U.S. patent application number 13/032744 was filed with the patent office on 2012-03-15 for methods and systems for following crowds.
This patent application is currently assigned to WALDECK TECHNOLOGY, LLC. Invention is credited to Christopher M. Amidon, Scott Curtis, Steven L. Petersen.
Application Number | 20120066614 13/032744 |
Document ID | / |
Family ID | 45807686 |
Filed Date | 2012-03-15 |
United States Patent
Application |
20120066614 |
Kind Code |
A1 |
Amidon; Christopher M. ; et
al. |
March 15, 2012 |
METHODS AND SYSTEMS FOR FOLLOWING CROWDS
Abstract
Systems and methods are disclosed for following status updates
sent by users in crowds of users. In one embodiment, a requestor is
enabled to follow status updates sent by users in a crowd of users
even after the users have dispersed from the crowd. More
specifically, in one embodiment, a requestor selects a crowd to
follow. Subsequently, after one or more users have left the crowd,
status updates from the users are obtained and sent to the
requestor. In another embodiment, a requestor selects a crowd to
follow. Subsequently, after some or all of the users in the crowd
have dispersed, status updates from users in new crowds in which
those users are located are obtained and sent to the requestor. In
another embodiment, a requestor is enabled to follow a user such
that the requestor receives status updates from crowds of users in
which the user is located.
Inventors: |
Amidon; Christopher M.;
(Apex, NC) ; Curtis; Scott; (Durham, NC) ;
Petersen; Steven L.; (Los Gatos, CA) |
Assignee: |
WALDECK TECHNOLOGY, LLC
Wilmington
DE
|
Family ID: |
45807686 |
Appl. No.: |
13/032744 |
Filed: |
February 23, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61309903 |
Mar 3, 2010 |
|
|
|
Current U.S.
Class: |
715/751 |
Current CPC
Class: |
G06F 16/9537 20190101;
H04L 51/20 20130101; H04L 67/306 20130101; G06Q 50/01 20130101;
H04L 51/32 20130101; G06F 16/1805 20190101; G06F 16/1734 20190101;
H04L 67/22 20130101; G06F 16/1834 20190101; G06F 16/40
20190101 |
Class at
Publication: |
715/751 |
International
Class: |
G06F 3/01 20060101
G06F003/01 |
Claims
1. A computer-implemented method comprising: receiving a crowd
selection of a requestor that identifies a first crowd of users
selected by the requestor; obtaining status updates sent by one or
more users from the first crowd of users after the one or more
users have left the first crowd of users; and sending the status
updates sent by the one or more users from the first crowd of users
to the requestor.
2. The method of claim 1 wherein: obtaining the status updates sent
by the one or more users from the first crowd of users after the
one or more users have left the first crowd of users comprises
obtaining status updates sent by users, including the one or more
users, in one or more second crowds of users in which the one or
more users from the first crowd of users are located after the one
or more users have left the first crowd of users; and sending the
status updates sent by the one or more users from the first crowd
of users to the requestor comprises sending the status updates sent
by the users in the one or more second crowds of users to the
requestor.
3. The method of claim 2 further comprising: recording a list of
users currently in the first crowd of users in response to
receiving the crowd selection of the requestor; and automatically
recording the requestor as a follower of the one or more second
crowds of users, wherein the one or more second crowds of users are
one or more crowds in which users in the list of users recorded for
the first crowd of users are located after the users have left the
first crowd of users.
4. The method of claim 3 wherein automatically recording the
requestor as a follower of the one or more second crowds of users
comprises: detecting that a user from the list of users recorded
for the first crowd of users is located in a new crowd; and
recording the requestor as a follower of the new crowd in response
to detecting that the user from the list of users is located in the
new crowd.
5. The method of claim 4 further comprising automatically removing
the requestor as a follower of the new crowd after no users from
the list of users recorded for the first crowd of users remain in
the new crowd.
6. The method of claim 3 wherein automatically recording the
requestor as a follower of the one or more second crowds of users
comprises: detecting when at least a predefined threshold number of
users from the list of users recorded for the first crowd of users
are located in a new crowd; and recording the requestor as a
follower of the new crowd in response to detecting that at least
the predefined threshold number of users from the list of users
recorded for the first crowd of users are located in the new
crowd.
7. The method of claim 6 further comprising automatically removing
the requestor as a follower of the new crowd after less than the at
least the predefined threshold number of users from the list of
users recorded for the first crowd of users remain in the new
crowd.
8. The method of claim 2 further comprising: recording a list of
users currently in the first crowd of users in response to
receiving the crowd selection of the requestor; subsequently
receiving a status update request from the requestor; and in
response to receiving the status update request from the requestor,
identifying one or more crowds of users, other than the first crowd
of users, in which users from the list of users are currently
located as the one or more second crowds of users; wherein
obtaining the status updates sent by the users in the one or more
second crowds of users and sending the status updates sent by the
users in the one or more second crowds of users are in response to
receiving the status update request from the requestor and
identifying the one or more crowds of users, other than the first
crowd of users, in which users from the list of users are currently
located as the one or more second crowds of users.
9. The method of claim 2 further comprising detecting the one or
more second crowds of users as one or more crowds of users that
split from the first crowd of users subsequent to receiving the
crowd selection of the requestor.
10. The method of claim 2 wherein the one or more second crowds of
users are one or more crowds of users, other than the first crowd
of users, in which one or more users from the first crowd of users
are located after the one or more users have left the first crowd
of users and that are located within a predefined geographic
region.
11. The method of claim 10 wherein the predefined geographic region
is a predefined geographic region that encompasses a location of
the first crowd of users at a time of receiving the crowd selection
of the requestor.
12. The method of claim 10 wherein the predefined geographic region
is a predefined geographic region that encompasses a current
location of the first crowd of users.
13. The method of claim 2 further comprising limiting the steps of
obtaining the status updates and sending the status updates to the
requestor to a predefined amount of time.
14. The method of claim 2 wherein obtaining the status updates sent
by the users in the one or more second crowds of users and sending
the status updates to the requestor are performed while the first
crowd of users still exists.
15. The method of claim 2 wherein obtaining the status updates sent
by the users in the one or more second crowds of users and sending
the status updates to the requestor are performed only after all of
the users in the first crowd of users have dispersed such that the
first crowd of users no longer exists.
16. The method of claim 2 wherein the first crowd of users is a
first group of spatially proximate users, and each of the one or
more second crowds of users is a corresponding group of spatially
proximate users.
17. The method of claim 1 wherein obtaining the status updates and
sending the status updates are subject to one or more geographic
limitations.
18. The method of claim 1 wherein sending the status updates to the
requestor is subject to one or more time limitations.
19. A computing device comprising: a communication interface
adapted to communicatively couple the computing device to a
network; and a controller associated with the communication
interface adapted to: receive, via the communication interface, a
crowd selection of a requestor that identifies a first crowd of
users selected by the requestor; obtain status updates sent by one
or more users from the first crowd of users after the one or more
users have left the first crowd of users; and send, via the
communication interface, the status updates sent by the one or more
users from the first crowd of users to the requestor.
20. A non-transitory computer-readable medium storing software for
instructing a controller of a computing device to: receive a crowd
selection of a requestor that identifies a first crowd of users
selected by the requestor; obtain status updates sent by one or
more users from the first crowd of users after the one or more
users have left the first crowd of users; and send the status
updates sent by the one or more users from the first crowd of users
to the requestor.
21. A computer-implemented method comprising: receiving a user
selection made by a requestor that identifies a select user
selected by the requestor; identifying a first crowd of users in
which the select user is located; recording the requestor as a
follower of the first crowd of users; obtaining status updates sent
by users in the first crowd of users; sending the status updates
sent by the users in the first crowd of users to the requestor as a
follower of the first crowd of users; identifying a second crowd of
users in which the select user is located after the select user has
left the first crowd of users; recording the requestor as a
follower of the second crowd of users; obtaining status updates
sent by users in the second crowd of users; and sending the status
updates sent by the users in the second crowd of users to the
requestor as a follower of the second crowd of users.
22. The method of claim 21 further comprising: detecting that the
select user has left the first crowd of users; and removing the
requestor as a follower of the first crowd of users in response to
detecting that the select user has left the first crowd of
users.
23. The method of claim 21 wherein the first crowd of users is a
first group of spatially proximate users, and each of the one or
more second crowds of users is a corresponding group of spatially
proximate users.
24. A computing device comprising: a communication interface
adapted to communicatively couple the computing device to a
network; and a controller associated with the communication
interface adapted to: receive, via the communication interface, a
user selection made by a requestor that identifies a select user
selected by the requestor; identify a first crowd of users in which
the select user is located; record the requestor as a follower of
the first crowd of users; obtain status updates sent by users in
the first crowd of users; send, via the communication interface,
the status updates sent by the users in the first crowd of users to
the requestor as a follower of the first crowd of users; identify a
second crowd of users in which the select user is located after the
select user has left the first crowd of users; record the requestor
as a follower of the second crowd of users; obtain status updates
sent by users in the second crowd of users; and send, via the
communication interface, the status updates sent by the users in
the second crowd of users to the requestor as a follower of the
second crowd of users.
Description
RELATED APPLICATIONS
[0001] This application claims the benefit of provisional patent
application Ser. No. 61/309,903, filed Mar. 3, 2010, the disclosure
of which is hereby incorporated herein by reference in its
entirety.
FIELD OF THE DISCLOSURE
[0002] The present disclosure relates to status updates from users
in crowds of users.
BACKGROUND
[0003] Status update services, such as the Twitter.RTM.
micro-blogging and social networking service, provide the ability
to follow status updates (e.g., tweets) of other users. However, in
many instances, it may be desirable to follow the status updates by
an entire crowd of users. For example, a person may desire to
follow status updates made by users in a crowd of users located at
a sporting event. Thus, there is a need for a system and method of
following status updates made by users in a crowd of users.
SUMMARY
[0004] Systems and methods are disclosed for following status
updates sent by users in crowds of users. In one embodiment, a
requestor is enabled to follow status updates sent by users in a
crowd of users even after the users have dispersed from the crowd.
More specifically, in one embodiment, a requestor selects a crowd
to follow. Subsequently, after one or more users have left the
crowd, status updates from the one or more users are obtained and
sent to the requestor. In another embodiment, a requestor selects a
crowd to follow. Subsequently, after some or all of the users in
the crowd have dispersed, status updates from users in new crowds
in which those users are located are obtained and sent to the
requestor.
[0005] In another embodiment, a requestor is enabled to follow a
user such that the requestor receives status updates from crowds of
users in which the user is located. More specifically, the
requestor selects a user to follow. In response, the requestor is
recorded as a follower of a crowd in which the user is currently
located, and status updates sent by users in the crowd are obtained
and sent to the requestor as a follower of the crowd. Thereafter,
when the user is located in a new crowd, the requestor is recorded
as a follower of the new crowd. Status updates from users in the
new crowd followed by the requestor are then obtained and sent to
the requestor.
[0006] Those skilled in the art will appreciate the scope of the
present disclosure 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
[0007] The accompanying drawing figures incorporated in and forming
a part of this specification illustrate several aspects of the
disclosure, and together with the description serve to explain the
principles of the disclosure.
