U.S. patent application number 12/963726 was filed with the patent office on 2012-03-15 for crowd and profile based communication addresses.
This patent application is currently assigned to WALDECK TECHNOLOGY, LLC. Invention is credited to Scott Curtis, Gregory M. Evans.
Application Number | 20120063367 12/963726 |
Document ID | / |
Family ID | 45806674 |
Filed Date | 2012-03-15 |
United States Patent
Application |
20120063367 |
Kind Code |
A1 |
Curtis; Scott ; et
al. |
March 15, 2012 |
CROWD AND PROFILE BASED COMMUNICATION ADDRESSES
Abstract
A system and method are disclosed for sending a message to a
select subset of users in a select crowd of users. In one
embodiment, a message to be delivered to a subset of users in a
select crowd of users is received from a user device of a sending
user. In response, one or more users in the crowd are selected as
the subset of the users in the crowd to which the message is to be
delivered. In one embodiment, the one or more users are selected
based on a profile matching process. The message is then sent to
the one or more users selected as the subset of the users in the
crowd to which the message is to be delivered. Preferably, the
message is sent to the one or more users anonymously such that the
message does not identify the sending user.
Inventors: |
Curtis; Scott; (Durham,
NC) ; Evans; Gregory M.; (Raleigh, NC) |
Assignee: |
WALDECK TECHNOLOGY, LLC
Wilmington
DE
|
Family ID: |
45806674 |
Appl. No.: |
12/963726 |
Filed: |
December 9, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61289107 |
Dec 22, 2009 |
|
|
|
Current U.S.
Class: |
370/270 |
Current CPC
Class: |
H04W 4/023 20130101;
G06F 3/0481 20130101; G06Q 30/0269 20130101; H04L 51/14 20130101;
G06Q 30/0261 20130101; G06Q 30/0251 20130101; H04L 51/38 20130101;
H04L 12/185 20130101 |
Class at
Publication: |
370/270 |
International
Class: |
H04L 12/16 20060101
H04L012/16 |
Claims
1. A computer-implemented method comprising: receiving, from a
device of a sending user, a message to be delivered to a subset of
users in a select crowd of users; selecting one or more users from
the users in the select crowd of users as the subset of the users
in the select crowd of users to which the message is to be
delivered; and sending the message to the one or more users
selected as the subset of the users in the select crowd of users to
which the message is to be delivered.
2. The method of claim 1 wherein the select crowd of users is one
of a plurality of dynamically defined crowds of users, and each
crowd of the plurality of dynamically defined crowds of users
includes a plurality of users that are spatially proximate to one
another.
3. The method of claim 1 wherein sending the message comprises
sending the message anonymously such that the message does not
identify the sending user.
4. The method of claim 1 wherein selecting the one or more users
comprises selecting the one or more users from the users in the
select crowd of users as the subset of the users in the select
crowd of users to which the message is to be delivered based on
profile matching.
5. The method of claim 4 wherein selecting the one or more users
from the users in the select crowd of users as the subset of the
users in the select crowd of users to which the message is to be
delivered based on profile matching comprises: for each user of at
least some of the users in the select crowd of users, generating a
match value based on a comparison of at least a subset of a user
profile of the sending user to at least a subset of a user profile
of the user; and selecting the one or more users from the users in
the select crowd of users as the subset of the users in the select
crowd of users to which the message is to be delivered based on the
match values of the at least some of the users in the select crowd
of users.
6. The method of claim 5 wherein the at least some of the users in
the select crowd of users are all of the users in the select crowd
of users.
7. The method of claim 5 wherein the at least some of the users in
the select crowd of users are one or more of the users in the
select crowd of users that have enabled receipt of messages.
8. The method of claim 5 wherein the user profile of the sending
user comprises one or more profile categories, and the at least a
subset of the user profile of the sending user is at least one
profile category selected from the one or more profile
categories.
9. The method of claim 8 wherein, for each user of the at least
some of the users in the select crowd of users, generating the
match value comprises generating the match value for the user based
on a comparison of the at least one profile category of the user
profile of the sending user to at least a subset of the user
profile of the user and at least one weight assigned to the at
least one profile category.
10. The method of claim 5 wherein selecting the one or more users
from the users in the select crowd of users as the subset of the
users in the select crowd of users to which the message is to be
delivered based on the match values of the at least some of the
users in the select crowd of users comprises: selecting a
predefined number of users from the at least some of the users in
the select crowd of users having the highest match values.
11. The method of claim 5 wherein selecting the one or more users
from the users in the select crowd of users as the subset of the
users in the select crowd of users to which the message is to be
delivered based on the match values of the at least some of the
users in the select crowd of users comprises: selecting one or more
users from the at least some of the users in the select crowd of
users having match values that are greater than a defined minimum
threshold score.
12. The method of claim 4 wherein selecting the one or more users
from the users in the select crowd of users as the subset of the
users in the select crowd of users to which the message is to be
delivered based on profile matching comprises: for each user of at
least some of the users in the select crowd of users: generating a
match value based on a comparison of at least a subset of a user
profile of the sending user to at least a subset of a user profile
of the user; and obtaining a referral rating for the user that is
indicative of a desirability of the user as a recipient of the
message; and selecting the one or more users from the users in the
select crowd of users as the subset of the users in the select
crowd of users to which the message is to be delivered based on the
match values and the referral ratings of the at least some of the
users in the select crowd of users.
13. The method of claim 12 wherein the referral rating for the user
is an implicit referral rating for the user.
14. The method of claim 12 wherein the referral rating for the user
is an explicit referral rating for the user.
15. The method of claim 4 wherein selecting the one or more users
from the users in the select crowd of users as the subset of the
users in the select crowd of users to which the message is to be
delivered based on profile matching comprises: for each user of at
least some of the users in the select crowd of users: generating a
match value based on a comparison of at least a subset of a user
profile of the sending user to at least a subset of a user profile
of the user; and obtaining a responsiveness rating for the user
that is indicative of whether the user is likely to respond to the
message from the sending user based on past actions of the user;
and selecting the one or more users from the users in the select
crowd of users as the subset of the users in the select crowd of
users to which the message is to be delivered based on the match
values and the responsiveness ratings of the at least some of the
users in the select crowd of users.
16. The method of claim 1 wherein selecting the one or more users
comprises: for each user of at least some of the users in the
select crowd of users, obtaining a referral rating for the user
that is indicative of a desirability of the user as a recipient of
the message; and selecting the one or more users from the users in
the select crowd of users as the subset of the users in the select
crowd of users to which the message is to be delivered based on the
referral ratings of the at least some of the users in the select
crowd of users.
17. The method of claim 16 wherein the referral rating for the user
is an implicit referral rating for the user.
18. The method of claim 16 wherein the referral rating for the user
is an explicit referral rating for the user.
19. The method of claim 1 wherein selecting the one or more users
comprises: for each user of at least some of the users in the
select crowd of users, obtaining a responsiveness rating for the
user that is indicative of a desirability of the user as a
recipient of the message; and selecting the one or more users from
the users in the select crowd of users as the subset of the users
in the select crowd of users to which the message is to be
delivered based on the responsiveness ratings of the at least some
of the users in the select crowd of users.
20. The method of claim 1 further comprising: receiving a response
from a responding user from the one or more users to which the
message was delivered; and sending the response to the sending user
at the device of the sending user.
21. The method of claim 20 wherein sending the response to the
sending user comprises sending the response anonymously such that
the response does not identify the responding user.
22. The method of claim 20 further comprising: receiving, from the
device of the sending user, a rating for the response assigned by
the sending user; and updating an explicit referral rating of the
responding user based on the rating for the response assigned by
the sending user.
23. The method of claim 20 further comprising: monitoring a
location of the sending user to detect whether the sending user
moves to a location of the select crowd of users; and updating an
implicit referral rating of the responding user based on whether
the sending user moves to the location of the select crowd of
users.
24. The method of claim 20 further comprising, for each user of the
one or more users to which the message was delivered, updating a
responsiveness rating of the user based on whether the user
responded to the message.
25. A server comprising: a communication interface; and a
controller associated with the communication interface and adapted
to: receive, from a device of a sending user, a message to be
delivered to a subset of users in a select crowd of users; select
one or more users from the users in the select crowd of users as
the subset of the users in the select crowd of users to which the
message is to be delivered; and send the message to the one or more
users selected as the subset of the users in the select crowd of
users to which the message is to be delivered.
26. A computer readable medium storing software for instructing a
controller of a computing device to: receive, from a device of a
sending user, a message to be delivered to a subset of users in a
select crowd of users; select one or more users from the users in
the select crowd of users as the subset of the users in the select
crowd of users to which the message is to be delivered; and send
the message to the one or more users selected as the subset of the
users in the select crowd of users to which the message is to be
delivered.
Description
RELATED APPLICATIONS
[0001] This application claims the benefit of provisional patent
application Ser. No. 61/289,107, filed Dec. 22, 2009, the
disclosure of which is hereby incorporated herein by reference in
its entirety.
FIELD OF THE DISCLOSURE
[0002] The present disclosure relates to delivering a message to a
select subset of users in a select crowd of users.
