US20120063367A1 - Crowd and profile based communication addresses - Google Patents

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.

Description

RELATED APPLICATIONS
• 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
• The present disclosure relates to delivering a message to a select subset of users in a select crowd of users.
BACKGROUND
• 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
• 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.
• 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
• 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.
• FIG. 1 illustrates a Mobile Aggregate Profile (MAP) system according to one embodiment of the present disclosure;
• FIG. 2 is a block diagram of the MAP server ofFIG. 1 according to one embodiment of the present disclosure;
• FIG. 3 is a block diagram of the MAP client of one of the mobile devices ofFIG. 1 according to one embodiment of the present disclosure;
• FIG. 4 illustrates the operation of the system ofFIG. 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;
• FIG. 5 illustrates the operation of the system ofFIG. 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;
• FIG. 6 is a flow chart for a spatial crowd formation process according to one embodiment of the present disclosure;
• FIGS. 7A through 7D graphically illustrate the crowd formation process ofFIG. 6 for an exemplary bounding box;
• FIGS. 8A through 8D illustrate a flow chart for a spatial crowd formation process according to another embodiment of the present disclosure;
• FIGS. 9A through 9D graphically illustrate the crowd formation process ofFIGS. 8A through 8D for a scenario where the crowd formation process is triggered by a location update for a user having no old location;
• FIGS. 10A through 10F graphically illustrate the crowd formation process ofFIGS. 8A through 8D for a scenario where the new and old bounding boxes overlap;
• FIGS. 11A through 11E graphically illustrate the crowd formation process ofFIGS. 8A through 8D in a scenario where the new and old bounding boxes do not overlap;
• FIG. 12 illustrates the operation of the system ofFIG. 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;
• 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;
• FIGS. 14A through 14F graphically illustrate the process ofFIG. 12 according to one exemplary embodiment of the present disclosure;
• FIG. 15 is a block diagram of the MAP server ofFIG. 1 according to one embodiment of the present disclosure; and
• FIG. 16 is a block diagram of one of the mobile devices ofFIG. 1 according to one embodiment of the present disclosure.
DETAILED DESCRIPTION
• 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.
• FIG. 1 illustrates a Mobile Aggregate Profile (MAP) system10 (hereinafter “system10”) 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 thesystem10 is exemplary and is not intended to limit the scope of the present disclosure. In this embodiment, thesystem10 includes aMAP server12, one ormore profile servers14, alocation server16, a number of mobile devices18-1 through18-N (generally referred to herein collectively asmobile devices18 and individually as mobile device18) having associated users20-1 through20-N (generally referred to herein collectively asusers20 and individually as user20), asubscriber device22 having anassociated subscriber24, and a third-party service26 communicatively coupled via anetwork28. Thenetwork28 may be any type of network or any combination of networks. Specifically, thenetwork28 may include wired components, wireless components, or both wired and wireless components. In one exemplary embodiment, thenetwork28 is a distributed public network such as the Internet, where themobile devices18 are enabled to connect to thenetwork28 via local wireless connections (e.g., WiFi® or IEEE 802.11 connections) or wireless telecommunications connections (e.g., 3G or 4G telecommunications connections such as GSM, LTE, W-CDMA, or WiMAX® connections).
• As discussed below in detail, theMAP server12 operates to obtain current locations, including location updates, and user profiles of theusers20 of themobile devices18. The current locations of theusers20 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 theusers20, theMAP server12 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 theusers20, generating aggregate profiles for crowds of users, and tracking crowds. Note that while theMAP server12 is illustrated as a single server for simplicity and ease of discussion, it should be appreciated that theMAP server12 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.
• In general, the one ormore profile servers14 operate to store user profiles for a number of persons including theusers20 of themobile devices18. For example, the one ormore profile servers14 may be servers providing social network services such as the Facebook® social networking service, the MySpace® social networking service, the LinkedIN® social networking service, or the like. As discussed below, using the one ormore profile servers14, theMAP server12 is enabled to directly or indirectly obtain the user profiles of theusers20 of themobile devices18. Thelocation server16 generally operates to receive location updates from themobile devices18 and make the location updates available to entities such as, for instance, theMAP server12. In one exemplary embodiment, thelocation server16 is a server operating to provide Yahoo!'s FireEagle service.
• Themobile devices18 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 themobile devices18 are the Apple® iPhone®, the Palm Pre®, the Samsung Rogue™, the Blackberry Storm™, the Motorola Droid or similar phone running Google's Android™ Operating System, an Apple® iPad™, and the Apple® iPod Touch® device. However, this list of exemplary mobile devices is not exhaustive and is not intended to limit the scope of the present disclosure.
• The mobile devices18-1 through18-N include MAP clients30-1 through30-N (generally referred to herein collectively asMAP clients30 or individually as MAP client30), MAP applications32-1 through32-N (generally referred to herein collectively asMAP applications32 or individually as MAP application32), third-party applications34-1 through34-N (generally referred to herein collectively as third-party applications34 or individually as third-party application34), and location functions36-1 through36-N (generally referred to herein collectively as location functions36 or individually as location function36), respectively. TheMAP client30 is preferably implemented in software. In general, in the preferred embodiment, theMAP client30 is a middleware layer operating to interface an application layer (i.e., theMAP application32 and the third-party applications34) to theMAP server12. More specifically, theMAP client30 enables theMAP application32 and the third-party applications34 to request and receive data from theMAP server12. In addition, theMAP client30 enables applications, such as theMAP application32 and the third-party applications34, to access data from theMAP server12.
• TheMAP application32 is also preferably implemented in software. TheMAP application32 generally provides a user interface component between theuser20 and theMAP server12. More specifically, among other things, theMAP application32 enables theuser20 to initiate requests for crowd data from theMAP server12 and present corresponding crowd data returned by theMAP server12 to theuser20 as well as enable theuser20 to send and receive messages as described below in detail. TheMAP application32 also enables theuser20 to configure various settings. For example, theMAP application32 may enable theuser20 to select a desired social networking service (e.g., Facebook®, MySpace®, LinkedIN®, etc.) from which to obtain the user profile of theuser20 and provide any necessary credentials (e.g., username and password) needed to access the user profile from the social networking service.
• The third-party applications34 are preferably implemented in software. The third-party applications34 operate to access theMAP server12 via theMAP client30. The third-party applications34 may utilize data obtained from theMAP server12 in any desired manner. As an example, one of the third-party applications34 may be a gaming application that utilizes crowd data to notify theuser20 of Points of Interest (POIs) or Areas of Interest (AOIs) where crowds of interest are currently located. It should be noted that while theMAP client30 is illustrated as being separate from theMAP application32 and the third-party applications34, the present disclosure is not limited thereto. The functionality of theMAP client30 may alternatively be incorporated into theMAP application32 and the third-party applications34.
• Thelocation function36 may be implemented in hardware, software, or a combination thereof. In general, thelocation function36 operates to determine or otherwise obtain the location of themobile device18. For example, thelocation function36 may be or include a Global Positioning System (GPS) receiver. In addition or alternatively, thelocation function36 may include hardware and/or software that enables improved location tracking in indoor environments such as, for example, shopping malls. For example, thelocation function36 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.
• Thesubscriber device22 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. Thesubscriber24 associated with thesubscriber device22 is a person or entity. In general, thesubscriber device22 enables thesubscriber24 to access theMAP server12 via aweb browser38 to obtain various types of data, preferably for a fee. For example, thesubscriber24 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 theweb browser38 is exemplary. In another embodiment, thesubscriber device22 is enabled to access theMAP server12 via a custom application.
