14, each
user in the lists of users for the location buckets has a
corresponding user record 90. The user record 90 includes a unique
user identifier (ID) for the user and the current location of the
user. In addition, in the embodiment where filtering of user
profiles is performed at the MAP server 12 (e.g., FIG. 4), the user
record 90 also includes both the user profile of the user and the
filtered user profile of the user. Alternatively, in the embodiment
where filtering of user profiles is performed by corresponding
mobile devices (e.g., FIG. 9), the user record 90 includes only the
filtered user profile of the user. The user profile includes
keywords for each of a number of profile categories, which are
stored in corresponding profile category records 92-1 through
92-M1. Each of the profile category records 92-1 through 92-M1
includes a user ID for the corresponding user which may be the same
user ID used in the user record 90, a category ID, and a list of
keywords for the profile category. Likewise, the filtered user
profile includes keywords for each of a number of profile
categories, which are stored in corresponding profile category
records 93-1 through 93-M2. Each of the profile category records
93-1 through 93-M2 includes a user ID for the corresponding user,
which may be the same user ID used in the user record 90, a
category ID, and a list of filtered keywords for the profile
category.
[0104] For anonymization, an anonymous user record 94 is created
from the user record 90. In the anonymous user record 94, the user
ID is replaced with a new user ID that is not connected back to the
user, which is also referred to herein as an anonymous user ID.
This new user ID is different than any other user ID used for
anonymous user records created from the user record of the user for
any previous or subsequent time periods. In this manner, anonymous
user records for a single user created over time cannot be linked
to one another.
[0105] In addition, anonymous profile category records 96-1 through
96-M2 are created for the profile category records 93-1 through
93-M2 for the filtered user profile. In the anonymous profile
category records 96-1 through 96-M2, the user ID is replaced with a
new user ID, which may be the same new user ID included in the
anonymous user record 94. The anonymous profile category records
96-1 through 96-M2 include the same category IDs and lists of
filtered keywords as the corresponding profile category records
93-1 through 93-M2. Note that the location of the user is not
stored in the anonymous user record 94. With respect to location,
it is sufficient that the anonymous user record 94 is linked to a
location bucket.
[0106] In another embodiment, the history manager 56 performs
anonymization in a manner similar to that described above with
respect to FIG. 14. However, in this embodiment, the profile
category records from the filtered user profiles of the group of
users in a location bucket, or the group of users in a number of
location buckets representing a node in a quadtree data structure
(see below), may be selectively randomized among the anonymous user
records of those users. In other words, each anonymous user record
would have a user profile including a selectively randomized set of
profile category records (including keywords) from a cumulative
list of profile category records from the filtered user profiles
for all of the users in the group.
[0107] In yet another embodiment, rather than creating anonymous
user records 94 for the users in the lists maintained for the
location buckets, the history manager 56 may perform anonymization
by storing an aggregate user profile for each location bucket, or
each group of location buckets representing a node in a quadtree
data structure (see below). The aggregate user profile may include
a list of all keywords and potentially the number of occurrences of
each keyword in the filtered user profiles of the corresponding
group of users. In this manner, the data stored by the history
manager 56 is not connected back to the users 20-1 through
20-N.
[0108] FIG. 15 is a flow chart illustrating the storing step (step
1704) of FIG. 13 in more detail according to one embodiment of the
present disclosure. First, the history manager 56 processes the
location buckets using a quadtree algorithm to produce a quadtree
data structure, where each node of the quadtree data structure
includes one or more of the location buckets having a combined
number of users that is at most a predefined maximum number of
users (step 1800). The history manager 56 then stores a history
object for each node in the quadtree data structure having at least
one user (step 1802).
[0109] Each history object includes location information, timing
information, data, and quadtree data structure information. The
location information included in the history object defines a
combined geographic area of the location bucket(s) forming the
corresponding node of the quadtree data structure. For example, the
location information may be latitude and longitude coordinates for
a northeast corner of the combined geographic area of the node of
the quadtree data structure and a southwest corner of the combined
geographic area for the node of the quadtree data structure. The
timing information includes information defining a time window for
the history object, which may be, for example, a start time for the
corresponding time interval and an end time for the corresponding
time interval. The data includes the anonymized user profile data
generated from the filtered user profiles of the users in the
list(s) maintained for the location bucket(s) forming the node of
the quadtree data structure for which the history object is stored.
In addition, the data may include a total number of users in the
location bucket(s) forming the node of the quadtree data structure.
Lastly, the quadtree data structure information includes
information defining a quadtree depth of the node in the quadtree
data structure.
[0110] FIG. 16 is a flow chart illustrating a quadtree algorithm
that may be used to process the location buckets to form the
quadtree data structure in step 1800 of FIG. 15 according to one
embodiment of the present disclosure. Initially, a geographic area
served by the MAP server 12 is divided into a number of geographic
regions, each including multiple location buckets. These geographic
regions are also referred to herein as base quadtree regions. The
geographic area served by the MAP server 12 may be, for example, a
city, a state, a country, or the like. Further, the geographic area
may be the only geographic area served by the MAP server 12 or one
of a number of geographic areas served by the MAP server 12.
Preferably, the base quadtree regions have a size of
2.sup.n.times.2.sup.n location buckets, where n is an integer
greater than or equal to 1.
[0111] In order to form the quadtree data structure, the history
manager 56 determines whether there are any more base quadtree
regions to process (step 1900). If there are more base quadtree
regions to process, the history manager 56 sets a current node to
the next base quadtree region to process, which for the first
iteration is the first base quadtree region (step 1902). The
history manager 56 then determines whether the number of users in
the current node is greater than a predefined maximum number of
users and whether a current quadtree depth is less than a maximum
quadtree depth (step 1904). In one embodiment, the maximum quadtree
depth may be reached when the current node corresponds to a single
location bucket. However, the maximum quadtree depth may be set
such that the maximum quadtree depth is reached before the current
node reaches a single location bucket.
[0112] If the number of users in the current node is greater than
the predefined maximum number of users and the current quadtree
depth is less than a maximum quadtree depth, the history manager 56
creates a number of child nodes for the current node (step 1906).
More specifically, the history manager 56 creates a child node for
each quadrant of the current node. The users in the current node
are then assigned to the appropriate child nodes based on the
location buckets in which the users are located (step 1908), and
the current node is then set to the first child node (step 1910).
At this point, the process returns to step 1904 and is
repeated.
[0113] Once the number of users in the current node is not greater
than the predefined maximum number of users or the maximum quadtree
depth has been reached, the history manager 56 determines whether
the current node has any more sibling nodes (step 1912). Sibling
nodes are child nodes of the same parent node. If so, the history
manager 56 sets the current node to the next sibling node of the
current node (step 1914), and the process returns to step 1904 and
is repeated. Once there are no more sibling nodes to process, the
history manager 56 determines whether the current node has a parent
node (step 1916). If so, since the parent node has already been
processed, the history manager 56 determines whether the parent
node has any sibling nodes that need to be processed (step 1918).
If the parent node has any sibling nodes that need to be processed,
the history manager 56 sets the next sibling node of the parent
node to be processed as the current node (step 1920). From this
point, the process returns to step 1904 and is repeated. Returning
to step 1916, if the current node does not have a parent node, the
process returns to step 1900 and is repeated until there are no
more base quadtree regions to process. Once there are no more base
quadtree regions to process, the finished quadtree data structure
is returned to the process of FIG. 15 such that the history manager
56 can then store the history objects for nodes in the quadtree
data structure having at least one user (step 1922).
[0114] FIGS. 17A through 17E graphically illustrate the process of
FIG. 16 for the generation of the quadtree data structure for one
exemplary base quadtree region 98. FIG. 17A illustrates the base
quadtree region 98. As illustrated, the base quadtree region 98 is
an 8.times.8 square of location buckets, where each of the small
squares represents a location bucket. First, the history manager 56
determines whether the number of users in the base quadtree region
98 is greater than the predetermined maximum number of users. In
this example, the predetermined maximum number of users is 3. Since
the number of users in the base quadtree region 98 is greater than
3, the history manager 56 divides the base quadtree region 98 into
four child nodes 100-1 through 100-4, as illustrated in FIG.
17B.