BACKGROUND
[0003] Location-aware mobile devices are prolific in today's
digital society. As a result, crowd-based applications that
identify and track crowds of users are emerging. One exemplary
system for identifying and tracking crowds of users is described in
U.S. patent application Ser. No. 12/645,532, entitled FORMING
CROWDS AND PROVIDING ACCESS TO CROWD DATA IN A MOBILE ENVIRONMENT,
which was filed Dec. 23, 2009; U.S. patent application Ser. No.
12/645,539, entitled ANONYMOUS CROWD TRACKING, which was filed Dec.
23, 2009; U.S. patent application Ser. No. 12/645,535, entitled
MAINTAINING A HISTORICAL RECORD OF ANONYMIZED USER PROFILE DATA BY
LOCATION FOR USERS IN A MOBILE ENVIRONMENT, which was filed Dec.
23, 2009; U.S. patent application Ser. No. 12/645,546, entitled
CROWD FORMATION FOR MOBILE DEVICE USERS, which was filed Dec. 23,
2009; U.S. patent application Ser. No. 12/645,556, entitled SERVING
A REQUEST FOR DATA FROM A HISTORICAL RECORD OF ANONYMIZED USER
PROFILE DATA IN A MOBILE ENVIRONMENT, which was filed Dec. 23,
2009; U.S. patent application Ser. No. 12/645,560, entitled
HANDLING CROWD REQUESTS FOR LARGE GEOGRAPHIC AREAS, which was filed
Dec. 23, 2009; and U.S. patent application Ser. No. 12/645,544,
entitled MODIFYING A USER'S CONTRIBUTION TO AN AGGREGATE PROFILE
BASED ON TIME BETWEEN LOCATION UPDATES AND EXTERNAL EVENTS, which
was filed Dec. 23, 2009; all of which are hereby incorporated
herein by reference in their entireties. With the emergence of such
crowd-based applications, there is a need for a system and method
that enables communication with users in crowds at desired
locations.
SUMMARY
[0004] The present disclosure relates to sending a message to a
select subset of users in a select crowd of users. In one
embodiment, a message to be delivered to a subset of users in a
select crowd of users is received from a user device of a sending
user. In response, one or more users in the crowd are selected as
the subset of the users in the crowd to which the message is to be
delivered. In one embodiment, the one or more users are selected
based on a profile matching process. For example, a predefined
number of users in the crowd having user profiles that most closely
match the user profile of the sending user, or a select portion of
the user profile of the sending user, may be selected as the subset
of the users in the crowd to which the message is to be delivered.
The message is then sent to the one or more users selected as the
subset of the users in the crowd to which the message is to be
delivered. Preferably, the message is sent to the one or more users
anonymously such that the message does not identify the sending
user.
[0005] 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
[0006] 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.
[0007] FIG. 1 illustrates a Mobile Aggregate Profile (MAP) system
according to one embodiment of the present disclosure;
[0008] FIG. 2 is a block diagram of the MAP server of FIG. 1
according to one embodiment of the present disclosure;
[0009] FIG. 3 is a block diagram of the MAP client of one of the
mobile devices of FIG. 1 according to one embodiment of the present
disclosure;
[0010] 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;
[0011] 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;
[0012] FIG. 6 is a flow chart for a spatial crowd formation process
according to one embodiment of the present disclosure;
[0013] FIGS. 7A through 7D graphically illustrate the crowd
formation process of FIG. 6 for an exemplary bounding box;
[0014] FIGS. 8A through 8D illustrate a flow chart for a spatial
crowd formation process according to another embodiment of the
present disclosure;
[0015] FIGS. 9A through 9D graphically illustrate the crowd
formation process of FIGS. 8A through 8D for a scenario where the
crowd formation process is triggered by a location update for a
user having no old location;
[0016] FIGS. 10A through 10F graphically illustrate the crowd
formation process of FIGS. 8A through 8D for a scenario where the
new and old bounding boxes overlap;
[0017] FIGS. 11A through 11E graphically illustrate the crowd
formation process of FIGS. 8A through 8D in a scenario where the
new and old bounding boxes do not overlap;
[0018] FIG. 12 illustrates the operation of the system of FIG. 1 to
generate and deliver a message to a select subset of users in a
crowd of users according to one embodiment of the present
disclosure;
[0019] FIG. 13 illustrates a process for selecting a subset of
users in a crowd of users to which to send a message according to
one embodiment of the present disclosure;
[0020] FIGS. 14A through 14F graphically illustrate the process of
FIG. 12 according to one exemplary embodiment of the present
disclosure;
[0021] FIG. 15 is a block diagram of the MAP server of FIG. 1
according to one embodiment of the present disclosure; and
[0022] FIG. 16 is a block diagram of one of the mobile devices of
FIG. 1 according to one embodiment of the present disclosure.
DETAILED DESCRIPTION
[0023] 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.
[0024] FIG. 1 illustrates a Mobile Aggregate Profile (MAP) system
10 (hereinafter "system 10") that enables a user to send a message
to a select subset of users in a select crowd of users 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, and a
third-party service 26 communicatively coupled via a network 28.
The network 28 may be any type of network or any combination of
networks. Specifically, the network 28 may include wired
components, wireless components, or both wired and wireless
components. In one exemplary embodiment, the network 28 is a
distributed public network such as the Internet, where the mobile
devices 18 are enabled to connect to the network 28 via local
wireless connections (e.g., WiFi.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).
[0025] 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, and
tracking crowds. Note that while the MAP server 12 is illustrated
as a single server for simplicity and ease of discussion, it should
be appreciated that the MAP server 12 may be implemented as a
single physical server or multiple physical servers operating in a
collaborative manner for purposes of redundancy and/or load
sharing.
[0026] 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 FireEagle service.
[0027] The mobile devices 18 may be mobile smart phones, portable
media player devices, mobile gaming devices, or the like. Some
exemplary mobile devices that may be programmed or otherwise
configured to operate as the mobile devices 18 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.TM., and
the Apple.RTM. iPod Touch.RTM. device. However, this list of
exemplary mobile devices is not exhaustive and is not intended to
limit the scope of the present disclosure.
[0028] The mobile devices 18-1 through 18-N include MAP clients
30-1 through 30-N (generally referred to herein collectively as MAP
clients 30 or individually as MAP client 30), MAP applications 32-1
through 32-N (generally referred to herein collectively as MAP
applications 32 or individually as MAP application 32), third-party
applications 34-1 through 34-N (generally referred to herein
collectively as third-party applications 34 or individually as
third-party application 34), and location functions 36-1 through
36-N (generally referred to herein collectively as location
functions 36 or individually as location function 36),
respectively. The MAP client 30 is preferably implemented in
software. In general, in the preferred embodiment, the MAP client
30 is a middleware layer operating to interface an application
layer (i.e., the MAP application 32 and the third-party
applications 34) to the MAP server 12. More specifically, the MAP
client 30 enables the MAP application 32 and the third-party
applications 34 to request and receive data from the MAP server 12.
In addition, the MAP client 30 enables applications, such as the
MAP application 32 and the third-party applications 34, to access
data from the MAP server 12.
[0029] The MAP application 32 is also preferably implemented in
software. The MAP application 32 generally provides a user
interface component between the user 20 and the MAP server 12. More
specifically, among other things, the MAP application 32 enables
the user 20 to initiate requests for crowd data from the MAP server
12 and present corresponding crowd data returned by the MAP server
12 to the user 20 as well as enable the user 20 to send and receive
messages as described below in detail. The MAP application 32 also
enables the user 20 to configure various settings. For example, the
MAP application 32 may enable the user 20 to select a desired
social networking service (e.g., Facebook.RTM., MySpace.RTM.,
LinkedIN.RTM., etc.) from which to obtain the user profile of the
user 20 and provide any necessary credentials (e.g., username and
password) needed to access the user profile from the social
networking service.
[0030] The third-party applications 34 are preferably implemented
in software. The third-party applications 34 operate to access the
MAP server 12 via the MAP client 30. The third-party applications
34 may utilize data obtained from the MAP server 12 in any desired
manner. As an example, one of the third-party applications 34 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 30 is illustrated as being separate from the
MAP application 32 and the third-party applications 34, the present
disclosure is not limited thereto. The functionality of the MAP
client 30 may alternatively be incorporated into the MAP
application 32 and the third-party applications 34.
[0031] The location function 36 may be implemented in hardware,
software, or a combination thereof. In general, the location
function 36 operates to determine or otherwise obtain the location
of the mobile device 18. For example, the location function 36 may
be or include a Global Positioning System (GPS) receiver. In
addition or alternatively, the location function 36 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 36 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.
[0032] The subscriber device 22 is a physical device such as a
personal computer, a mobile computer (e.g., a notebook computer, a
netbook computer, a tablet computer, etc.), a mobile smart phone,
or the like. The subscriber 24 associated with the subscriber
device 22 is a person or entity. In general, the subscriber device
22 enables the subscriber 24 to access the MAP server 12 via a web
browser 38 to obtain various types of data, preferably for a fee.
For example, the subscriber 24 may pay a fee to have access to
crowd data such as aggregate profiles for crowds located at one or
more POIs and/or located in one or more AOIs, pay a fee to track
crowds, or the like. Note that the web browser 38 is exemplary. In
another embodiment, the subscriber device 22 is enabled to access
the MAP server 12 via a custom application.