• Lastly, the third-party service26 is a service that has access to data from theMAP server12 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 theMAP server12, the third-party service26 operates to provide a service such as, for example, targeted advertising. For example, the third-party service26 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 service26, other types of third-party services26 may additionally or alternatively be provided. Other types of third-party services26 that may be provided will be apparent to one of ordinary skill in the art upon reading this disclosure.
• Before proceeding, it should be noted that while thesystem10 ofFIG. 1 illustrates an embodiment where the one ormore profile servers14 and thelocation server16 are separate from theMAP server12, the present disclosure is not limited thereto. In an alternative embodiment, the functionality of the one ormore profile servers14 and/or thelocation server16 may be implemented within theMAP server12.
• FIG. 2 is a block diagram of theMAP server12 ofFIG. 1 according to one embodiment of the present disclosure. As illustrated, theMAP server12 includes anapplication layer40, abusiness logic layer42, and apersistence layer44. Theapplication layer40 includes a user web application46, a mobile client/server protocol component48, and one or more data Application Programming Interfaces (APIs)50. The user web application46 is preferably implemented in software and operates to provide a web interface for users, such as thesubscriber24, to access theMAP server12 via a web browser. The mobile client/server protocol component48 is preferably implemented in software and operates to provide an interface between theMAP server12 and theMAP clients30 hosted by themobile devices18. Thedata APIs50 enable third-party services, such as the third-party service26, to access theMAP server12.
• Thebusiness logic layer42 includes aprofile manager52, alocation manager54, ahistory manager56, acrowd analyzer58, anaggregation engine60, and amessage delivery function62 each of which is preferably implemented in software. Theprofile manager52 generally operates to obtain the user profiles of theusers20 directly or indirectly from the one ormore profile servers14 and store the user profiles in thepersistence layer44. Thelocation manager54 operates to obtain the current locations of theusers20 including location updates. As discussed below, the current locations of theusers20 may be obtained directly from themobile devices18 and/or obtained from thelocation server16.
• Thehistory manager56 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. Thecrowd analyzer58 operates to form crowds of users. In one embodiment, thecrowd analyzer58 utilizes a spatial crowd formation algorithm. However, the present disclosure is not limited thereto. In addition, thecrowd analyzer58 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, thecrowd analyzer58 may also operate to track crowds. Theaggregation engine60 generally operates to provide aggregate profile data in response to requests from themobile devices18, thesubscriber device22, and the third-party service26. 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 theprofile manager52, thelocation manager54, thehistory manager56, thecrowd analyzer58, and theaggregation engine60, 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.
• As discussed below in detail, themessage delivery function62 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 theusers20 selects a desired crowd and defines a message to be delivered to a subset of theusers20 in the desired crowd. Themessage delivery function62 selects one or more of theusers20 in the desired crowd as the subset ofusers20 in the desired crowd to which the message is to be delivered. Preferably, the one ormore users20 are selected based on profile matching and, optionally, one or more ratings assigned to theusers20 in the crowd that reflect the desirability of theusers20 as recipients of the message. Themessage delivery function62 then sends the message to the one ormore users20 selected for message delivery. Preferably, the message is anonymous (i.e., does not identify the sender). Themessage delivery function62 may also enable the one ormore users20 that receive the message to respond to the message if desired. Any response is also preferably anonymous.
• Thepersistence layer44 includes anobject mapping layer63 and adatastore64. Theobject mapping layer63 is preferably implemented in software. Thedatastore64 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, thebusiness logic layer42 is implemented in an object-oriented programming language such as, for example, Java. As such, theobject mapping layer63 operates to map objects used in thebusiness logic layer42 to relational database entities stored in thedatastore64. Note that, in one embodiment, data is stored in thedatastore64 in a Resource Description Framework (RDF) compatible format.
• In an alternative embodiment, rather than being a relational database, thedatastore64 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, theMAP server12 may be designed to accept raw FOAF files describing persons, their friends, and their interests. These FOAF files are currently output by some social networking services such as LiveJournal® and Facebook®. TheMAP server12 may then persist RDF descriptions of theusers20 as a proprietary extension of the FOAF vocabulary that includes additional properties desired for thesystem10.
• FIG. 3 illustrates theMAP client30 ofFIG. 1 in more detail according to one embodiment of the present disclosure. As illustrated, in this embodiment, theMAP client30 includes aMAP access API66, aMAP middleware component68, and a mobile client/server protocol component70. TheMAP access API66 is implemented in software and provides an interface by which theMAP client30 and the third-party applications34 are enabled to access theMAP client30. TheMAP middleware component68 is implemented in software and performs the operations needed for theMAP client30 to operate as an interface between theMAP application32 and the third-party applications34 at themobile device18 and theMAP server12. The mobile client/server protocol component70 enables communication between theMAP client30 and theMAP server12 via a defined protocol.
• FIG. 4 illustrates the operation of thesystem10 ofFIG. 1 to provide the user profile of one of theusers20 of one of themobile devices18 to theMAP server12 according to one embodiment of the present disclosure. This discussion is equally applicable to theother users20 of the othermobile devices18. First, an authentication process is performed (step1000). For authentication, in this embodiment, themobile device18 authenticates with the profile server14 (step1000A) and the MAP server12 (step1000B). In addition, theMAP server12 authenticates with the profile server14 (step1000C). Preferably, authentication is performed using OpenID or similar technology. However, authentication may alternatively be performed using separate credentials (e.g., username and password) of theuser20 for access to theMAP server12 and theprofile server14. Assuming that authentication is successful, theprofile server14 returns an authentication succeeded message to the MAP server12 (step1000D), and theprofile server14 returns an authentication succeeded message to theMAP client30 of the mobile device18 (step1000E).
• At some point after authentication is complete, a user profile process is performed such that a user profile of theuser20 is obtained from theprofile server14 and delivered to the MAP server12 (step1002). In this embodiment, theMAP client30 of themobile device18 sends a profile request to the profile server14 (step1002A). In response, theprofile server14 returns the user profile of theuser20 to the mobile device18 (step1002B). TheMAP client30 of themobile device18 then sends the user profile of theuser20 to the MAP server12 (step1002C). Note that while in this embodiment theMAP client30 sends the complete user profile of theuser20 to theMAP server12, in an alternative embodiment, theMAP client30 may filter the user profile of theuser20 according to criteria specified by theuser20. For example, the user profile of theuser20 may include demographic information, general interests, music interests, and movie interests, and theuser20 may specify that the demographic information or some subset thereof is to be filtered, or removed, before sending the user profile to theMAP server12.
• Upon receiving the user profile of theuser20 from theMAP client30 of themobile device18, theprofile manager52 of theMAP server12 processes the user profile (step1002D). More specifically, in the preferred embodiment, theprofile manager52 includes social network handlers for the social network services supported by theMAP server12 that operate to map the user profiles of theusers20 obtained from the social network services to a common format utilized by theMAP server12. 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 theMAP server12 supports user profiles from Facebook®, MySpace®, and LinkedIN®, theprofile manager52 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 theusers20 in the common format used by theMAP server12. For this example assume that the user profile of theuser20 is from Facebook®. Theprofile manager52 uses a Facebook handler to process the user profile of theuser20 to map the user profile of theuser20 from Facebook® to a user profile for theuser20 for theMAP server12 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 theuser20 from Facebook® may be processed by the Facebook handler of theprofile manager52 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, theprofile manager52 may use natural language processing or semantic analysis. For example, if the Facebook® user profile of theuser20 states that theuser20 is 20 years old, semantic analysis may result in the keyword of 18-24 years old being stored in the user profile of theuser20 for theMAP server12.