[0115] Next, the history manager 56 determines whether the number
of users in the child node 100-1 is greater than the predetermined
maximum, which again for this example is 3. Since the number of
users in the child node 100-1 is greater than 3, the history
manager 56 divides the child node 100-1 into four child nodes 102-1
through 102-4, as illustrated in FIG. 17C. The child nodes 102-1
through 102-4 are children of the child node 100-1. The history
manager 56 then determines whether the number of users in the child
node 102-1 is greater than the predetermined maximum number of
users, which again is 3. Since there are more than 3 users in the
child node 102-1, the history manager 56 further divides the child
node 102-1 into four child nodes 104-1 through 104-N, as
illustrated in FIG. 17D.
[0116] The history manager 56 then determines whether the number of
users in the child node 104-1 is greater than the predetermined
maximum number of users, which again is 3. Since the number of
users in the child node 104-1 is not greater than the predetermined
maximum number of users, the child node 104-1 is identified as a
node for the finished quadtree data structure, and the history
manager 56 proceeds to process the sibling nodes of the child node
104-1, which are the child nodes 104-2 through 104-4. Since the
number of users in each of the child nodes 104-2 through 104-4 is
less than the predetermined maximum number of users, the child
nodes 104-2 through 104-4 are also identified as nodes for the
finished quadtree data structure.
[0117] Once the history manager 56 has finished processing the
child nodes 104-1 through 104-4, the history manager 56 identifies
the parent node of the child nodes 104-1 through 104-4, which in
this case is the child node 102-1. The history manager 56 then
processes the sibling nodes of the child node 102-1, which are the
child nodes 102-2 through 102-4. In this example, the number of
users in each of the child nodes 102-2 through 102-4 is less than
the predetermined maximum number of users. As such, the child nodes
102-2 through 102-4 are identified as nodes for the finished
quadtree data structure.
[0118] Once the history manager 56 has finished processing the
child nodes 102-1 through 102-4, the history manager 56 identifies
the parent node of the child nodes 102-1 through 102-4, which in
this case is the child node 100-1. The history manager 56 then
processes the sibling nodes of the child node 100-1, which are the
child nodes 100-2 through 100-4. More specifically, the history
manager 56 determines that the child node 100-2 includes more than
the predetermined maximum number of users and, as such, divides the
child node 100-2 into four child nodes 106-1 through 106-4, as
illustrated in FIG. 17E. Because the number of users in each of the
child nodes 106-1 through 106-4 is not greater than the
predetermined maximum number of users, the child nodes 106-1
through 106-4 are identified as nodes for the finished quadtree
data structure. Then, the history manager 56 proceeds to process
the child nodes 100-3 and 100-4. Since the number of users in each
of the child nodes 100-3 and 100-4 is not greater than the
predetermined maximum number of users, the child nodes 100-3 and
100-4 are identified as nodes for the finished quadtree data
structure. Thus, at completion, the quadtree data structure for the
base quadtree region 98 includes the child nodes 104-1 through
104-4, the child nodes 102-2 through 102-4, the child nodes 106-1
through 106-4, and the child nodes 100-3 and 100-4, as illustrated
in FIG. 17E.
[0119] As discussed above, the history manager 56 stores a history
object for each of the nodes in the quadtree data structure
including at least one user. As such, in this example, the history
manager 56 stores history objects for the child nodes 104-2 and
104-3, the child nodes 102-2 and 102-4, the child nodes 106-1 and
106-4, and the child node 100-3. However, no history objects are
stored for the nodes that do not have any users (i.e., the child
nodes 104-1 and 104-4, the child node 102-3, the child nodes 106-2
and 106-3, and the child node 100-4).
[0120] FIG. 18 illustrates a process for generating historical
aggregate profile data according to one embodiment of the present
disclosure. First, upon receiving a historical request, the history
manager 56 establishes a bounding box for the historical request
(step 2000). In this embodiment, the historical request is a
historical request for historical aggregate profile data for a user
for which user profile filtering is to be performed, as described
above with respect to FIGS. 4 and 9. In one embodiment where user
profile filtering is performed at the MAP server 12 (e.g., FIG. 4),
the historical request is from the profile manager 52 of the MAP
server 12. In another embodiment where user profile filtering is
performed at the mobile devices 18-1 through 18-N (e.g., FIG. 9),
the historical request is from one of the mobile devices 18-1
through 18-N as part of the filtering process. Using the user 20-1
as an example, the historical request is for historical aggregate
profile data for the current location of the user 20-1. As such,
the bounding box is a geographic area of a predetermined shape and
size centered at or otherwise encompassing the current location of
the user 20-1. Note that while a bounding box is used in this
example, other geographic shapes may be used to define a bounding
region for the historical request (e.g., a bounding circle).
[0121] In addition to establishing the bounding box, the history
manager 56 establishes a time window for the historical request
(step 2002). In one embodiment, the historical request defines a
relative time period such as, for example, the last week, the last
month, or the like. The time window for the historical request may
then be determined based on the relative time period. For example,
if the historical request is for the last week and the current date
and time are Sep. 17, 2009 at 10:00 pm, the history manager 56 may
generate the time window as Sep. 10, 2009 at 10:00 pm through Sep.
17, 2009 at 10:00 pm. In another embodiment, a system-defined time
window relative to the current time may be used as the time window
for the historical request.
[0122] Next, the history manager 56 obtains history objects
relevant to the bounding box and the time window for the historical
request from the datastore 64 of the MAP server 12 (step 2004). The
relevant history objects are history objects recorded for time
periods within or intersecting the time window and for locations,
or geographic areas, within or intersecting the bounding box for
the historical request. The history manager 56 also determines an
equivalent depth of the bounding box (D.sub.BB) within the quadtree
data structures used to store the history objects (step 2006). More
specifically, the area of the base quadtree region (e.g., the base
quadtree region 98) is referred to as A.sub.BASE. Then, at each
depth of the quadtree, the area of the corresponding quadtree nodes
is (1/4).sup.D*A.sub.BASE. In other words, the area of a child node
is 1/4.sup.th of the area of the parent node of that child node.
The history manager 56 determines the equivalent depth of the
bounding box (D.sub.BB) by determining a quadtree depth at which
the area of the corresponding quadtree nodes most closely matches
an area of the bounding box (A.sub.BB).
[0123] Note that equivalent quadtree depth of the bounding box
(D.sub.BB) determined in step 2006 is used below in order to
efficiently determine the ratios of the area of the bounding box
(A.sub.BB) to areas of the relevant history objects (A.sub.HO).
However, in an alternative embodiment, the ratios of the area of
the bounding box (A.sub.BB) to the areas of the relevant history
objects (A.sub.HO) may be otherwise computed, in which case step
2006 would not be needed.
[0124] At this point, the history manager 56 gets the next history
object from the relevant history objects obtained in step 2004
(step 2008). Next, the history manager 56 sets a relevancy weight
for the history object, where the relevancy weight is indicative of
a relevancy of the history object to the bounding box (step 2010).
For instance, a history object includes anonymized user profile
data for a corresponding geographic area. If that geographic area
is within or significantly overlaps the bounding box, then the
history object will have a high relevancy weight. However, if the
geographic area only overlaps the bounding box slightly, then the
history object will have a low relevancy weight. In this
embodiment, the relevancy weight for the history object is set to
an approximate ratio of the area of the bounding box (A.sub.BB) to
an area of the history object (A.sub.HO) computed based on a
difference between the quadtree depth of the history object
(D.sub.HO) and the equivalent quadtree depth of the bounding box
(D.sub.EQ). The quadtree depth of the history object (D.sub.HO) is
stored in the history object. More specifically, in one embodiment,
the relevancy weight of the history object is set according to the
following:
relevancy = A BB A HO .apprxeq. ( 1 4 ) D HO - D BB ,
##EQU00002##
for D.sub.HO>D.sub.BB, and
[0125] relevancy=1, for D.sub.HO.ltoreq.D.sub.BB.
[0126] Next, the history manager 56 generates an aggregate profile
for the history object by comparing the filtered user profiles of
the anonymous user records stored in the history object to one
another (step 2012). The resulting aggregate profile for the
history object includes an aggregate list of interests and
corresponding representation values for the interests in the
aggregate list of interests. The aggregate list of interests
includes all of the interests, which are expressed as keywords,
from the filtered user profiles of the anonymous user records
stored in the history object. Further, for each interest, or
keyword, in the aggregate list of interests, the aggregate profile
for the history object includes a representation value defining a
degree to which the interest is expressed in the filtered user
profiles of the anonymous user records stored in the history
object. In one embodiment, for each interest, the representation
value for the interest is a number of user matches, or occurrences,
for the interest across all of the filtered user profiles of the
anonymous user records stored in the history object. In another
embodiment, for each interest, the representation value for the
interest is a ratio, or percentage, of the number of user matches
for the interest across all of the filtered user profiles of the
anonymous user records stored in the history object over a total
number of anonymous user records stored in the history object.
