Patent Publication Number: US-9407598-B2

Title: Ad-hoc micro-blogging groups

Description:
RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 13/037,546, which was filed Mar. 1, 2011, which claims the benefit of provisional patent application Ser. No. 61/309,903, filed Mar. 3, 2010, the disclosures of which are hereby incorporated herein by reference in their entirety. 
    
    
     FIELD OF THE DISCLOSURE 
     The present disclosure relates to micro-blog posts and more specifically relates to tagging micro-blog posts with crowd identifiers of crowds of users in which originators of the micro-blog posts are located. 
     BACKGROUND 
     Publish-subscribe micro-blogging services, such as the Twitter® micro-blogging and social networking service, have become extremely popular. However, current publish-subscribe micro-blogging services do not support many-to-many ad-hoc micro-blogging groups. As such, there is a need for a system and method of providing many-to-many ad-hoc micro-blogging groups. 
     SUMMARY 
     Systems and methods are disclosed for distributing micro-blog posts to ad-hoc micro-blogging groups. In one embodiment, a micro-blog post of a user is obtained. A crowd in which the user is located is determined, where the crowd is a group of spatially proximate users. The micro-blog post of the user is tagged with a crowd identifier of the crowd in which the user is located such that the micro-blog post includes a crowd identifier tag. Publication of the micro-blog post including the crowd identifier tag is then effected. 
     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 of  FIG. 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 of  FIG. 1  according to one embodiment of the present disclosure; 
         FIG. 4  illustrates the operation of the system of  FIG. 1  to provide user profiles and current locations of the users of the mobile devices to the MAP server according to one embodiment of the present disclosure; 
         FIG. 5  illustrates the operation of the system of  FIG. 1  to provide user profiles and current locations of the users of the mobile devices to the MAP server according to another embodiment of the present disclosure; 
         FIGS. 6A through 6D  illustrate 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 of  FIGS. 6A through 6D  for a scenario where the crowd formation process is triggered by a location update for a user having no old location; 
         FIGS. 8A through 8F  graphically illustrate the crowd formation process of  FIGS. 6A through 6D  for a scenario where the new and old bounding boxes overlap; 
         FIGS. 9A through 9E  graphically illustrate the crowd formation process of  FIGS. 6A through 6D  in a scenario where the new and old bounding boxes do not overlap; 
         FIG. 10  illustrates a process for tagging micro-blog posts of users with corresponding crowd identifier (ID) tags and effecting publication of the micro-blog posts including the crowd ID tags according to one embodiment of the present disclosure; 
         FIG. 11  illustrates the operation of the system of  FIG. 1  to tag micro-blog posts of users with corresponding crowd ID tags and publish the micro-blog posts including the crowd ID tags according to one embodiment of the present disclosure; 
         FIG. 12  illustrates a process for publishing the micro-blog posts of  FIG. 11  according to one embodiment of the present disclosure; 
         FIG. 13  illustrates a process for publishing the micro-blog posts of  FIG. 11  according to another embodiment of the present disclosure; 
         FIG. 14  illustrates the operation of the system of  FIG. 1  to tag micro-blog posts of users with corresponding crowd ID tags and publish the micro-blog posts including the crowd ID tags according to another embodiment of the present disclosure; 
         FIG. 15  illustrates a process for publishing the micro-blog post of  FIG. 14  according to one embodiment of the present disclosure; 
         FIG. 16  illustrates a process for publishing the micro-blog post of  FIG. 14  according to another embodiment of the present disclosure; 
         FIG. 17  illustrates the operation of the system of  FIG. 1  to tag micro-blog posts of users with corresponding crowd ID tags and publish the micro-blog posts including the crowd ID tags according to another embodiment of the present disclosure; 
         FIG. 18  illustrates the operation of the system of  FIG. 1  to tag micro-blog posts of users with corresponding crowd ID tags and publish the micro-blog posts including the crowd ID tags according to one embodiment of the present disclosure; 
         FIG. 19  illustrates the operation of the system of  FIG. 1  to tag micro-blog posts of users with corresponding crowd ID tags and publish the micro-blog posts including the crowd ID tags according to one embodiment of the present disclosure; 
         FIG. 20  is a block diagram of the MAP server of  FIG. 1  according to one embodiment of the present disclosure; 
         FIG. 21  is a block diagram of one of the mobile devices of  FIG. 1  according to one embodiment of the present disclosure; 
         FIG. 22  is a block diagram of the subscriber device of  FIG. 1  according to one embodiment of the present disclosure; and 
         FIG. 23  is a block diagram of a server computer hosting the third-party service of  FIG. 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) system  10  (hereinafter “system  10 ”) that enables ad-hoc micro-blogging groups according to one embodiment of the present disclosure. Note that the system  10  is exemplary and is not intended to limit the scope of the present disclosure. In this embodiment, the system  10  includes a MAP server  12 , one or more profile servers  14 , a location server  16 , a number of mobile devices  18 - 1  through  18 -N (generally referred to herein collectively as mobile devices  18  and individually as mobile device  18 ) having associated users  20 - 1  through  20 -N (generally referred to herein collectively as users  20  and individually as user  20 ), a subscriber device  22  having an associated subscriber  24 , and a micro-blogging service  26  communicatively coupled via a network  28 . The network  28  may be any type of network or any combination of networks. Specifically, the network  28  may include wired components, wireless components, or both wired and wireless components. In one exemplary embodiment, the network  28  is a distributed public network such as the Internet, where the mobile devices  18  are enabled to connect to the network  28  via local wireless connections (e.g., WiFi® 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, the MAP server  12  operates to obtain current locations, including location updates, and user profiles of the users  20  of the mobile devices  18 . The current locations of the users  20  can be expressed as positional geographic coordinates such as latitude-longitude pairs, and a height vector (if applicable), or any other similar information capable of identifying a given physical point in space in a two-dimensional or three-dimensional coordinate system. Using the current locations and user profiles of the users  20 , the MAP server  12  is enabled to provide a number of features such as, but not limited to, forming crowds of users using current locations and/or user profiles of the users  20 , generating aggregate profiles for crowds of users, and tracking crowds. Note that while the MAP server  12  is illustrated as a single server for simplicity and ease of discussion, it should be appreciated that the MAP server  12  may be implemented as a single physical server or multiple physical servers operating in a collaborative manner for purposes of redundancy and/or load sharing. 