[0033] Lastly, the third-party service 26 is a service that has
access to data from the MAP server 12 such as 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.
[0034] 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.
[0035] FIG. 2 is a block diagram of the MAP server 12 of FIG. 1
according to one embodiment of the present disclosure. As
illustrated, the MAP server 12 includes an application layer 40, a
business logic layer 42, and a persistence layer 44. The
application layer 40 includes a user web application 46, a mobile
client/server protocol component 48, and one or more data
Application Programming Interfaces (APIs) 50. The user web
application 46 is preferably implemented in software and operates
to provide a web interface for users, such as the subscriber 24, to
access the MAP server 12 via a web browser. The mobile
client/server protocol component 48 is preferably implemented in
software and operates to provide an interface between the MAP
server 12 and the MAP clients 30 hosted by the mobile devices 18.
The data APIs 50 enable third-party services, such as the
third-party service 26, to access the MAP server 12.
[0036] The business logic layer 42 includes a profile manager 52, a
location manager 54, a history manager 56, a crowd analyzer 58, an
aggregation engine 60, and a message delivery function 62 each of
which is preferably implemented in software. The profile manager 52
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 44. The location
manager 54 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.
[0037] The history manager 56 generally operates to maintain a
historical record of anonymized user profile data by location. Note
that while the user profile data stored in the historical record is
preferably anonymized, it is not limited thereto. 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.
For additional information regarding the operation of the profile
manager 52, the location manager 54, the history manager 56, the
crowd analyzer 58, and the aggregation engine 60, the interested
reader is directed to U.S. patent application Ser. No. 12/645,532,
entitled FORMING CROWDS AND PROVIDING ACCESS TO CROWD DATA IN A
MOBILE ENVIRONMENT, which was filed Dec. 23, 2009; U.S. patent
application Ser. No. 12/645,539, entitled ANONYMOUS CROWD TRACKING,
which was filed Dec. 23, 2009; U.S. patent application Ser. No.
12/645,535, entitled MAINTAINING A HISTORICAL RECORD OF ANONYMIZED
USER PROFILE DATA BY LOCATION FOR USERS IN A MOBILE ENVIRONMENT,
which was filed Dec. 23, 2009; U.S. patent application Ser. No.
12/645,546, entitled CROWD FORMATION FOR MOBILE DEVICE USERS, which
was filed Dec. 23, 2009; U.S. patent application Ser. No.
12/645,556, entitled SERVING A REQUEST FOR DATA FROM A HISTORICAL
RECORD OF ANONYMIZED USER PROFILE DATA IN A MOBILE ENVIRONMENT,
which was filed Dec. 23, 2009; U.S. patent application Ser. No.
12/645,560, entitled HANDLING CROWD REQUESTS FOR LARGE GEOGRAPHIC
AREAS, which was filed Dec. 23, 2009; and U.S. patent application
Ser. No. 12/645,544, entitled MODIFYING A USER'S CONTRIBUTION TO AN
AGGREGATE PROFILE BASED ON TIME BETWEEN LOCATION UPDATES AND
EXTERNAL EVENTS, which was filed Dec. 23, 2009; all of which have
been incorporated herein by reference in their entireties.
[0038] As discussed below in detail, the message delivery function
62 enables delivery, or sending, of messages to select subsets of
users in select crowds. In general, a user such as, but not limited
to, one of the users 20 selects a desired crowd and defines a
message to be delivered to a subset of the users 20 in the desired
crowd. The message delivery function 62 selects one or more of the
users 20 in the desired crowd as the subset of users 20 in the
desired crowd to which the message is to be delivered. Preferably,
the one or more users 20 are selected based on profile matching
and, optionally, one or more ratings assigned to the users 20 in
the crowd that reflect the desirability of the users 20 as
recipients of the message. The message delivery function 62 then
sends the message to the one or more users 20 selected for message
delivery. Preferably, the message is anonymous (i.e., does not
identify the sender). The message delivery function 62 may also
enable the one or more users 20 that receive the message to respond
to the message if desired. Any response is also preferably
anonymous.
[0039] The persistence layer 44 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 42 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
42 to relational database entities stored in the datastore 64. Note
that, in one embodiment, data is stored in the datastore 64 in a
Resource Description Framework (RDF) compatible format.
[0040] 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.
[0041] FIG. 3 illustrates the MAP client 30 of FIG. 1 in more
detail according to one embodiment of the present disclosure. As
illustrated, in this embodiment, the MAP client 30 includes a MAP
access API 66, a MAP middleware component 68, and a mobile
client/server protocol component 70. The MAP access API 66 is
implemented in software and provides an interface by which the MAP
client 30 and the third-party applications 34 are enabled to access
the MAP client 30. The MAP middleware component 68 is implemented
in software and performs the operations needed for the MAP client
30 to operate as an interface between the MAP application 32 and
the third-party applications 34 at the mobile device 18 and the MAP
server 12. The mobile client/server protocol component 70 enables
communication between the MAP client 30 and the MAP server 12 via a
defined protocol.
[0042] 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 30 of the mobile device 18 (step
1000E).
[0043] 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 30 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 30 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 30 sends the complete user
profile of the user 20 to the MAP server 12, in an alternative
embodiment, the MAP client 30 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.
[0044] Upon receiving the user profile of the user 20 from the MAP
client 30 of the mobile device 18, the profile manager 52 of the
MAP server 12 processes the user profile (step 1002D). More
specifically, in the preferred embodiment, the profile manager 52
includes social network handlers for the social network services
supported by the MAP server 12 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. For example, if the MAP server 12
supports user profiles from Facebook.RTM., MySpace.RTM., and
LinkedIN.RTM., the profile manager 52 may include a Facebook
handler, a MySpace handler, and a LinkedlN 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 52 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 52 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 52 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.
[0045] After processing the user profile of the user 20, the
profile manager 52 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 32.
[0046] 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 52 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.
[0047] 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 32 of the mobile device 18 obtains
the current location of the mobile device 18 from the location
function 36 of the mobile device 18. The MAP application 32 then
provides the current location of the mobile device 18 to the MAP
client 30, and the MAP client 30 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 32 to provide location updates for the user 20
to the MAP server 12.
[0048] In response to receiving the current location of the mobile
device 18, the location manager 54 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. Note
that, in the preferred embodiment, only the current location of the
user 20 is stored in the user record of the user 20. In this
manner, the MAP server 12 maintains privacy for the user 20 since
the MAP server 12 does not maintain a historical record of the
location of the user 20. Any historical data maintained by the MAP
server 12 is preferably anonymized by the history manager 56 in
order to maintain the privacy of the users 20.
[0049] In addition to storing the current location of the user 20,
the location manager 54 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 32 will not be able to provide location updates for the
user 20 to the MAP server 12 unless the MAP application 32 is
active. Therefore, when the MAP application 32 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 54 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 32 is not active at the mobile device 18.
[0050] 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 30 of the mobile device 18
(step 1100D).
[0051] 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 52
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 52 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.
[0052] Upon receiving the user profile of the user 20, the profile
manager 52 of the MAP server 12 processes the user profile (step
1102C). More specifically, as discussed above, in the preferred
embodiment, the profile manager 52 includes social network handlers
for the social network services supported by the MAP server 12. The
social network handlers process user profiles to generate user
profiles for the MAP server 12 that include lists of keywords for
each of a number of profile categories, or profile slices.
[0053] After processing the user profile of the user 20, the
profile manager 52 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 32.
[0054] 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 52 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.
[0055] 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 32 of the mobile device 18 obtains
the current location of the mobile device 18 from the location
function 36 of the mobile device 18. The MAP application 32 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.
[0056] In response to receiving the current location of the mobile
device 18, the location manager 54 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. Note
that, in the preferred embodiment, only the current location of the
user 20 is stored in the user record of the user 20. In this
manner, the MAP server 12 maintains privacy for the user 20 since
the MAP server 12 does not maintain a historical record of the
location of the user 20. As discussed below in detail, historical
data maintained by the MAP server 12 is preferably anonymized in
order to maintain the privacy of the users 20.
[0057] 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 32 will not
provide location updates for the user 20 to the location server 16
unless the MAP application 32 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 32 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 54 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 32
is not active at the mobile device 18.
[0058] 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 is a flow chart for a
spatial crowd formation process according to one embodiment of the
present disclosure. Note that, in one embodiment, this process is
performed in response to a request for crowd data for a POI or an
AOI or in response to a crowdsearch request. In another embodiment,
this process may be performed proactively by the crowd analyzer 58
as, for example, a background process.
[0059] First, the crowd analyzer 58 establishes a bounding box for
the crowd formation process (step 1200). Note that while a bounding
box is used in this example, other geographic shapes may be used to
define a bounding region for the crowd formation process (e.g., a
bounding circle). In one embodiment, if crowd formation is
performed in response to a specific request, the bounding box is
established based on the POI or the AOI of the request. If the
request is for a POI, then the bounding box is a geographic area of
a predetermined size centered at the POI. If the request is for an
AOI, the bounding box is the AOI. Alternatively, if the crowd
formation process is performed proactively, the bounding box is a
bounding box of a predefined size.