[0127] The history manager 56 then determines whether there are
more relevant history objects to process (step 2014). If so, the
process returns to step 2008 and is repeated until all of the
relevant history objects obtained in step 2004 have been processed.
Once all of the relevant history objects have been processed, the
history manager 56 combines the aggregate profiles of the history
objects to provide a combined aggregate profile, which is referred
to herein as historical aggregate profile data for the current
location of the user 20-1. More specifically, in this embodiment,
the history manager 56 computes a weighted average of the aggregate
profiles for the history objects using the relevancy weights of the
history objects (step 2016). In order to compute the weighted
average of the aggregate profiles for the history objects, the
history manager 56 combines the interests in the aggregate lists of
interests from the aggregate profiles generated for the history
objects to provide a combined list of interests that includes all
of the interests from the aggregate profiles generated for the
history objects. In addition, for each interest in the combined
list of interests, the history manager 56 computes a weighted
average of the representation value(s) for that interest in the
aggregate profiles generated for the history objects. For example,
the weighted average of the representation values for each of the
interests in the combined list of interests may be computed as:
representation_value AVG , INTEREST _ j = i = 1 n ( relevancy i
representation_value i , INTEREST _ j ) i = 1 n relevancy i
##EQU00003##
where relevancy.sub.i is the relevancy weight computed in step 2010
for the i-th history object,
representation_value.sub.i,INTEREST.sub.--.sub.j is the relevancy
value for j-th interest, or keyword, in the i-th history object,
and n is the number of relevant history objects. In a similar
manner, if the representation values and thus the weighted averages
of the representation values for the interests are expressed as
numbers of user matches, or occurrences, in one embodiment, the
history manager 56 further computes a weighted average of the total
number of users for the relevant history objects, which may be
computed as:
total_users AVG = i = 1 n ( relevancy i total_users i ) i = 1 n
relevancy i , ##EQU00004##
[0128] where relevancy.sub.i is the relevancy weight computed in
step 2010 for the i-th history object, total_users.sub.i is the
total number of users from the aggregate profile of the i-th
history object, and n is the number of history objects in the list
for the output time band.
[0129] Lastly, the history manager 56 outputs the weighted average
of the aggregate profiles of the history objects computed in step
2016 as the historical aggregate profile data for the current
location of the user 20-1 (step 2018). More specifically, in this
embodiment, the historical aggregate profile data includes the
combined list of interests for the aggregate profiles generated for
the history objects in step 2012. In addition, the historical
aggregate profile data includes, for each interest in the combined
list of interests, the weighted average of the representation
values for that interest across the history objects. For each
interest in the historical aggregate profile data, the weighted
average of the representation values for that interests across the
history objects is referred to herein as a representation value for
that interest in the historical aggregate profile data. Lastly, in
one embodiment, the historical aggregate profile data may include
the weighted average of the total number of users across the
history objects.
[0130] It should be noted that the process for generating
historical aggregate profile data above is exemplary and not
intended to limit the scope of the present disclosure. Other
processes for generating historical aggregate profile data may be
used. For example, crowds of users may formed using, for instance,
the crowd formation processes described below. Crowds of users may
then be tracked by storing corresponding crowd snapshots of those
crowds over time, where the crowd snapshots include location data
defining the locations of the corresponding crowds and profile data
(e.g., anonymized user profile data) for the users in the
corresponding crowds at the times at which the crowd snapshots were
created and stored. Historical aggregate profile data may then be
generated for the current location of, for example, the user 20-1
by combining the profile data from crowd snapshots relevant to a
bounding region of a predetermined shape and size that encompasses
(e.g., is centered at) the current location of the user 20-1. While
not essential, for more details regarding crowd tracking, the
interested reader is directed to U.S. patent application Ser. No.
12/645,539 entitled ANONYMOUS CROWD TRACKING, which was filed on
Dec. 23, 2009 and has been incorporated herein by reference in its
entirety.
[0131] FIGS. 19 through 25 illustrate the operation of the MAP
server 12 to form crowds of users and to provide current aggregate
profile data based thereon according to one embodiment of the
present disclosure. Current aggregate profile data provided by the
MAP server 12 using the following processes may be used to filter
user profile data as described herein. For example, current
aggregate profile data may be generated using the processes
described below and used either by the MAP server 12 to filter user
profiles of one or more of the users 20-1 through 20-N (e.g., FIG.
4) or by one or more of the mobile devices 18-1 through 18-N to
filter the user profiles of the corresponding users 20-1 through
20-N (e.g., FIG. 9).
[0132] FIG. 19 is a flow chart for a spatial crowd formation
process according to one embodiment of the present disclosure. Note
that, in one embodiment, this process is performed in response to a
request for aggregate profile data for the current location of a
user from the profile manager 52 of the MAP server 12 as part of
the user profile filtering process described above with respect to
FIG. 4. In another embodiment, this process is performed in
response to a request for aggregate profile data for the current
location of a user from a corresponding mobile device as part of
the user profile filtering process described above with respect to
FIG. 9. However, this process is not limited thereto.
[0133] First, the crowd analyzer 58 establishes a bounding box for
the crowd formation process (step 2100). Note that while a bounding
box is used in this example, other geographic shapes may be used to
define a bounding region for the crowd formation process (e.g., a
bounding circle). In one embodiment, if crowd formation is
performed in response to a specific request for the current
location of, for example, the user 20-1, the bounding box is
established based on the current location of the user 20-1 such
that the bounding box is a geographic area of a predetermined size
centered at or otherwise encompassing the current location of the
user 20-1.
[0134] The crowd analyzer 58 then creates a crowd for each
individual user in the bounding box (step 2102). More specifically,
the crowd analyzer 58 queries the datastore 64 of the MAP server 12
to identify users currently located within the bounding box. Then,
a crowd of one user is created for each user currently located
within the bounding box. Next, the crowd analyzer 58 determines the
two closest crowds in the bounding box (step 2104) and determines a
distance between the two crowds (step 2106). The distance between
the two crowds is a distance between crowd centers of the two
crowds. Note that the crowd center of a crowd of one is the current
location of the user in the crowd. The crowd analyzer 58 then
determines whether the distance between the two crowds is less than
an optimal inclusion distance (step 2108). In this embodiment, the
optimal inclusion distance is a predefined static distance. If the
distance between the two crowds is less than the optimal inclusion
distance, the crowd analyzer 58 combines the two crowds (step 2110)
and computes a new crowd center for the resulting crowd (step
2112). The crowd center may be computed based on the current
locations of the users in the crowd using a center of mass
algorithm. At this point the process returns to step 2104 and is
repeated until the distance between the two closest crowds is not
less than the optimal inclusion distance. At that point, the crowd
analyzer 58 discards any crowds with less than three users (step
2114). Note that throughout this disclosure crowds are only
maintained if the crowds include three or more users. However,
while three users is the preferred minimum number of users in a
crowd, the present disclosure is not limited thereto. The minimum
number of users in a crowd may be defined as any number greater
than or equal to two users.
[0135] FIGS. 20A through 20D graphically illustrate the crowd
formation process of FIG. 19 for an exemplary bounding box 108. In
FIGS. 20A through 20D, crowds are noted by dashed circles, and the
crowd centers are noted by cross-hairs (+). As illustrated in FIG.
20A, initially, the crowd analyzer 58 creates crowds 110 through
118 for the users in the geographic area, where, at this point,
each of the crowds 110 through 118 includes one user. The current
locations of the users are the crowd centers of the crowds 110
through 118. Next, the crowd analyzer 58 determines the two closest
crowds and a distance between the two closest crowds. In this
example, at this point, the two closest crowds are crowds 112 and
114, and the distance between the two closest crowds 112 and 114 is
less than the optimal inclusion distance. As such, the two closest
crowds 112 and 114 are combined by merging crowd 114 into crowd
112, and a new crowd center (+) is computed for the crowd 112, as
illustrated in FIG. 20B. Next, the crowd analyzer 58 again
determines the two closest crowds, which are now crowds 110 and
112. The crowd analyzer 58 then determines a distance between the
crowds 110 and 112. Since the distance is less than the optimal
inclusion distance, the crowd analyzer 58 combines the two crowds
110 and 112 by merging the crowd 110 into the crowd 112, and a new
crowd center (+) is computed for the crowd 112, as illustrated in
FIG. 20C. At this point, there are no more crowds separated by less
than the optimal inclusion distance. As such, the crowd analyzer 58
discards crowds having less than three users, which in this example
are crowds 116 and 118. As a result, at the end of the crowd
formation process, the crowd 112 has been formed with three users,
as illustrated in FIG. 20D.