• After processing the user profile of theuser20, theprofile manager52 of theMAP server12 stores the resulting user profile for the user20 (step1002E). More specifically, in one embodiment, theMAP server12 stores user records for theusers20 in the datastore64 (FIG. 2). The user profile of theuser20 is stored in the user record of theuser20. The user record of theuser20 includes a unique identifier of theuser20, the user profile of theuser20, and, as discussed below, a current location of theuser20. Note that the user profile of theuser20 may be updated as desired. For example, in one embodiment, the user profile of theuser20 is updated by repeatingstep1002 each time theuser20 activates theMAP application32.
• Note that while the discussion herein focuses on an embodiment where the user profiles of theusers20 are obtained from the one ormore profile servers14, the user profiles of theusers20 may be obtained in any desired manner. For example, in one alternative embodiment, theuser20 may identify one or more favorite websites. Theprofile manager52 of theMAP server12 may then crawl the one or more favorite websites of theuser20 to obtain keywords appearing in the one or more favorite websites of theuser20. These keywords may then be stored as the user profile of theuser20.
• At some point, a process is performed such that a current location of themobile device18 and thus a current location of theuser20 is obtained by the MAP server12 (step1004). In this embodiment, theMAP application32 of themobile device18 obtains the current location of themobile device18 from thelocation function36 of themobile device18. TheMAP application32 then provides the current location of themobile device18 to theMAP client30, and theMAP client30 then provides the current location of themobile device18 to the MAP server12 (step1004A). Note thatstep1004A may be repeated periodically or in response to a change in the current location of themobile device18 in order for theMAP application32 to provide location updates for theuser20 to theMAP server12.
• In response to receiving the current location of themobile device18, thelocation manager54 of theMAP server12 stores the current location of themobile device18 as the current location of the user20 (step1004B). More specifically, in one embodiment, the current location of theuser20 is stored in the user record of theuser20 maintained in thedatastore64 of theMAP server12. Note that, in the preferred embodiment, only the current location of theuser20 is stored in the user record of theuser20. In this manner, theMAP server12 maintains privacy for theuser20 since theMAP server12 does not maintain a historical record of the location of theuser20. Any historical data maintained by theMAP server12 is preferably anonymized by thehistory manager56 in order to maintain the privacy of theusers20.
• In addition to storing the current location of theuser20, thelocation manager54 sends the current location of theuser20 to the location server16 (step1004C). In this embodiment, by providing location updates to thelocation server16, theMAP server12 in return receives location updates for theuser20 from thelocation server16. This is particularly beneficial when themobile device18 does not permit background processes. If themobile device18 does not permit background processes, theMAP application32 will not be able to provide location updates for theuser20 to theMAP server12 unless theMAP application32 is active. Therefore, when theMAP application32 is not active, other applications running on the mobile device18 (or some other device of the user20) may directly or indirectly provide location updates to thelocation server16 for theuser20. This is illustrated instep1006 where thelocation server16 receives a location update for theuser20 directly or indirectly from another application running on themobile device18 or an application running on another device of the user20 (step1006A). Thelocation server16 then provides the location update for theuser20 to the MAP server12 (step1006B). In response, thelocation manager54 updates and stores the current location of theuser20 in the user record of the user20 (step1006C). In this manner, theMAP server12 is enabled to obtain location updates for theuser20 even when theMAP application32 is not active at themobile device18.
• FIG. 5 illustrates the operation of thesystem10 ofFIG. 1 to provide the user profile of theuser20 of one of themobile devices18 to theMAP server12 according to another embodiment of the present disclosure. This discussion is equally applicable to user profiles of theusers20 of the othermobile devices18. First, an authentication process is performed (step1100). For authentication, in this embodiment, themobile device18 authenticates with the MAP server12 (step1100A), and theMAP server12 authenticates with the profile server14 (step1100B). Preferably, authentication is performed using OpenID or similar technology. However, authentication may alternatively be performed using separate credentials (e.g., username and password) of theuser20 for access to theMAP server12 and theprofile server14. Assuming that authentication is successful, theprofile server14 returns an authentication succeeded message to the MAP server12 (step1100C), and theMAP server12 returns an authentication succeeded message to theMAP client30 of the mobile device18 (step1100D).
• At some point after authentication is complete, a user profile process is performed such that a user profile of theuser20 is obtained from theprofile server14 and delivered to the MAP server12 (step1102). In this embodiment, theprofile manager52 of theMAP server12 sends a profile request to the profile server14 (step1102A). In response, theprofile server14 returns the user profile of theuser20 to theprofile manager52 of the MAP server12 (step1102B). Note that while in this embodiment theprofile server14 returns the complete user profile of theuser20 to theMAP server12, in an alternative embodiment, theprofile server14 may return a filtered version of the user profile of theuser20 to theMAP server12. Theprofile server14 may filter the user profile of theuser20 according to criteria specified by theuser20. For example, the user profile of theuser20 may include demographic information, general interests, music interests, and movie interests, and theuser20 may specify that the demographic information or some subset thereof is to be filtered, or removed, before sending the user profile to theMAP server12.
• Upon receiving the user profile of theuser20, theprofile manager52 of theMAP server12 processes the user profile (step1102C). More specifically, as discussed above, in the preferred embodiment, theprofile manager52 includes social network handlers for the social network services supported by theMAP server12. The social network handlers process user profiles to generate user profiles for theMAP server12 that include lists of keywords for each of a number of profile categories, or profile slices.
• After processing the user profile of theuser20, theprofile manager52 of theMAP server12 stores the resulting user profile for the user20 (step1102D). More specifically, in one embodiment, theMAP server12 stores user records for theusers20 in the datastore64 (FIG. 2). The user profile of theuser20 is stored in the user record of theuser20. The user record of theuser20 includes a unique identifier of theuser20, the user profile of theuser20, and, as discussed below, a current location of theuser20. Note that the user profile of theuser20 may be updated as desired. For example, in one embodiment, the user profile of theuser20 is updated by repeatingstep1102 each time theuser20 activates theMAP application32.
• Note that while the discussion herein focuses on an embodiment where the user profiles of theusers20 are obtained from the one ormore profile servers14, the user profiles of theusers20 may be obtained in any desired manner. For example, in one alternative embodiment, theuser20 may identify one or more favorite websites. Theprofile manager52 of theMAP server12 may then crawl the one or more favorite websites of theuser20 to obtain keywords appearing in the one or more favorite websites of theuser20. These keywords may then be stored as the user profile of theuser20.
• At some point, a process is performed such that a current location of themobile device18 and thus a current location of theuser20 is obtained by the MAP server12 (step1104). In this embodiment, theMAP application32 of themobile device18 obtains the current location of themobile device18 from thelocation function36 of themobile device18. TheMAP application32 then provides the current location of theuser20 of themobile device18 to the location server16 (step1104A). Note thatstep1104A may be repeated periodically or in response to changes in the location of themobile device18 in order to provide location updates for theuser20 to theMAP server12. Thelocation server16 then provides the current location of theuser20 to the MAP server12 (step1104B). Thelocation server16 may provide the current location of theuser20 to theMAP server12 automatically in response to receiving the current location of theuser20 from themobile device18 or in response to a request from theMAP server12.