[0060] The crowd analyzer 58 then creates a crowd for each
individual user in the bounding box (step 1202). More specifically,
the crowd analyzer 58 queries the datastore 64 of the MAP server 12
to identify users currently located within the bounding box. Then,
a crowd of one user is created for each user currently located
within the bounding box. Next, the crowd analyzer 58 determines the
two closest crowds in the bounding box (step 1204) and determines a
distance between the two crowds (step 1206). The distance between
the two crowds is a distance between crowd centers of the two
crowds. Note that the crowd center of a crowd of one is the current
location of the user in the crowd. The crowd analyzer 58 then
determines whether the distance between the two crowds is less than
an optimal inclusion distance (step 1208). In this embodiment, the
optimal inclusion distance is a predefined static distance. If the
distance between the two crowds is less than the optimal inclusion
distance, the crowd analyzer 58 combines the two crowds (step 1210)
and computes a new crowd center for the resulting crowd (step
1212). The crowd center may be computed based on the current
locations of the users in the crowd using a center of mass
algorithm. At this point the process returns to step 1204 and is
repeated until the distance between the two closest crowds is not
less than the optimal inclusion distance. At that point, the crowd
analyzer 58 discards any crowds with less than three users (step
1214). Note that throughout this disclosure crowds are only
maintained if the crowds include three or more users. However,
while three users is the preferred minimum number of users in a
crowd, the present disclosure is not limited thereto. The minimum
number of users in a crowd may be defined as any number greater
than or equal to two users.
[0061] FIGS. 7A through 7D graphically illustrate the crowd
formation process of FIG. 6 for an exemplary bounding box 72. In
FIGS. 7A through 7D, crowds are noted by dashed circles, and the
crowd centers are noted by cross-hairs (+). As illustrated in FIG.
7A, initially, the crowd analyzer 58 creates crowds 74 through 82
for the users in the geographic area defined by the bounding box
72, where, at this point, each of the crowds 74 through 82 includes
one user. The current locations of the users are the crowd centers
of the crowds 74 through 82. Next, the crowd analyzer 58 determines
the two closest crowds and a distance between the two closest
crowds. In this example, at this point, the two closest crowds are
crowds 76 and 78, and the distance between the two closest crowds
76 and 78 is less than the optimal inclusion distance. As such, the
two closest crowds 76 and 78 are combined by merging crowd 78 into
crowd 76, and a new crowd center (+) is computed for the crowd 76,
as illustrated in FIG. 7B. Next, the crowd analyzer 58 again
determines the two closest crowds, which are now crowds 74 and 76.
The crowd analyzer 58 then determines a distance between the crowds
74 and 76. Since the distance is less than the optimal inclusion
distance, the crowd analyzer 58 combines the two crowds 74 and 76
by merging the crowd 74 into the crowd 76, and a new crowd center
(+) is computed for the crowd 76, as illustrated in FIG. 7C. At
this point, there are no more crowds separated by less than the
optimal inclusion distance. As such, the crowd analyzer 58 discards
crowds having less than three users, which in this example are
crowds 80 and 82. As a result, at the end of the crowd formation
process, the crowd 76 has been formed with three users, as
illustrated in FIG. 7D.
[0062] FIGS. 8A through 8D illustrate a flow chart for a spatial
crowd formation process according to another 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 the users 20. As such, first, the crowd
analyzer 58 receives a location update, or a new location, for a
user (step 1300). Assume that, for this example, the location
update is received for the user 20-1. In response, the crowd
analyzer 58 retrieves an old location of the user 20-1, if any
(step 1302). The old location is the current location of the user
20-1 prior to receiving the new location. The crowd analyzer 58
then creates a new bounding box of a predetermined size centered at
the new location of the user 20-1 (step 1304) and an old bounding
box of a predetermined size centered at the old location of the
user 20-1, if any (step 1306). The predetermined size of the new
and old bounding boxes may be any desired size. As one example, the
predetermined size of the new and old bounding boxes is 40 meters
by 40 meters. Note that if the user 20-1 does not have an old
location (i.e., the location received in step 1300 is the first
location received for the user 20-1), then the old bounding box is
essentially null. Also note that while bounding "boxes" are used in
this example, the bounding areas may be of any desired shape.
[0063] Next, the crowd analyzer 58 determines whether the new and
old bounding boxes overlap (step 1308). If so, the crowd analyzer
58 creates a bounding box encompassing the new and old bounding
boxes (step 1310). For example, if the new and old bounding boxes
are 40.times.40 meter regions and a 1.times.1 meter square at the
northeast corner of the new bounding box overlaps a 1.times.1 meter
square at the southwest corner of the old bounding box, the crowd
analyzer 58 may create a 79.times.79 meter square bounding box
encompassing both the new and old bounding boxes.
[0064] The crowd analyzer 58 then determines the individual users
and crowds relevant to the bounding box created in step 1310 (step
1312). The crowds relevant to the bounding box are crowds that are
within or overlap the bounding box (e.g., have at least one user
located within the bounding box). The individual users relevant to
the bounding box are users that are currently located within the
bounding box and not already part of a crowd. Next, the crowd
analyzer 58 computes an optimal inclusion distance for individual
users based on user density within the bounding box (step 1314).
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
, Eqn . ( 1 ) ##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.
[0065] The crowd analyzer 58 then creates a crowd for each
individual user within the bounding box that is not already
included in a crowd and sets the optimal inclusion distance for the
crowds to the initial optimal inclusion distance (step 1316). At
this point, the process proceeds to FIG. 8B where the crowd
analyzer 58 analyzes the crowds relevant to the bounding box to
determine whether any of the crowd members (i.e., users in the
crowds) violate the optimal inclusion distance of their crowds
(step 1318). Any crowd member that violates the optimal inclusion
distance of his or her crowd is then removed from that crowd (step
1320). The crowd analyzer 58 then creates a crowd of one user for
each of the users removed from their crowds in step 1320 and sets
the optimal inclusion distance for the newly created crowds to the
initial optimal inclusion distance (step 1322).
[0066] Next, the crowd analyzer 58 determines the two closest
crowds for the bounding box (step 1324) and a distance between the
two closest crowds (step 1326). 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
1328). 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.
[0067] If the distance between the two closest crowds is not less
than the optimal inclusion distance, then the process proceeds to
step 1338. Otherwise, the two closest crowds are combined or merged
(step 1330), and a new crowd center for the resulting crowd is
computed (step 1332). Again, a center of mass algorithm may be used
to compute the crowd center of a crowd. In addition, a new optimal
inclusion distance for the resulting crowd is computed (step 1334).
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
) , Eqn . ( 2 ) optimal_inclusion _dist = average + ( 1 n i = 1 n (
d i - average ) 2 ) Eqn . ( 3 ) ##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.
[0068] At this point, the crowd analyzer 58 determines whether a
maximum number of iterations have been performed (step 1336). The
maximum number of iterations is a predefined number that ensures
that the crowd formation process does not indefinitely loop over
steps 1318 through 1334 or loop over steps 1318 through 1334 more
than a desired maximum number of times. If the maximum number of
iterations has not been reached, the process returns to step 1318
and is repeated until either the distance between the two closest
crowds is not less than the optimal inclusion distance of the
larger crowd or the maximum number of iterations has been reached.
At that point, the crowd analyzer 58 discards crowds with less than
three users, or members (step 1338) and the process ends.
[0069] Returning to step 1308 in FIG. 8A, if the new and old
bounding boxes do not overlap, the process proceeds to FIG. 8C and
the bounding box to be processed is set to the old bounding box
(step 1340). In general, the crowd analyzer 58 then processes the
old bounding box in much the same manner as described above with
respect to steps 1312 through 1338. More specifically, the crowd
analyzer 58 determines the individual users and crowds relevant to
the bounding box (step 1342). The crowds relevant to the bounding
box are crowds that are within or overlap the bounding box (e.g.,
have at least one user located within the bounding box). The
individual users relevant to the bounding box are users that are
currently located within the bounding box and not already part of a
crowd. Next, the crowd analyzer 58 computes an optimal inclusion
distance for individual users based on user density within the
bounding box (step 1344). 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
, Eqn . ( 4 ) ##EQU00003##
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.
[0070] 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 1346). At
this point, the crowd analyzer 58 analyzes the crowds for the
bounding box to determine whether any crowd members (i.e., users in
the crowds) violate the optimal inclusion distance of their crowds
(step 1348). Any crowd member that violates the optimal inclusion
distance of his or her crowd is then removed from that crowd (step
1350). The crowd analyzer 58 then creates a crowd of one user for
each of the users removed from their crowds in step 1350 and sets
the optimal inclusion distance for the newly created crowds to the
initial optimal inclusion distance (step 1352).
[0071] Next, the crowd analyzer 58 determines the two closest
crowds in the bounding box (step 1354) and a distance between the
two closest crowds (step 1356). 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
1358). 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.
[0072] If the distance between the two closest crowds is not less
than the optimal inclusion distance, the process proceeds to step
1368. Otherwise, the two closest crowds are combined or merged
(step 1360), and a new crowd center for the resulting crowd is
computed (step 1362). Again, a center of mass algorithm may be used
to compute the crowd center of a crowd. In addition, a new optimal
inclusion distance for the resulting crowd is computed (step 1364).