[0008] FIG. 1 illustrates a system that enables crowd following in
order to receive status updates from users in followed crowds
according to one embodiment of the present disclosure;
[0009] FIG. 2 is a more detailed illustration of the Mobile
Aggregate Profile (MAP) server of FIG. 1 according to one
embodiment of the present disclosure;
[0010] FIG. 3 is a more detailed illustration of the MAP
application of one of the mobile devices of FIG. 1 according to one
embodiment of the present disclosure;
[0011] FIG. 4 illustrates the operation of the system of FIG. 1 to
provide user profiles and current locations of the users of the
mobile devices to the MAP server according to one embodiment of the
present disclosure;
[0012] FIG. 5 illustrates the operation of the system of FIG. 1 to
provide user profiles and current locations of the users of the
mobile devices to the MAP server according to another embodiment of
the present disclosure;
[0013] FIG. 6 illustrates exemplary data records that may be used
to represent crowds, users, crowd snapshots, and anonymous users
according to one embodiment of the present disclosure;
[0014] FIGS. 7A through 7D illustrate one embodiment of a spatial
crowd formation process that may be used to enable crowd tracking
according to one embodiment of the present disclosure;
[0015] FIGS. 8A through 8D graphically illustrate the crowd
formation process of FIGS. 7A through 7D for a scenario where the
crowd formation process is triggered by a location update for a
user having no old location;
[0016] FIGS. 9A through 9F graphically illustrate the crowd
formation process of FIGS. 7A through 7D for a scenario where the
new and old bounding boxes overlap;
[0017] FIGS. 10A through 10E graphically illustrate the crowd
formation process of FIGS. 7A through 7D in a scenario where the
new and old bounding boxes do not overlap;
[0018] FIG. 11 illustrates a process for creating crowd snapshots
according to one embodiment of the present disclosure;
[0019] FIG. 12 illustrates a process that may be used to
re-establish crowds and detect crowd splits according to one
embodiment of the present disclosure;
[0020] FIG. 13 graphically illustrates the process of
re-establishing a crowd for an exemplary crowd according to one
embodiment of the present disclosure;
[0021] FIG. 14 graphically illustrates the process for capturing a
crowd split for an exemplary crowd according to one embodiment of
the present disclosure;
[0022] FIG. 15 graphically illustrates the merging of two exemplary
pre-existing crowds according to one embodiment of the present
disclosure;
[0023] FIG. 16 illustrates the operation of the status update
processor of the MAP server of FIG. 1 to enable a requestor to
follow status updates from users in a select crowd even after the
users in the select crowd disperse according to one embodiment of
the present disclosure;
[0024] FIG. 17 illustrates the operation of the status update
processor of the MAP server of FIG. 1 to receive and distribute
status updates sent by users to followers of corresponding crowds
of users according to one embodiment of the present disclosure;
[0025] FIG. 18 illustrates the operation of the status update
processor to automatically record a requestor as a follower of
crowds of users including users dispersing from a select crowd of
users according to one embodiment of the present disclosure;
[0026] FIG. 19 illustrates the operation of the status update
processor of the MAP server according to another embodiment of the
present disclosure;
[0027] FIG. 20 illustrates the operation of the status update
processor of the MAP server according to yet another embodiment of
the present disclosure in which a requestor is automatically added
as a follower of crowds split from a select crowd;
[0028] FIG. 21 illustrates the operation of the status update
processor of the MAP server according to yet another embodiment of
the present disclosure in which a requestor is enabled to follow
crowds of users in which a select user is located;
[0029] FIG. 22 illustrates the operation of the status update
processor of the MAP server according to yet another embodiment of
the present disclosure in which a requestor is enabled to receive
status updates from users in a select crowd of users even after
those users have dispersed from the select crowd of users;
[0030] FIG. 23 is a block diagram of the MAP server of FIG. 1
according to one embodiment of the present disclosure;
[0031] FIG. 24 is a block diagram of one of the mobile devices of
FIG. 1 according to one embodiment of the present disclosure;
[0032] FIG. 25 is a block diagram of the subscriber device of FIG.
1 according to one embodiment of the present disclosure; and
[0033] FIG. 26 is a block diagram of a computing device operating
to host the status update service of FIG. 1 according to one
embodiment of the present disclosure.
DETAILED DESCRIPTION
[0034] 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.
[0035] The present disclosure relates to following status updates
from users in crowds of users. As used herein, a status update is a
message provided by a user for publication via a status update, or
micro-blogging, service such as, for example, the Twitter.RTM.
micro-blogging and social networking service or the Facebook.RTM.
social networking service. The status update may include a
text-based status update, an audio status update, a video status
update, an image status update, or any combination thereof. As an
example, a status update may be a tweet provided by a user of the
Twitter.RTM. micro-blogging and social networking service, which is
referred to herein as one example of a status updating service. As
another example, a status update may be a status update posted by a
user of the Facebook.RTM. social networking service. Note, however,
that status updates are not limited to Twitter.RTM. tweets or
Facebook.RTM. status updates. Other types of status updates may
additionally or alternatively be used.
[0036] FIG. 1 illustrates a Mobile Aggregate Profiling (MAP) system
10 (hereinafter "system 10") that operates to enable crowd
following in order to receive status updates from followed crowds
according to one embodiment of the present disclosure. Note that
the system 10 is exemplary and is not intended to limit the scope
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
(generally referred to herein collectively as mobile devices 18 and
individually as mobile device 18) having associated users 20-1
through 20-N (generally referred to herein collectively as users 20
and individually as user 20), a subscriber device 22 having an
associated subscriber 24, a third-party service 26, and a status
update service 28 communicatively coupled via a network 30. The
network 30 may be any type of network or any combination of
networks. Specifically, the network 30 may include wired
components, wireless components, or both wired and wireless
components. In one exemplary embodiment, the network 30 is a
distributed public network such as the Internet, where the mobile
devices 18 are enabled to connect to the network 30 via local
wireless connections (e.g., Wi-Fi.RTM. or IEEE 802.11 connections)
or wireless telecommunications connections (e.g., 3G or 4G
telecommunications connections such as GSM, LTE, W-CDMA, or
WiMAX.RTM. connections).
[0037] As discussed below in detail, the MAP server 12 operates to
obtain current locations, including location updates, and user
profiles of the users 20 of the mobile devices 18. The current
locations of the users 20 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, the MAP server 12 is enabled to
provide a number of features such as, but not limited to, forming
crowds of users using current locations and/or user profiles of the
users 20, generating aggregate profiles for crowds of users,
tracking crowds of users, and distributing status updates from the
users 20 obtained from the status update service 28. 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, load sharing, and/or the like.
[0038] In general, the one or more profile servers 14 operate to
store user profiles for a number of persons including the users 20
of the mobile devices 18. 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, using the one or more
profile servers 14, the MAP server 12 is enabled to directly or
indirectly obtain the user profiles of the users 20 of the mobile
devices 18. The location server 16 generally operates to receive
location updates from the mobile devices 18 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 Fire Eagle.RTM. service.
[0039] The mobile devices 18 may be mobile smart phones, portable
media player devices, mobile gaming devices, mobile computers
(e.g., laptop computers), or the like. Some exemplary mobile
devices that may be programmed or otherwise configured to operate
as the mobile devices 18 are the Apple.RTM. iPhone.RTM., the Palm
Pre.RTM., the Samsung Rogue.TM., the Blackberry Storm.TM., the
Motorola DROID or similar phone running Google's Android.TM.
Operating System, an Apple.RTM. iPad.RTM., 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.
[0040] The mobile devices 18-1 through 18-N include MAP clients
32-1 through 32-N (generally referred to herein collectively as MAP
clients 32 or individually as MAP client 32), MAP applications 34-1
through 34-N (generally referred to herein collectively as MAP
applications 34 or individually as MAP application 34), third-party
applications 36-1 through 36-N (generally referred to herein
collectively as third-party applications 36 or individually as
third-party application 36), and location functions 38-1 through
38-N (generally referred to herein collectively as location
functions 38 or individually as location function 38),
respectively. The MAP client 32 is preferably implemented in
software. In general, in the preferred embodiment, the MAP client
32 is a middleware layer operating to interface an application
layer (i.e., the MAP application 34 and the third-party
applications 36) to the MAP server 12. More specifically, the MAP
client 32 enables the MAP application 34 and the third-party
applications 36 to request and receive data from the MAP server 12.
In addition, the MAP client 32 enables applications, such as the
MAP application 34 and the third-party applications 36, to access
data from the MAP server 12.
[0041] The MAP application 34 is also preferably implemented in
software. The MAP application 34 generally provides a user
interface component between the user 20 and the MAP server 12. For
example, the MAP application 34 may enable the user 20 to initiate
crowd search requests or requests for crowd data from the MAP
server 12 and presents corresponding data returned by the MAP
server 12 to the user 20. As another example, as described below in
detail, the MAP application 34 may enable the user 20 to follow
status updates of crowds of users, which is more generally referred
to herein as following crowds of users. The MAP application 34 also
enables the user 20 to configure various settings.
[0042] For example, the MAP application 34 may enable the user 20
to select a desired social networking service (e.g., Facebook.RTM.,
MySpace.RTM., LinkedlN.RTM., etc.) from which to obtain the user
profile of the user 20 and provide any necessary credentials (e.g.,
username and password) needed to access the user profile from the
social networking service.
[0043] The third-party applications 36 are preferably implemented
in software. The third-party applications 36 operate to access the
MAP server 12 via the MAP client 32. The third-party applications
36 may utilize data obtained from the MAP server 12 in any desired
manner. As an example, one of the third-party applications 36 may
be a gaming application that utilizes crowd data to notify the user
20 of Points of Interest (POIs) or Areas of Interest (AOIs) where
crowds of interest are currently located. It should be noted that
while the MAP client 32 is illustrated as being separate from the
MAP application 34 and the third-party applications 36, the present
disclosure is not limited thereto. The functionality of the MAP
client 32 may alternatively be incorporated into the MAP
application 34 and the third-party applications 36.
[0044] The location function 38 may be implemented in hardware,
software, or a combination thereof. In general, the location
function 38 operates to determine or otherwise obtain the location
of the mobile device 18. For example, the location function 38 may
be or include a Global Positioning System (GPS) receiver. In
addition or alternatively, the location function 38 may include
hardware and/or software that enables improved location tracking in
indoor environments such as, for example, shopping malls. For
example, the location function 38 may be part of or compatible with
the InvisiTrack Location System provided by InvisiTrack and
described in U.S. Pat. No. 7,423,580 entitled "Method and System of
Three-Dimensional Positional Finding" which issued on Sep. 9, 2008,
U.S. Pat. No. 7,787,886 entitled "System and Method for Locating a
Target using RFID" which issued on Aug. 31, 2010, and U.S. Patent
Application Publication No. 2007/0075898 entitled "Method and
System for Positional Finding Using RF, Continuous and/or Combined
Movement" which published on Apr. 5, 2007, all of which are hereby
incorporated herein by reference for their teachings regarding
location tracking.
[0045] 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 40 to obtain various types of data, preferably for a fee.
For example, the subscriber 24 may 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. As another example, the subscriber 24 may be
enabled to follow status updates from crowds of users. Note that
the web browser 40 is exemplary. In another embodiment, the
subscriber device 22 is enabled to access the MAP server 12 via a
custom application.
[0046] The third-party service 26 is a service that has access to
data from the MAP server 12 such as, for example, aggregate
profiles for one or more crowds at one or more POIs or within one
or more AOIs. 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.
[0047] Lastly, the status update service 28 is preferably
implemented in software and hosted by a physical server or a number
of physical servers operating in a collaborative manner for
purposes of load sharing, redundancy, or the like. Note that while
only one status update service 28 is illustrated, there may be
multiple status update services 28. The status update service 28
enables users, such as the users 20, to register with the status
update service 28. In response, corresponding user accounts are
created by the status update service 28. For each of the users 20
that is registered with the status update service 28, the user
account of the user 20 may include a user identifier (ID) of the
user 20 such as a screen name or username of the user 20 for the
status update service 28 and, in some embodiments, an indicator
such as a flag that indicates whether status updates from the user
20 are to be shared with the MAP server 12. In some embodiments,
the user account of the user 20 may also include a user profile of
the user 20 that defines one or more interests of the user 20.
[0048] As discussed below in detail, the status update service 28
receives status updates from the users 20 that are registered with
the status update service 28 via the mobile devices 18 of the users
20 over the network 30. Each status update preferably includes the
user ID of the user 20 from which the status update originated and
a body of the status update. Each status update may also include
information such as, for example, a timestamp defining a time and
date on which the status update was sent from the mobile device 18
of the user 20 to the status update service 28, a location of the
user 20 at the time the status update was sent from the mobile
device 18 to the status update service 28, or the like. Upon
receiving status updates from the mobile devices 18 of the users
20, the status update service 28 stores the status updates and/or
delivers the status updates to the MAP server 12 if the
corresponding users 20 have chosen to share their status updates
with the MAP server 12. The status updates may be sent to the MAP
server 12 as they are received, in a batch process, or the
like.
[0049] 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. In addition, while the system 10 of FIG.
1 illustrates an embodiment where the status update service 28 is
separate from the MAP server 12, the one or more profile servers
14, and the location server 16, the present disclosure is not
limited thereto. The status update service 28 may alternatively be
implemented with the MAP server 12, the one or more profile servers
14, or the location server 16. For example, a social networking
service such as the Facebook.RTM. social networking service may
function as both the profile server 14 and the status update
service 28.
[0050] 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 42, a
business logic layer 44, and a persistence layer 46. The
application layer 42 includes a user web application 48, a mobile
client/server protocol component 50, and one or more data
Application Programming Interfaces (APIs) 52. The user web
application 48 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 50 is preferably implemented in
software and operates to provide an interface between the MAP
server 12 and the MAP clients 32 hosted by the mobile devices 18.
The data APIs 52 enable third-party services, such as the
third-party service 26, to access the MAP server 12.
[0051] The business logic layer 44 includes a profile manager 54, a
location manager 56, a crowd analyzer 58, an aggregation engine 60,
and a status update processor 62 each of which is preferably
implemented in software. The profile manager 54 generally operates
to obtain the user profiles of the users 20 directly or indirectly
from the one or more profile servers 14 and store the user profiles
in the persistence layer 46. The location manager 56 operates to
obtain the current locations of the users 20 including location
updates. As discussed below, the current locations of the users 20
may be obtained directly from the mobile devices 18 and/or obtained
from the location server 16.