• In response to receiving the current location of themobile device18, thelocation manager54 of theMAP server12 stores the current location of themobile device18 as the current location of the user20 (step1104C). More specifically, in one embodiment, the current location of theuser20 is stored in the user record of theuser20 maintained in thedatastore64 of theMAP server12. Note that, in the preferred embodiment, only the current location of theuser20 is stored in the user record of theuser20. In this manner, theMAP server12 maintains privacy for theuser20 since theMAP server12 does not maintain a historical record of the location of theuser20. As discussed below in detail, historical data maintained by theMAP server12 is preferably anonymized in order to maintain the privacy of theusers20.
• As discussed above, the use of thelocation server16 is particularly beneficial when themobile device18 does not permit background processes. As such, if themobile device18 does not permit background processes, theMAP application32 will not provide location updates for theuser20 to thelocation server16 unless theMAP application32 is active. However, other applications running on the mobile device18 (or some other device of the user20) may provide location updates to thelocation server16 for theuser20 when theMAP application32 is not active. This is illustrated instep1106 where thelocation server16 receives a location update for theuser20 from another application running on themobile device18 or an application running on another device of the user20 (step1106A). Thelocation server16 then provides the location update for theuser20 to the MAP server12 (step1106B). In response, thelocation manager54 updates and stores the current location of theuser20 in the user record of the user20 (step1106C). In this manner, theMAP server12 is enabled to obtain location updates for theuser20 even when theMAP application32 is not active at themobile device18.
• FIG. 6 begins a discussion of the operation of thecrowd analyzer58 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 thecrowd analyzer58 as, for example, a background process.
• First, thecrowd analyzer58 establishes a bounding box for the crowd formation process (step1200). 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.
• Thecrowd analyzer58 then creates a crowd for each individual user in the bounding box (step1202). More specifically, thecrowd analyzer58 queries thedatastore64 of theMAP server12 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, thecrowd analyzer58 determines the two closest crowds in the bounding box (step1204) and determines a distance between the two crowds (step1206). 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. Thecrowd analyzer58 then determines whether the distance between the two crowds is less than an optimal inclusion distance (step1208). 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, thecrowd analyzer58 combines the two crowds (step1210) and computes a new crowd center for the resulting crowd (step1212). 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 step1204 and is repeated until the distance between the two closest crowds is not less than the optimal inclusion distance. At that point, thecrowd analyzer58 discards any crowds with less than three users (step1214). 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.
• FIGS. 7A through 7D graphically illustrate the crowd formation process ofFIG. 6 for anexemplary bounding box72. InFIGS. 7A through 7D, crowds are noted by dashed circles, and the crowd centers are noted by cross-hairs (+). As illustrated inFIG. 7A, initially, thecrowd analyzer58 createscrowds74 through82 for the users in the geographic area defined by thebounding box72, where, at this point, each of thecrowds74 through82 includes one user. The current locations of the users are the crowd centers of thecrowds74 through82. Next, thecrowd analyzer58 determines the two closest crowds and a distance between the two closest crowds. In this example, at this point, the two closest crowds arecrowds76 and78, and the distance between the twoclosest crowds76 and78 is less than the optimal inclusion distance. As such, the twoclosest crowds76 and78 are combined by mergingcrowd78 intocrowd76, and a new crowd center (+) is computed for thecrowd76, as illustrated inFIG. 7B. Next, thecrowd analyzer58 again determines the two closest crowds, which are now crowds74 and76. Thecrowd analyzer58 then determines a distance between thecrowds74 and76. Since the distance is less than the optimal inclusion distance, thecrowd analyzer58 combines the twocrowds74 and76 by merging thecrowd74 into thecrowd76, and a new crowd center (+) is computed for thecrowd76, as illustrated inFIG. 7C. At this point, there are no more crowds separated by less than the optimal inclusion distance. As such, thecrowd analyzer58 discards crowds having less than three users, which in this example arecrowds80 and82. As a result, at the end of the crowd formation process, thecrowd76 has been formed with three users, as illustrated inFIG. 7D.
• 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 theusers20 and is preferably repeated for each location update received for theusers20. As such, first, thecrowd analyzer58 receives a location update, or a new location, for a user (step1300). Assume that, for this example, the location update is received for the user20-1. In response, thecrowd analyzer58 retrieves an old location of the user20-1, if any (step1302). The old location is the current location of the user20-1 prior to receiving the new location. Thecrowd analyzer58 then creates a new bounding box of a predetermined size centered at the new location of the user20-1 (step1304) and an old bounding box of a predetermined size centered at the old location of the user20-1, if any (step1306). 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 user20-1 does not have an old location (i.e., the location received instep1300 is the first location received for the user20-1), 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.
• Next, thecrowd analyzer58 determines whether the new and old bounding boxes overlap (step1308). If so, thecrowd analyzer58 creates a bounding box encompassing the new and old bounding boxes (step1310). For example, if the new and old bounding boxes are 40×40 meter regions and a 1×1 meter square at the northeast corner of the new bounding box overlaps a 1×1 meter square at the southwest corner of the old bounding box, thecrowd analyzer58 may create a 79×79 meter square bounding box encompassing both the new and old bounding boxes.
• Thecrowd analyzer58 then determines the individual users and crowds relevant to the bounding box created in step1310 (step1312). 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, thecrowd analyzer58 computes an optimal inclusion distance for individual users based on user density within the bounding box (step1314). 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:
• $\begin{array}{cc}\mathrm{initial\_optimal}\mathrm{\_inclusion}\mathrm{\_dist}=a·\sqrt{\frac{A_{\mathrm{BoundingBox}}}{\mathrm{number\_of}\mathrm{\_users}}},& \mathrm{Eqn}.\left(1\right)\end{array}$
• where a is a number between 0 and 1, A_BoundingBoxis 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 ⅔.
• Thecrowd analyzer58 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 (step1316). At this point, the process proceeds toFIG. 8B where thecrowd analyzer58 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 (step1318). Any crowd member that violates the optimal inclusion distance of his or her crowd is then removed from that crowd (step1320). Thecrowd analyzer58 then creates a crowd of one user for each of the users removed from their crowds instep1320 and sets the optimal inclusion distance for the newly created crowds to the initial optimal inclusion distance (step1322).
• Next, thecrowd analyzer58 determines the two closest crowds for the bounding box (step1324) and a distance between the two closest crowds (step1326). The distance between the two closest crowds is the distance between the crowd centers of the two closest crowds. Thecrowd analyzer58 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 (step1328). 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 thecrowd analyzer58 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, thecrowd analyzer58 may compare the distance between the two closest crowds to an average of the optimal inclusion distances of the two closest crowds.
• If the distance between the two closest crowds is not less than the optimal inclusion distance, then the process proceeds to step1338. Otherwise, the two closest crowds are combined or merged (step1330), and a new crowd center for the resulting crowd is computed (step1332). 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 (step1334). In one embodiment, the new optimal inclusion distance for the resulting crowd is computed as:
• $\begin{array}{cc}\mathrm{average}=\frac{1}{n+1}·\left(\mathrm{initial\_optimal}\mathrm{\_inclusion}\mathrm{\_dist}+\sum _{i=1}^nd_i\right),& \mathrm{Eqn}.\left(2\right)\\ \mathrm{optimal\_inclusion}\mathrm{\_dist}=\mathrm{average}+\sqrt{\left(\frac{1}{n}·\sum _{i=1}^n{\left(d_i-\mathrm{average}\right)}^2\right)}& \mathrm{Eqn}.\left(3\right)\end{array}$
• where n is the number of users in the crowd and d_iis 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.