     In general, the one or more profile servers  14  operate to store user profiles for a number of persons including the users  20  of the mobile devices  18 . For example, the one or more profile servers  14  may be servers providing social network services such as the Facebook® social networking service, the MySpace® social networking service, the LinkedIN® social networking service, or the like. As discussed below, using the one or more profile servers  14 , the MAP server  12  is enabled to directly or indirectly obtain the user profiles of the users  20  of the mobile devices  18 . The location server  16  generally operates to receive location updates from the mobile devices  18  and make the location updates available to entities such as, for instance, the MAP server  12 . In one exemplary embodiment, the location server  16  is a server operating to provide Yahoo!&#39;s Fire Eagle® service. 
     The mobile devices  18  may be mobile smart phones, portable media player devices, mobile gaming devices, or the like. Some exemplary mobile devices that may be programmed or otherwise configured to operate as the mobile devices  18  are the Apple® iPhone®, the Palm Pre®, the Samsung Rogue™, the Blackberry Storm™, the Motorola Droid or similar phone running Google&#39;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 devices  18 - 1  through  18 -N include MAP clients  30 - 1  through  30 -N (generally referred to herein collectively as MAP clients  30  or individually as MAP client  30 ), MAP applications  32 - 1  through  32 -N (generally referred to herein collectively as MAP applications  32  or individually as MAP application  32 ), third-party applications  34 - 1  through  34 -N (generally referred to herein collectively as third-party applications  34  or individually as third-party application  34 ), and location functions  36 - 1  through  36 -N (generally referred to herein collectively as location functions  36  or individually as location function  36 ), respectively. The MAP client  30  is preferably implemented in software. In general, in the preferred embodiment, the MAP client  30  is a middleware layer operating to interface an application layer (i.e., the MAP application  32  and the third-party applications  34 ) to the MAP server  12 . More specifically, the MAP client  30  enables the MAP application  32  and the third-party applications  34  to request and receive data from the MAP server  12 . In addition, the MAP client  30  enables applications, such as the MAP application  32  and the third-party applications  34 , to access data from the MAP server  12 . 
     The MAP application  32  is also preferably implemented in software. The MAP application  32  generally provides a user interface component between the user  20  and the MAP server  12 . More specifically, among other things, the MAP application  32  enables the user  20  to initiate requests for crowd data from the MAP server  12  and present corresponding crowd data returned by the MAP server  12  to the user  20  as well as enable the user  20  to follow micro-blog posts sent by users in desired crowds. The MAP application  32  also enables the user  20  to configure various settings. For example, the MAP application  32  may enable the user  20  to select a desired social networking service (e.g., Facebook®, MySpace®, LinkedIN®, etc.) from which to obtain the user profile of the user  20  and provide any necessary credentials (e.g., username and password) needed to access the user profile from the social networking service. 
     The third-party applications  34  are preferably implemented in software. The third-party applications  34  operate to access the MAP server  12  via the MAP client  30 . The third-party applications  34  may utilize data obtained from the MAP server  12  in any desired manner. As an example, one of the third-party applications  34  may be a gaming application that utilizes crowd data to notify the user  20  of Points of Interest (POIs) or Areas of Interest (AOIs) where crowds of interest are currently located. It should be noted that while the MAP client  30  is illustrated as being separate from the MAP application  32  and the third-party applications  34 , the present disclosure is not limited thereto. The functionality of the MAP client  30  may alternatively be incorporated into the MAP application  32  and the third-party applications  34 . 
     The location function  36  may be implemented in hardware, software, or a combination thereof. In general, the location function  36  operates to determine or otherwise obtain the location of the mobile device  18 . For example, the location function  36  may be or include a Global Positioning System (GPS) receiver. In addition or alternatively, the location function  36  may include hardware and/or software that enables improved location tracking in indoor environments such as, for example, shopping malls. For example, the location function  36  may be part of or compatible with the InvisiTrack Location System provided by InvisiTrack and described in U.S. Pat. No. 7,423,580 entitled “Method and System of Three-Dimensional Positional Finding” which issued on Sep. 9, 2008, U.S. Pat. No. 7,787,886 entitled “System and Method for Locating a Target using RFID” which issued on Aug. 31, 2010, and U.S. Patent Application Publication No. 2007/0075898 entitled “Method and System for Positional Finding Using RF, Continuous and/or Combined Movement” which published on Apr. 5, 2007, all of which are hereby incorporated herein by reference for their teachings regarding location tracking. 