[0136] FIGS. 21A through 21D illustrate a flow chart for a spatial
crowd formation process according to another embodiment of the
present disclosure. This process may be used to proactively form
and maintain crowds of users at the MAP server 12. Subsequently,
the filtered user profiles of users in one or more crowds of users
at or near the current location of, for example, the user 20-1 may
be combined to provide current aggregate profile data for the
current location of the user 20-1, which may then be used to filter
the user profile of the user 20-1 as described above.
[0137] In this embodiment, the spatial crowd formation process is
triggered in response to receiving a location update for one of the
users 20-1 through 20-N and is preferably repeated for each
location update received for the users 20-1 through 20-N. As such,
first, the crowd analyzer 58 receives a location update, or a new
location, for a user (step 2200). Assume that, for this example,
the location update is received for the user 20-1. In response, the
crowd analyzer 58 retrieves an old location of the user 20-1, if
any (step 2202). The old location is the current location of the
user 20-1 prior to receiving the new location. The crowd analyzer
58 then creates a new bounding box of a predetermined size centered
at the new location of the user 20-1 (step 2204) and an old
bounding box of a predetermined size centered at the old location
of the user 20-1, if any (step 2206). The predetermined size of the
new and old bounding boxes may be any desired size. As one example,
the predetermined size of the new and old bounding boxes is 40
meters by 40 meters. Note that if the user 20-1 does not have an
old location (i.e., the location received in step 2200 is the first
location received for the user 20-1), then the old bounding box is
essentially null. Also note that while bounding "boxes" are used in
this example, the bounding areas may be of any desired shape.
[0138] Next, the crowd analyzer 58 determines whether the new and
old bounding boxes overlap (step 2208). If so, the crowd analyzer
58 creates a bounding box encompassing the new and old bounding
boxes (step 2210). For example, if the new and old bounding boxes
are 40.times.40 meter regions and a 1.times.1 meter square at the
northeast corner of the new bounding box overlaps a 1.times.1 meter
square at the southwest corner of the old bounding box, the crowd
analyzer 58 may create a 79.times.79 meter square bounding box
encompassing both the new and old bounding boxes.
[0139] The crowd analyzer 58 then determines the individual users
and crowds relevant to the bounding box created in step 2210 (step
2212). The crowds relevant to the bounding box are crowds that are
within or overlap the bounding box (e.g., have at least one user
located within the bounding box). The individual users relevant to
the bounding box are users that are currently located within the
bounding box and not already part of a crowd. Next, the crowd
analyzer 58 computes an optimal inclusion distance for individual
users based on user density within the bounding box (step 2214).
More specifically, in one embodiment, the optimal inclusion
distance for individuals, which is also referred to herein as an
initial optimal inclusion distance, is set according to the
following equation:
initial_optimal _inclusion _dist = a A BoundingBox number_of _users
, ##EQU00005##
where a is a number between 0 and 1, A.sub.BoundingBox is an area
of the bounding box, and number_of_users is the total number of
users in the bounding box. The total number of users in the
bounding box includes both individual users that are not already in
a crowd and users that are already in a crowd. In one embodiment, a
is 2/3.
[0140] The crowd analyzer 58 then creates a crowd for each
individual user within the bounding box that is not already
included in a crowd and sets the optimal inclusion distance for the
crowds to the initial optimal inclusion distance (step 2216). At
this point, the process proceeds to FIG. 21B where the crowd
analyzer 58 analyzes the crowds relevant to the bounding box to
determine whether any of the crowd members (i.e., users in the
crowds) violate the optimal inclusion distance of their crowds
(step 2218). Any crowd member that violates the optimal inclusion
distance of his or her crowd is then removed from that crowd (step
2220). The crowd analyzer 58 then creates a crowd of one user for
each of the users removed from their crowds in step 2220 and sets
the optimal inclusion distance for the newly created crowds to the
initial optimal inclusion distance (step 2222).
[0141] Next, the crowd analyzer 58 determines the two closest
crowds for the bounding box (step 2224) and a distance between the
two closest crowds (step 2226). The distance between the two
closest crowds is the distance between the crowd centers of the two
closest crowds. The crowd analyzer 58 then determines whether the
distance between the two closest crowds is less than the optimal
inclusion distance of a larger of the two closest crowds (step
2228). If the two closest crowds are of the same size (i.e., have
the same number of users), then the optimal inclusion distance of
either of the two closest crowds may be used. Alternatively, if the
two closest crowds are of the same size, the optimal inclusion
distances of both of the two closest crowds may be used such that
the crowd analyzer 58 determines whether the distance between the
two closest crowds is less than the optimal inclusion distances of
both of the two closest crowds. As another alternative, if the two
closest crowds are of the same size, the crowd analyzer 58 may
compare the distance between the two closest crowds to an average
of the optimal inclusion distances of the two closest crowds.
[0142] If the distance between the two closest crowds is less than
the optimal inclusion distance, the two closest crowds are combined
or merged (step 2230), and a new crowd center for the resulting
crowd is computed (step 2232). Again, a center of mass algorithm
may be used to compute the crowd center of a crowd. In addition, a
new optimal inclusion distance for the resulting crowd is computed
(step 2234). In one embodiment, the new optimal inclusion distance
for the resulting crowd is computed as:
average = 1 n + 1 ( initial_optimal _inclusion _dist + i = 1 n d i
) , optimial_inclusion _dist = average + ( 1 n i = 1 n ( d i -
average ) 2 ) , ##EQU00006##
where n is the number of users in the crowd and d.sub.i is a
distance between the ith user and the crowd center. In other words,
the new optimal inclusion distance is computed as the average of
the initial optimal inclusion distance and the distances between
the users in the crowd and the crowd center plus one standard
deviation.
[0143] At this point, the crowd analyzer 58 determines whether a
maximum number of iterations have been performed (step 2236). The
maximum number of iterations is a predefined number that ensures
that the crowd formation process does not indefinitely loop over
steps 2218 through 2234 or loop over steps 2218 through 2234 more
than a desired maximum number of times. If the maximum number of
iterations has not been reached, the process returns to step 2218
and is repeated until either the distance between the two closest
crowds is not less than the optimal inclusion distance of the
larger crowd or the maximum number of iterations has been reached.
At that point, the crowd analyzer 58 discards crowds with less than
three users, or members (step 2238) and the process ends.
[0144] Returning to step 2208 in FIG. 21A, if the new and old
bounding boxes do not overlap, the process proceeds to FIG. 21C and
the bounding box to be processed is set to the old bounding box
(step 2240). In general, the crowd analyzer 58 then processes the
old bounding box in much the same manner as described above with
respect to steps 2212 through 2238. More specifically, the crowd
analyzer 58 determines the individual users and crowds relevant to
the bounding box (step 2242). The crowds relevant to the bounding
box are crowds that are within or overlap the bounding box (e.g.,
have at least one user located within the bounding box). The
individual users relevant to the bounding box are users that are
currently located within the bounding box and not already part of a
crowd. Next, the crowd analyzer 58 computes an optimal inclusion
distance for individual users based on user density within the
bounding box (step 2244). More specifically, in one embodiment, the
optimal inclusion distance for individuals, which is also referred
to herein as an initial optimal inclusion distance, is set
according to the following equation:
initial_optimal _inclusion _dist = a A BoundingBox number_of _users
, ##EQU00007##
where a is a number between 0 and 1, A.sub.BoundingBox is an area
of the bounding box, and number_of_users is the total number of
users in the bounding box. The total number of users in the
bounding box includes both individual users that are not already in
a crowd and users that are already in a crowd. In one embodiment, a
is 2/3.