• At this point, thecrowd analyzer58 determines whether a maximum number of iterations have been performed (step1336). The maximum number of iterations is a predefined number that ensures that the crowd formation process does not indefinitely loop oversteps1318 through1334 or loop oversteps1318 through1334 more than a desired maximum number of times. If the maximum number of iterations has not been reached, the process returns to step1318 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, thecrowd analyzer58 discards crowds with less than three users, or members (step1338) and the process ends.
• Returning to step1308 inFIG. 8A, if the new and old bounding boxes do not overlap, the process proceeds toFIG. 8C and the bounding box to be processed is set to the old bounding box (step1340). In general, thecrowd analyzer58 then processes the old bounding box in much the same manner as described above with respect tosteps1312 through1338. More specifically, thecrowd analyzer58 determines the individual users and crowds relevant to the bounding box (step1342). 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, thecrowd analyzer58 computes an optimal inclusion distance for individual users based on user density within the bounding box (step1344). 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:
• $\begin{array}{cc}\mathrm{initial\_optimal}\mathrm{\_inclusion}\mathrm{\_dist}=a·\sqrt{\frac{A_{\mathrm{BoundingBox}}}{\mathrm{number\_of}\mathrm{\_users}}},& \mathrm{Eqn}.\left(4\right)\end{array}$
• where a is a number between 0 and 1, A_BoundingBoxis 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 ⅔.
• Thecrowd analyzer58 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 (step1346). At this point, thecrowd analyzer58 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 (step1348). Any crowd member that violates the optimal inclusion distance of his or her crowd is then removed from that crowd (step1350). Thecrowd analyzer58 then creates a crowd of one user for each of the users removed from their crowds instep1350 and sets the optimal inclusion distance for the newly created crowds to the initial optimal inclusion distance (step1352).
• Next, thecrowd analyzer58 determines the two closest crowds in the bounding box (step1354) and a distance between the two closest crowds (step1356). The distance between the two closest crowds is the distance between the crowd centers of the two closest crowds. Thecrowd analyzer58 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 (step1358). 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 thecrowd analyzer58 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, thecrowd analyzer58 may compare the distance between the two closest crowds to an average of the optimal inclusion distances of the two closest crowds.
• If the distance between the two closest crowds is not less than the optimal inclusion distance, the process proceeds to step1368. Otherwise, the two closest crowds are combined or merged (step1360), and a new crowd center for the resulting crowd is computed (step1362). 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 (step1364). As discussed above, in one embodiment, the new optimal inclusion distance for the resulting crowd is computed as:
• $\begin{array}{cc}\mathrm{average}=\frac{1}{n+1}·\left(\mathrm{initial\_optimal}\mathrm{\_inclusion}\mathrm{\_dist}+\sum _{i=1}^nd_i\right),& \mathrm{Eqn}.\left(5\right)\\ \mathrm{optimal\_inclusion}\mathrm{\_dist}=\mathrm{average}+\sqrt{\left(\frac{1}{n}·\sum _{i=1}^n{\left(d_i-\mathrm{average}\right)}^2\right)}& \mathrm{Eqn}.\left(6\right)\end{array}$
• where n is the number of users in the crowd and d_iis 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.
• At this point, thecrowd analyzer58 determines whether a maximum number of iterations have been performed (step1366). If the maximum number of iterations has not been reached, the process returns to step1348 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, thecrowd analyzer58 discards crowds with less than three users, or members (step1368). Thecrowd analyzer58 then determines whether the crowd formation process for the new and old bounding boxes is done (step1370). In other words, thecrowd analyzer58 determines whether both the new and old bounding boxes have been processed. If not, the bounding box is set to the new bounding box (step1372), and the process returns to step1342 and is repeated for the new bounding box. Once both the new and old bounding boxes have been processed, the crowd formation process ends.
• FIGS. 9A through 9D graphically illustrate the crowd formation process ofFIGS. 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, thecrowd analyzer58 creates anew bounding box84 for the new location of the user, and thenew bounding box84 is set as the bounding box to be processed for crowd formation. Then, as illustrated inFIG. 9A, thecrowd analyzer58 identifies all individual users currently located within thenew bounding box84 and all crowds located within or overlapping thenew bounding box84. In this example,crowd86 is an existing crowd relevant to thenew bounding box84. Crowds are indicated by dashed circles, crowd centers are indicated by cross-hairs (+), and users are indicated as dots. Next, as illustrated inFIG. 9B, thecrowd analyzer58 createscrowds88 through92 of one user for the individual users, and the optional inclusion distances of thecrowds88 through92 are set to the initial optimal inclusion distance. As discussed above, the initial optimal inclusion distance is computed by thecrowd analyzer58 based on a density of users within thenew bounding box84.
• Thecrowd analyzer58 then identifies the twoclosest crowds88 and90 in thenew bounding box84 and determines a distance between the twoclosest crowds88 and90. In this example, the distance between the twoclosest crowds88 and90 is less than the optimal inclusion distance. As such, the twoclosest crowds88 and90 are merged and a new crowd center and new optimal inclusion distance are computed, as illustrated inFIG. 9C. Thecrowd analyzer58 then repeats the process such that the twoclosest crowds88 and92 in thenew bounding box84 are again merged, as illustrated inFIG. 9D. At this point, the distance between the twoclosest crowds86 and88 is greater than the appropriate optimal inclusion distance. As such, the crowd formation process is complete.
• FIGS. 10A through 10F graphically illustrate the crowd formation process ofFIGS. 8A through 8D for a scenario where the new and old bounding boxes overlap. As illustrated inFIG. 10A, a user moves from an old location to a new location, as indicated by an arrow. Thecrowd analyzer58 receives a location update for the user giving the new location of the user. In response, thecrowd analyzer58 creates anold bounding box94 for the old location of the user and anew bounding box96 for the new location of the user.Crowd98 exists in theold bounding box94, andcrowd100 exists in thenew bounding box96.
• Since theold bounding box94 and thenew bounding box96 overlap, thecrowd analyzer58 creates abounding box102 that encompasses both theold bounding box94 and thenew bounding box96, as illustrated inFIG. 10B. In addition, thecrowd analyzer58 createscrowds104 through110 for individual users currently located within thebounding box102. The optimal inclusion distances of thecrowds104 through110 are set to the initial optimal inclusion distance computed by thecrowd analyzer58 based on the density of users in thebounding box102.
• Next, thecrowd analyzer58 analyzes thecrowds98,100, and104 through110 to determine whether any members of thecrowds98,100, and104 through110 violate the optimal inclusion distances of thecrowds98,100, and104 through110. In this example, as a result of the user leaving thecrowd98 and moving to his new location, both of the remaining members of thecrowd98 violate the optimal inclusion distance of thecrowd98. As such, thecrowd analyzer58 removes the remaining users from thecrowd98 and createscrowds112 and114 of one user each for those users, as illustrated inFIG. 10C.