     The subscriber device  22  is a physical device such as a personal computer, a mobile computer (e.g., a notebook computer, a netbook computer, a tablet computer, etc.), a mobile smart phone, or the like. The subscriber  24  associated with the subscriber device  22  is a person or entity. In general, the subscriber device  22  enables the subscriber  24  to access the MAP server  12  via a web browser  38  to obtain various types of data, preferably for a fee. For example, the subscriber  24  may pay a fee to have access to crowd data such as aggregate profiles for crowds located at one or more POIs and/or located in one or more AOIs, pay a fee to track crowds, or the like. Note that the web browser  38  is exemplary. In another embodiment, the subscriber device  22  is enabled to access the MAP server  12  via a custom application. 
     Lastly, the micro-blogging service  26  is a service that enables the users  20  to send and receive micro-blog posts. As used herein, a micro-blog post is a message posted by a user for publication via a micro-blogging service. A micro-blog post may include text, audio, video, an image, or any combination thereof. As an example, a micro-blog post may be a tweet posted by a user of the Twitter® micro-blogging and social networking service or a post made by a user of the Facebook® social networking service. As discussed below in detail, requestors (e.g., the users  20  or the subscriber  24 ) are enabled to obtain micro-blog posts from the users  20  in desired crowds of users. 
     Before proceeding, it should be noted that while the system  10  of  FIG. 1  illustrates an embodiment where the one or more profile servers  14 , the location server  16 , and the micro-blogging service  26  are separate from the MAP server  12 , the present disclosure is not limited thereto. In an alternative embodiment, the functionality of the one or more profile servers  14 , the location server  16 , and/or the micro-blogging service  26  may be implemented within the MAP server  12 . Further, while the profile servers  14 , the location server  16 , and the micro-blogging service  26  are separate in the embodiment of  FIG. 1 , in another embodiment, the functionality of the profile servers  14 , the location server  16 , and/or the micro-blogging service  26  may be implemented in a single service. 
       FIG. 2  is a block diagram of the MAP server  12  of  FIG. 1  according to one embodiment of the present disclosure. As illustrated, the MAP server  12  includes an application layer  40 , a business logic layer  42 , and a persistence layer  44 . The application layer  40  includes a user web application  46 , a mobile client/server protocol component  48 , and one or more data Application Programming Interfaces (APIs)  50 . The user web application  46  is preferably implemented in software and operates to provide a web interface for users, such as the subscriber  24 , to access the MAP server  12  via a web browser. The mobile client/server protocol component  48  is preferably implemented in software and operates to provide an interface between the MAP server  12  and the MAP clients  30  hosted by the mobile devices  18 . The data APIs  50  enable third-party services, such as the micro-blogging service  26 , to access the MAP server  12 . 
     The business logic layer  42  includes a profile manager  52 , a location manager  54 , a crowd analyzer  56 , an aggregation engine  58 , and a micro-blog function  60  each of which is preferably implemented in software. The profile manager  52  generally operates to obtain the user profiles of the users  20  directly or indirectly from the one or more profile servers  14  and store the user profiles in the persistence layer  44 . The location manager  54  operates to obtain the current locations of the users  20  including location updates. As discussed below, the current locations of the users  20  may be obtained directly from the mobile devices  18  and/or obtained from the location server  16 . 
     The crowd analyzer  56  operates to form crowds of users. In one embodiment, the crowd analyzer  56  utilizes a spatial crowd formation algorithm. However, the present disclosure is not limited thereto. In addition, the crowd analyzer  56  may further characterize crowds to reflect degree of fragmentation, best-case and worst-case degree of separation (DOS), and/or degree of bi-directionality. Still further, the crowd analyzer  56  may also operate to track crowds. The aggregation engine  58  generally operates to provide aggregate profile data. The aggregate profile data may be aggregate profile data for crowd(s) of users. As discussed below in detail, the micro-blog function  60  enables requestors, such as but not limited to the users  20  and the subscriber  24 , to obtain micro-blog posts from users in desired crowds of users by tagging micro-blog posts made by the users  20  with crowd identifiers (IDs) of the crowds in which the corresponding users  20  are located. For additional information regarding the operation of the profile manager  52 , the location manager  54 , the crowd analyzer  56 , and the aggregation engine  58 , the interested reader is directed to U.S. Patent Application Publication No. 2010/0198828, entitled FORMING CROWDS AND PROVIDING ACCESS TO CROWD DATA IN A MOBILE ENVIRONMENT, which published on Aug. 5, 2010; U.S. Patent Application Publication No. 2010/0197318, entitled ANONYMOUS CROWD TRACKING, which published on Aug. 5, 2010; U.S. Patent Application Publication No. 2010/0198826, entitled MAINTAINING A HISTORICAL RECORD OF ANONYMIZED USER PROFILE DATA BY LOCATION FOR USERS IN A MOBILE ENVIRONMENT, which published on Aug. 5, 2010; U.S. Patent Application Publication No. 2010/0198917, entitled CROWD FORMATION FOR MOBILE DEVICE USERS, which published on Aug. 5, 2010; U.S. Patent Application Publication No. 2010/0198870, entitled SERVING A REQUEST FOR DATA FROM A HISTORICAL RECORD OF ANONYMIZED USER PROFILE DATA IN A MOBILE ENVIRONMENT, which published on Aug. 5, 2010; U.S. Patent Application Publication No. 2010/0198862, entitled HANDLING CROWD REQUESTS FOR LARGE GEOGRAPHIC AREAS, which published on Aug. 5, 2010; and U.S. Patent Application Publication No. 2010/0197319, entitled MODIFYING A USER&#39;S CONTRIBUTION TO AN AGGREGATE PROFILE BASED ON TIME BETWEEN LOCATION UPDATES AND EXTERNAL EVENTS, which published on Aug. 5, 2010; all of which are hereby incorporated herein by reference in their entireties. 