[0052] 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 further
characterize crowds to reflect degree of fragmentation, best-case
and worst-case degree of separation (DOS), and/or degree of
bi-directionality. Still further, 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, the subscriber device 22, and the
third-party service 26. The aggregate profile data may be
historical aggregate profile data for one or more POIs or one or
more AOIs or aggregate profile data for crowd(s) currently at one
or more POIs or within one or more AOIs. As discussed below in
detail, the status update processor 62 operates to obtain status
updates sent by the users 20 from the status update service 28 and
deliver the status updates to followers of the corresponding crowds
of the users 20.
[0053] For additional information regarding the operation of the
profile manager 54, the location manager 56, the crowd analyzer 58,
and the aggregation engine 60, the interested reader is directed to
U.S. Patent Application Publication No. 2010/0198828, entitled
"Forming Crowds And Providing Access To Crowd Data In A Mobile
Environment," which was filed Dec. 23, 2009 and published Aug. 5,
2010; U.S. Patent Application Publication No. 2010/0197318,
entitled "Anonymous Crowd Tracking," which was filed Dec. 23, 2009
and published Aug. 5, 2010; U.S. Patent Application Publication No.
2010/0198826, entitled "Maintaining A Historical Record Of
Anonymized User Profile Data By Location For Users In A Mobile
Environment," which was filed Dec. 23, 2009 and published Aug. 5,
2010; U.S. Patent Application Publication No. 2010/0198917,
entitled "Crowd Formation For Mobile Device Users," which was filed
Dec. 23, 2009 and published Aug. 5, 2010; U.S. Patent Application
Publication No. 2010/0198870, entitled "Serving A Request For Data
From A Historical Record Of Anonymized User Profile Data In A
Mobile Environment," which was filed Dec. 23, 2009 and published
Aug. 5, 2010; U.S. Patent Application Publication No. 2010/0198862,
entitled "Handling Crowd Requests For Large Geographic Areas,"
which was filed Dec. 23, 2009 and published Aug. 5, 2010; and U.S.
Patent Application Publication No. 2010/0197319, entitled
"Modifying A User's Contribution To An Aggregate Profile Based On
Time Between Location Updates And External Events," which was filed
Dec. 23, 2009 and published Aug. 5, 2010; all of which are hereby
incorporated herein by reference in their entireties.
[0054] The persistence layer 46 includes an object mapping layer 63
and a datastore 64. The object mapping layer 63 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 44 is implemented in an object-oriented programming
language such as, for example, Java. As such, the object mapping
layer 63 operates to map objects used in the business logic layer
44 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.
[0055] 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.RTM. and Facebook.RTM.. The MAP server
12 may then persist RDF descriptions of the users 20 as a
proprietary extension of the FOAF vocabulary that includes
additional properties desired for the system 10.
[0056] FIG. 3 illustrates the MAP client 32 of FIG. 1 in more
detail according to one embodiment of the present disclosure. As
illustrated, in this embodiment, the MAP client 32 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
application 34 and the third-party applications 36 are enabled to
access the MAP client 32. The MAP middleware component 68 is
implemented in software and performs the operations needed for the
MAP client 32 to operate as an interface between the MAP
application 34 and the third-party applications 36 at the mobile
device 18 and the MAP server 12. The mobile client/server protocol
component 70 enables communication between the MAP client 32 and
the MAP server 12 via a defined protocol.
[0057] The present disclosure is primarily focused on obtaining and
distributing status updates to followers of corresponding crowds of
users. However, before discussing this in detail, it is beneficial
to discuss other features of the MAP server 12, namely, the
operation of the MAP server 12 to obtain user profiles and location
updates and to create and track crowds of users. As described
below, the crowds of users are utilized for distributing status
updates from users in the crowds to followers of the crowds.
[0058] FIG. 4 illustrates the operation of the system 10 of FIG. 1
to provide the user profile of one of the users 20 of one of the
mobile devices 18 to the MAP server 12 according to one embodiment
of the present disclosure. This discussion is equally applicable to
the other users 20 of the other mobile devices 18. First, an
authentication process is performed (step 1000). For
authentication, in this embodiment, the mobile device 18
authenticates with the profile server 14 (step 1000A) and the MAP
server 12 (step 1000B). In addition, the MAP server 12
authenticates with the profile server 14 (step 1000C). 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
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 1000D),
and the profile server 14 returns an authentication succeeded
message to the MAP client 32 of the mobile device 18 (step
1000E).
[0059] At some point after authentication is complete, a user
profile process is performed such that a user profile of the user
20 is obtained from the profile server 14 and delivered to the MAP
server 12 (step 1002). In this embodiment, the MAP client 32 of the
mobile device 18 sends a profile request to the profile server 14
(step 1002A). In response, the profile server 14 returns the user
profile of the user 20 to the mobile device 18 (step 1002B). The
MAP client 32 of the mobile device 18 then sends the user profile
of the user 20 to the MAP server 12 (step 1002C). Note that while
in this embodiment the MAP client 32 sends the complete user
profile of the user 20 to the MAP server 12, in an alternative
embodiment, the MAP client 32 may filter the user profile of the
user 20 according to criteria specified by the user 20. For
example, the user profile of the user 20 may include demographic
information, general interests, music interests, and movie
interests, and the user 20 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.
[0060] Upon receiving the user profile of the user 20 from the MAP
client 32 of the mobile device 18, the profile manager 54 of the
MAP server 12 processes the user profile (step 1002D). More
specifically, in the preferred embodiment, the profile manager 54
includes social network handlers for the social network services
supported by the MAP server 12 that operate to map the user
profiles of the users 20 obtained from the social network services
to a common format utilized by the MAP server 12. This common
format includes a number of user profile categories, or user
profile slices, such as, for example, a demographic profile
category, a social interaction profile category, a general
interests category, a music interests profile category, and a movie
interests profile category.
[0061] For example, if the MAP server 12 supports user profiles
from Facebook.RTM., MySpace.RTM., and LinkedIN.RTM., the profile
manager 54 may include a Facebook handler, a MySpace handler, and a
LinkedIN handler. The social network handlers process user profiles
from the corresponding social network services to generate user
profiles for the users 20 in the common format used by the MAP
server 12. For this example assume that the user profile of the
user 20 is from Facebook.RTM.. The profile manager 54 uses a
Facebook handler to process the user profile of the user 20 to map
the user profile of the user 20 from Facebook.RTM. to a user
profile for the user 20 for the MAP server 12 that includes lists
of keywords for a number of predefined profile categories, or
profile slices, such as, for example, 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 from Facebook.RTM. may be processed by the Facebook
handler of the profile manager 54 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, movie genres, or the like for the movie
interests profile category. In one embodiment, the profile manager
54 may use natural language processing or semantic analysis. For
example, if the Facebook.RTM. user profile of the user 20 states
that the user 20 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 for the MAP server 12.
[0062] After processing the user profile of the user 20, the
profile manager 54 of the MAP server 12 stores the resulting user
profile for the user 20 (step 1002E). More specifically, in one
embodiment, the MAP server 12 stores user records for the users 20
in the datastore 64 (FIG. 2). The user profile of the user 20 is
stored in the user record of the user 20. The user record of the
user 20 includes a unique identifier of the user 20, the user
profile of the user 20, and, as discussed below, a current location
of the user 20. Note that the user profile of the user 20 may be
updated as desired. For example, in one embodiment, the user
profile of the user 20 is updated by repeating step 1002 each time
the user 20 activates the MAP application 34.
[0063] Note that while the discussion herein focuses on an
embodiment where the user profiles of the users 20 are obtained
from the one or more profile servers 14, the user profiles of the
users 20 may be obtained in any desired manner. For example, in one
alternative embodiment, the user 20 may identify one or more
favorite websites. The profile manager 54 of the MAP server 12 may
then crawl the one or more favorite websites of the user 20 to
obtain keywords appearing in the one or more favorite websites of
the user 20. These keywords may then be stored as the user profile
of the user 20.
[0064] At some point, a process is performed such that a current
location of the mobile device 18 and thus a current location of the
user 20 is obtained by the MAP server 12 (step 1004). In this
embodiment, the MAP application 34 of the mobile device 18 obtains
the current location of the mobile device 18 from the location
function 38 of the mobile device 18. The MAP application 34 then
provides the current location of the mobile device 18 to the MAP
client 32, and the MAP client 32 then provides the current location
of the mobile device 18 to the MAP server 12 (step 1004A). Note
that step 1004A may be repeated periodically or in response to a
change in the current location of the mobile device 18 in order for
the MAP application 34 to provide location updates for the user 20
to the MAP server 12.
[0065] In response to receiving the current location of the mobile
device 18, the location manager 56 of the MAP server 12 stores the
current location of the mobile device 18 as the current location of
the user 20 (step 1004B). More specifically, in one embodiment, the
current location of the user 20 is stored in the user record of the
user 20 maintained in the datastore 64 of the MAP server 12.
[0066] In addition to storing the current location of the user 20,
the location manager 56 sends the current location of the user 20
to the location server 16 (step 1004C). In this embodiment, by
providing location updates to the location server 16, the MAP
server 12 in return receives location updates for the user 20 from
the location server 16. This is particularly beneficial when the
mobile device 18 does not permit background processes. If the
mobile device 18 does not permit background processes, the MAP
application 34 will not be able to provide location updates for the
user 20 to the MAP server 12 unless the MAP application 34 is
active. Therefore, when the MAP application 34 is not active, other
applications running on the mobile device 18 (or some other device
of the user 20) may directly or indirectly provide location updates
to the location server 16 for the user 20. This is illustrated in
step 1006 where the location server 16 receives a location update
for the user 20 directly or indirectly from another application
running on the mobile device 18 or an application running on
another device of the user 20 (step 1006A). The location server 16
then provides the location update for the user 20 to the MAP server
12 (step 1006B). In response, the location manager 56 updates and
stores the current location of the user 20 in the user record of
the user 20 (step 1006C). In this manner, the MAP server 12 is
enabled to obtain location updates for the user 20 even when the
MAP application 34 is not active at the mobile device 18.
[0067] FIG. 5 illustrates the operation of the system 10 of FIG. 1
to provide the user profile of the user 20 of one of the mobile
devices 18 to the MAP server 12 according to another embodiment of
the present disclosure. This discussion is equally applicable to
user profiles of the users 20 of the other mobile devices 18.
First, an authentication process is performed (step 1100). For
authentication, in this embodiment, the mobile device 18
authenticates with the MAP server 12 (step 1100A), and the MAP
server 12 authenticates with the profile server 14 (step 1100B).
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 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 1100C), and the MAP server 12 returns an authentication
succeeded message to the MAP client 32 of the mobile device 18
(step 1100D).
[0068] At some point after authentication is complete, a user
profile process is performed such that a user profile of the user
20 is obtained from the profile server 14 and delivered to the MAP
server 12 (step 1102). In this embodiment, the profile manager 54
of the MAP server 12 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 to the profile manager 54 of the MAP
server 12 (step 1102B). Note that while in this embodiment the
profile server 14 returns the complete user profile of the user 20
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 to the MAP server 12. The profile server 14 may filter the
user profile of the user 20 according to criteria specified by the
user 20. For example, the user profile of the user 20 may include
demographic information, general interests, music interests, and
movie interests, and the user 20 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.
[0069] Upon receiving the user profile of the user 20, the profile
manager 54 of the MAP server 12 processes the user profile (step
1102C). More specifically, as discussed above, in the preferred
embodiment, the profile manager 54 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, or profile slices.
[0070] After processing the user profile of the user 20, the
profile manager 54 of the MAP server 12 stores the resulting user
profile for the user 20 (step 1102D). More specifically, in one
embodiment, the MAP server 12 stores user records for the users 20
in the datastore 64 (FIG. 2). The user profile of the user 20 is
stored in the user record of the user 20. The user record of the
user 20 includes a unique identifier of the user 20, the user
profile of the user 20, and, as discussed below, a current location
of the user 20. Note that the user profile of the user 20 may be
updated as desired. For example, in one embodiment, the user
profile of the user 20 is updated by repeating step 1102 each time
the user 20 activates the MAP application 34.
[0071] Note that while the discussion herein focuses on an
embodiment where the user profiles of the users 20 are obtained
from the one or more profile servers 14, the user profiles of the
users 20 may be obtained in any desired manner. For example, in one
alternative embodiment, the user 20 may identify one or more
favorite websites. The profile manager 54 of the MAP server 12 may
then crawl the one or more favorite websites of the user 20 to
obtain keywords appearing in the one or more favorite websites of
the user 20. These keywords may then be stored as the user profile
of the user 20.
[0072] At some point, a process is performed such that a current
location of the mobile device 18 and thus a current location of the
user 20 is obtained by the MAP server 12 (step 1104). In this
embodiment, the MAP application 34 of the mobile device 18 obtains
the current location of the mobile device 18 from the location
function 38 of the mobile device 18. The MAP application 34 then
provides the current location of the user 20 of the mobile device
18 to the location server 16 (step 1104A). Note that step 1104A may
be repeated periodically or in response to changes in the location
of the mobile device 18 in order to provide location updates for
the user 20 to the MAP server 12. The location server 16 then
provides the current location of the user 20 to the MAP server 12
(step 1104B). The location server 16 may provide the current
location of the user 20 to the MAP server 12 automatically in
response to receiving the current location of the user 20 from the
mobile device 18 or in response to a request from the MAP server
12. In response to receiving the current location of the mobile
device 18, the location manager 56 of the MAP server 12 stores the
current location of the mobile device 18 as the current location of
the user 20 (step 1104C). More specifically, in one embodiment, the
current location of the user 20 is stored in the user record of the
user 20 maintained in the datastore 64 of the MAP server 12.