• Thecrowd analyzer58 then identifies the two closest crowds in thebounding box102, which in this example are thecrowds108 and110. Next, thecrowd analyzer58 computes a distance between the twocrowds108 and110. In this example, the distance between the twocrowds108 and110 is less than the initial optimal inclusion distance and, as such, the twocrowds108 and110 are combined. In this example, crowds are combined by merging the smaller crowd into the larger crowd. Since the twocrowds108 and110 are of the same size, thecrowd analyzer58 merges thecrowd110 into thecrowd108, as illustrated inFIG. 10D. A new crowd center and new optimal inclusion distance are then computed for thecrowd108.
• At this point, thecrowd analyzer58 repeats the process and determines that thecrowds100 and106 are now the two closest crowds. In this example, the distance between the twocrowds100 and106 is less than the optimal inclusion distance of the larger of the twocrowds100 and106, which is thecrowd100. As such, thecrowd106 is merged into thecrowd100 and a new crowd center and optimal inclusion distance are computed for thecrowd100, as illustrated inFIG. 10E. At this point, there are no two crowds closer than the optimal inclusion distance of the larger of the two crowds. As such, thecrowd analyzer58 discards any crowds having less than three members, as illustrated inFIG. 10F. In this example, thecrowds104,108,112, and114 have less than three members and are therefore removed. Thecrowd100 has three or more members and, as such, is not removed. At this point, the crowd formation process is complete.
• FIGS. 11A through 11E graphically illustrate the crowd formation process ofFIGS. 8A through 8D in a scenario where the new and old bounding boxes do not overlap. As illustrated inFIG. 11A, in this example, the user moves from an old location to a new location. Thecrowd analyzer58 creates anold bounding box116 for the old location of the user and anew bounding box118 for the new location of the user.Crowds120 and122 exist in theold bounding box116, andcrowd124 exists in thenew bounding box118. In this example, since the old andnew bounding boxes116 and118 do not overlap, thecrowd analyzer58 processes the old andnew bounding boxes116 and118 separately.
• More specifically, as illustrated inFIG. 11B, as a result of the movement of the user from the old location to the new location, the remaining users in the crowd120 no longer satisfy the optimal inclusion distance for the crowd120. As such, the remaining users in the crowd120 are removed from the crowd120, andcrowds126 and128 of one user each are created for the removed users as shown inFIG. 11C. In this example, no two crowds in theold bounding box116 are close enough to be combined. As such, crowds having less than three users are removed, and processing of theold bounding box116 is complete, and thecrowd analyzer58 proceeds to process thenew bounding box118.
• As illustrated inFIG. 11D, processing of thenew bounding box118 begins by thecrowd analyzer58 creating acrowd130 of one user for the user. Thecrowd analyzer58 then identifies thecrowds124 and130 as the two closest crowds in thenew bounding box118 and determines a distance between the twocrowds124 and130. In this example, the distance between the twocrowds124 and130 is less than the optimal inclusion distance of the larger crowd, which is thecrowd124. As such, thecrowd analyzer58 combines thecrowds124 and130 by merging thecrowd130 into thecrowd124, as illustrated inFIG. 11E. A new crowd center and new optimal inclusion distance are then computed for thecrowd124. At this point, the crowd formation process is complete. Note that the crowd formation processes described above with respect toFIGS. 6 through 11D are exemplary. The present disclosure is not limited thereto. Any type of crowd formation process may be used.
• FIG. 12 illustrates the operation of thesystem10 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 device18-1 sends a crowd request to the MAP server12 (step1400). Note that while in this example the crowd request and, as discussed below, message originate from the mobile device18-1 of the user20-1, this discussion is equally applicable to crowd requests and messages originating from themobile devices18 of any of theusers20. 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 user20-1 of the mobile device18-1 via the MAP application32-1 or may be initiated automatically by the MAP application32-1 in response to an event such as, for example, start-up of the MAP application32-1, movement of the user20-1, or the like.
• In one embodiment, the crowd request is for a POI, where the POI is a POI corresponding to the current location of the user20-1, a POI selected from a list of POIs defined by the user20-1, a POI selected from a list of POIs defined by the MAP application32-1 or theMAP server12, a POI selected by the user20-1 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 user20-1 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 user20-1, or both. Note that in some embodiments, the user20-1 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.
• 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 user20-1, an AOI selected from a list of AOIs defined by the user20-1, an AOI selected from a list of AOIs defined by the MAP application32-1 or theMAP server12, an AOI selected by the user20-1 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 user20-1 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 user20-1, or both. Note that in some embodiments, the user20-1 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 user20-1 via the MAP application32-1. In yet another embodiment, the MAP application32-1 automatically uses the current location of the user20-1 as the POI or as a center point for an AOI of a predefined shape and size.
• Upon receiving the crowd request, theMAP server12 identifies one or more crowds relevant to the crowd request (step1402). More specifically, in one embodiment, thecrowd analyzer58 performs a crowd formation process such as that described above inFIG. 6 to form one or more crowds relevant to the POI or the AOI of the crowd request. In another embodiment, thecrowd analyzer58 proactively forms crowds using a process such as that described above inFIGS. 8A through 8D and stores corresponding crowd records in thedatastore64 of theMAP server12. Then, rather than forming the relevant crowds in response to the crowd request, thecrowd analyzer58 queries thedatastore64 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.
• Once thecrowd analyzer58 has identified the crowds relevant to the crowd request, theMAP server12 generates or otherwise obtains crowd data for the identified crowds (step1404). 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. TheMAP server12 then returns the crowd data to the mobile device18-1 (step1406).
• Upon receiving the crowd data, the MAP application32-1 of the mobile device18-1 presents the crowd data to the user20-1 (step1408). The manner in which the crowd data is presented depends on the particular implementation of the MAP application32-1. 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 user20-1 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.
• Next, the MAP application32-1 of the mobile device18-1 receives user input from the user20-1 that selects a crowd (also referred to herein as a desired crowd) to which to send a message (step1410). In response, the MAP application32-1 of the mobile device18-1 generates a message to be sent to a subset of users in the crowd (step1412). More specifically, in the preferred embodiment, the message includes information that identifies the desired crowd selected in step1410, 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 theMAP server12. The message body is preferably a text message input by the user20-1 of the mobile device18-1 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).
• 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:
• • one or more select profile categories from the user profile of the user20-1 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,
  • weights assigned to the one or more select profile categories,
  • 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,
  • a weight assigned to the implicit referral rating criterion,
  • 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,
  • a weight assigned to the explicit referral rating criterion,
  • 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
  • a weight assigned to the responsiveness rating criterion.
• 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 user20-1. Alternatively, the user20-1 may choose to use predefined default message delivery criteria.
• Once the message is generated, the MAP application32-1 of the mobile device18-1 sends the message to the MAP server12 (step1414). In response to receiving the message, themessage delivery function62 of theMAP server12 selects the subset of users in the desired crowd to which to send the message (step1416). As discussed below in detail, in the preferred embodiment, themessage delivery function62 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, themessage delivery function62 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 user20-1 and stored at theMAP server12. 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 theMAP server12 or embedded into the operation of themessage delivery function62.
• In this example, the users20-2 and20-3 are selected as the subset of the users in the desired crowd to which the message is to be delivered. As such, themessage delivery function62 of theMAP server12 sends the message to the mobile devices18-2 and18-3 of the users20-2 and20-3 (steps1418 and1420). Note that the message sent insteps1418 and1420 may be a version of the message received by theMAP server12 instep1414 that includes the message body but that does not include the message delivery criteria. Further, in the preferred embodiment, the message sent insteps1418 and1420 is sent anonymously such that the message does not identify the user20-1 (i.e., the sender of the message is anonymous). Also, note that an advertisement may be inserted into the message sent to the users20-2 and20-3 using any desired targeted advertisement scheme.