     The persistence layer  44  includes an object mapping layer  62  and a datastore  64 . The object mapping layer  62  is preferably implemented in software. The datastore  64  is preferably a relational database, which is implemented in a combination of hardware (i.e., physical data storage hardware) and software (i.e., relational database software). In this embodiment, the business logic layer  42  is implemented in an object-oriented programming language such as, for example, Java. As such, the object mapping layer  62  operates to map objects used in the business logic layer  42  to relational database entities stored in the datastore  64 . Note that, in one embodiment, data is stored in the datastore  64  in a Resource Description Framework (RDF) compatible format. 
     In an alternative embodiment, rather than being a relational database, the datastore  64  may be implemented as an RDF datastore. More specifically, the RDF datastore may be compatible with RDF technology adopted by Semantic Web activities. Namely, the RDF datastore may use the Friend-Of-A-Friend (FOAF) vocabulary for describing people, their social networks, and their interests. In this embodiment, the MAP server  12  may be designed to accept raw FOAF files describing persons, their friends, and their interests. These FOAF files are currently output by some social networking services such as LiveJournal® and Facebook®. The MAP server  12  may then persist RDF descriptions of the users  20  as a proprietary extension of the FOAF vocabulary that includes additional properties desired for the system  10 . 
       FIG. 3  illustrates the MAP client  30  of  FIG. 1  in more detail according to one embodiment of the present disclosure. As illustrated, in this embodiment, the MAP client  30  includes a MAP access API  66 , a MAP middleware component  68 , and a mobile client/server protocol component  70 . The MAP access API  66  is implemented in software and provides an interface by which the MAP application  32  and the third-party applications  34  are enabled to access the MAP client  30 . The MAP middleware component  68  is implemented in software and performs the operations needed for the MAP client  30  to operate as an interface between the MAP application  32  and the third-party applications  34  at the mobile device  18  and the MAP server  12 . The mobile client/server protocol component  70  enables communication between the MAP client  30  and the MAP server  12  via a defined protocol. 
       FIG. 4  illustrates the operation of the system  10  of  FIG. 1  to provide the user profile of one of the users  20  of one of the mobile devices  18  to the MAP server  12  according to one embodiment of the present disclosure. This discussion is equally applicable to the other users  20  of the other mobile devices  18 . First, an authentication process is performed (step  1000 ). For authentication, in this embodiment, the mobile device  18  authenticates with the profile server  14  (step  1000 A) and the MAP server  12  (step  1000 B). In addition, the MAP server  12  authenticates with the profile server  14  (step  1000 C). Preferably, authentication is performed using OpenID or similar technology. However, authentication may alternatively be performed using separate credentials (e.g., username and password) of the user  20  for access to the MAP server  12  and the profile server  14 . Assuming that authentication is successful, the profile server  14  returns an authentication succeeded message to the MAP server  12  (step  1000 D), and the profile server  14  returns an authentication succeeded message to the MAP client  30  of the mobile device  18  (step  1000 E). 
     At some point after authentication is complete, a user profile process is performed such that a user profile of the user  20  is obtained from the profile server  14  and delivered to the MAP server  12  (step  1002 ). In this embodiment, the MAP client  30  of the mobile device  18  sends a profile request to the profile server  14  (step  1002 A). In response, the profile server  14  returns the user profile of the user  20  to the mobile device  18  (step  1002 B). The MAP client  30  of the mobile device  18  then sends the user profile of the user  20  to the MAP server  12  (step  1002 C). Note that while in this embodiment the MAP client  30  sends the complete user profile of the user  20  to the MAP server  12 , in an alternative embodiment, the MAP client  30  may filter the user profile of the user  20  according to criteria specified by the user  20 . For example, the user profile of the user  20  may include demographic information, general interests, music interests, and movie interests, and the user  20  may specify that the demographic information or some subset thereof is to be filtered, or removed, before sending the user profile to the MAP server  12 . 
     Upon receiving the user profile of the user  20  from the MAP client  30  of the mobile device  18 , the profile manager  52  of the MAP server  12  processes the user profile (step  1002 D). More specifically, in the preferred embodiment, the profile manager  52  includes social network handlers for the social network services supported by the MAP server  12  that operate to map the user profiles of the users  20  obtained from the social network services to a common format utilized by the MAP server  12 . This common format includes a number of user profile categories, or user profile slices, such as, for example, a demographic profile category, a social interaction profile category, a general interests category, a music interests profile category, and a movie interests profile category. For example, if the MAP server  12  supports user profiles from Facebook®, MySpace®, and LinkedIN®, the profile manager  52  may include a Facebook handler, a MySpace handler, and a LinkedIN handler. The social network handlers process user profiles from the corresponding social network services to generate user profiles for the users  20  in the common format used by the MAP server  12 . For this example assume that the user profile of the user  20  is from Facebook®. The profile manager  52  uses a Facebook handler to process the user profile of the user  20  to map the user profile of the user  20  from Facebook® to a user profile for the user  20  for the MAP server  12  that includes lists of keywords for a number of predefined profile categories, or profile slices, such as, for example, a demographic profile category, a social interaction profile category, a general interests profile category, a music interests profile category, and a movie interests profile category. As such, the user profile of the user  20  from Facebook® may be processed by the Facebook handler of the profile manager  52  to create a list of keywords such as, for example, liberal, High School Graduate, 35-44, College Graduate, etc. for the demographic profile category; a list of keywords such as Seeking Friendship for the social interaction profile category; a list of keywords such as politics, technology, photography, books, etc. for the general interests profile category; a list of keywords including music genres, artist names, album names, or the like for the music interests profile category; and a list of keywords including movie titles, actor or actress names, director names, movie genres, or the like for the movie interests profile category. In one embodiment, the profile manager  52  may use natural language processing or semantic analysis. For example, if the Facebook® user profile of the user  20  states that the user  20  is 20 years old, semantic analysis may result in the keyword of 18-24 years old being stored in the user profile of the user  20  for the MAP server  12 . 