[0145] The crowd analyzer 58 then creates a crowd of one user for
each individual user within the bounding box that is not already
included in a crowd and sets the optimal inclusion distance for the
crowds to the initial optimal inclusion distance (step 2246). At
this point, the crowd analyzer 58 analyzes the crowds for the
bounding box to determine whether any crowd members (i.e., users in
the crowds) violate the optimal inclusion distance of their crowds
(step 2248). Any crowd member that violates the optimal inclusion
distance of his or her crowd is then removed from that crowd (step
2250). The crowd analyzer 58 then creates a crowd of one user for
each of the users removed from their crowds in step 2250 and sets
the optimal inclusion distance for the newly created crowds to the
initial optimal inclusion distance (step 2252).
[0146] Next, the crowd analyzer 58 determines the two closest
crowds in the bounding box (step 2254) and a distance between the
two closest crowds (step 2256). The distance between the two
closest crowds is the distance between the crowd centers of the two
closest crowds. The crowd analyzer 58 then determines whether the
distance between the two closest crowds is less than the optimal
inclusion distance of a larger of the two closest crowds (step
2258). If the two closest crowds are of the same size (i.e., have
the same number of users), then the optimal inclusion distance of
either of the two closest crowds may be used. Alternatively, if the
two closest crowds are of the same size, the optimal inclusion
distances of both of the two closest crowds may be used such that
the crowd analyzer 58 determines whether the distance between the
two closest crowds is less than the optimal inclusion distances of
both of the two closest crowds. As another alternative, if the two
closest crowds are of the same size, the crowd analyzer 58 may
compare the distance between the two closest crowds to an average
of the optimal inclusion distances of the two closest crowds.
[0147] If the distance between the two closest crowds is less than
the optimal inclusion distance, the two closest crowds are combined
or merged (step 2260), and a new crowd center for the resulting
crowd is computed (step 2262). Again, a center of mass algorithm
may be used to compute the crowd center of a crowd. In addition, a
new optimal inclusion distance for the resulting crowd is computed
(step 2264). As discussed above, in one embodiment, the new optimal
inclusion distance for the resulting crowd is computed as:
average = 1 n + 1 ( initial_optimal _inclusion _dist + i = 1 n d i
) , optimal_inclusion _dist = average + ( 1 n i = 1 n ( d i -
average ) 2 ) , ##EQU00008##
where n is the number of users in the crowd and d.sub.i is a
distance between the ith user and the crowd center. In other words,
the new optimal inclusion distance is computed as the average of
the initial optimal inclusion distance and the distances between
the users in the crowd and the crowd center plus one standard
deviation.
[0148] At this point, the crowd analyzer 58 determines whether a
maximum number of iterations have been performed (step 2266). If
the maximum number of iterations has not been reached, the process
returns to step 2248 and is repeated until either the distance
between the two closest crowds is not less than the optimal
inclusion distance of the larger crowd or the maximum number of
iterations has been reached. At that point, the crowd analyzer 58
discards crowds with less than three users, or members (step 2268).
The crowd analyzer 58 then determines whether the crowd formation
process for the new and old bounding boxes is done (step 2270). In
other words, the crowd analyzer 58 determines whether both the new
and old bounding boxes have been processed. If not, the bounding
box is set to the new bounding box (step 2272), and the process
returns to step 2242 and is repeated for the new bounding box. Once
both the new and old bounding box have been processed, the crowd
formation process ends.
[0149] FIGS. 22A through 22D graphically illustrate the crowd
formation process of FIGS. 21A through 21D for a scenario where the
crowd formation process is triggered by a location update for a
user having no old location. In this scenario, the crowd analyzer
58 creates a new bounding box 120 for the new location of the user,
and the new bounding box 120 is set as the bounding box to be
processed for crowd formation. Then, as illustrated in FIG. 22A,
the crowd analyzer 58 identifies all individual users currently
located within the bounding box 120 and all crowds located within
or overlapping the bounding box. In this example, crowd 122 is an
existing crowd relevant to the bounding box 120. Crowds are
indicated by dashed circles, crowd centers are indicated by
cross-hairs (+), and users are indicated as dots. Next, as
illustrated in FIG. 22B, the crowd analyzer 58 creates crowds 124
through 128 of one user for the individual users, and the optional
inclusion distances of the crowds 124 through 128 are set to the
initial optimal inclusion distance. As discussed above, the initial
optimal inclusion distance is computed by the crowd analyzer 58
based on a density of users within the bounding box 120.
[0150] The crowd analyzer 58 then identifies the two closest crowds
124 and 126 in the bounding box 120 and determines a distance
between the two closest crowds 124 and 126. In this example, the
distance between the two closest crowds 124 and 126 is less than
the optimal inclusion distance. As such, the two closest crowds 124
and 126 are merged and a new crowd center and new optimal inclusion
distance are computed, as illustrated in FIG. 22C. The crowd
analyzer 58 then repeats the process such that the two closest
crowds 124 and 128 in the bounding box 120 are again merged, as
illustrated in FIG. 22D. At this point, the distance between the
two closest crowds 122 and 124 is greater than the appropriate
optimal inclusion distance. As such, the crowd formation process is
complete.
[0151] FIGS. 23A through 23F graphically illustrate the crowd
formation process of FIGS. 21A through 21D for a scenario where the
new and old bounding boxes overlap. As illustrated in FIG. 23A, a
user moves from an old location to a new location, as indicated by
an arrow. The crowd analyzer 58 receives a location update for the
user giving the new location of the user. In response, the crowd
analyzer 58 creates an old bounding box 130 for the old location of
the user and a new bounding box 132 for the new location of the
user. Crowd 134 exists in the old bounding box 130, and crowd 136
exists in the new bounding box 132.
[0152] Since the old bounding box 130 and the new bounding box 132
overlap, the crowd analyzer 58 creates a bounding box 138 that
encompasses both the old bounding box 130 and the new bounding box
132, as illustrated in FIG. 23B. In addition, the crowd analyzer 58
creates crowds 140 through 146 for individual users currently
located within the bounding box 138. The optimal inclusion
distances of the crowds 140 through 146 are set to the initial
optimal inclusion distance computed by the crowd analyzer 58 based
on the density of users in the bounding box 138.
[0153] Next, the crowd analyzer 58 analyzes the crowds 134, 136,
and 140 through 146 to determine whether any members of the crowds
134, 136, and 140 through 146 violate the optimal inclusion
distances of the crowds 134, 136, and 140 through 146. In this
example, as a result of the user leaving the crowd 134 and moving
to his new location, both of the remaining members of the crowd 134
violate the optimal inclusion distance of the crowd 134. As such,
the crowd analyzer 58 removes the remaining users from the crowd
134 and creates crowds 148 and 150 of one user each for those
users, as illustrated in FIG. 23C.
[0154] The crowd analyzer 58 then identifies the two closest crowds
in the bounding box 138, which in this example are the crowds 144
and 146. Next, the crowd analyzer 58 computes a distance between
the two crowds 144 and 146. In this example, the distance between
the two crowds 144 and 146 is less than the initial optimal
inclusion distance and, as such, the two crowds 144 and 146 are
combined. In this example, crowds are combined by merging the
smaller crowd into the larger crowd. Since the two crowds 144 and
146 are of the same size, the crowd analyzer 58 merges the crowd
146 into the crowd 144, as illustrated in FIG. 23D. A new crowd
center and new optimal inclusion distance are then computed for the
crowd 144.
[0155] At this point, the crowd analyzer 58 repeats the process and
determines that the crowds 136 and 142 are now the two closest
crowds. In this example, the distance between the two crowds 136
and 142 is less than the optimal inclusion distance of the larger
of the two crowds 136 and 142, which is the crowd 136. As such, the
crowd 142 is merged into the crowd 136 and a new crowd center and
optimal inclusion distance are computed for the crowd 136, as
illustrated in FIG. 23E. At this point, there are no two crowds
closer than the optimal inclusion distance of the larger of the two
crowds. As such, the crowd analyzer 58 discards any crowds having
less than three members, as illustrated in FIG. 23F. In this
example, the crowds 140, 144, 148, and 150 have less than three
members and are therefore removed. The crowd 136 has three or more
members and, as such, is not removed. At this point, the crowd
formation process is complete.
[0156] FIGS. 24A through 24E graphically illustrate the crowd
formation process of FIGS. 21A through 21D in a scenario where the
new and old bounding boxes do not overlap. As illustrated in FIG.
24A, in this example, the user moves from an old location to a new
location. The crowd analyzer 58 creates an old bounding box 152 for
the old location of the user and a new bounding box 154 for the new
location of the user. Crowds 156 and 158 exist in the old bounding
box 152, and crowd 160 exists in the new bounding box 154. In this
example, since the old and new bounding boxes 152 and 154 do not
overlap, the crowd analyzer 58 processes the old and new bounding
boxes 152 and 154 separately.