As discussed above, in one embodiment, the new optimal inclusion
distance for the resulting crowd is computed as:
average = 1 n + 1 ( initial_optimal _inclusion _dist + i = 1 n d i
) , Eqn . ( 5 ) optimal_inclusion _dist = average + ( 1 n i = 1 n (
d i - average ) 2 ) Eqn . ( 6 ) ##EQU00004##
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.
[0073] At this point, the crowd analyzer 58 determines whether a
maximum number of iterations have been performed (step 1366). If
the maximum number of iterations has not been reached, the process
returns to step 1348 and is repeated until either the distance
between the two closest crowds is not less than the optimal
inclusion distance of the larger crowd or the maximum number of
iterations has been reached. At that point, the crowd analyzer 58
discards crowds with less than three users, or members (step 1368).
The crowd analyzer 58 then determines whether the crowd formation
process for the new and old bounding boxes is done (step 1370). 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 1372), and the process
returns to step 1342 and is repeated for the new bounding box. Once
both the new and old bounding boxes have been processed, the crowd
formation process ends.
[0074] FIGS. 9A through 9D graphically illustrate the crowd
formation process of FIGS. 8A through 8D for a scenario where the
crowd formation process is triggered by a location update for a
user having no old location. In this scenario, the crowd analyzer
58 creates a new bounding box 84 for the new location of the user,
and the new bounding box 84 is set as the bounding box to be
processed for crowd formation. Then, as illustrated in FIG. 9A, the
crowd analyzer 58 identifies all individual users currently located
within the new bounding box 84 and all crowds located within or
overlapping the new bounding box 84. In this example, crowd 86 is
an existing crowd relevant to the new bounding box 84. Crowds are
indicated by dashed circles, crowd centers are indicated by
cross-hairs (+), and users are indicated as dots. Next, as
illustrated in FIG. 9B, the crowd analyzer 58 creates crowds 88
through 92 of one user for the individual users, and the optional
inclusion distances of the crowds 88 through 92 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 new bounding box 84.
[0075] The crowd analyzer 58 then identifies the two closest crowds
88 and 90 in the new bounding box 84 and determines a distance
between the two closest crowds 88 and 90. In this example, the
distance between the two closest crowds 88 and 90 is less than the
optimal inclusion distance. As such, the two closest crowds 88 and
90 are merged and a new crowd center and new optimal inclusion
distance are computed, as illustrated in FIG. 9C. The crowd
analyzer 58 then repeats the process such that the two closest
crowds 88 and 92 in the new bounding box 84 are again merged, as
illustrated in FIG. 9D. At this point, the distance between the two
closest crowds 86 and 88 is greater than the appropriate optimal
inclusion distance. As such, the crowd formation process is
complete.
[0076] FIGS. 10A through 10F graphically illustrate the crowd
formation process of FIGS. 8A through 8D for a scenario where the
new and old bounding boxes overlap. As illustrated in FIG. 10A, a
user moves from an old location to a new location, as indicated by
an arrow. The crowd analyzer 58 receives a location update for the
user giving the new location of the user. In response, the crowd
analyzer 58 creates an old bounding box 94 for the old location of
the user and a new bounding box 96 for the new location of the
user. Crowd 98 exists in the old bounding box 94, and crowd 100
exists in the new bounding box 96.
[0077] Since the old bounding box 94 and the new bounding box 96
overlap, the crowd analyzer 58 creates a bounding box 102 that
encompasses both the old bounding box 94 and the new bounding box
96, as illustrated in FIG. 10B. In addition, the crowd analyzer 58
creates crowds 104 through 110 for individual users currently
located within the bounding box 102. The optimal inclusion
distances of the crowds 104 through 110 are set to the initial
optimal inclusion distance computed by the crowd analyzer 58 based
on the density of users in the bounding box 102.
[0078] Next, the crowd analyzer 58 analyzes the crowds 98, 100, and
104 through 110 to determine whether any members of the crowds 98,
100, and 104 through 110 violate the optimal inclusion distances of
the crowds 98, 100, and 104 through 110. In this example, as a
result of the user leaving the crowd 98 and moving to his new
location, both of the remaining members of the crowd 98 violate the
optimal inclusion distance of the crowd 98. As such, the crowd
analyzer 58 removes the remaining users from the crowd 98 and
creates crowds 112 and 114 of one user each for those users, as
illustrated in FIG. 10C.
[0079] The crowd analyzer 58 then identifies the two closest crowds
in the bounding box 102, which in this example are the crowds 108
and 110. Next, the crowd analyzer 58 computes a distance between
the two crowds 108 and 110. In this example, the distance between
the two crowds 108 and 110 is less than the initial optimal
inclusion distance and, as such, the two crowds 108 and 110 are
combined. In this example, crowds are combined by merging the
smaller crowd into the larger crowd. Since the two crowds 108 and
110 are of the same size, the crowd analyzer 58 merges the crowd
110 into the crowd 108, as illustrated in FIG. 10D. A new crowd
center and new optimal inclusion distance are then computed for the
crowd 108.
[0080] At this point, the crowd analyzer 58 repeats the process and
determines that the crowds 100 and 106 are now the two closest
crowds. In this example, the distance between the two crowds 100
and 106 is less than the optimal inclusion distance of the larger
of the two crowds 100 and 106, which is the crowd 100. As such, the
crowd 106 is merged into the crowd 100 and a new crowd center and
optimal inclusion distance are computed for the crowd 100, as
illustrated in FIG. 10E. 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. 10F. In this
example, the crowds 104, 108, 112, and 114 have less than three
members and are therefore removed. The crowd 100 has three or more
members and, as such, is not removed. At this point, the crowd
formation process is complete.
[0081] FIGS. 11A through 11E graphically illustrate the crowd
formation process of FIGS. 8A through 8D in a scenario where the
new and old bounding boxes do not overlap. As illustrated in FIG.
11A, in this example, the user moves from an old location to a new
location. The crowd analyzer 58 creates an old bounding box 116 for
the old location of the user and a new bounding box 118 for the new
location of the user. Crowds 120 and 122 exist in the old bounding
box 116, and crowd 124 exists in the new bounding box 118. In this
example, since the old and new bounding boxes 116 and 118 do not
overlap, the crowd analyzer 58 processes the old and new bounding
boxes 116 and 118 separately.
[0082] More specifically, as illustrated in FIG. 11B, as a result
of the movement of the user from the old location to the new
location, the remaining users in the crowd 120 no longer satisfy
the optimal inclusion distance for the crowd 120. As such, the
remaining users in the crowd 120 are removed from the crowd 120,
and crowds 126 and 128 of one user each are created for the removed
users as shown in FIG. 11C. In this example, no two crowds in the
old bounding box 116 are close enough to be combined. As such,
crowds having less than three users are removed, and processing of
the old bounding box 116 is complete, and the crowd analyzer 58
proceeds to process the new bounding box 118.
[0083] As illustrated in FIG. 11D, processing of the new bounding
box 118 begins by the crowd analyzer 58 creating a crowd 130 of one
user for the user. The crowd analyzer 58 then identifies the crowds
124 and 130 as the two closest crowds in the new bounding box 118
and determines a distance between the two crowds 124 and 130. In
this example, the distance between the two crowds 124 and 130 is
less than the optimal inclusion distance of the larger crowd, which
is the crowd 124. As such, the crowd analyzer 58 combines the
crowds 124 and 130 by merging the crowd 130 into the crowd 124, as
illustrated in FIG. 11E. A new crowd center and new optimal
inclusion distance are then computed for the crowd 124. At this
point, the crowd formation process is complete. Note that the crowd
formation processes described above with respect to FIGS. 6 through
11D are exemplary. The present disclosure is not limited thereto.
Any type of crowd formation process may be used.
[0084] FIG. 12 illustrates the operation of the system 10 to enable
generation and delivery of a message to a select subset of users in
a select crowd according to one embodiment of the present
disclosure. First, the mobile device 18-1 sends a crowd request to
the MAP server 12 (step 1400). Note that while in this example the
crowd request and, as discussed below, message originate from the
mobile device 18-1 of the user 20-1, this discussion is equally
applicable to crowd requests and messages originating from the
mobile devices 18 of any of the users 20. The crowd request is a
request for crowd data for crowds currently formed near a specified
POI or within a specified AOI. The crowd request may be initiated
by the user 20-1 of the mobile device 18-1 via the MAP application
32-1 or may be initiated automatically by the MAP application 32-1
in response to an event such as, for example, start-up of the MAP
application 32-1, movement of the user 20-1, or the like.
[0085] In one embodiment, the crowd request is for a POI, where the
POI is a POI corresponding to the current location of the user
20-1, a POI selected from a list of POIs defined by the user 20-1,
a POI selected from a list of POIs defined by the MAP application
32-1 or the MAP server 12, a POI selected by the user 20-1 from a
map, a POI implicitly defined via a separate application (e.g., the
POI is implicitly defined as the location of the nearest Starbucks
coffee house in response to the user 20-1 performing a Google
search for "Starbucks"), or the like. If the POI is selected from a
list of POIs, the list of POIs may include static POIs which may be
defined by street addresses or latitude and longitude coordinates,
dynamic POIs which may be defined as the current locations of one
or more friends of the user 20-1, or both. Note that in some
embodiments, the user 20-1 may be enabled to define a POI by
selecting a crowd center of a crowd as a POI, where the POI would
thereafter remain static at that point and would not follow the
crowd.