[0073] As discussed above, the use of the location server 16 is
particularly beneficial when the mobile device 18 does not permit
background processes. As such, if the mobile device 18 does not
permit background processes, the MAP application 34 will not
provide location updates for the user 20 to the location server 16
unless the MAP application 34 is active. However, other
applications running on the mobile device 18 (or some other device
of the user 20) may provide location updates to the location server
16 for the user 20 when the MAP application 34 is not active. This
is illustrated in step 1106 where the location server 16 receives a
location update for the user 20 from another application running on
the mobile device 18 or an application running on another device of
the user 20 (step 1106A). The location server 16 then provides the
location update for the user 20 to the MAP server 12 (step 1106B).
In response, the location manager 56 updates and stores the current
location of the user 20 in the user record of the user 20 (step
1106C). In this manner, the MAP server 12 is enabled to obtain
location updates for the user 20 even when the MAP application 34
is not active at the mobile device 18.
[0074] FIG. 6 begins a discussion of the operation of the crowd
analyzer 58 to form crowds of users according to one embodiment of
the present disclosure. Specifically, FIG. 6 illustrates exemplary
data records that may be used to represent crowds, users, crowd
snapshots used for crowd tracking, and anonymous users according to
one embodiment of the present disclosure. As illustrated, for each
crowd created by the crowd analyzer 58 of the MAP server 12, a
corresponding crowd record 72 is created and stored in the
datastore 64 of the MAP server 12. The crowd record 72 for a crowd
includes a users field, a North-East (NE) corner field, a
South-West (SW) corner field, a center field, a crowd snapshots
field, a split from field, and a combined into field. The users
field stores a set or list of user records 74 corresponding to a
subset of the users 20 that are currently in the crowd. The NE
corner field stores a location corresponding to a NE corner of a
bounding box for the crowd. The NE corner may be defined by
latitude and longitude coordinates and optionally an altitude.
Similarly, the SW corner field stores a location of a SW corner of
the bounding box for the crowd. Like the NE corner, the SW corner
may be defined by latitude and longitude coordinates and optionally
an altitude. Together, the NE corner and the SW corner define a
bounding box for the crowd, where the edges of the bounding box
pass through the current locations of the outermost users 20 in the
crowd. The center field stores a location corresponding to a center
of the crowd. The center of the crowd may be defined by latitude
and longitude coordinates and optionally an altitude. The center of
the crowd may be computed based on the current locations of the
users 20 in the crowd using a center of mass algorithm. Together,
the NE corner, the SW corner, and the center of the crowd form
spatial information defining the location of the crowd. Note,
however, that the spatial information defining the location of the
crowd may include additional or alternative information depending
on the particular implementation. The crowd snapshots field stores
a list of crowd snapshot records 76 corresponding to crowd
snapshots for the crowd created and stored over time. As discussed
below in detail, the split from field may be used to store a
reference to a crowd record corresponding to another crowd from
which the crowd split, and the combined into field may be used to
store a reference to a crowd record corresponding to another crowd
into which the crowd has been merged.
[0075] Each of the user records 74 includes an ID field, a location
field, a profile field, a crowd field, and a previous crowd field.
The ID field stores a unique ID for the user 20 represented by the
user record 74. The location field stores the current location of
the user 20, which may be defined by latitude and longitude
coordinates and optionally an altitude. The profile field stores
the user profile of the user 20, which may be defined as a list of
keywords for one or more profile categories. The crowd field is
used to store a reference to a crowd record of a crowd of which the
user 20 is currently a member. The previous crowd field may be used
to store a reference to a crowd record of a crowd of which the user
20 was previously a member.
[0076] Each of the crowd snapshot records 76 includes an anonymous
users field, a NE corner field, a SW corner field, a center field,
a sample time field, and a vertices field. The anonymous users
field stores a set or list of anonymous user records 78, which are
anonymized versions of user records for the users 20 that are in
the crowd at a time the crowd snapshot was created. The NE corner
field stores a location corresponding to a NE corner of a bounding
box for the crowd at the time the crowd snapshot was created. The
NE corner may be defined by latitude and longitude coordinates and
optionally an altitude. Similarly, the SW corner field stores a
location of a SW corner of the bounding box for the crowd at the
time the crowd snapshot was created. Like the NE corner, the SW
corner may be defined by latitude and longitude coordinates and
optionally an altitude. The center field stores a location
corresponding to a center of the crowd at the time the crowd
snapshot was created. The center of the crowd may be defined by
latitude and longitude coordinates and optionally an altitude.
Together, the NE corner, the SW corner, and the center of the crowd
form spatial information defining the location of the crowd at the
time the crowd snapshot was created. Note, however, that the
spatial information defining the location of the crowd at the time
the crowd snapshot was created may include additional or
alternative information depending on the particular implementation.
The sample time field stores a timestamp indicating a time at which
the crowd snapshot was created. The timestamp preferably includes a
date and a time of day at which the crowd snapshot was created. The
vertices field stores locations of a number of the users 20 in the
crowd at the time the crowd snapshot was created that define an
actual outer boundary of the crowd (e.g., as a polygon) at the time
the crowd snapshot was created. Note that the actual outer boundary
of a crowd may be used to show the location of the crowd when
displayed to a user.
[0077] Each of the anonymous user records 78 includes an anonymous
ID field and a profile field. The anonymous ID field stores an
anonymous user ID, which is preferably a unique user ID that is not
tied, or linked, back to any of the users 20 and particularly not
tied back to the user 20 or the user record 74 for which the
anonymous user record 78 has been created. In one embodiment, the
anonymous user records 78 for a crowd snapshot record 76 are
anonymized versions of the user records 74 of the users in the
crowd at the time the crowd snapshot was created. The profile field
stores an anonymized user profile of the anonymous user, which may
be defined as a list of keywords for one or more profile
categories.
[0078] FIGS. 7A through 7D illustrate one embodiment of a spatial
crowd formation process that may be performed by the crowd analyzer
58 to enable a crowd tracking feature according to one embodiment
of the present disclosure. In this embodiment, the spatial crowd
formation process is triggered in response to receiving a location
update for one of the users 20 and is preferably repeated for each
location update received for any one of the users 20. As such,
first, the crowd analyzer 58 receives a location update, or a new
location, for one of the users 20 (step 1200). In response, the
crowd analyzer 58 retrieves an old location of the user 20, if any
(step 1202). The old location is the current location of the user
20 prior to receiving the new location of the user 20. The crowd
analyzer 58 then creates a new bounding box of a predetermined size
centered at or otherwise encompassing the new location of the user
20 (step 1204) and an old bounding box of a predetermined size
centered at or otherwise encompassing the old location of the user
20, if any (step 1206). The predetermined size of the new and old
bounding boxes may be any desired size.
[0079] 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
does not have an old location (i.e., the location received in step
1200 is the first location received for the user 20), then the old
bounding box is essentially null. Also note that while bounding
"boxes" are used in this example, the bounding regions may be of
any desired shape.
[0080] Next, the crowd analyzer 58 determines whether the new and
old bounding boxes overlap (step 1208). If so, the crowd analyzer
58 creates a bounding box encompassing the new and old bounding
boxes (step 1210). For example, if the new and old bounding boxes
are 40.times.40 meter regions and a 1.times.1 meter square at the
North-East corner of the new bounding box overlaps a 1.times.1
meter square at the South-West 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.
[0081] The crowd analyzer 58 then determines the individual users
and crowds relevant to the bounding box created in step 1210 (step
1212). Note that the crowds relevant to the bounding box are
pre-existing crowds resulting from previous iterations of the
spatial crowd formation process. In this embodiment, the crowds
relevant to the bounding box are crowds having crowd bounding boxes
that are within or overlap the bounding box established in step
1210. Alternatively, the crowds relevant to the bounding box may be
crowds having crowd centers located within the bounding box or
crowds having at least one user currently located within the
bounding box. In order to determine the relevant crowds, the crowd
analyzer 58 queries the datastore 64 of the MAP server 12 to obtain
crowd records for crowds that are within or overlap the bounding
box established in step 1210. The individual users relevant to the
bounding box are any of the users 20 that are currently located
within the bounding box and are not already members of a crowd. In
order to identify the relevant individual users, the crowd analyzer
58 queries the datastore 64 of the MAP server 12 for the user
records 74 of the users 20 that are currently located in the
bounding box created in step 1210 and are not already members 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 1214). 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
, ##EQU00001##
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.
[0082] The crowd analyzer 58 then creates a crowd of one user for
each individual user within the bounding box established in step
1210 that is not already included in a crowd and sets the optimal
inclusion distance for those crowds to the initial optimal
inclusion distance (step 1216). The crowds created for the
individual users are temporary crowds created for purposes of
performing the crowd formation process. At this point, the process
proceeds to FIG. 7B where the crowd analyzer 58 analyzes the crowds
in the bounding box established in step 1210 to determine whether
any of the crowd members (i.e., users in the crowds) violate the
optimal inclusion distance of their crowds (step 1218). Any crowd
member that violates the optimal inclusion distance of his or her
crowd is then removed from that crowd and the previous crowd fields
in the corresponding user records 74 are set (step 1220). More
specifically, in this embodiment, a member is removed from a crowd
by removing the user record 74 of the member from the set or list
of user records in the crowd record 72 of the crowd and setting the
previous crowd stored in the user record 74 of the member to the
crowd from which the member has been removed. The crowd analyzer 58
then creates a crowd of one user for each of the users 20 removed
from their crowds in step 1220 and sets the optimal inclusion
distance for the newly created crowds to the initial optimal
inclusion distance (step 1222).
[0083] Next, the crowd analyzer 58 determines the two closest
crowds in the bounding box (step 1224) and a distance between the
two closest crowds (step 1226). The distance between the two
closest crowds is the distance between the crowd centers of the two
closest crowds, which are stored in the crowd records for 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
1228). 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 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 crowds.
[0084] If the distance between the two closest crowds is greater
than the optimal inclusion distance, the process proceeds to step
1240. However, if the distance between the two closest crowds is
less than the optimal inclusion distance, the two crowds are merged
(step 1230). The manner in which the two crowds are merged differs
depending on whether the two crowds are pre-existing crowds or
temporary crowds created for the spatial crowd formation process.
If both crowds are pre-existing crowds, one of the two crowds is
selected as a non-surviving crowd and the other is selected as the
surviving crowd. If one crowd is larger than the other, the smaller
crowd is selected as the non-surviving crowd and the larger crowd
is selected as the surviving crowd. If the two crowds are of the
same size, one of the crowds is selected as the surviving crowd and
the other crowd is selected as the non-surviving crowd using any
desired technique. The non-surviving crowd is then merged into the
surviving crowd by adding the set or list of user records for the
non-surviving crowd to the set or list of user records for the
surviving crowd and setting the merged into field of the
non-surviving crowd to a reference to the crowd record of the
surviving crowd. In addition, the crowd analyzer 58 sets the
previous crowd fields of the user records 74 in the set or list of
user records from the non-surviving crowd to a reference to the
crowd record 72 of the non-surviving crowd.
[0085] If one of the crowds is a temporary crowd and the other
crowd is a pre-existing crowd, the temporary crowd is selected as
the non-surviving crowd, and the pre-existing crowd is selected as
the surviving crowd. The non-surviving crowd is then merged into
the surviving crowd by adding the set or list of user records from
the crowd record 72 of the non-surviving crowd to the set or list
of user records in the crowd record 72 of the surviving crowd.
However, since the non-surviving crowd is a temporary crowd, the
previous crowd field(s) of the user record(s) 74 of the user(s) 20
in the non-surviving crowd are not set to a reference to the crowd
record 72 of the non-surviving crowd. Similarly, the crowd record
72 of the temporary crowd may not have a merged into field, but, if
it does, the merged into field is not set to a reference to the
surviving crowd.
[0086] If both the crowds are temporary crowds, one of the two
crowds is selected as a non-surviving crowd and the other is
selected as a surviving crowd. If one crowd is larger than the
other, the smaller crowd is selected as the non-surviving crowd and
the larger crowd is selected as the surviving crowd. If the two
crowds are of the same size, one of the crowds is selected as the
surviving crowd and the other crowd is selected as the
non-surviving crowd using any desired technique. The non-surviving
crowd is then merged into the surviving crowd by adding the set or
list of user records for the non-surviving crowd to the set or list
of user records for the surviving crowd. However, since the
non-surviving crowd is a temporary crowd, the previous crowd
field(s) of the user record(s) 74 of the user(s) 20 in the
non-surviving crowd are not set to a reference to the crowd record
72 of the non-surviving crowd. Similarly, the crowd record 72 of
the temporary crowd may not have a merged into field, but, if it
does, the merged into field is not set to a reference to the
surviving crowd.