[0157] More specifically, as illustrated in FIG. 24B, as a result
of the movement of the user from the old location to the new
location, the remaining users in the crowd 156 no longer satisfy
the optimal inclusion distance for the crowd 156. As such, the
remaining users in the crowd 156 are removed from the crowd 156,
and crowds 162 and 164 of one user each are created for the removed
users as shown in FIG. 24C. In this example, no two crowds in the
old bounding box 152 are close enough to be combined. As such,
processing of the old bounding box 152 is complete, and the crowd
analyzer 58 proceeds to process the new bounding box 154.
[0158] As illustrated in FIG. 24D, processing of the new bounding
box 154 begins by the crowd analyzer 58 creating a crowd 166 of one
user for the user. The crowd analyzer 58 then identifies the crowds
160 and 166 as the two closest crowds in the new bounding box 154
and determines a distance between the two crowds 160 and 166. In
this example, the distance between the two crowds 160 and 166 is
less than the optimal inclusion distance of the larger crowd, which
is the crowd 160. As such, the crowd analyzer 58 combines the
crowds 160 and 166 by merging the crowd 166 into the crowd 160, as
illustrated in FIG. 24E. A new crowd center and new optimal
inclusion distance are then computed for the crowd 160. At this
point, the crowd formation process is complete.
[0159] FIG. 25 illustrates a process for generating aggregate
profile data, or more specifically current aggregate profile data,
for one or more crowds in response to a request for aggregate
profile data for a current location of a user according to one
embodiment of the present disclosure. First, a request for
aggregate profile data for a current location of a user is received
by the crowd analyzer 58 (step 2300). In this example, the request
is for aggregate profile data for the current location of the user
20-1. In one embodiment, the request is received from the profile
manager 52 as part of the profile filtering process of FIG. 4. In
another embodiment, the request is received from the mobile device
18-1 of the user 20-1 as part of the profile filtering process of
FIG. 9. Next, the crowd analyzer 58 establishes a bounding box for
the request (step 2302). The bounding box is a geographic area of a
predetermined size centered at or otherwise encompassing the
current location of the user 20-1. While a bounding "box" is used
in this example, a bounding region of any desired shape and size
may be used.
[0160] Next, the crowd analyzer 58 identifies one or more crowds
that are relevant to the bounding box established for the request
(step 2304). The one or more crowds that are relevant to the
bounding box are preferably crowds located within or overlapping
the bounding box at the current time. After the crowd analyzer 58
has identified the one or more relevant crowds, the relevant crowds
are passed to the aggregation engine 60. The aggregation engine 60
then selects a next crowd to process from the one or more relevant
crowds, which for the first iteration is the first relevant crowd
(step 2306). The aggregation engine 60 then generates an aggregate
profile for the crowd based on a comparison of the filtered user
profiles of the users in the crowd to one another (step 2308). Note
that in this embodiment is it assumed that filtered user profiles
are created and stored for all of the users 20-1 through 20-N.
However, the present disclosure is not limited thereto. In an
alternative embodiment, profile filtering may not be performed for
some of the users 20-1 through 20-N. In this case, the user
profiles of any users in the crowd for which filtered user profiles
have not been created and stored are used when generating the
aggregate profile for the crowd.
[0161] The aggregate profile for the crowd includes an aggregate
list of interests of the users in the crowd and representation
values for the interests in the aggregate list of interests for the
crowd. As discussed above, in one embodiment, the interests are
expressed as keywords in a number of profile categories. In one
embodiment, for each interest, or keyword, the representation value
for the interest is a number of user matches, or occurrences, of
the interest across all of the filtered user profiles of the users
in the crowd. In another embodiment, for each interest, or keyword,
the representation value for the interest is a ratio of a number of
user matches, or occurrences, of the interest across the filtered
user profiles of all of the users in the crowd over a total number
of users in the crowd.
[0162] Once the aggregate profile of the crowd is generated, the
aggregation engine 60 determines whether there are more crowds to
process (step 2310). If so, the process returns to step 2306 and is
repeated for the next crowd. Once aggregate profiles have been
generated for all of the relevant crowds, the aggregate profiles
for the crowds are combined (step 2312). The aggregate profiles may
be combined by generating a combined list of interests that
includes all of the interests from the aggregate profiles of the
crowds. Then, for each interest in the combined list of interests,
the representation values for that interest in the aggregate
profiles are combined to provide a combined representation value
for the interest. For example, if the representation values are
expressed as numbers of user matches, then the number of user
matches for a particular interest in each of the aggregate profiles
for the crowds that include that interest may be summed to provide
the combined representation value for that interest. As another
example, if the representation values are expressed as ratios of
the user matches to total numbers of users, then the ratios for a
particular interest in each of the aggregate profiles for the
crowds that include that interest may be averaged to provide the
combined representation value for that interest.
[0163] Lastly, the aggregation engine 60 outputs the combined
aggregate profile for the one or more crowds as the current
aggregate profile data for the current location of the user 20-1
(step 2314). The combined aggregate profile includes the combined
list of interests generated from the aggregate profiles for the one
or more relevant crowds and corresponding combined representation
values for the interests in the combined list of interests. The
combined representation values may also be referred to herein as
representation values for the corresponding interests in the
current aggregate profile data.
[0164] Before proceeding, an alternative embodiment should be
discussed. In an alternative embodiment, rather than establishing a
bounding box for identifying the one or more relevant crowds, the
crowd analyzer 58 may identify a current crowd in which the user
20-1 is located or a current crowd in which the user 20-1 is to be
located based on the current location of the user 20-1. This
identified crowd may then be the only relevant crowd considered for
purposes of current aggregate profile generation. The aggregation
engine 60 may then generate an aggregate profile for the identified
crowd and output the aggregate profile of the identified crowd as
the aggregate profile data for the current location of the user
20-1. In this alternative embodiment, steps 2302, 2306, 2310, and
2312 of FIG. 25 would not be needed. Also, in step 2314, the
aggregate profile of the crowd would be output as the aggregate
profile data for the current location of the user 20-1.
[0165] In another alternative embodiment, rather than utilizing
crowds to generate current aggregate profile data, the MAP server
12 may establish a bounding region of a predetermined shape and
size that is centered at or that otherwise encompasses the current
location of the user 20-1. The MAP server 12 may then identify all
of the other users 20-2 through 20-N that are currently located
within the bounding region and aggregate the filtered user profiles
of the identified users to provide the current aggregate profile
data for the current location of the user 20-1.
[0166] In the embodiments described above, user profile filtering
is performed at either the MAP server 12 or the corresponding
mobile devices 18-1 through 18-N prior to utilizing the user
profiles at the MAP server 12 to provide aggregate profile data.
FIG. 26 illustrates an embodiment where filtering is performed on
aggregate profile data after receiving the aggregate profile data
from the MAP server 12 according to another embodiment of the
present disclosure. Using the MAP application 32-1 of the mobile
device 18-1 as an example, first, the MAP application 32-1 sends a
request to the MAP server 12 for current and/or historical
aggregate profile data (step 2400). In response, the MAP server 12
generates the requested aggregate profile data. Note that in this
embodiment, the MAP server 12 preferably does not create or store
filtered user profiles for the users 20-1 through 20-N. As such,
the user profiles, rather than the filtered user profiles, of the
users 20-1 through 20-N are used to generate aggregate profile
data. The MAP server 12 then returns the requested aggregate
profile data to the MAP application 32-1 of the mobile device
18-1.
[0167] At this point, the MAP application 32-1 receives the
requested aggregate profile data from the MAP server 12 (step
2402). Next, the MAP application 32-1 may obtain additional
information, if any, for a corresponding location for which the
aggregate profile data was requested in a manner similar to that
described above (step 2404). In one embodiment, the aggregate
profile data is for the current location of the user 20-1 and, as
such, the additional information is also for the current location
of the user 20-1. The MAP application 32-1 then filters aggregate
profile data based on the aggregate profile data itself, the
additional information, and one or more filtering rules (step
2406). Lastly, the MAP application 32-1 utilizes the filtered
aggregate profile data (step 2408). For example, the MAP
application 32-1 may present the filtered aggregate profile data to
the user 20-1. As another example, the MAP application 32-1 may
utilize the filtered aggregate profile data to perform a desired
function.