[0086] In another embodiment, the crowd request is for an AOI,
where the AOI may be an AOI of a predefined shape and size centered
at the current location of the user 20-1, an AOI selected from a
list of AOIs defined by the user 20-1, an AOI selected from a list
of AOIs defined by the MAP application 32-1 or the MAP server 12,
an AOI selected by the user 20-1 from a map, an AOI implicitly
defined via a separate application (e.g., the AOI is implicitly
defined as an area of a predefined shape and size centered at the
location of the nearest Starbucks coffee house in response to the
user 20-1 performing a Google search for "Starbucks"), or the like.
If the AOI is selected from a list of AOIs, the list of AOIs may
include static AOIs, dynamic AOIs which may be defined as areas of
a predefined shape and size centered at the current locations of
one or more friends of the user 20-1, or both. Note that in some
embodiments, the user 20-1 may be enabled to define an AOI by
selecting a crowd such that an AOI is created of a predefined shape
and size centered at the crowd center of the selected crowd. The
AOI would thereafter remain static and would not follow the crowd.
The POI or the AOI of the crowd request may be selected by the user
20-1 via the MAP application 32-1. In yet another embodiment, the
MAP application 32-1 automatically uses the current location of the
user 20-1 as the POI or as a center point for an AOI of a
predefined shape and size.
[0087] Upon receiving the crowd request, the MAP server 12
identifies one or more crowds relevant to the crowd request (step
1402). More specifically, in one embodiment, the crowd analyzer 58
performs a crowd formation process such as that described above in
FIG. 6 to form one or more crowds relevant to the POI or the AOI of
the crowd request. In another embodiment, the crowd analyzer 58
proactively forms crowds using a process such as that described
above in FIGS. 8A through 8D and stores corresponding crowd records
in the datastore 64 of the MAP server 12. Then, rather than forming
the relevant crowds in response to the crowd request, the crowd
analyzer 58 queries the datastore 64 to identify the crowds that
are relevant to the crowd request. The crowds relevant to the crowd
request may be those crowds within or intersecting a bounding
region, such as a bounding box, for the crowd request (e.g., crowds
having crowd centers within the bounding region for the crowd
request, crowds having one or more users located within the
bounding region for the crowd request, or crowds having crowd
perimeters that are within or overlap the bounding region for the
crowd request). If the crowd request is for a POI, the bounding
region is a geographic region of a predefined shape and size
centered at the POI. If the crowd request is for an AOI, the
bounding region is the AOI.
[0088] Once the crowd analyzer 58 has identified the crowds
relevant to the crowd request, the MAP server 12 generates or
otherwise obtains crowd data for the identified crowds (step 1404).
The crowd data for the identified crowds preferably includes
spatial information defining the locations of the crowds (e.g., the
crowd centers of the crowds or North-West and South-East corners of
a bounding box passing through the outermost users in the crowd),
aggregate profiles for the crowds, information characterizing the
crowds, or both. In addition, the crowd data may include the number
of users in the crowds, the amount of time the crowds have been
located at or near the POI or within the AOI of the crowd request,
or the like. The MAP server 12 then returns the crowd data to the
mobile device 18-1 (step 1406).
[0089] Upon receiving the crowd data, the MAP application 32-1 of
the mobile device 18-1 presents the crowd data to the user 20-1
(step 1408). The manner in which the crowd data is presented
depends on the particular implementation of the MAP application
32-1. In one embodiment, the crowd data is overlaid upon a map. For
example, the crowds may be represented by corresponding indicators
overlaid on a map. The user 20-1 may then select a crowd in order
to view additional crowd data regarding that crowd such as, for
example, the aggregate profile of that crowd, characteristics of
that crowd, or the like.
[0090] Next, the MAP application 32-1 of the mobile device 18-1
receives user input from the user 20-1 that selects a crowd (also
referred to herein as a desired crowd) to which to send a message
(step 1410). In response, the MAP application 32-1 of the mobile
device 18-1 generates a message to be sent to a subset of users in
the crowd (step 1412). More specifically, in the preferred
embodiment, the message includes information that identifies the
desired crowd selected in step 1410, a message body, and one or
more message delivery criteria. Thus, the message is essentially
addressed using the information that identifies the desired crowd
and the one or more message delivery criteria. The information that
identifies the desired crowd may be, for example, a unique
identifier assigned to the crowd by the MAP server 12. The message
body is preferably a text message input by the user 20-1 of the
mobile device 18-1 but is not limited thereto. For example, the
message body may alternatively be an audio message (e.g., a voice
message) or an audio-visual message (e.g., a video message).
[0091] The message delivery criteria preferably include one or more
user scoring criteria and one or more selection criteria. The one
or more user scoring criteria preferably include one or more of the
following: [0092] one or more select profile categories from the
user profile of the user 20-1 to be matched against user profiles
of the users in the desired crowd (or corresponding profile
categories in the user profiles of the users in the desired crowd)
in order to select the subset of the users in the crowd to which to
deliver the message, [0093] weights assigned to the one or more
select profile categories, [0094] an implicit referral rating
criterion such that the subset of users in the desired crowd to
which to deliver the message are selected based on implicit
referral ratings of the users in the desired crowd, [0095] a weight
assigned to the implicit referral rating criterion, [0096] an
explicit referral rating criterion such that the subset of users in
the desired crowd to which to deliver the message are selected
based on explicit referral ratings of the users in the desired
crowd, [0097] a weight assigned to the explicit referral rating
criterion, [0098] a responsiveness rating criterion such that the
subset of users in the desired crowd to which to deliver the
message are selected based on responsiveness ratings of the users
in the desired crowd, and [0099] a weight assigned to the
responsiveness rating criterion.
[0100] The one or more selection criteria preferably include one or
more criteria for selecting the subset of users to which to deliver
the message based on scores assigned to each of at least some, and
possibly all, of the users in the desired crowd based on the one or
more user scoring criteria. For example, the one or more selection
criteria may include a criterion that a defined number (e.g., 5) of
the highest scored users in the crowd are to be selected as the
subset of users in the crowd to which the message is to be
delivered. As another example, the one or more selection criteria
may include a criterion that the users in the crowd that are scored
above a defined minimum threshold score (e.g., 50) are to be
selected as the subset of users in the crowd to which the message
is to be delivered. The message delivery criteria are preferably
selected by the user 20-1. Alternatively, the user 20-1 may choose
to use predefined default message delivery criteria.
[0101] Once the message is generated, the MAP application 32-1 of
the mobile device 18-1 sends the message to the MAP server 12 (step
1414). In response to receiving the message, the message delivery
function 62 of the MAP server 12 selects the subset of users in the
desired crowd to which to send the message (step 1416). As
discussed below in detail, in the preferred embodiment, the message
delivery function 62 scores at least some of the users in the
desired crowd, and possibly all of the users in the desired crowd,
based on the one or more user selection criteria included in the
message delivery criteria. Then, the message delivery function 62
selects the subset of the users in the desired crowd to which the
message is to be delivered based on the scores assigned to the
users in the desired crowd and the one or more selection criteria
included in the message delivery criteria. It should be noted that
while in the embodiments described herein all of the message
delivery criteria are included in the message, the present
disclosure is not limited thereto. For instance, some or all of the
message delivery criteria may be predefined criteria selected by
the user 20-1 and stored at the MAP server 12. As another example,
some of the message delivery criteria described herein may be
system-defined criteria rather than user-defined criteria. For
instance, the selection criteria may be system-defined criteria
that is stored at the MAP server 12 or embedded into the operation
of the message delivery function 62.
[0102] In this example, the users 20-2 and 20-3 are selected as the
subset of the users in the desired crowd to which the message is to
be delivered. As such, the message delivery function 62 of the MAP
server 12 sends the message to the mobile devices 18-2 and 18-3 of
the users 20-2 and 20-3 (steps 1418 and 1420). Note that the
message sent in steps 1418 and 1420 may be a version of the message
received by the MAP server 12 in step 1414 that includes the
message body but that does not include the message delivery
criteria. Further, in the preferred embodiment, the message sent in
steps 1418 and 1420 is sent anonymously such that the message does
not identify the user 20-1 (i.e., the sender of the message is
anonymous). Also, note that an advertisement may be inserted into
the message sent to the users 20-2 and 20-3 using any desired
targeted advertisement scheme.