[0087] Next, the crowd analyzer 58 removes the non-surviving crowd
(step 1232). In this embodiment, the manner in which the
non-surviving crowd is removed depends on whether the non-surviving
crowd is a pre-existing crowd or a temporary crowd. If the
non-surviving crowd is a pre-existing crowd, the removal process is
performed by removing or nulling the users field, the NE corner
field, the SW corner field, and the center field of the crowd
record 72 of the non-surviving crowd. In this manner, the spatial
information for the non-surviving crowd is removed from the
corresponding crowd record such that the non-surviving or removed
crowd will no longer be found in response to spatial-based queries
on the datastore 64. However, the crowd snapshots for the
non-surviving crowd are still available via the crowd record 72 for
the non-surviving crowd. In contrast, if the non-surviving crowd is
a temporary crowd, the crowd analyzer 58 may remove the crowd by
deleting the corresponding crowd record 72.
[0088] The crowd analyzer 58 also computes a new crowd center for
the surviving crowd (step 1234). 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 surviving crowd is computed
(step 1236). 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 ) , ##EQU00002##
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.
[0089] At this point, the crowd analyzer 58 determines whether a
maximum number of iterations have been performed (step 1238). The
maximum number of iterations is a predefined number that ensures
that the crowd formation process does not indefinitely loop over
steps 1218 through 1236 or loop over steps 1218 through 1236 more
than a desired maximum number of times. If the maximum number of
iterations has not been reached, the process returns to step 1218
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 removes crowds with less than
three users, or members (step 1240), and the process ends. As
discussed above, in this embodiment, the manner in which a crowd is
removed depends on whether the crowd is a pre-existing crowd or a
temporary crowd. If the crowd is a pre-existing crowd, a removal
process is performed by removing or nulling the users field, the NE
corner field, the SW corner field, and the center field of the
crowd record 72 of the crowd. In this manner, the spatial
information for the crowd is removed from the corresponding crowd
record 72 such that the crowd will no longer be found in response
to spatial-based queries on the datastore 64. However, the crowd
snapshots for the crowd are still available via the crowd record 72
for the crowd. In contrast, if the crowd is a temporary crowd, the
crowd analyzer 58 may remove the crowd by deleting the
corresponding crowd record 72. In this manner, crowds having less
than three members are removed in order to maintain privacy of
individuals as well as groups of two users (e.g., a couple). Note
that in this example, the minimum number of users required for a
crowd is 3. However, the present disclosure is not limited thereto.
The minimum number of users for a crowd may be any desired number
greater than or equal to 2.
[0090] Returning to step 1208 in FIG. 7A, if the new and old
bounding boxes do not overlap, the process proceeds to FIG. 7C and
the bounding box to be processed is set to the old bounding box
(step 1242). In general, the crowd analyzer 58 then processes the
old bounding box in much that same manner as described above with
respect to steps 1212 through 1240. More specifically, the crowd
analyzer 58 determines the individual users and crowds relevant to
the bounding box (step 1244). Next, the crowd analyzer 58 computes
an optimal inclusion distance for individual users based on user
density within the bounding box (step 1246). The optimal inclusion
distance may be computed as described above with respect to step
1214.
[0091] 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 1248). The
crowds created for the individual users are temporary crowds
created for purposes of performing the crowd formation process. At
this point, the crowd analyzer 58 analyzes the crowds in the
bounding box to determine whether any crowd members (i.e., users in
the crowds) violate the optimal inclusion distance of their crowds
(step 1250). Any crowd member that violates the optimal inclusion
distance of his or her crowd is then removed from that crowd and
the previous crowd fields in the corresponding user records 74 are
set (step 1252). More specifically, in this embodiment, a member is
removed from a crowd by removing the user record 74 of the member
from the set or list of user records in the crowd record 72 of the
crowd and setting the previous crowd stored in the user record 74
of the member to the crowd from which the member has been removed.
The crowd analyzer 58 then creates a crowd for each of the users 20
removed from their crowds in step 1252 and sets the optimal
inclusion distance for the newly created crowds to the initial
optimal inclusion distance (step 1254).
[0092] Next, the crowd analyzer 58 determines the two closest
crowds in the bounding box (step 1256) and a distance between the
two closest crowds (step 1258). 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
1260). 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.
[0093] If the distance between the two closest crowds is greater
than the optimal inclusion distance, the process proceeds to step
1272. However, if the distance between the two closest crowds is
less than the optimal inclusion distance, the two crowds are merged
(step 1262). The manner in which the two crowds are merged differs
depending on whether the two crowds are pre-existing crowds or
temporary crowds created for the spatial crowd formation process.
If both crowds are pre-existing crowds, one of the two crowds is
selected as a non-surviving crowd and the other is selected as a
surviving crowd. If one crowd is larger than the other, the smaller
crowd is selected as the non-surviving crowd and the larger crowd
is selected as the surviving crowd. If the two crowds are of the
same size, one of the crowds is selected as the surviving crowd and
the other crowd is selected as the non-surviving crowd using any
desired technique. The non-surviving crowd is then merged into the
surviving crowd by adding the set or list of user records for the
non-surviving crowd to the set or list of user records for the
surviving crowd and setting the merged into field of the
non-surviving crowd to a reference to the crowd record 72 of the
surviving crowd. In addition, the crowd analyzer 58 sets the
previous crowd fields of the set or list of user records from the
non-surviving crowd to a reference to the crowd record 72 of the
non-surviving crowd.
[0094] If one of the crowds is a temporary crowd and the other
crowd is a pre-existing crowd, the temporary crowd is selected as
the non-surviving crowd, and the pre-existing crowd is selected as
the surviving crowd. The non-surviving crowd is then merged into
the surviving crowd by adding the user records 74 from the set or
list of user records from the crowd record 72 of the non-surviving
crowd to the set or list of user records in the crowd record 72 of
the surviving crowd. However, since the non-surviving crowd is a
temporary crowd, the previous crowd field(s) of the user record(s)
74 of the user(s) 20 in the non-surviving crowd are not set to a
reference to the crowd record 72 of the non-surviving crowd.
Similarly, the crowd record 72 of the temporary crowd may not have
a merged into field, but, if it does, the merged into field is not
set to a reference to the surviving crowd.
[0095] If both the crowds are temporary crowds, one of the two
crowds is selected as a non-surviving crowd and the other is
selected as a surviving crowd. If one crowd is larger than the
other, the smaller crowd is selected as the non-surviving crowd and
the larger crowd is selected as a surviving crowd. If the two
crowds are of the same size, one of the crowds is selected as the
surviving crowd and the other crowd is selected as the
non-surviving crowd using any desired technique. The non-surviving
crowd is then merged into the surviving crowd by adding the set or
list of user records for the non-surviving crowd to the set or list
of user records for the surviving crowd. However, since the
non-surviving crowd is a temporary crowd, the previous crowd
field(s) of the user record(s) 74 of the user(s) 20 in the
non-surviving crowd are not set to a reference to the crowd record
72 of the non-surviving crowd. Similarly, the crowd record 72 of
the temporary crowd may not have a merged into field, but, if it
does, the merged into field is not set to a reference to the
surviving crowd.
[0096] Next, the crowd analyzer 58 removes the non-surviving crowd
(step 1264). In this embodiment, the manner in which the
non-surviving crowd is removed depends on whether the non-surviving
crowd is a pre-existing crowd or a temporary crowd. If the
non-surviving crowd is a pre-existing crowd, the removal process is
performed by removing or nulling the users field, the NE corner
field, the SW corner field, and the center field of the crowd
record 72 of the non-surviving crowd. In this manner, the spatial
information for the non-surviving crowd is removed from the
corresponding crowd record 72 such that the non-surviving or
removed crowd will no longer be found in response to spatial-based
queries on the datastore 64. However, the crowd snapshots for the
non-surviving crowd are still available via the crowd record 72 for
the non-surviving crowd. In contrast, if the non-surviving crowd is
a temporary crowd, the crowd analyzer 58 may remove the crowd by
deleting the corresponding crowd record 72.
[0097] The crowd analyzer 58 also computes a new crowd center for
the surviving crowd (step 1266). 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 surviving crowd is computed
(step 1268). In one embodiment, the new optimal inclusion distance
for the surviving crowd is computed in the manner described above
with respect to step 1234.
[0098] At this point, the crowd analyzer 58 determines whether a
maximum number of iterations have been performed (step 1270). If
the maximum number of iterations has not been reached, the process
returns to step 1250 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
removes crowds with less than three users, or members (step 1272).
As discussed above, in this embodiment, the manner in which a crowd
is removed depends on whether the crowd is a pre-existing crowd or
a temporary crowd. If the crowd is a pre-existing crowd, a removal
process is performed by removing or nulling the users field, the NE
corner field, the SW corner field, and the center field of the
crowd record 72 of the crowd. In this manner, the spatial
information for the crowd is removed from the corresponding crowd
record 72 such that the crowd will no longer be found in response
to spatial-based queries on the datastore 64. However, the crowd
snapshots for the crowd are still available via the crowd record 72
for the crowd. In contrast, if the crowd is a temporary crowd, the
crowd analyzer 58 may remove the crowd by deleting the
corresponding crowd record 72. In this manner, crowds having less
than three members are removed in order to maintain privacy of
individuals as well as groups of two users (e.g., a couple). Again,
note that in this example the minimum number of users required for
a crowd is 3. However, the present disclosure is not limited
thereto. The minimum number of users for a crowd may be any desired
number greater than or equal to 2.
[0099] The crowd analyzer 58 then determines whether the crowd
formation process for the new and old bounding boxes is done (step
1274). 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 1276), and
the process returns to step 1244 and is repeated for the new
bounding box. Once both the new and old bounding boxes have been
processed, the crowd formation process ends.
[0100] FIGS. 8A through 8D graphically illustrate the crowd
formation process of FIGS. 7A through 7D for a scenario where the
crowd formation process is triggered by a location update for one
of the users 20 having no old location. In this scenario, the crowd
analyzer 58 creates a new bounding box 80 for the new location of
the user 20, and the new bounding box 80 is set as the bounding box
to be processed for crowd formation. Then, as illustrated in FIG.
8A, the crowd analyzer 58 identifies all individual users currently
located within the bounding box 80 and all crowds located within or
overlapping the bounding box 80. In this example, crowd 82 is an
existing crowd relevant to the bounding box 80. Crowds are
indicated by dashed circles, crowd centers are indicated by
cross-hairs (.+-.), and users are indicated as dots. Next, as
illustrated in FIG. 8B, the crowd analyzer 58 creates crowds 84
through 88 of one user for the individual users, and the optimal
inclusion distances of the crowds 84 through 88 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 80.
[0101] The crowd analyzer 58 then identifies the two closest crowds
84 and 86 in the bounding box 80 and determines a distance between
the two closest crowds 84 and 86. In this example, the distance
between the two closest crowds 84 and 86 is less than the optimal
inclusion distance. As such, the two closest crowds 84 and 86 are
merged and a new crowd center and new optimal inclusion distance
are computed, as illustrated in FIG. 8C. The crowd analyzer 58 then
repeats the process such that the two closest crowds 84 and 88 in
the bounding box 80 are merged, as illustrated in FIG. 8D. At this
point, the distance between the two closest crowds 82 and 84 is
greater than the appropriate optimal inclusion distance. As such,
the crowd formation process is complete.
[0102] FIGS. 9A through 9F graphically illustrate the crowd
formation process of FIGS. 7A through 7D for a scenario where the
new and old bounding boxes overlap. As illustrated in FIG. 9A, one
of the users 20 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 20 giving the new location of the user 20. In
response, the crowd analyzer 58 creates an old bounding box 90 for
the old location of the user and a new bounding box 92 for the new
location of the user. Crowd 94 exists in the old bounding box 90,
and crowd 96 exists in the new bounding box 92.
[0103] Since the old bounding box 90 and the new bounding box 92
overlap, the crowd analyzer 58 creates a bounding box 98 that
encompasses both the old bounding box 90 and the new bounding box
92, as illustrated in FIG. 9B. In addition, the crowd analyzer 58
creates crowds 100 through 106 for individual users currently
located within the bounding box 98. The optimal inclusion distances
of the crowds 100 through 106 are set to the initial optimal
inclusion distance computed by the crowd analyzer 58 based on the
density of users in the bounding box 98.
[0104] Next, the crowd analyzer 58 analyzes the crowds 94, 96, and
100 through 106 to determine whether any members of the crowds 94,
96, and 100 through 106 violate the optimal inclusion distances of
the crowds 94, 96, and 100 through 106. In this example, as a
result of the user leaving the crowd 94 and moving to his new
location, both of the remaining members of the crowd 94 violate the
optimal inclusion distance of the crowd 94. As such, the crowd
analyzer 58 removes the remaining users 20 from the crowd 94 and
creates crowds 108 and 110 of one user each for those users, as
illustrated in FIG. 9C.