• The mobile devices18-2 and18-3, and preferably the MAP applications32-2 and32-3 of the mobile devices18-2 and18-3, present the message to the users20-2 and20-3 (steps1422 and1424). In this example, the MAP application32-2 of the mobile device18-2 generates a response to the message based on user input from the user20-2 (step1426). The response preferably includes a text, audio, or audio-visual message input by the user20-2 in response to the message received instep1422. The MAP application32-2 then sends the response to the MAP server12 (step1428), and themessage delivery function62 of theMAP server12 then sends the response to the mobile device18-1 of the user20-1 (step1430). Preferably, the response sent to the mobile device18-1 is sent anonymously such that the response does not identify the user20-2 (i.e., the responder is anonymous). Also, note that an advertisement may be inserted into the response sent to the user20-1 using any desired targeted advertisement scheme.
• Next, the MAP application32-1 of the mobile device18-1 presents the response to the user20-1 (step1432). Optionally, in this embodiment, the MAP application32-1 of the mobile device18-1 receives a rating assigned to the response by the user20-1 (step1434). The rating may be, for example, a numerical rating (e.g., 1 to 5), a like/dislike rating, or the like. The MAP application32-1 then sends the rating to the MAP server12 (step1436). Themessage delivery function62 of theMAP server12 then updates an explicit referral rating of the user20-2 that sent the response based on the rating assigned to the response by the user20-1 and received by theMAP server12 in step1436 (step1438). For example, the explicit referral rating of the user20-2 may be an average rating assigned to responses sent by the user20-2 in response to messages sent to the user20-2 by themessage delivery function62 of theMAP server12 scaled to a value between 0 and 100. In a similar manner, explicit referral ratings may also be maintained for theother users20 in thesystem10.
• In this embodiment, themessage delivery function62 of theMAP server12 also maintains implicit referral ratings for theusers20 in thesystem10 based on the ability of theusers20 to submit responses that drawother users20 to their crowds or, alternatively, POI. More specifically, in this example, themessage delivery function62 monitors the location of the user20-1 (i.e., the sender) to detect whether the user20-1 joins the crowd of the user20-2 (i.e., the responder) within a predefined amount of time after the response is sent in step1430 (step1440). Alternatively, themessage delivery function62 may detect whether the user20-1 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 user20-1 joins the crowd of the user20-2 within the predefined amount of time (e.g., 1 hour), themessage delivery function62 determines that the response of the user20-2 enticed the user20-1 to come and join the desired crowd of the user20-2.
• After the user20-1 has joined the crowd of the user20-2 or after the predefined amount of time has expired without the user20-1 joining the crowd of the user20-2, themessage delivery function62 updates the implicit referral rating of the user20-2 (step1442). In one embodiment, themessage delivery function62 includes a counter for the user20-2 that is incremented if the user20-1 joins the crowd of the user20-2 within the predefined amount of time. This counter defines the number of times that the user20-2 has provided responses that resulted in the other user coming to join the crowd of the user20-2. The implicit referral rating of the user20-2 may then be computed as:
• $\begin{array}{cc}{\mathrm{referral\_rating}}_{\mathrm{implicit}}=\frac{\mathrm{counter}}{\mathrm{Number\_of}\mathrm{\_Responses}}\times 100,& \mathrm{Eqn},\left(7\right)\end{array}$
• where referral_rating_implicitis the implicit referral rating of the user20-2, counter is the counter maintained for the number of times the user20-2 has provided responses that resulted in the other user coming to join the crowd of the user20-2, and Number_of_Responses is the total number of responses sent by the user20-2 to messages sent to the user20-2 by themessage delivery function62 of theMAP server12. In a similar manner, implicit referral ratings are preferably maintained for theother users20.
• In addition to the explicit and implicit referral ratings, themessage delivery function62 may also maintain responsiveness ratings for theusers20. In this example, themessage delivery function62 updates the responsiveness ratings of the users20-2 and20-3 to which the message was sent insteps1418 and1420 (step1444). As an example, the responsiveness rating of each of theusers20 may be computed based on the following equation:
• $\begin{array}{cc}\mathrm{responsiveness\_rating}=\frac{\mathrm{Number\_of}\mathrm{\_Responses}}{\mathrm{Number\_of}\mathrm{\_Messages}\mathrm{\_Recieved}}\times 100,& \mathrm{Eqn}.\left(8\right)\end{array}$
• where responsiveness_rating is the responsiveness rating of theuser20, Number_of_Responses is the number of responses sent by theuser20 in response to messages sent to theuser20 by themessage delivery function62 of theMAP server12, and Number_of_Messages Received is the number of messages sent to theuser20 by themessage delivery function62.
• Before proceeding, again, note that advertisements may be inserted into the message sent to the users20-2 and20-3 in the crowd and/or the response sent to the user20-1 using any desired targeted advertisement scheme. For example, the response to the user20-1 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 user20-1 that is indicative of the likelihood that the user20-1 will go to and join the crowd of the responder after receiving the response. This later rating may be maintained by themessage delivery function62 by tracking whether the user20-1 goes to and joins crowds after sending messages to subsets of users in those crowds and receiving responses from users in those crowds.
• FIG. 13 illustratesstep1416 ofFIG. 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, themessage delivery function62 first identifies users in the desired crowd that have enabled a messaging feature that allows messages to be sent to them via themessage delivery function62 of the MAP server12 (step1500). Note thatstep1500 is optional. Alternatively, messages may be sent to all of theusers20 as part of the normal operation of thesystem10 where theusers20 cannot disable the messaging feature provided by themessage delivery function62.
• Next, a counter i is set to 1 (step1502). Themessage delivery function62 then scores user i of the users identified in step1500 (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 (step1504). 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 user20-1; 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 user20-1 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 user20-1 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 user20-1. 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:
• $\begin{array}{cc}\mathrm{match\_value}=\frac{\mathrm{Number\_of}\mathrm{\_Matches}}{\mathrm{Total\_Number}\mathrm{\_of}\mathrm{\_Keywords}}\times 100,& \mathrm{Eqn}.\left(9\right)\end{array}$
• 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 user20-1.
• Once profile matching is complete, user i is scored based on the following equation:
• $\begin{array}{cc}{\mathrm{score}}_i=\frac{\sum _{j=1}^M\left(w_j\times {\mathrm{paramter}}_j\right)}{\sum _{j=1}^Mw_j},& \mathrm{Eqn}.\left(10\right)\end{array}$
• where score, is the score for user i, parameter, is a j-th parameter used for the scoring process, w_jis 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 user20-1, the implicit referral rating of user i, the explicit referral rating of user i, and the responsiveness rating of user i.
• Next, themessage delivery function62 determines whether user i is the last user in the users identified in step1500 (or alternatively all users in the desired crowd) (step1506). If not, the counter i is incremented (step1508) and the process is repeated for the next user. Once all of the users have been scored, themessage delivery function62 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 step1504 (step1510). 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. Themessage delivery function62 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 themessage delivery function62 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 themessage delivery function62 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.