     After processing the user profile of the user  20 , the profile manager  52  of the MAP server  12  stores the resulting user profile for the user  20  (step  1002 E). More specifically, in one embodiment, the MAP server  12  stores user records for the users  20  in the datastore  64  ( FIG. 2 ). The user profile of the user  20  is stored in the user record of the user  20 . The user record of the user  20  includes a unique identifier of the user  20 , the user profile of the user  20 , and, as discussed below, a current location of the user  20 . Note that the user profile of the user  20  may be updated as desired. For example, in one embodiment, the user profile of the user  20  is updated by repeating step  1002  each time the user  20  activates the MAP application  32 . 
     Note that while the discussion herein focuses on an embodiment where the user profiles of the users  20  are obtained from the one or more profile servers  14 , the user profiles of the users  20  may be obtained in any desired manner. For example, in one alternative embodiment, the user  20  may identify one or more favorite websites. The profile manager  52  of the MAP server  12  may then crawl the one or more favorite websites of the user  20  to obtain keywords appearing in the one or more favorite websites of the user  20 . These keywords may then be stored as the user profile of the user  20 . 
     At some point, a process is performed such that a current location of the mobile device  18  and thus a current location of the user  20  is obtained by the MAP server  12  (step  1004 ). In this embodiment, the MAP application  32  of the mobile device  18  obtains the current location of the mobile device  18  from the location function  36  of the mobile device  18 . The MAP application  32  then provides the current location of the mobile device  18  to the MAP client  30 , and the MAP client  30  then provides the current location of the mobile device  18  to the MAP server  12  (step  1004 A). Note that step  1004 A may be repeated periodically or in response to a change in the current location of the mobile device  18  in order for the MAP application  32  to provide location updates for the user  20  to the MAP server  12 . 
     In response to receiving the current location of the mobile device  18 , the location manager  54  of the MAP server  12  stores the current location of the mobile device  18  as the current location of the user  20  (step  1004 B). More specifically, in one embodiment, the current location of the user  20  is stored in the user record of the user  20  maintained in the datastore  64  of the MAP server  12 . Note that, in the preferred embodiment, only the current location of the user  20  is stored in the user record of the user  20 . In this manner, the MAP server  12  maintains privacy for the user  20  since the MAP server  12  does not maintain a historical record of the location of the user  20 . Any historical data maintained by the MAP server  12  is preferably anonymized in order to maintain the privacy of the users  20 . 
     In addition to storing the current location of the user  20 , the location manager  54  sends the current location of the user  20  to the location server  16  (step  1004 C). In this embodiment, by providing location updates to the location server  16 , the MAP server  12  in return receives location updates for the user  20  from the location server  16 . This is particularly beneficial when the mobile device  18  does not permit background processes. If the mobile device  18  does not permit background processes, the MAP application  32  will not be able to provide location updates for the user  20  to the MAP server  12  unless the MAP application  32  is active. Therefore, when the MAP application  32  is not active, other applications running on the mobile device  18  (or some other device of the user  20 ) may directly or indirectly provide location updates to the location server  16  for the user  20 . This is illustrated in step  1006  where the location server  16  receives a location update for the user  20  directly or indirectly from another application running on the mobile device  18  or an application running on another device of the user  20  (step  1006 A). The location server  16  then provides the location update for the user  20  to the MAP server  12  (step  1006 B). In response, the location manager  54  updates and stores the current location of the user  20  in the user record of the user  20  (step  1006 C). In this manner, the MAP server  12  is enabled to obtain location updates for the user  20  even when the MAP application  32  is not active at the mobile device  18 . 
       FIG. 5  illustrates the operation of the system  10  of  FIG. 1  to provide the user profile of the user  20  of one of the mobile devices  18  to the MAP server  12  according to another embodiment of the present disclosure. This discussion is equally applicable to user profiles of the users  20  of the other mobile devices  18 . First, an authentication process is performed (step  1100 ). For authentication, in this embodiment, the mobile device  18  authenticates with the MAP server  12  (step  1100 A), and the MAP server  12  authenticates with the profile server  14  (step  1100 B). Preferably, authentication is performed using OpenID or similar technology. However, authentication may alternatively be performed using separate credentials (e.g., username and password) of the user  20  for access to the MAP server  12  and the profile server  14 . Assuming that authentication is successful, the profile server  14  returns an authentication succeeded message to the MAP server  12  (step  1100 C), and the MAP server  12  returns an authentication succeeded message to the MAP client  30  of the mobile device  18  (step  1100 D). 