[0105] The crowd analyzer 58 then identifies the two closest crowds
in the bounding box 98, which in this example are the crowds 104
and 106. Next, the crowd analyzer 58 computes a distance between
the two crowds 104 and 106. In this example, the distance between
the two crowds 104 and 106 is less than the initial optimal
inclusion distance and, as such, the two crowds 104 and 106 are
merged. In this example, the crowd analyzer 58 merges the crowd 106
into the crowd 104, as illustrated in FIG. 9D. A new crowd center
and new optimal inclusion distance are then computed for the crowd
104.
[0106] At this point, the crowd analyzer 58 repeats the process and
determines that the crowds 96 and 102 are now the two closest
crowds. In this example, the distance between the two crowds 96 and
102 is less than the optimal inclusion distance of the larger of
the two crowds 96 and 102, which is the crowd 96. As such, the
crowd 102 is merged into the crowd 96 and a new crowd center and
optimal inclusion distance are computed for the crowd 96, as
illustrated in FIG. 9E. 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. 9F. In this
example, the crowds 100, 104, 108, and 110 have less than three
members and are therefore removed. The crowd 96 has three or more
members and, as such, is not removed. At this point, the crowd
formation process is complete.
[0107] FIGS. 10A through 10E graphically illustrate the crowd
formation process of FIGS. 7A through 7D in a scenario where the
new and old bounding boxes do not overlap. As illustrated in FIG.
10A, in this example, the user 20 moves from an old location to a
new location. The crowd analyzer 58 creates an old bounding box 112
for the old location of the user 20 and a new bounding box 114 for
the new location of the user 20. Crowds 116 and 118 exist in the
old bounding box 112, and crowd 120 exists in the new bounding box
114. In this example, since the old and new bounding boxes 112 and
114 do not overlap, the crowd analyzer 58 processes the old and new
bounding boxes 112 and 114 separately.
[0108] More specifically, as illustrated in FIG. 10B, as a result
of the movement of the user 20 from the old location to the new
location, the remaining users 20 in the crowd 116 no longer satisfy
the optimal inclusion distance for the crowd 116. As such, the
remaining users 20 in the crowd 116 are removed from the crowd 116,
and crowds 122 and 124 of one user each are created for the removed
users as shown in FIG. 10B. In this example, no two crowds in the
old bounding box 112 are close enough to be combined. As such,
since the crowds 122 and 124 do not have at least 3 users, the
crowds 122 and 124 are discarded as shown in FIG. 10C, and
processing of the old bounding box 112 is complete. The crowd
analyzer 58 then proceeds to process the new bounding box 114.
[0109] As illustrated in FIG. 10D, processing of the new bounding
box 114 begins by the crowd analyzer 58 creating a crowd 126 of one
user for the user 20. The crowd analyzer 58 then identifies the
crowds 120 and 126 as the two closest crowds in the new bounding
box 114 and determines a distance between the two crowds 120 and
126. In this example, the distance between the two crowds 120 and
126 is less than the optimal inclusion distance of the larger
crowd, which is the crowd 120. As such, the crowd analyzer 58
merges the crowd 126 into the crowd 120, as illustrated in FIG.
10E. A new crowd center and new optimal inclusion distance are then
computed for the crowd 120. At this point, the crowd formation
process is complete.
[0110] FIG. 11 illustrates a process for creating crowd snapshots
according to one embodiment of the present disclosure. In this
embodiment, after the spatial crowd formation process of FIGS. 7A
through 7D is performed in response to a location update for one of
the users 20, the crowd analyzer 58 detects crowd change events, if
any, for the relevant crowds (step 1300). The relevant crowds are
pre-existing crowds that are relevant to the bounding region(s)
processed during the spatial crowd formation process in response to
the location update for the user 20. The crowd analyzer 58 may
detect crowd change events by comparing the crowd records 72 of the
relevant crowds before and after performing the spatial crowd
formation process in response to the location update for the user
20. The crowd change events may be a change in the users 20 in the
crowd, a change to a location of one of the users 20 within the
crowd, or a change in the spatial information for the crowd (e.g.,
the NE corner, the SW corner, or the crowd center). Note that if
multiple crowd change events are detected for a single crowd, then
those crowd change events are preferably consolidated into a single
crowd change event.
[0111] Next, the crowd analyzer 58 determines whether there are any
crowd change events (step 1302). If not, the process ends.
Otherwise, the crowd analyzer 58 gets the next crowd change event
(step 1304) and generates a crowd snapshot for a corresponding
crowd (step 1306). More specifically, the crowd change event
identifies the crowd record 72 stored for the crowd for which the
crowd change event was detected. A crowd snapshot is then created
for that crowd by creating a new crowd snapshot record 76 for the
crowd and adding the new crowd snapshot record 76 to the list of
crowd snapshots stored in the crowd record 72 for the crowd. As
discussed above, in this embodiment, the crowd snapshot record 76
includes a set or list of anonymous user records 78, which are an
anonymized version of the user records 74 for the users 20 in the
crowd at the current time. In addition, the crowd snapshot record
includes the NE corner, the SW corner, and the center of the crowd
at the current time as well as a timestamp defining the current
time as the sample time at which the crowd snapshot record 76 was
created. Lastly, locations of the users 20 in the crowd that define
the outer boundary of the crowd at the current time are stored in
the crowd snapshot record 76 as the vertices of the crowd. After
creating the crowd snapshot, the crowd analyzer 58 determines
whether there are any more crowd change events (step 1308). If so,
the process returns to step 1304 and is repeated for the next crowd
change event. Once all of the crowd change events are processed,
the process ends.
[0112] FIG. 12 illustrates a process that may be used to
re-establish crowds and detect crowd splits according to one
embodiment of the present disclosure. In general, in order to
accurately track a crowd, it is preferable to enable crowds that
have been removed to be re-established in the future. For example,
a crowd may be removed as a result of users in the crowd
deactivating their MAP applications (or powering down their mobile
devices). If those users then move together to a different location
and then reactivate their MAP applications (or power on their
mobile devices), it is preferable for the resulting crowd to be
identified as the same crowd that was previously removed. In other
words, it is desirable to re-establish the crowd. In addition, in
order to accurately track a crowd, it is desirable to capture when
the crowd splits into two or more crowds.
[0113] Accordingly, in this embodiment, the spatial crowd formation
process of FIGS. 7A through 7D is performed in response to a
location update for one of the users 20. The crowd analyzer 58 then
gets a next relevant crowd (step 1400). The relevant crowds are
pre-existing and new crowds that are within the bounding region(s)
processed during the spatial crowd formation process in response to
the location update for the user 20. Note that, for the first
iteration, the next relevant crowd is the first relevant crowd. The
crowd analyzer 58 then determines a maximum number of users in the
crowd from a common previous crowd (step 1402). More specifically,
the crowd analyzer 58 examines the previous crowd fields of the
user records of all of the users 20 in the crowd to identify users
from a common previous crowd. For each previous crowd found in the
user records of the users 20 in the crowd, the crowd analyzer 58
counts the number of users in the crowd that are from that previous
crowd. The crowd analyzer 58 then selects the previous crowd having
the highest number of users, and determines that the number of
users counted for the selected previous crowd is the maximum number
of users in the crowd from a common previous crowd.
[0114] The crowd analyzer 58 then determines whether the maximum
number of users in the crowd from a common previous crowd is
greater than a predefined threshold number of users (step 1404). In
an alternative embodiment, rather than determining the maximum
number of users from a common previous crowd and comparing that
number to a predefined threshold number of users, a maximum
percentage of users in the crowd from a common previous crowd may
be determined and compared to a predefined threshold percentage. If
the maximum number of users in the crowd from a common previous
crowd is not greater than the predefined threshold number of users,
the process proceeds to step 1410. Otherwise, the crowd analyzer 58
determines whether the common previous crowd has been removed (step
1406). If so, then the crowd is re-established as the common
previous crowd (step 1408). More specifically, in this embodiment,
the crowd is re-established as the common previous crowd by storing
the set or list of user records, the NE corner, the SW corner, and
the center from the crowd record of the crowd in the crowd record
of the common previous crowd. The crowd record for the crowd may
then be deleted. In addition, the previous crowd fields of the
users from the common previous crowd may be set to null or
otherwise cleared. Once the common previous crowd is
re-established, the crowd analyzer 58 determines whether there are
more relevant crowds to process (step 1410). If so, the process
returns to step 1400 and is repeated until all relevant crowds are
processed.
[0115] Returning to step 1406, if the common previous crowd has not
been removed, the crowd analyzer 58 identifies the crowd as being
split from the common previous crowd (step 1412). More
specifically, in this embodiment, the crowd analyzer 58 stores a
reference to the crowd record of the common previous crowd in the
split from field of the crowd record of the crowd. At this point,
the crowd analyzer 58 then determines whether there are more
relevant crowds to process (step 1410). If so, the process returns
to step 1400 and is repeated until all relevant crowds are
processed, at which time the process ends.
[0116] FIG. 13 graphically illustrates the process of
re-establishing a crowd for an exemplary crowd according to one
embodiment of the present disclosure. As illustrated, at TIME 1,
CROWD A has been formed and a corresponding crowd record has been
created and stored. Between TIME 1 and TIME 2, three users from
CROWD A have moved, thereby resulting in the removal of those three
users from CROWD A as well as the removal of CROWD A. Again, CROWD
A has been removed by removing the set or list of user records and
spatial information from the crowd record for CROWD A. At TIME 2, a
new crowd, CROWD B, has been formed for the three users that were
previously in CROWD A. As such, the previous crowd fields for the
three users now in CROWD B indicate that the three users are from
CROWD A. Using the process of FIG. 12, the crowd analyzer 58
determines that the three users in CROWD B have a common previous
crowd, namely, CROWD A. As a result, the crowd analyzer 58
re-establishes CROWD B as CROWD A, as shown at TIME 2'.
[0117] FIG. 14 graphically illustrates the process for capturing a
crowd split for an exemplary crowd according to one embodiment of
the present disclosure. As illustrated, at TIME 1, CROWD A has been
formed and a corresponding crowd record has been created and
stored. Between TIME 1 and TIME 2, four users from CROWD A have
separated from the other three users of CROWD A. As a result, a new
crowd, CROWD B, has been formed at TIME 2 for the four users from
CROWD A. Using the process of FIG. 12, the crowd analyzer 58
determines that the four users in CROWD B are all from CROWD A and
therefore identifies CROWD B as being split from CROWD A.
[0118] FIG. 15 graphically illustrates the merging of two exemplary
pre-existing crowds according to one embodiment of the present
disclosure. As discussed above, the merger of crowds is performed
during the spatial crowd formation process of FIGS. 7A through 7D.
As illustrated, at TIME 1, CROWD A and CROWD B have been formed and
corresponding crowd records have been created and stored. Between
TIME 1 and TIME 2, CROWD A and CROWD B move close to one another
such that the distance between CROWD A and CROWD B is less than the
optimal inclusion distance(s) at TIME 2. As such, the crowd
analyzer 58 merges CROWD A into CROWD B at TIME 2'. As part of the
merger, CROWD A is removed, and the merged into field of the crowd
record for CROWD A is set to a reference to the crowd record for
CROWD B. In addition, the previous crowd fields in the user records
of the users from CROWD A are set to a reference to the crowd
record of CROWD A.
[0119] Up until this point, the disclosure has primarily focused on
the operation of the system 10 to form and track crowds of users.
Now, the discussion will turn to processes by which the system 10
enables crowd following in order to receive status updates from the
users 20 in the followed crowds according to various embodiments of
the present disclosure. FIG. 16 illustrates the operation of the
status update processor 62 of the MAP server 12 to enable a
requestor to follow status updates from users in a select crowd of
users even after users in the select crowd have dispersed according
to one embodiment of the present disclosure. To give context,
consider the scenario where a number of the users 20 form a crowd
of users attending a college football game. The system 10 may
enable a requestor, such as one of the users 20 in the crowd at the
football game, to receive status updates from the users 20 in the
crowd while at the football game. However, in this scenario, it is
very likely that the users 20 will post status updates about the
football game even after the football game has ended and the users
20 in the crowd have dispersed. The process of FIG. 16 enables a
requestor, such as but not limited to one of the users 20 in the
crowd, to receive status updates from other crowds of users in
which the users 20 in the select crowd are located after the
football game has ended and the crowd has dispersed. So, for
example, if a number of the users 20 that were in the crowd at the
football game move to a new crowd at a nearby sports bar after the
football game has ended, the requestor will receive status updates
from the users in the new crowd at the nearby sports bar. In this
manner, the requestor will continue to receive relevant status
updates regarding the football game even after the crowd at the
football game has dispersed.