• FIGS. 14A through 14F graphically illustrate the process ofFIG. 12 for an exemplary message and response according to one embodiment of the present disclosure. As illustrated inFIG. 14A, after sending the crowd request and receiving corresponding crowd data from theMAP server12, the MAP application32-1 of the mobile device18-1 of the user20-1 presents the crowd data to the user20-1 in the form of a number ofcrowd indicators132 through140 overlaid on a map. Thecrowd indicators132 through140 show the locations of corresponding crowds.
• As also illustrated inFIG. 14A, the user20-1 is enabled to select a desired crowd by selecting the crowd indicator138 of the desired crowd. In response, amenu142 is presented to the user20-1. Themenu142 includes a crowddata display area144 in which information regarding the crowd is presented to the user20-1 and a menu selection area146. 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 user20-1 (or one or more select profile categories of the user profile of the user20-1), and a number of users in the crowd that are addressable (i.e., have enabled the messaging feature provided by themessage delivery function62 of the MAP server12). 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 user20-1 or one or more select profile categories of the user profile of the user20-1) over the total number of users in the crowd multiplied by 100.
• The menu selection area146 provides a list of actions that may be selected by the user20-1. Specifically, in this example, the menu selection area146 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 user20-1, causes the MAP application32-1 to route the user20-1 to the POI at which the crowd is located. The “Send a MSG to All Members” action, if selected, enables the user20-1 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 themessage delivery function62 in much the same manner as described above. However, themessage delivery function62 would not select a subset of users in the crowd to which to deliver the message. Rather, themessage delivery function62 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 themessage delivery function62. The “Send a MSG to Some Members” action, if selected by the user20-1, causes the MAP application32-1 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 user20-1, causes the MAP application32-1 to present more actions to the user20-1.
• In this example, the user20-1 selects the “Send a MSG to Some Members” action. In response, amessage creation screen148 is presented to the user20-1 as illustrated inFIG. 14B. Themessage creation screen148 includes atext entry area150 that enables the user20-1 to enter text for the body of the message. Themessage creation screen148 also includes a “Send”button152 and an “Advanced”button154. The user20-1 causes the message to be sent by selecting the “Send”button152. If the user20-1 does not first select the “Advanced”button154 to define message delivery criteria for the message before selecting the “Send”button152, default message delivery criteria may be used.
• In this example, the user20-1 selects the “Advanced”button154. In response, a message delivery criteria screen156 is presented to the user20-1 as illustrated inFIG. 14C. The message delivery criteria screen156 includes a “Profile Matching”checkbox158 andcheckboxes160 through166 for profile categories in the user profile of the user20-1. The user20-1 selects the “Profile Matching”checkbox158 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 user20-1 selects one or more of thecheckboxes160 through166 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 user20-1 has selected the “Demographics” profile category and the “Music” profile category. In addition, the message delivery criteria screen156 includes slider bars168 through174 for assigning weights to the corresponding profile categories.
• The message delivery criteria screen156 also includes a “Ratings”checkbox176 that, when selected by the user20-1, 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”checkbox176 is selected, in this embodiment, the user20-1 is enabled to select one or more rating types to use when selecting the subset of the users in the crowd by selecting correspondingcheckboxes178 and180. In this example, the user20-1 has enabled the use of both implicit and explicit referral ratings by selecting thecheckbox178 and enabled the use of responsiveness ratings by selecting thecheckbox180. In this example, the message delivery criteria screen156 also includes slider bars182 and184 for assigning weights to the referral ratings and responsiveness ratings, respectively.
• Lastly, the message delivery criteria screen156 includes a pull-down menu186 that enables the user20-1 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 user20-1 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 user20-1 has finished configuring the message delivery criteria, the user20-1 selects a “Done”button188. The user20-1 can reset the message delivery criteria to their default settings by selecting a “Default”button190.
• Once the message has been sent to the mobile device18-2 of the user20-2, the mobile device18-2 presents the message to the user20-2 via amessage display screen192, as illustrated inFIG. 14D. The user20-2 may then read the message and either ignore the message by selecting an “Ignore”button194 or respond to the message by selecting a “Respond”button196. In this example, the user20-2 selects the “Respond”button196. In response, aresponse generation screen198 is presented to the user20-2, as illustrated inFIG. 14E. Theresponse generation screen198 includes atext entry area200 where the user20-2 is enabled to enter text for a response to be sent to the user20-1. Once the user20-2 is ready to send the response, the user20-2 selects a “Send”button202. In response, the message is sent to the user20-1, preferably anonymously, via themessage delivery function62. The user20-2 may cancel the response generation process by selecting a “Cancel”button203. Once the response is received by the mobile device18-1 of the user20-1, the mobile device18-1 presents the response to the user20-1 via aresponse display screen204, as illustrated inFIG. 14F. The user20-1 may then rate the response by selecting either a “Dislike”button206 or a “Like”button208.
• FIG. 15 is a block diagram of theMAP server12 according to one embodiment of the present disclosure. As illustrated, theMAP server12 includes acontroller210 connected tomemory212, one or moresecondary storage devices214, and acommunication interface216 by abus218 or similar mechanism. Thecontroller210 is a microprocessor, digital Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), or the like. In this embodiment, thecontroller210 is a microprocessor, and theapplication layer40, thebusiness logic layer42, and the object mapping layer63 (FIG. 2) are implemented in software and stored in thememory212 for execution by thecontroller210. Further, the datastore64 (FIG. 2) may be implemented in the one or moresecondary storage devices214. Thesecondary storage devices214 are digital data storage devices such as, for example, one or more hard disk drives. Thecommunication interface216 is a wired or wireless communication interface that communicatively couples theMAP server12 to the network28 (FIG. 1). For example, thecommunication interface216 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.
• FIG. 16 is a block diagram of the mobile device18-1 according to one embodiment of the present disclosure. This discussion is equally applicable to the other mobile devices18-2 through18-N. As illustrated, the mobile device18-1 includes acontroller220 connected tomemory222, acommunication interface224, one or more user interface components226, and the location function36-1 by abus228 or similar mechanism. Thecontroller220 is a microprocessor, digital ASIC, FPGA, or the like. In this embodiment, thecontroller220 is a microprocessor, and the MAP client30-1, the MAP application32-1, and the third-party applications34-1 are implemented in software and stored in thememory222 for execution by thecontroller220. In this embodiment, the location function36-1 is a hardware component such as, for example, a GPS receiver. Thecommunication interface224 is a wireless communication interface that communicatively couples the mobile device18-1 to the network28 (FIG. 1). For example, thecommunication interface224 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® interface), or the like. The one or more user interface components226 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.
• 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.

Claims (26)

What is claimed is:

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 ofclaim 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 ofclaim 1 wherein sending the message comprises sending the message anonymously such that the message does not identify the sending user.

4. The method ofclaim 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 ofclaim 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 ofclaim 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 ofclaim 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 ofclaim 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 ofclaim 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 ofclaim 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 ofclaim 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 ofclaim 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 ofclaim 12 wherein the referral rating for the user is an implicit referral rating for the user.

14. The method ofclaim 12 wherein the referral rating for the user is an explicit referral rating for the user.

15. The method ofclaim 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 ofclaim 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 ofclaim 16 wherein the referral rating for the user is an implicit referral rating for the user.

18. The method ofclaim 16 wherein the referral rating for the user is an explicit referral rating for the user.

19. The method ofclaim 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 ofclaim 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 ofclaim 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 ofclaim 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 ofclaim 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 ofclaim 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.

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