     At some point after authentication is complete, a user profile process is performed such that a user profile of the user  20  is obtained from the profile server  14  and delivered to the MAP server  12  (step  1102 ). In this embodiment, the profile manager  52  of the MAP server  12  sends a profile request to the profile server  14  (step  1102 A). In response, the profile server  14  returns the user profile of the user  20  to the profile manager  52  of the MAP server  12  (step  1102 B). Note that while in this embodiment the profile server  14  returns the complete user profile of the user  20  to the MAP server  12 , in an alternative embodiment, the profile server  14  may return a filtered version of the user profile of the user  20  to the MAP server  12 . The profile server  14  may filter the user profile of the user  20  according to criteria specified by the user  20 . For example, the user profile of the user  20  may include demographic information, general interests, music interests, and movie interests, and the user  20  may specify that the demographic information or some subset thereof is to be filtered, or removed, before sending the user profile to the MAP server  12 . 
     Upon receiving the user profile of the user  20 , the profile manager  52  of the MAP server  12  processes the user profile (step  1102 C). More specifically, as discussed above, in the preferred embodiment, the profile manager  52  includes social network handlers for the social network services supported by the MAP server  12 . The social network handlers process user profiles to generate user profiles for the MAP server  12  that include lists of keywords for each of a number of profile categories, or profile slices. 
     After processing the user profile of the user  20 , the profile manager  52  of the MAP server  12  stores the resulting user profile for the user  20  (step  1102 D). More specifically, in one embodiment, the MAP server  12  stores user records for the users  20  in the datastore  64  ( FIG. 2 ). The user profile of the user  20  is stored in the user record of the user  20 . The user record of the user  20  includes a unique identifier of the user  20 , the user profile of the user  20 , and, as discussed below, a current location of the user  20 . Note that the user profile of the user  20  may be updated as desired. For example, in one embodiment, the user profile of the user  20  is updated by repeating step  1102  each time the user  20  activates the MAP application  32 . 
     Note that while the discussion herein focuses on an embodiment where the user profiles of the users  20  are obtained from the one or more profile servers  14 , the user profiles of the users  20  may be obtained in any desired manner. For example, in one alternative embodiment, the user  20  may identify one or more favorite websites. The profile manager  52  of the MAP server  12  may then crawl the one or more favorite websites of the user  20  to obtain keywords appearing in the one or more favorite websites of the user  20 . These keywords may then be stored as the user profile of the user  20 . 
     At some point, a process is performed such that a current location of the mobile device  18  and thus a current location of the user  20  is obtained by the MAP server  12  (step  1104 ). In this embodiment, the MAP application  32  of the mobile device  18  obtains the current location of the mobile device  18  from the location function  36  of the mobile device  18 . The MAP application  32  then provides the current location of the user  20  of the mobile device  18  to the location server  16  (step  1104 A). Note that step  1104 A may be repeated periodically or in response to changes in the location of the mobile device  18  in order to provide location updates for the user  20  to the MAP server  12 . The location server  16  then provides the current location of the user  20  to the MAP server  12  (step  1104 B). The location server  16  may provide the current location of the user  20  to the MAP server  12  automatically in response to receiving the current location of the user  20  from the mobile device  18  or in response to a request from the MAP server  12 . 
     In response to receiving the current location of the mobile device  18 , the location manager  54  of the MAP server  12  stores the current location of the mobile device  18  as the current location of the user  20  (step  1104 C). More specifically, in one embodiment, the current location of the user  20  is stored in the user record of the user  20  maintained in the datastore  64  of the MAP server  12 . Note that, in the preferred embodiment, only the current location of the user  20  is stored in the user record of the user  20 . In this manner, the MAP server  12  maintains privacy for the user  20  since the MAP server  12  does not maintain a historical record of the location of the user  20 . As discussed below in detail, historical data maintained by the MAP server  12  is preferably anonymized in order to maintain the privacy of the users  20 . 
     As discussed above, the use of the location server  16  is particularly beneficial when the mobile device  18  does not permit background processes. As such, if the mobile device  18  does not permit background processes, the MAP application  32  will not provide location updates for the user  20  to the location server  16  unless the MAP application  32  is active. However, other applications running on the mobile device  18  (or some other device of the user  20 ) may provide location updates to the location server  16  for the user  20  when the MAP application  32  is not active. This is illustrated in step  1106  where the location server  16  receives a location update for the user  20  from another application running on the mobile device  18  or an application running on another device of the user  20  (step  1106 A). The location server  16  then provides the location update for the user  20  to the MAP server  12  (step  1106 B). In response, the location manager  54  updates and stores the current location of the user  20  in the user record of the user  20  (step  1106 C). In this manner, the MAP server  12  is enabled to obtain location updates for the user  20  even when the MAP application  32  is not active at the mobile device  18 . 
       FIGS. 6A through 6D  begin a discussion of the operation of the crowd analyzer  56  to form crowds of users according to one embodiment of the present disclosure.  FIGS. 6A through 6D  illustrate a flow chart for a spatial crowd formation process according to one embodiment of the present disclosure. In this embodiment, the spatial crowd formation process is triggered in response to receiving a location update for one of the users  20  and is preferably repeated for each location update received for the users  20 . As such, first, the crowd analyzer  56  receives a location update, or a new location, for one of the users  20  (step  1200 ). In response, the crowd analyzer  56  retrieves an old location of the user  20 , if any (step  1202 ). The old location is the current location of the user  20  prior to receiving the new location. The crowd analyzer  56  then creates a new bounding box of a predetermined size centered at the new location of the user  20  (step  1204 ) and an old bounding box of a predetermined size centered at the old location of the user  20 , if any (step  1206 ). The predetermined size of the new and old bounding boxes may be any desired size. As one example, the predetermined size of the new and old bounding boxes is 40 meters by 40 meters. Note that if the user  20  does not have an old location (i.e., the location received in step  1200  is the first location received for the user  20 ), then the old bounding box is essentially null. Also note that while bounding “boxes” are used in this example, the bounding areas may be of any desired shape. 