[0103] The mobile devices 18-2 and 18-3, and preferably the MAP
applications 32-2 and 32-3 of the mobile devices 18-2 and 18-3,
present the message to the users 20-2 and 20-3 (steps 1422 and
1424). In this example, the MAP application 32-2 of the mobile
device 18-2 generates a response to the message based on user input
from the user 20-2 (step 1426). The response preferably includes a
text, audio, or audio-visual message input by the user 20-2 in
response to the message received in step 1422. The MAP application
32-2 then sends the response to the MAP server 12 (step 1428), and
the message delivery function 62 of the MAP server 12 then sends
the response to the mobile device 18-1 of the user 20-1 (step
1430). Preferably, the response sent to the mobile device 18-1 is
sent anonymously such that the response does not identify the user
20-2 (i.e., the responder is anonymous). Also, note that an
advertisement may be inserted into the response sent to the user
20-1 using any desired targeted advertisement scheme.
[0104] Next, the MAP application 32-1 of the mobile device 18-1
presents the response to the user 20-1 (step 1432). Optionally, in
this embodiment, the MAP application 32-1 of the mobile device 18-1
receives a rating assigned to the response by the user 20-1 (step
1434). The rating may be, for example, a numerical rating (e.g., 1
to 5), a like/dislike rating, or the like. The MAP application 32-1
then sends the rating to the MAP server 12 (step 1436). The message
delivery function 62 of the MAP server 12 then updates an explicit
referral rating of the user 20-2 that sent the response based on
the rating assigned to the response by the user 20-1 and received
by the MAP server 12 in step 1436 (step 1438). For example, the
explicit referral rating of the user 20-2 may be an average rating
assigned to responses sent by the user 20-2 in response to messages
sent to the user 20-2 by the message delivery function 62 of the
MAP server 12 scaled to a value between 0 and 100. In a similar
manner, explicit referral ratings may also be maintained for the
other users 20 in the system 10.
[0105] In this embodiment, the message delivery function 62 of the
MAP server 12 also maintains implicit referral ratings for the
users 20 in the system 10 based on the ability of the users 20 to
submit responses that draw other users 20 to their crowds or,
alternatively, POI. More specifically, in this example, the message
delivery function 62 monitors the location of the user 20-1 (i.e.,
the sender) to detect whether the user 20-1 joins the crowd of the
user 20-2 (i.e., the responder) within a predefined amount of time
after the response is sent in step 1430 (step 1440). Alternatively,
the message delivery function 62 may detect whether the user 20-1
comes to the POI at which the desired crowd was located at the time
of sending the message within the predefined amount of time. If the
user 20-1 joins the crowd of the user 20-2 within the predefined
amount of time (e.g., 1 hour), the message delivery function 62
determines that the response of the user 20-2 enticed the user 20-1
to come and join the desired crowd of the user 20-2.
[0106] After the user 20-1 has joined the crowd of the user 20-2 or
after the predefined amount of time has expired without the user
20-1 joining the crowd of the user 20-2, the message delivery
function 62 updates the implicit referral rating of the user 20-2
(step 1442). In one embodiment, the message delivery function 62
includes a counter for the user 20-2 that is incremented if the
user 20-1 joins the crowd of the user 20-2 within the predefined
amount of time. This counter defines the number of times that the
user 20-2 has provided responses that resulted in the other user
coming to join the crowd of the user 20-2. The implicit referral
rating of the user 20-2 may then be computed as:
referral_rating implicit = counter Number_of _Responses .times. 100
, Eqn , ( 7 ) ##EQU00005##
where referral_rating.sub.implicit is the implicit referral rating
of the user 20-2, counter is the counter maintained for the number
of times the user 20-2 has provided responses that resulted in the
other user coming to join the crowd of the user 20-2, and
Number_of_Responses is the total number of responses sent by the
user 20-2 to messages sent to the user 20-2 by the message delivery
function 62 of the MAP server 12. In a similar manner, implicit
referral ratings are preferably maintained for the other users
20.
[0107] In addition to the explicit and implicit referral ratings,
the message delivery function 62 may also maintain responsiveness
ratings for the users 20. In this example, the message delivery
function 62 updates the responsiveness ratings of the users 20-2
and 20-3 to which the message was sent in steps 1418 and 1420 (step
1444). As an example, the responsiveness rating of each of the
users 20 may be computed based on the following equation:
responsiveness_rating = Number_of _Responses Number_of _Messages
_Recieved .times. 100 , Eqn . ( 8 ) ##EQU00006##
where responsiveness_rating is the responsiveness rating of the
user 20, Number_of_Responses is the number of responses sent by the
user 20 in response to messages sent to the user 20 by the message
delivery function 62 of the MAP server 12, and Number_of_Messages
Received is the number of messages sent to the user 20 by the
message delivery function 62.
[0108] Before proceeding, again, note that advertisements may be
inserted into the message sent to the users 20-2 and 20-3 in the
crowd and/or the response sent to the user 20-1 using any desired
targeted advertisement scheme. For example, the response to the
user 20-1 may include an advertisement for a POI at which the crowd
is located. In such a scenario, an ad value may be determined for
the advertisement based on the implicit and/or explicit referral
rating of the responder and/or a rating for the user 20-1 that is
indicative of the likelihood that the user 20-1 will go to and join
the crowd of the responder after receiving the response. This later
rating may be maintained by the message delivery function 62 by
tracking whether the user 20-1 goes to and joins crowds after
sending messages to subsets of users in those crowds and receiving
responses from users in those crowds.
[0109] FIG. 13 illustrates step 1416 of FIG. 12 in more detail
according to one embodiment of the present disclosure. In order to
select the subset of the users in the desired crowd to which to
send the message, the message delivery function 62 first identifies
users in the desired crowd that have enabled a messaging feature
that allows messages to be sent to them via the message delivery
function 62 of the MAP server 12 (step 1500). Note that step 1500
is optional. Alternatively, messages may be sent to all of the
users 20 as part of the normal operation of the system 10 where the
users 20 cannot disable the messaging feature provided by the
message delivery function 62.
[0110] Next, a counter i is set to 1 (step 1502). The message
delivery function 62 then scores user i of the users identified in
step 1500 (or alternatively user i of the users in the desired
crowd) based on one or more user scoring criteria included in the
message delivery criteria (step 1504). More specifically, in this
embodiment, the user scoring criteria include one or more select
profile categories from the user profile of the sending user, which
in this example is the user 20-1; weights assigned to the one or
more select profile categories; an implicit referral rating
criterion; a weight assigned to the implicit referral rating
criterion; an explicit referral rating criterion; a weight assigned
to the explicit referral rating criterion; a responsiveness rating
criterion; and a weight assigned to the responsiveness rating
criterion. As such, user i is scored by first comparing the one or
more select profile categories from the user profile of the user
20-1 to the user profile of user i to determine, for each select
profile category, a match value indicative of a degree of
similarity between the keywords, or interests, in the select
profile category of the user 20-1 and keywords, or interests, in
the user profile of user i. The profile matching process may
consider all keywords in the user profile of user i or only those
keywords in the profile categories of the user profile of user i
that correspond to the one or more select profile categories of the
user 20-1. In the preferred embodiment, for each of the select
profile categories, the match value is the ratio of the number of
keywords in the select profile category that have matching keywords
in the user profile of user i over the total number of keywords in
the select profile category scaled to a value between 0 and 100.
Note that scaling to a value between 0 and 100 is exemplary and not
intended to limit the scope of the present disclosure. As used
herein, two keywords match if the two keywords match to at least a
predefined threshold degree. The predefined threshold degree may be
an exact match or something less than an exact match (e.g., NC
State University determined to match NCSU). For example, for each
profile category, the match value may be computed as:
match_value = Number_of _Matches Total_Number _of _Keywords .times.
100 , Eqn . ( 9 ) ##EQU00007##
where match_value is the match value for the profile category,
Number_of_Matches is the number of keywords in the profile category
for which a matching keyword was found in the user profile of user
i, and Total_Number_of_Keywords is the total number of keywords in
the select profile category of the user profile of the user
20-1.
[0111] Once profile matching is complete, user i is scored based on
the following equation:
score i = j = 1 M ( w j .times. paramter j ) j = 1 M w j , Eqn . (
10 ) ##EQU00008##
where score, is the score for user i, parameter, is a j-th
parameter used for the scoring process, w.sub.j is a weight
assigned to the j-th parameter, and M is the total number of
parameters used for the scoring process. In this example, the
parameters are the match values computed for the select profile
categories of the user profile of the user 20-1, the implicit
referral rating of user i, the explicit referral rating of user i,
and the responsiveness rating of user i.
[0112] Next, the message delivery function 62 determines whether
user i is the last user in the users identified in step 1500 (or
alternatively all users in the desired crowd) (step 1506). If not,
the counter i is incremented (step 1508) and the process is
repeated for the next user. Once all of the users have been scored,
the message delivery function 62 selects the subset of users to
which to send the message based on the one or more selection
criteria included in the message delivery criteria and the scores
assigned to the users in step 1504 (step 1510). As one example, the
one or more selection criteria define a maximum number of users to
which to send the message, where the maximum number of users to
which to send the message is preferably less than the total number
of users in the desired crowd. The message delivery function 62
then selects the maximum number of users in the desired crowd
having the highest scores. For example, if the maximum number of
users is 5, then the message delivery function 62 selects the 5
highest scored users as the subset of the users in the desired
crowd to which to send the message. As another example, the one or
more selection criteria may define a minimum threshold score, and
the message delivery function 62 selects users having scores that
are greater than the minimum threshold score as the subset of users
in the desired crowd to which the message is to be sent. The
minimum threshold score defined by the one or more selection
criteria is preferably greater than a lowest possible score, which
in the exemplary embodiment described above is a score of 0.