[0120] More specifically, first, the status update processor 62 of
the MAP server 12 receives a crowd selection of a requestor (step
1500). The crowd selection identifies a crowd selected by the
requestor. The requestor may be one of the users 20 in the selected
crowd or one of the users 20 that is not in the selected crowd
where the crowd selection is sent to the MAP server 12 via the MAP
application 34 or alternatively one of the third-party applications
36 of the corresponding mobile device 18. Alternatively, the
requestor may be the subscriber 24 at the subscriber device 22
where the crowd selection is sent to the MAP server 12 via, for
example, the web browser 40 of the subscriber device 22. As yet
another alternative, the requestor may be the third-party service
26.
[0121] Upon receiving the crowd selection of the requestor, the
status update processor 62 of the MAP server 12 records a list of
users currently in the selected crowd (step 1502). More
specifically, as discussed above, the crowd record stored for the
selected crowd includes a list of users that are currently in the
crowd. As such, the status update processor 62 may store a copy of
the list of users in the crowd record of the selected crowd in
response to receiving the crowd selection of the requestor.
Thereafter, the status update processor 62 records the requestor as
a follower of new crowds in which the users 20 in the recorded list
of users are located after leaving, or dispersing from, the
selected crowd (step 1504). In this manner, the requestor is
automatically added as a follower of any new crowds in which the
users 20 in the recorded list of users are located after leaving
the selected crowd of users. Notably, step 1504 may be performed
even while the selected crowd still exists (i.e., before all of the
users in the selected crowd have dispersed) or may be performed
only after all of the users in the selected crowd have dispersed
(i.e., performed only after the selected crowd has been removed or
no longer exists).
[0122] FIG. 17 illustrates the operation of the status update
processor 62 to distribute status updates sent by the users 20 to
followers of the corresponding crowds in which the users 20 are
located according to one embodiment of the present disclosure. In
one embodiment, this process is performed in parallel with the
process of FIG. 16. First, the status update processor 62 receives
a status update sent by one of the users 20 from the status update
service 28 (step 1600). In one embodiment, the status update
service 28 automatically sends status updates sent by the users 20
to the MAP server 12 as they are received by the status update
service 28 or in a batch process. In another embodiment, the status
update processor 62 of the MAP server 12 periodically requests
status updates sent by the users 20 from the status update service
28.
[0123] Upon receiving the status update sent by the user 20, the
status update processor 62 determines whether there are any
followers of the crowd in which the user 20 is currently located
(step 1602). If not, the process returns to step 1600 and is
repeated for the next status update received for one of the users
20. If there are one or more followers of the crowd in which the
user 20 that sent the status update is currently located, the
status update processor 62 sends the status update to each of the
followers (step 1604). For example, if one of the followers is one
of the other users 20, then the status update processor 62
preferably sends the status update to the mobile device 18 of the
other user 20 for presentation to the other user 20 via, for
instance, the MAP application 34 or one of the third-party
applications 36 of the mobile device 18, depending on the
particular implementation. If one of the followers is the
subscriber 24, then the status update processor 62 preferably sends
the status update for display to the subscriber 24 via the web
browser 40 of the subscriber device 22. The process then returns to
step 1600 and is repeated for the next status update received for
one of the users 20.
[0124] Notably, in the scenario where the requestor is one of the
users 20, the requestor may be provided with one or more features
to assist the user 20 in viewing status updates from followed
crowds. These features may be provided by the MAP application 34 or
one of the third-party applications 36 depending on which is
enabling the requestor to follow the crowds. For example, the
requestor may be enabled to select a desired crowd from a list of
crowds followed by that requestor. In response, the requestor may
be presented with a consolidated list of status updates resulting
from following the selected crowd. In addition or alternatively,
the requestor may be presented with a map and/or list that
illustrates how the selected crowd has dispersed. The requestor may
then be enabled to select any crowd to which the users in the
selected crowd have dispersed to view only the status updates from
that particular crowd.
[0125] FIG. 18 illustrates step 1504 of FIG. 16 in more detail
according to one embodiment of the present disclosure. More
specifically, FIG. 18 is a more detailed illustration of a process
by which the status update processor 62 of the MAP server 12 is
enabled to automatically record, or add, the requestor as a
follower of new crowds in which the users in the recorded list of
users are located after leaving the crowd selected by the
requestor. First, the status update processor 62 determines whether
a predefined time limit has expired since the requestor selected
the crowd in step 1500 (FIG. 16) (step 1700). The predefined time
limit may be defined by the requestor or may be system-defined. For
example, the status update processor 62 may use a system-defined
time limit of 1 hour. Such a time limit may be particularly
beneficial where the requestor is not automatically added as a
follower of new crowds of the users in the recorded list of users
for the crowd selected by the requestor until the crowd selected by
the requestor has completely dispersed (i.e., has been removed or
no longer exists). If the predefined time limit has expired, then
the requestor is removed as a follower of the current crowds of the
users in the recorded list of users for the crowd selected by the
requestor (step 1702). Note that steps 1700 and 1702 are optional.
If the predefined time limit has not expired, the status update
processor 62 sets a counter i to 1 (step 1704). Next, the status
update processor 62 determines whether the ith user (user i) in the
recorded list of users has left his previous crowd (step 1706). If
user i has not left his previous crowd (i.e., if user i remains in
the same crowd as before), then the process proceeds to step
1712.
[0126] In this embodiment, if user i has left his previous crowd,
the status update processor 62 determines whether less than a
predefined threshold number of users from the recorded list of
users remain in the previous crowd of user i (step 1708). The
predefined threshold number of users may be expressed as an
absolute number of users (e.g., 3 users) or a percentage of the
number of users in the recorded list (e.g., 5% of the users in the
recorded list of users for the crowd selected by the requestor). If
more than the predefined threshold number of users from the
recorded list of users remain in the previous crowd of user i, then
the requestor remains a follower of that crowd and the process
proceeds to step 1712. If less than the predefined threshold number
of users from the recorded list of users remain in the previous
crowd of user i, then the status update processor 62 removes the
requestor as a follower of the previous crowd of user i (step
1710).
[0127] Next, whether proceeding from step 1706, 1708, or 1710, the
status update processor 62 determines whether user i is located in
a new crowd (step 1712). If not, the process proceeds to step 1720.
If user i is located in a new crowd, in this embodiment, the status
update processor 62 determines whether the new crowd of user i
satisfies a predefined geographical limitation (step 1714). Note
that step 1714 is optional. The predefined geographical limitation
may be, for example, that the new crowd of user i is within a
predefined geographical boundary that is centered at or otherwise
encompasses the location of the crowd selected by the requestor at
the time that the crowd was selected by the requestor (e.g., within
a defined maximum distance from the location of the crowd selected
by the requestor at the time the crowd was selected by the
requestor). Alternatively, if the crowd selected by the requestor
still exists, the predefined geographical limitation may be, for
example, that the new crowd of user i is within a predefined
geographical boundary that is centered at or otherwise encompasses
a current location of the crowd selected by the requestor (e.g.,
within a defined maximum distance from the current location of the
crowd selected by the requestor). If the new crowd of user i does
not satisfy the predefined geographical limitation, then the
process proceeds to step 1720.
[0128] If the new crowd of user i does satisfy the predefined
geographical limitation, in this embodiment, the status update
processor 62 determines whether a threshold number of users in the
recorded list of users for the crowd selected by the requestor are
in the new crowd of user i (step 1716). Notably, the thresholds in
steps 1708 and 1716 may be the same threshold values or different
threshold values. The threshold number of users is a predefined
threshold and may be expressed as an absolute number of users
(e.g., 3 users) or a percentage of the number of users in the
recorded list (e.g., 5% of the users in the recorded list of users
for the crowd selected by the requestor). If at least the threshold
number of users in the recorded list of users for the crowd
selected by the requestor is not included in the new crowd of user
i, then the process proceeds to step 1720. Otherwise, the status
update processor 62 adds the requestor as a follower of the new
crowd of user i (step 1718). Note that step 1716 is optional. In
another embodiment, the requestor is added as a follower of the new
crowd of user i without first requiring that at least the threshold
number of users in the recorded list of users for the crowd
selected by the requestor (i.e., the original crowd) to be in the
new crowd of user i.
[0129] At this point, whether proceeding from step 1712, 1714,
1716, or 1718, the status update processor 62 determines whether
user i is the last user in the list of users recorded for the crowd
of users selected by the requestor (step 1720). If not, the status
update processor 62 increments the counter i (step 1722), and then
the process returns to step 1706 and is repeated for the next user.
Once the last user in the recorded list of users for the crowd
selected by the requestor has been processed, the process returns
to step 1700 and is repeated.
[0130] FIG. 19 illustrates the operation of the status update
processor 62 of the MAP server 12 according to another embodiment
of the present disclosure. First, the status update processor 62 of
the MAP server 12 receives a crowd selection of a requestor (step
1800). The crowd selection identifies a crowd selected by the
requestor. The requestor may be one of the users 20 in the selected
crowd or one of the users 20 that is not in the selected crowd
where the crowd selection is sent to the MAP server 12 via the MAP
application 34 or alternatively one of the third-party applications
36 of the corresponding mobile device 18. Alternatively, the
requestor may be the subscriber 24 at the subscriber device 22
where the crowd selection is sent to the MAP server 12 via, for
example, the web browser 40 of the subscriber device 22. As yet
another alternative, the requestor may be the third-party service
26. Upon receiving the crowd selection of the requestor, the status
update processor 62 of the MAP server 12 records a list of users
currently in the selected crowd (step 1802). More specifically, as
discussed above, the crowd record stored for the selected crowd
includes a list of users that are currently in the crowd. As such,
the status update processor 62 may store a copy of the list of
users in the crowd record of the selected crowd at the time of the
crowd selection as the recorded list of users in the selected
crowd.
[0131] Some time thereafter, the status update processor 62
receives a status update request from the requestor (step 1804).
The status update request may be manually initiated by the
requestor or automatically sent on behalf of the requestor. In
response, the status update processor 62 identifies the crowds in
which the users in the recorded list of users for the selected
crowd are currently located (step 1806). The status update
processor 62 then obtains status updates sent by the users 20 in
the identified crowds (step 1808) and returns those status updates
to the requestor (step 1810). More specifically, the requestor may
be added as a follower of the identified crowds. Thereafter, as
status updates are sent by users in the identified crowds, the
status updates are returned to the requestor as a follower of those
crowds. The requestor may remain a follower of the identified
crowds indefinitely, for a predefined amount of time, or the like,
depending on the particular implementation. Further, while not
illustrated, the identified crowds may be filtered based on
geographic limitations such that the requestor receives status
updates only for those crowds that satisfy one or more predefined
geographic limitations in a manner similar to that described above
with respect to step 1714 of FIG. 18. Notably, steps 1804 through
1810 may be performed while the selected crowd still exists (i.e.,
before all of the users in the selected crowd have dispersed) or
may be performed only after all of the users in the selected crowd
have dispersed (i.e., performed only after the selected crowd has
been removed or no longer exists). Also, steps 1804 through 1810
may be repeated.
[0132] FIG. 20 illustrates the operation of the status update
processor 62 of the MAP server 12 according to yet another
embodiment of the present disclosure. First, the status update
processor 62 of the MAP server 12 receives a crowd selection of a
requestor (step 1900). The crowd selection identifies a crowd
selected by the requestor. The requestor may be one of the users 20
in the selected crowd or one of the users 20 that is not in the
selected crowd where the crowd selection is sent to the MAP server
12 via the MAP application 34 or alternatively one of the
third-party applications 36 of the corresponding mobile device 18.
Alternatively, the requestor may be the subscriber 24 at the
subscriber device 22 where the crowd selection is sent to the MAP
server 12 via, for example, the web browser 40 of the subscriber
device 22. As yet another alternative, the requestor may be the
third-party service 26.
[0133] Sometime after receiving the crowd selection of the
requestor, the status update processor 62 of the MAP server 12
detects a crowd split from the selected crowd (step 1902). In one
embodiment, the detected crowd is a crowd that directly split from
the selected crowd. In another embodiment, the detected crowd is
either a crowd that directly split from the selected crowd or
indirectly split from the selected crowd. As used herein, a crowd
that indirectly split from the selected crowd is a crowd that split
from a crowd that split from the selected crowd, a crowd that split
from a crowd that split from a crowd that split from the selected
crowd, or so on. Upon detecting a crowd that split from the
selected crowd, the status update processor 62 adds the requestor
as a follower of the detected crowd (step 1904). The process then
returns to step 1902. While not illustrated, in another embodiment,
the requestor is not added as a follower of the detected crowd if
the detected crowd does not satisfy one or more predefined
geographic limitations in a manner similar to that described above
with respect to step 1714 of FIG. 18. In addition or alternatively,
steps 1902 through 1904 may be repeated until a predefined time
limit has expired. The predefined time limit may be defined by the
requestor or may be system defined. After the predefined time limit
has expired, the requestor is removed as a follower of the crowds
that split from the selected crowd. Note that in the embodiment of
FIG. 20, the status update processor 62 also performs the process
of FIG. 17 in order to deliver status updates to the requestor as a
follower of the crowds detected in step 1902.