     Next, the crowd analyzer  56  determines whether the new and old bounding boxes overlap (step  1208 ). If so, the crowd analyzer  56  creates a bounding box encompassing the new and old bounding boxes (step  1210 ). 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, the crowd analyzer  56  may create a 79×79 meter square bounding box encompassing both the new and old bounding boxes. 
     The crowd analyzer  56  then determines the individual users and crowds relevant to the bounding box created in step  1210  (step  1212 ). The crowds relevant to the bounding box are crowds that are within or overlap the bounding box (e.g., have at least one user located within the bounding box). The individual users relevant to the bounding box are users that are currently located within the bounding box and not already part of a crowd. Next, the crowd analyzer  56  computes an optimal inclusion distance for individual users based on user density within the bounding box (step  1214 ). More specifically, in one embodiment, the optimal inclusion distance for individuals, which is also referred to herein as an initial optimal inclusion distance, is set according to the following equation: 
                         initial   —     ⁢     optimal   —     ⁢     inclusion   —     ⁢   dist     =     a   ·         A   BoundingBox         number   —     ⁢     of   —     ⁢   users             ,           Eqn   .           ⁢     (   1   )                 
where a is a number between 0 and 1, A BoundingBox  is an area of the bounding box, and number_of_users is the total number of users in the bounding box. The total number of users in the bounding box includes both individual users that are not already in a crowd and users that are already in a crowd. In one embodiment, a is ⅔.
 
     The crowd analyzer  56  then creates a crowd for each individual user within the bounding box that is not already included in a crowd and sets the optimal inclusion distance for the crowds to the initial optimal inclusion distance (step  1216 ). At this point, the process proceeds to  FIG. 6B  where the crowd analyzer  56  analyzes the crowds relevant to the bounding box to determine whether any of the crowd members (i.e., users in the crowds) violate the optimal inclusion distance of their crowds (step  1218 ). Any crowd member that violates the optimal inclusion distance of his or her crowd is then removed from that crowd (step  1220 ). The crowd analyzer  56  then creates a crowd of one user for each of the users removed from their crowds in step  1220  and sets the optimal inclusion distance for the newly created crowds to the initial optimal inclusion distance (step  1222 ). 
     Next, the crowd analyzer  56  determines the two closest crowds for the bounding box (step  1224 ) and a distance between the two closest crowds (step  1226 ). The distance between the two closest crowds is the distance between the crowd centers of the two closest crowds. The crowd analyzer  56  then determines whether the distance between the two closest crowds is less than the optimal inclusion distance of a larger of the two closest crowds (step  1228 ). If the two closest crowds are of the same size (i.e., have the same number of users), then the optimal inclusion distance of either of the two closest crowds may be used. Alternatively, if the two closest crowds are of the same size, the optimal inclusion distances of both of the two closest crowds may be used such that the crowd analyzer  56  determines whether the distance between the two closest crowds is less than the optimal inclusion distances of both of the two closest crowds. As another alternative, if the two closest crowds are of the same size, the crowd analyzer  56  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 step  1238 . Otherwise, the two closest crowds are combined or merged (step  1230 ), and a new crowd center for the resulting crowd is computed (step  1232 ). A center of mass algorithm may be used to compute the crowd center of a crowd. In addition, a new optimal inclusion distance for the resulting crowd is computed (step  1234 ). In one embodiment, the new optimal inclusion distance for the resulting crowd is computed as: 
                     average   =       1     n   +   1       ·     (         initial   —     ⁢     optimal   —     ⁢     inclusion   —     ⁢   dist     +       ∑     i   =   1     n     ⁢           ⁢     d   i         )         ,           Eqn   .           ⁢     (   2   )                     optimal   —     ⁢     inclusion   —     ⁢   dist     =     average   +       (       1   n     ·       ∑     i   =   1     n     ⁢           ⁢       (       d   i     -   average     )     2         )                 Eqn   .           ⁢     (   3   )                 
where n is the number of users in the crowd and d i  is a distance between the ith user and the crowd center. In other words, the new optimal inclusion distance is computed as the average of the initial optimal inclusion distance and the distances between the users in the crowd and the crowd center plus one standard deviation.
 
     At this point, the crowd analyzer  56  determines whether a maximum number of iterations have been performed (step  1236 ). The maximum number of iterations is a predefined number that ensures that the crowd formation process does not indefinitely loop over steps  1218  through  1234  or loop over steps  1218  through  1234  more than a desired maximum number of times. If the maximum number of iterations has not been reached, the process returns to step  1218  and is repeated until either the distance between the two closest crowds is not less than the optimal inclusion distance of the larger crowd or the maximum number of iterations has been reached. At that point, the crowd analyzer  56  discards crowds with less than three users, or members (step  1238 ), and the process ends. 