[0168] FIG. 27 illustrates step 2408 of FIG. 26 in more detail
according to one embodiment of the present disclosure. In order to
filter the aggregate profile data, the MAP application 32-1 first
determines a cut-off value (step 2500). The cut-off value may be
determined in the same manner as that described above with respect
to step 1400 of FIG. 8. For example, the cut-off value may be a
function of the highest representation value of the representation
values for the interests in the aggregate profile data. Further, if
the location for which the aggregate profile data has been obtained
is the current location of the user 20-1, the cut-off value may
also be a function of an amount of time that the user 20-1 has been
at the same location, a frequency at which the user 20-1 visits the
location, a number of times that the user 20-1 has visited the
location, or a combination thereof.
[0169] After determining the cut-off value, filtered aggregate
profile data is initialized (step 2502). The filtered aggregate
profile is initialized by creating a version of the aggregate
profile data having no interests. Then, the representation value
for the next interest in the aggregate profile data is obtained
(step 2504). A determination is then made as to whether the
representation value is greater than or equal to the cut-off value
(step 2506). If not, the process proceeds to step 2510. Otherwise,
the interest is added to the filtered aggregate profile data (step
2508). At this point, whether proceeding from step 2506 or 2508, a
determination is made as to whether the last interest in the
aggregate profile data has been processed (step 2510). If not, the
process returns to step 2504 and is repeated.
[0170] Once all of the interests in the aggregate profile data have
been processed, the filtered aggregate profile data may be further
filtered based on the additional information obtained for the
corresponding location in a manner similar to that described above
(step 2512). Note that step 2512 is optional. In addition, in this
embodiment, the filtered aggregate profile data may be further
filtered based on one or more situational awareness rules in the
manner described above (step 2514). Note that like step 2512, step
2514 is also optional.
[0171] The processes described above enable either user profile
filtering or aggregate profile filtering to control what profile
information is revealed through aggregate profile data depending on
the environments in which the users 20-1 through 20-N are located.
However, a user may attempt to thwart this control mechanism by,
for example, altering his user profile to include a particular
interest in an attempt to bypass filtering rules configured to hide
that there are one or more other users with that interest nearby.
For example, user A may define a filtering rule such that interest
A in user A's user profile is always filtered, or hidden,
regardless of the amount of time that user A has remained at the
same location unless the aggregate profile data for user A's
current location indicates that there is at least one other user at
or near user A's current location having a user profile that
includes interest A. User B may attempt to bypass user A's rule by
altering his user profile to include interest A even though user B
does not in fact have an interest in interest A in an attempt to
have user A's interests in interest A revealed via the aggregate
profile data for the current location.
[0172] The following discussion describes processes by which such
bypass attempts can be thwarted based on tracking ages of interests
in the user profiles, and thus the filtered user profiles, of the
users 20-1 through 20-N. In order to track the ages of the
interests in the user profiles, and thus filtered user profiles, of
the users 20-1 through 20-N, the profile manager 52 stores an age
timestamp for each interest in the user profiles, and thus filtered
user profiles, of the users 20-1 through 20-N. Using the user 20-1
as an example, the profile manager 52 initially sets age timestamps
for the interests in the user profile of the user 20-1 to a current
time at the time when the profile manager 52 first obtains the user
profile of the user 20-1. Thereafter, each time an interest is
added to the user profile of the user 20-1, an age timestamp
corresponding to the time at which the interest was added is
created and stored for that interest in the user profile of the
user 20-1. Similarly, each time an interest is modified in the user
profile of the user 20-1, the age timestamp for that interest is
updated to reflect the time at which the interest was modified.
Using the age timestamps, ages of the interests in the user
profiles of the users 20-1 through 20-N can be determined. Note
that the age timestamps for the interests in the user profiles of
the users 20-1 through 20-N are also stored in the filtered user
profiles of the users 20-1 through 20-N for the interests remaining
after filtering.
[0173] In one embodiment, the ages of the interests in the filtered
user profiles of the users 20-1 through 20-N are used when
generating aggregate profile data at the MAP server 12 to be used
in the profile filtering process of FIG. 4, FIG. 9, or FIG. 26.
More specifically, when generating historical aggregate profile
data according to the process of FIG. 18, in step 2012, interests
in the filtered user profiles of the anonymous user records stored
in the history object that have ages that are less than a
predetermined threshold age (e.g., 1 day, 1 week, or the like) are
ignored, or not considered, when generating the aggregate profile
for the history object. In this manner, interests that were
recently added or modified at the time of creating the history
object do not contribute to the aggregate profile generated for the
history object and thus the historical aggregate profile data.
[0174] In an alternative embodiment, when generating historical
aggregate profile data according to the process of FIG. 18, in step
2012, interests in the filtered user profiles of the anonymous user
records stored in the history object that are relevant to one or
more filtering rules of the user for which the historical aggregate
profile data is being generated (e.g., the user whose user profile
is to be filtered based on the historical aggregate profile data)
and that have ages that are less than a predetermined threshold age
are ignored, or not considered, when generating the aggregate
profile for the history object. An interest is relevant to a
filtering rule when that interest, if included in the historical
aggregate profile data, would be utilized for determining whether
the filtering rule is satisfied. In this manner, interests recently
added or modified at the time of creating and storing the history
object do not contribute to the historical aggregate profile
data.
[0175] In a similar manner, when generating current aggregate
profile data according to the process of FIG. 25, in step 2308,
interests in the filtered user profiles of the users in the crowd
that have ages that are less than a predetermined threshold age
(e.g., 1 day, 1 week, or the like) are ignored, or not considered,
when generating the aggregate profile for the crowd. In this
manner, interests recently added or modified in the filtered user
profiles of the users in the crowd do not contribute to the
aggregate profile data for the crowd. As such, any attempt to
thwart the filtering rules defined for filtering a user profile of
a user cannot be bypassed by another user in a relevant crowd by
that other user adding interests to his user profile.
[0176] In an alternative embodiment, when generating current
aggregate profile data according to the process of FIG. 25, in step
2308, interests in the filtered user profiles of the users in the
crowd that are relevant to one or more filtering rules of the user
for which the current aggregate profile data is being generated
(e.g., the user whose user profile is to be filtered based on the
current aggregate profile data) and that have ages that are less
than a predetermined threshold age are ignored, or not considered,
when generating the aggregate profile for the crowd. An interest is
relevant to a filtering rule when that interest, if included in the
current aggregate profile data, would be utilized for determining
whether the filtering rule is satisfied. In this manner, interests
recently added or modified in the filtered user profiles of the
users in the crowd that would alter the filtering of the user
profile of the user do not contribute to the aggregate profile data
for the crowd. As such, any attempt to thwart the filtering rules
defined for filtering the user profile of the user cannot be
bypassed by another user in a relevant crowd by that other user
adding interests to his user profile.
[0177] In order to illustrate some of the concepts described above,
the following exemplary use cases are provided. However, these use
cases are exemplary and are not intended to limit the scope of the
present disclosure.
Ted at a Conference:
[0178] 1. Ted is at a business conference for restaurant equipment.
Since Ted still likes to share information about the fact that he
is involved with a pizza franchise and see how many other
conference attendees are as well, he leaves his MAP application on
his mobile device running. Normally, this might cause some of Ted's
more personal information from his user profile to be shown in the
aggregate profile for the conference but he has set up rules around
the more personal parts of his profile so that only people that
share his love for UNC can see that in the aggregate profile for
the conference. 2. Ted enters the building where the conference is
being held and the MAP application on his mobile device (e.g.,
mobile phone) sends an update to the server for his current
location, his current situation, and, if not already sent, his user
profile. [0179] Determining the Appropriate Rules and Situations:
[0180] 1. The MAP server gathers information about the event
associated with that location and the current aggregate profile for
the location and finds that it matches the criteria that Ted has
identified as a "work" situation, which tells the MAP server to
send an alert to Ted's MAP client to see if he wants to go into a
Blend mode to hide his more personal and unrelated information. 3.