[0113] FIGS. 14A through 14F graphically illustrate the process of
FIG. 12 for an exemplary message and response according to one
embodiment of the present disclosure. As illustrated in FIG. 14A,
after sending the crowd request and receiving corresponding crowd
data from the MAP server 12, the MAP application 32-1 of the mobile
device 18-1 of the user 20-1 presents the crowd data to the user
20-1 in the form of a number of crowd indicators 132 through 140
overlaid on a map. The crowd indicators 132 through 140 show the
locations of corresponding crowds.
[0114] As also illustrated in FIG. 14A, the user 20-1 is enabled to
select a desired crowd by selecting the crowd indicator 138 of the
desired crowd. In response, a menu 142 is presented to the user
20-1. The menu 142 includes a crowd data display area 144 in which
information regarding the crowd is presented to the user 20-1 and a
menu selection area 146. In this example, the information regarding
the crowd includes information describing a POI at which the crowd
is currently located and aggregate profile data for the crowd. In
this example, the information describing the POI is the name of the
POI, the address of the POI, and the telephone number for the POI.
Further, in this example, the aggregate profile data for the crowd
includes the size of the crowd (i.e., the total number of users in
the crowd), a degree of similarity between the crowd and the user
profile of the user 20-1 (or one or more select profile categories
of the user profile of the user 20-1), and a number of users in the
crowd that are addressable (i.e., have enabled the messaging
feature provided by the message delivery function 62 of the MAP
server 12). Note that the degree of similarity may be calculated as
the ratio of the total number of user matches (i.e., the total
number of users in the crowd that have at least one keyword that
matches a keyword in the user profile of the user 20-1 or one or
more select profile categories of the user profile of the user
20-1) over the total number of users in the crowd multiplied by
100.
[0115] The menu selection area 146 provides a list of actions that
may be selected by the user 20-1. Specifically, in this example,
the menu selection area 146 lists a "Take Me There" action, a "Send
a MSG to All Members" action, a "Send a MSG to Some Members"
action, and a "More . . . " action. The "Take Me There" action, if
selected by the user 20-1, causes the MAP application 32-1 to route
the user 20-1 to the POI at which the crowd is located. The "Send a
MSG to All Members" action, if selected, enables the user 20-1 to
send a message (preferably anonymously) to all of the users in the
crowd. A message sent to all of the users in the crowd would be
delivered by the message delivery function 62 in much the same
manner as described above. However, the message delivery function
62 would not select a subset of users in the crowd to which to
deliver the message. Rather, the message delivery function 62 would
send the message to all of the users in the crowd, or at least all
users in the crowd that have enabled the messaging feature of the
message delivery function 62. The "Send a MSG to Some Members"
action, if selected by the user 20-1, causes the MAP application
32-1 to generate a message to be delivered to a subset of the users
in the crowd in the manner described above. The "More . . . "
action, if selected by the user 20-1, causes the MAP application
32-1 to present more actions to the user 20-1.
[0116] In this example, the user 20-1 selects the "Send a MSG to
Some Members" action. In response, a message creation screen 148 is
presented to the user 20-1 as illustrated in FIG. 14B. The message
creation screen 148 includes a text entry area 150 that enables the
user 20-1 to enter text for the body of the message. The message
creation screen 148 also includes a "Send" button 152 and an
"Advanced" button 154. The user 20-1 causes the message to be sent
by selecting the "Send" button 152. If the user 20-1 does not first
select the "Advanced" button 154 to define message delivery
criteria for the message before selecting the "Send" button 152,
default message delivery criteria may be used.
[0117] In this example, the user 20-1 selects the "Advanced" button
154. In response, a message delivery criteria screen 156 is
presented to the user 20-1 as illustrated in FIG. 14C. The message
delivery criteria screen 156 includes a "Profile Matching" checkbox
158 and checkboxes 160 through 166 for profile categories in the
user profile of the user 20-1. The user 20-1 selects the "Profile
Matching" checkbox 158 to cause profile matching to be used when
selecting the subset of users in the desired crowd to which the
message is to be delivered. Once profile matching is selected, the
user 20-1 selects one or more of the checkboxes 160 through 166 to
select the corresponding profile categories for use in the profile
matching process when selecting the subset of users in the desired
crowd to which to deliver the message. In this example, the user
20-1 has selected the "Demographics" profile category and the
"Music" profile category. In addition, the message delivery
criteria screen 156 includes slider bars 168 through 174 for
assigning weights to the corresponding profile categories.
[0118] The message delivery criteria screen 156 also includes a
"Ratings" checkbox 176 that, when selected by the user 20-1, causes
ratings to be used when selecting the subset of users in the
desired crowd to which the message is to be delivered. Once the
"Ratings" checkbox 176 is selected, in this embodiment, the user
20-1 is enabled to select one or more rating types to use when
selecting the subset of the users in the crowd by selecting
corresponding checkboxes 178 and 180. In this example, the user
20-1 has enabled the use of both implicit and explicit referral
ratings by selecting the checkbox 178 and enabled the use of
responsiveness ratings by selecting the checkbox 180. In this
example, the message delivery criteria screen 156 also includes
slider bars 182 and 184 for assigning weights to the referral
ratings and responsiveness ratings, respectively.
[0119] Lastly, the message delivery criteria screen 156 includes a
pull-down menu 186 that enables the user 20-1 to select a selection
criterion to be used to select the subset of users in the desired
crowd to which to deliver the message based on the scores assigned
to the users in the crowd based on the user scoring criteria. In
this example, the user 20-1 has set the selection criterion such
that the message is to be delivered to the 5 highest matching
(i.e., the 5 highest scored) users in the desired crowd. Once the
user 20-1 has finished configuring the message delivery criteria,
the user 20-1 selects a "Done" button 188. The user 20-1 can reset
the message delivery criteria to their default settings by
selecting a "Default" button 190.
[0120] Once the message has been sent to the mobile device 18-2 of
the user 20-2, the mobile device 18-2 presents the message to the
user 20-2 via a message display screen 192, as illustrated in FIG.
14D. The user 20-2 may then read the message and either ignore the
message by selecting an "Ignore" button 194 or respond to the
message by selecting a "Respond" button 196. In this example, the
user 20-2 selects the "Respond" button 196. In response, a response
generation screen 198 is presented to the user 20-2, as illustrated
in FIG. 14E. The response generation screen 198 includes a text
entry area 200 where the user 20-2 is enabled to enter text for a
response to be sent to the user 20-1. Once the user 20-2 is ready
to send the response, the user 20-2 selects a "Send" button 202. In
response, the message is sent to the user 20-1, preferably
anonymously, via the message delivery function 62. The user 20-2
may cancel the response generation process by selecting a "Cancel"
button 203. Once the response is received by the mobile device 18-1
of the user 20-1, the mobile device 18-1 presents the response to
the user 20-1 via a response display screen 204, as illustrated in
FIG. 14F. The user 20-1 may then rate the response by selecting
either a "Dislike" button 206 or a "Like" button 208.
[0121] FIG. 15 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 210 connected to memory 212, one or
more secondary storage devices 214, and a communication interface
216 by a bus 218 or similar mechanism. The controller 210 is a
microprocessor, digital Application Specific Integrated Circuit
(ASIC), Field Programmable Gate Array (FPGA), or the like. In this
embodiment, the controller 210 is a microprocessor, and the
application layer 40, the business logic layer 42, and the object
mapping layer 63 (FIG. 2) are implemented in software and stored in
the memory 212 for execution by the controller 210. Further, the
datastore 64 (FIG. 2) may be implemented in the one or more
secondary storage devices 214. The secondary storage devices 214
are digital data storage devices such as, for example, one or more
hard disk drives. The communication interface 216 is a wired or
wireless communication interface that communicatively couples the
MAP server 12 to the network 28 (FIG. 1). For example, the
communication interface 216 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.
[0122] FIG. 16 is a block diagram of the mobile device 18-1
according to one embodiment of the present disclosure. This
discussion is equally applicable to the other mobile devices 18-2
through 18-N. As illustrated, the mobile device 18-1 includes a
controller 220 connected to memory 222, a communication interface
224, one or more user interface components 226, and the location
function 36-1 by a bus 228 or similar mechanism. The controller 220
is a microprocessor, digital ASIC, FPGA, or the like. In this
embodiment, the controller 220 is a microprocessor, and the MAP
client 30-1, the MAP application 32-1, and the third-party
applications 34-1 are implemented in software and stored in the
memory 222 for execution by the controller 220. In this embodiment,
the location function 36-1 is a hardware component such as, for
example, a GPS receiver. The communication interface 224 is a
wireless communication interface that communicatively couples the
mobile device 18-1 to the network 28 (FIG. 1). For example, the
communication interface 224 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 cellular interface
such as, for example, a Global System for Mobile communications
(GSM) interface or a W-CDMA interface, or a 4G cellular interface
such as a Long Term Evolution (LTE) or WiMAX.RTM. interface), or
the like. The one or more user interface components 226 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.
[0123] 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.
* * * * *