[0134] FIG. 21 illustrates the operation of the status update
processor 62 according to yet another embodiment of the present
disclosure. In this embodiment, a requestor selects one of the
users 20 to follow and, in response, is provided with status
updates of the crowds in which the user 20 is located. Thus, as the
user 20 moves from a first crowd to a second crowd, the requestor
is removed as a follower of the first crowd and added as a follower
of the second crowd. In this manner, as the user 20 moves among
crowds over time, the requestor is enabled to follow the crowds in
which the user 20 is located.
[0135] More specifically, first, the status update processor 62 of
the MAP server 12 receives a user selection made by a requestor
(step 2000). The user selection identifies one of the users 20
selected by the requestor. The requestor may be one of the users
20, where the selected user 20 is another one of the users 20. In
this case, the user selection is sent to the MAP server 12 via the
MAP application 34 or alternatively one of the third-party
applications 36 of the corresponding mobile device 18.
Alternatively, the requestor may be the subscriber 24 at the
subscriber device 22 where the user selection is sent to the MAP
server 12 via, for example, the web browser 40 of the subscriber
device 22. As yet another alternative, the requestor may be the
third-party service 26.
[0136] Upon receiving the user selection made by the requestor, the
status update processor 62 of the MAP server 12 identifies the
crowd in which the selected user is located (step 2002) and records
the requestor as a follower of the identified crowd (step 2004).
Next, the status update processor 62 determines whether the
selected user has left the crowd previously identified as the crowd
of the selected user (step 2006). If not, the process proceeds to
step 2010. If so, the status update processor 62 removes the
requestor as a follower of the previous crowd of the selected user
(step 2008). In other words, once the selected user leaves a crowd,
the requestor is removed as a follower of that crowd. The status
update processor 62 then determines whether the selected user has
joined a new crowd (step 2010). If not, the process returns to step
2006 and is repeated. If the selected user has joined a new crowd,
the status update processor 62 records the requestor as a follower
of the new crowd of the selected user (step 2012). The process then
returns to step 2006 and is repeated. Note that in the embodiment
of FIG. 21, the status update processor 62 also performs the
process of FIG. 17 in order to deliver status updates sent by users
in the crowd of the selected user to the requestor as a follower of
the crowd of the selected user.
[0137] FIG. 22 illustrates the operation of the status update
processor 62 of the MAP server 12 to enable a requestor to follow
status updates sent by users in a select crowd even after those
users have dispersed from the select crowd according to another
embodiment of the present disclosure. First, the status update
processor 62 of the MAP server 12 receives a crowd selection of a
requestor (step 2100). The crowd selection identifies a crowd
selected by the requestor. The requestor may be one of the users 20
in the selected crowd or one of the users 20 that is not in the
selected crowd where the crowd selection is sent to the MAP server
12 via the MAP application 34 or alternatively one of the
third-party applications 36 of the corresponding mobile device 18.
Alternatively, the requestor may be the subscriber 24 at the
subscriber device 22 where the crowd selection is sent to the MAP
server 12 via, for example, the web browser 40 of the subscriber
device 22. As yet another alternative, the requestor may be the
third-party service 26. Upon receiving the crowd selection of the
requestor, the status update processor 62 of the MAP server 12
records a list of users currently in the selected crowd (step
2102). More specifically, as discussed above, the crowd record
stored for the selected crowd includes a list of users that are
currently in the crowd. As such, the status update processor 62 may
store a copy of the list of users in the crowd record of the
selected crowd at the time of the crowd selection as the recorded
list of users in the selected crowd.
[0138] Thereafter, the status update processor 62 obtains status
updates from the users 20 in the recorded list of users for the
select crowd and sends the status updates to the requestor even
after the users 20 in the recorded list of users have dispersed, or
left, the selected crowd (step 2104). Optionally, step 2104 may
include time and/or geographic limitations. For example, status
updates from the users 20 in the recorded list of users may be
obtained and sent to the requestor only for a predefined amount of
time after the users 20 leave the select crowd or, alternatively,
for only a predefined amount of time after the select crowd has
completely dispersed (i.e., no longer exists). The time limit may
be per individual user 20 in the recorded list. For example, there
may be a separate 1 hour time limit for each user 20 in the
recorded list since the user 20 may leave the crowd at different
times. The time limit may alternatively be a single time limit that
is applicable to the entire list of users. For example, there may
be a single 1 hour time limit that begins when the last user in the
recorded list of users leaves the selected crowd. In addition or
alternatively, status updates from the users 20 in the recorded
list of users may be obtained and sent to the requestor only while
the users 20 are subject to geographic limitations. For example,
the status updates may be obtained and sent to the requestor as
long as the users 20 in the recorded list remain in a predefined
geographic boundary. This predefined geographic boundary may be a
predefined geographic boundary that is centered at or otherwise
encompasses the location of the selected crowd at the time the
crowd was selected by the requestor, a predefined geographic
boundary that is centered at or otherwise encompasses the current
location of the selected crowd, or the like. The geographic
limitation may be per user such that status updates for any one of
the users 20 in the recorded list are obtained and sent to the
requestor as long as the geographic limitation is satisfied.
Alternatively, the geographic limitation may be for the recorded
list of users as a whole where the status updates are obtained and
sent to the requestor for all of the users 20 in the recorded list
as long as any one of the users 20 in the recorded list satisfies
the geographic limitation or, alternatively, all of the users 20 in
the recorded list satisfy the geographic limitation.
[0139] FIG. 23 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 128 connected to memory 130, one or
more secondary storage devices 132, and a communication interface
134 by a bus 136 or similar mechanism. The controller 128 is a
microprocessor, digital Application Specific Integrated Circuit
(ASIC), Field Programmable Gate Array (FPGA), or similar hardware
component. In this embodiment, the controller 128 is a
microprocessor, and the application layer 42, the business logic
layer 44, and the object mapping layer 63 (FIG. 2) are implemented
in software and stored in the memory 130 for execution by the
controller 128. Further, the datastore 64 (FIG. 2) may be
implemented in the one or more secondary storage devices 132. The
secondary storage devices 132 are digital data storage devices such
as, for example, one or more hard disk drives. The communication
interface 134 is a wired or wireless communication interface that
communicatively couples the MAP server 12 to the network 30 (FIG.
1). For example, the communication interface 134 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.
[0140] FIG. 24 is a block diagram of one of the mobile devices 18
according to one embodiment of the present disclosure. This
discussion is equally applicable to the other mobile devices 18. As
illustrated, the mobile device 18 includes a controller 138
connected to memory 140, a communication interface 142, one or more
user interface components 144, and the location function 38 by a
bus 146 or similar mechanism. The controller 138 is a
microprocessor, digital ASIC, FPGA, or similar hardware component.
In this embodiment, the controller 138 is a microprocessor, and the
MAP client 32, the MAP application 34, and the third-party
applications 36 are implemented in software and stored in the
memory 140 for execution by the controller 138. In this embodiment,
the location function 38 is a hardware component such as, for
example, a GPS receiver. The communication interface 142 is a
wireless communication interface that communicatively couples the
mobile device 18 to the network 30 (FIG. 1). For example, the
communication interface 142 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 (e.g., 3G telecommunications
interface such as a Global System for Mobile communications (GSM)
interface or the like, or a 4G telecommunications interface such as
Long Term Evolution (LTE), or the like. The one or more user
interface components 144 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.
[0141] FIG. 25 is a block diagram of the subscriber device 22
according to one embodiment of the present disclosure. As
illustrated, the subscriber device 22 includes a controller 148
connected to memory 150, one or more secondary storage devices 152,
a communication interface 154, and one or more user interface
components 156 by a bus 158 or similar mechanism. The controller
148 is a microprocessor, digital ASIC, FPGA, or similar hardware
component. In this embodiment, the controller 148 is a
microprocessor, and the web browser 40 (FIG. 1) is implemented in
software and stored in the memory 150 for execution by the
controller 148. The one or more secondary storage devices 152 are
digital storage devices such as, for example, one or more hard disk
drives. The communication interface 154 is a wired or wireless
communication interface that communicatively couples the subscriber
device 22 to the network 30 (FIG. 1). For example, the
communication interface 154 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, a mobile
communications interface such as a cellular telecommunications
interface, or the like. The one or more user interface components
156 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.
[0142] FIG. 26 is a block diagram of a computing device 160
operating to host the status update service 28 according to one
embodiment of the present disclosure. The computing device 160 may
be, for example, a physical server. As illustrated, the computing
device 160 includes a controller 162 connected to memory 164, one
or more secondary storage devices 166, a communication interface
168, and one or more user interface components 170 by a bus 172 or
similar mechanism. The controller 162 is a microprocessor, digital
ASIC, FPGA, or similar hardware component. In this embodiment, the
controller 162 is a microprocessor, and the status update service
28 is implemented in software and stored in the memory 164 for
execution by the controller 162. The one or more secondary storage
devices 166 are digital storage devices such as, for example, one
or more hard disk drives. The communication interface 168 is a
wired or wireless communication interface that communicatively
couples the computing device 160 to the network 30 (FIG. 1). For
example, the communication interface 168 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, a
mobile communications interface such as a cellular
telecommunications interface, or the like. The one or more user
interface components 170 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.
[0143] The following use cases illustrate some aspects of the
systems and methods described above. However, these use cases are
exemplary and are not intended to limit the scope of the present
disclosure.
[0144] Use Case #1: Following a crowd in order to continue
receiving status updates when the crowd disperses. [0145] 1. Jim
and his friend are in a crowd at the N.C. State football game.
[0146] 2. State ends up losing and the crowd begins to disperse.
[0147] 3. Jim wants to view status updates sent by users that are
in the crowd at the game even after the crowd disperses so he
selects to follow his current crowd. [0148] 4. The MAP server 12
takes a snapshot of the users in the crowd. [0149] 5. Everyone goes
their own way, but several of the fans continue sending status
updates about the game. [0150] 6. Jim is able to see all of the
status updates from the fans that are continuing to post. Jim joins
in.
[0151] Use Case #1a: Creating and utilizing sub-groups from the
original followed group. (Continuing from the previous use case).
[0152] 1. After the game, Jim and his friend decide to go hang out
on Hillsborough Street. [0153] 2. Jim opens up the MAP application
34 on his mobile device 18, and he selects the crowd that he is
following (i.e., the "Game Crowd" that has now dispersed or that is
continuing to disperse). [0154] 3. The MAP server 12 detects that
two new crowds have split from the "Game Crowd" and are located at
neighboring bars on Hillsborough Street--Porter's Tavern and
Mitch's Tavern. [0155] 4. Jim is able to select each of the crowds
using his MAP application 34 to view their ongoing status updates.
[0156] 5. One of the crowds (the one at Mitch's Tavern) is still
harping on how bad the team played, how the coach should be fired,
etc., while the other crowd (at Porter's Tavern) is discussing how
much fun they had at the game and the (few) bright spots from the
game. [0157] 6. Additionally, the MAP application 34 allows Jim to
quickly see various and useful crowd characteristics of each of the
two crowds that he is monitoring. [0158] 7. Jim can see that the
more upbeat crowd also has a predominance of members who have a
history of both gathering at and sending status updates from
Porter's Tavern. The crowd at Mitch's Tavern on the other hand does
not appear to be a "regular" crowd at that location. [0159] 8. Jim
and his friend decide to join the more upbeat and "regular" crowd
for a few drinks at Porter's Tavern.
[0160] Use Case #2: Following a User. [0161] 1. Amy enjoys the
insightful comments of a mobile industry analyst and follows him on
Twitter.RTM.. [0162] 2. Amy sees that the analyst is also a user
registered with the MAP server 12. [0163] 3. Amy decides to follow
the analyst via the MAP server 12 as well. [0164] 4. A mobile
conference is taking place in N.Y. and Amy isn't able to go. [0165]
5. However, because Amy is following the analyst via the MAP server
12, Amy is able to follow the status updates from the users in the
crowds in which the analyst is located. [0166] 6. Amy occasionally
checks the crowd status updates from the crowds in which the
analyst is located in order to get the analyst's latest thoughts as
well as perspectives and viewpoints of other users in the analyst's
crowds.
[0167] The present disclosure provides substantial opportunity for
variation without departing from the spirit or scope of the
disclosure. For example, FIGS. 16 through 22 focus on embodiments
where the MAP server 12 obtains status updates sent by the users 20
from the status update service 28 and then distributes the status
updates to the followers of the corresponding crowds. However, the
present disclosure is not limited thereto. In an alternative
embodiment, the MAP server 12 may record followers of crowds in the
manner described above with respect to FIGS. 16 and 18 through 22
and then communicate with the status update service 28 such that
the status update service 28 distributes status updates sent by the
users 20 to the followers of the corresponding crowds of users.
Other variations will be apparent to one of ordinary skill in the
art upon reading this disclosure and are to be considered to be
within the scope of the present disclosure.
[0168] Those skilled in the art will recognize improvements and
modifications to the preferred embodiments of the present
disclosure. All such improvements and modifications are considered
within the scope of the concepts disclosed herein and the claims
that follow.
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