Ted's MAP application causes his mobile device to beep at him, and
he pulls his mobile device out and sees that it is asking if he
wants to enter Blend mode. Ted tells the system to remember his
preference for the conference event and that he does want to switch
to Blend mode. [0181] Blend Mode: [0182] 1. In Blend mode, Ted's
user profile will be filtered such that his filtered user profile
includes only those interests that are related to the aggregate
profile data for his current location and the attributes (i.e.,
additional information) obtained for the event. In this case, the
additional information for Ted's current location indicates that he
is currently at a business type event related to the food industry
and equipment. [0183] 2. Based on the aggregate profile data and
the additional information obtained for his current location, Ted's
user profile is filtered such that his filtered user profile
includes only those portions dealing with the franchises he is
involved with and the fact that he is involved mainly with pizza
restaurant equipment and portions dealing with some related awards
and articles referenced in his user profile. All of these portions
of Ted's user profile are shown as part of the conference aggregate
profile, but his love for college basketball, and particularly UNC,
are not shared since they are not strongly represented by the
location metadata or aggregate profile.
Ted on the Town:
[0184] 1. After a long day at the conference, Ted has seen some
friendly faces and made some new friends and they go out on the
town. Ted's MAP application on his mobile device updates the MAP
server on his current location, and the MAP server determines that
it is not associated with the conference anymore. As such,
operation switches Ted's profile filtering setting to a Sharing
mode. 2. Ted and his friends find a nice local pub and grab some
food and drinks. [0185] Sharing Mode: [0186] 1. Ted's MAP
application is constantly updating the MAP server on his location.
Even though Ted is in Sharing mode now, when he is first associated
with a location, his profile is filtered as if he was in the Blend
mode. [0187] 2. As Ted spends more time at a location, the
filtering of his user profile becomes less restrictive such that
more of his user profile begins to show in the aggregate profile
data for the location. By the time they have spent a half hour at
the restaurant, some of Ted's interests in bowling and college
sports are starting to appear in the aggregate profile data for the
location. After another half an hour or so, Ted's interest in
college basketball appears in the aggregate profile data. 3. After
about an hour has passed, everyone pays their part of the bill, and
they all use their MAP-enabled mobile devices to find some fun
places to hop around to, but don't end up staying for very long at
any of the places. [0188] Restarting the Loosening Process at New
Locations: [0189] 1. The system continues to get updates about
Ted's location. Once Ted's location is no longer associated with
the restaurant, his profile restarts the loosening process, meaning
that his profile is once again restricted to the Blend mode view.
4. Finally, Ted and the three most stalwart of the crowd end up at
an all night diner where they talk late into the night and the next
morning. Soon they are the majority of who is left in the diner
such that the aggregate profile data for the diner begins to show
some more personal information about the remaining users. It comes
to light via conversation that there are a few rather vehement Duke
fans among the remaining group. Ted is a staunch UNC fan, but
having just met these individuals and considering that these
individuals could be good potential business contacts, the general
level of intoxication, and the fact that UNC recently lost to Duke,
Ted would rather not let it be known that he is a UNC fan. [0190]
Automatic Screening through Rules: [0191] 1. The system had
identified that there at least two Duke fans present based off of
the user profiles of two of the four people present and had
followed the rule that Ted had set for his UNC affiliation that his
interest as a UNC fan is to be revealed only if the ratio of UNC to
Duke fans was at least even. In addition or alternatively,
aggregate profile data may be generated such that Ted's interest as
a UNC fan would only be revealed in the aggregate profile data
generated for other UNC fans but not for Duke fans unless the ratio
of UNC to Duke fans was at least even. 5. Ted heads to the
bathroom, but his new business contacts are a little curious about
the lukewarm response that their UNC bashing jokes got from Ted.
They have Nosy Ned pull out his mobile device and use his MAP
application to look at the aggregate profile data for the diner.
Ned doesn't see any UNC fan representation in the diner's aggregate
profile data. Ned does not have a Duke fan affiliation in his
profile so taking it a step further he tries falsely adding that he
is a UNC fan to his user profile in order to see if he can
potentially sidestep rules Ted may have set upon his UNC fan
information sharing if Ted actually is a UNC fan. [0192] Profile
Attribute Aging to Protect against Interest Falsification: [0193]
1. The system acknowledges the update to Ned's profile. However,
since the system has all changes to Ned's user profile
time-stamped, it quickly identifies that this change was very
recently made and therefore will not acknowledge it as qualifying
Ned for any rules requiring this newly added interest until this
newly added interest has been in the Ned's user profile for a
sufficient amount of time. 6. As Ted comes back out he checks his
MAP application but he does not see Ned's update. Therefore, to
Ted, the aggregate profile contains no UNC fans. [0194]
Anti-Fishing and Anti-Reverse Persecution Hiding: [0195] 1. The
system recognizes that Ned is not allowed to see Ted's UNC fan
interest. So, in order to protect Ned from being identified as
someone who recently changed his profile, potentially in order to
try and see information he could not before, and to keep Ted from
mistakenly trying to find someone at the location who shares his
UNC fan interest, the system hides Ned's profile update from Ted
until Ned's update is old enough to be counted toward Ted's rules.
[0196] 2. The system allows all other users who have no rules
excluding Ned from seeing their UNC fan attribute, even if it is
simply allowing Ned to see that they do not have the UNC attribute.
As such, Ned's other two Duke fan friends are able to see a UNC fan
appear in the aggregate profile.
Ted Over Time:
[0197] 1. The next morning at the second day of the conference, Ted
arrives bleary-eyed and forgets that he should switch to Blend
mode, but the system recognizes the conference event when it
receives the update to Ted's location and therefore automatically
switches to Blend mode again. 2. Ted doesn't stay out as late the
next night but does meet some of his friends including the three
Duke fans from the evening before for drinks and food at a new
restaurant. [0198] Crowd Awareness [0199] 1. The system identifies
that Ted is with a similar set of people as from the other night so
it adjusts the interval at which the restrictions on his profile
are loosened in order to confuse any attempts the other users might
make to tie particular interests to him. 3. Ted continues to meet
with friends including the three Duke fans over the rest of the
week while he is attending the conference and they start to
frequent one or two locations. [0200] Location Awareness [0201] 1.
The system identifies that Ted is frequenting certain locations and
therefore once again adjusts the loosening interval for his profile
in order to keep people at those locations from being able to tie
particular changes in the aggregate profile to him.
[0202] FIG. 28 is a block diagram of the MAP server 12 according to
one embodiment of the present disclosure. As illustrated, the MAP
server 12 includes a controller 168 connected to memory 170, one or
more secondary storage devices 172, and a communication interface
174 by a bus 176 or similar mechanism. The controller 168 is a
microprocessor, digital Application Specific Integrated Circuit
(ASIC), Field Programmable Gate Array (FPGA), or the like. In this
embodiment, the controller 168 is a microprocessor, and the
application layer 40, the business logic layer 42, and the object
mapping layer 62 (FIG. 2) are implemented in software and stored in
the memory 170 for execution by the controller 168. Further, the
datastore 64 (FIG. 2) may be implemented in the one or more
secondary storage devices 172. The secondary storage devices 172
are digital data storage devices such as, for example, one or more
hard disk drives. The communication interface 174 is a wired or
wireless communication interface that communicatively couples the
MAP server 12 to the network 28 (FIG. 1). For example, the
communication interface 174 may be an Ethernet interface, local
wireless interface such as a wireless interface operating according
to one of the suite of IEEE 802.11 standards, or the like.
[0203] FIG. 29 is a block diagram of the mobile device 18-1
according to one embodiment of the present disclosure. This
discussion is equally applicable to the other mobile devices 18-2
through 18-N. As illustrated, the mobile device 18-1 includes a
controller 178 connected to memory 180, a communication interface
182, one or more user interface components 184, and the location
function 36-1 by a bus 186 or similar mechanism. The controller 178
is a microprocessor, digital ASIC, FPGA, or the like. In this
embodiment, the controller 178 is a microprocessor, and the MAP
client 30-1, the MAP application 32-1, and the third-party
applications 34-1 are implemented in software and stored in the
memory 180 for execution by the controller 178. In this embodiment,
the location function 36-1 is a hardware component such as, for
example, a GPS receiver. The communication interface 182 is a
wireless communication interface that communicatively couples the
mobile device 18-1 to the network 28 (FIG. 1). For example, the
communication interface 182 may be a local wireless interface such
as a wireless interface operating according to one of the suite of
IEEE 802.11 standards, a mobile communications interface such as a
cellular telecommunications interface, or the like. The one or more
user interface components 184 include, for example, a touchscreen,
a display, one or more user input components (e.g., a keypad), a
speaker, or the like, or any combination thereof.
[0204] Those skilled in the art will recognize improvements and
modifications to the preferred embodiments of the present
invention. All such improvements and modifications are considered
within the scope of the concepts disclosed herein and the claims
that follow.
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