     Returning to step  1208  in  FIG. 6A , if the new and old bounding boxes do not overlap, the process proceeds to  FIG. 6C  and the bounding box to be processed is set to the old bounding box (step  1240 ). In general, the crowd analyzer  56  then processes the old bounding box in much the same manner as described above with respect to steps  1212  through  1238 . More specifically, the crowd analyzer  56  determines the individual users and crowds relevant to the bounding box (step  1242 ). The crowds relevant to the bounding box are crowds that are within or overlap the bounding box (e.g., have at least one user located within the bounding box). The individual users relevant to the bounding box are users that are currently located within the bounding box and not already part of a crowd. Next, the crowd analyzer  56  computes an optimal inclusion distance for individual users based on user density within the bounding box (step  1244 ). More specifically, in one embodiment, the optimal inclusion distance for individuals, which is also referred to herein as an initial optimal inclusion distance, is set according to the following equation: 
                         initial   —     ⁢     optimal   —     ⁢     inclusion   —     ⁢   dist     =     a   ·         A   BoundingBox         number   —     ⁢     of   —     ⁢   users             ,           Eqn   .           ⁢     (   4   )                 
where a is a number between 0 and 1, A BoundingBox  is an area of the bounding box, and number_of_users is the total number of users in the bounding box. The total number of users in the bounding box includes both individual users that are not already in a crowd and users that are already in a crowd. In one embodiment, a is ⅔.
 
     The crowd analyzer  56  then creates a crowd of one user for each individual user within the bounding box that is not already included in a crowd and sets the optimal inclusion distance for the crowds to the initial optimal inclusion distance (step  1246 ). At this point, the crowd analyzer  56  analyzes the crowds for the bounding box to determine whether any crowd members (i.e., users in the crowds) violate the optimal inclusion distance of their crowds (step  1248 ). Any crowd member that violates the optimal inclusion distance of his or her crowd is then removed from that crowd (step  1250 ). The crowd analyzer  56  then creates a crowd of one user for each of the users removed from their crowds in step  1250  and sets the optimal inclusion distance for the newly created crowds to the initial optimal inclusion distance (step  1252 ). 
     Next, the crowd analyzer  56  determines the two closest crowds in the bounding box (step  1254 ) and a distance between the two closest crowds (step  1256 ). The distance between the two closest crowds is the distance between the crowd centers of the two closest crowds. The crowd analyzer  56  then determines whether the distance between the two closest crowds is less than the optimal inclusion distance of a larger of the two closest crowds (step  1258 ). If the two closest crowds are of the same size (i.e., have the same number of users), then the optimal inclusion distance of either of the two closest crowds may be used. Alternatively, if the two closest crowds are of the same size, the optimal inclusion distances of both of the two closest crowds may be used such that the crowd analyzer  56  determines whether the distance between the two closest crowds is less than the optimal inclusion distances of both of the two closest crowds. As another alternative, if the two closest crowds are of the same size, the crowd analyzer  56  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 step  1268 . Otherwise, the two closest crowds are combined or merged (step  1260 ), and a new crowd center for the resulting crowd is computed (step  1262 ). Again, a center of mass algorithm may be used to compute the crowd center of a crowd. In addition, a new optimal inclusion distance for the resulting crowd is computed (step  1264 ). As discussed above, in one embodiment, the new optimal inclusion distance for the resulting crowd is computed as: 
                     average   =       1     n   +   1       ·     (         initial   —     ⁢     optimal   —     ⁢     inclusion   —     ⁢   dist     +       ∑     i   =   1     n     ⁢           ⁢     d   i         )         ,           Eqn   .           ⁢     (   5   )                     optimal   —     ⁢     inclusion   —     ⁢   dist     =     average   +       (       1   n     ·       ∑     i   =   1     n     ⁢           ⁢       (       d   i     -   average     )     2         )                 Eqn   .           ⁢     (   6   )                 
where n is the number of users in the crowd and d i  is a distance between the ith user and the crowd center. In other words, the new optimal inclusion distance is computed as the average of the initial optimal inclusion distance and the distances between the users in the crowd and the crowd center plus one standard deviation.
 
     At this point, the crowd analyzer  56  determines whether a maximum number of iterations have been performed (step  1266 ). If the maximum number of iterations has not been reached, the process returns to step  1248  and is repeated until either the distance between the two closest crowds is not less than the optimal inclusion distance of the larger crowd or the maximum number of iterations has been reached. At that point, the crowd analyzer  56  discards crowds with less than three users, or members (step  1268 ). The crowd analyzer  56  then determines whether the crowd formation process for the new and old bounding boxes is done (step  1270 ). In other words, the crowd analyzer  56  determines whether both the new and old bounding boxes have been processed. If not, the bounding box is set to the new bounding box (step  1272 ), and the process returns to step  1242  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. 7A through 7D  graphically illustrate the crowd formation process of  FIGS. 6A through 6D  for a scenario where the crowd formation process is triggered by a location update for a user having no old location. In this scenario, the crowd analyzer  56  creates a new bounding box  72  for the new location of the user, and the new bounding box  72  is set as the bounding box to be processed for crowd formation. Then, as illustrated in  FIG. 7A , the crowd analyzer  56  identifies all individual users currently located within the new bounding box  72  and all crowds located within or overlapping the new bounding box  72 . In this example, crowd  74  is an existing crowd relevant to the new bounding box  72 . Crowds are indicated by dashed circles, crowd centers are indicated by cross-hairs (+), and users are indicated as dots. Next, as illustrated in  FIG. 7B , the crowd analyzer  56  creates crowds  76  through  80  of one user for the individual users, and the optimal inclusion distances of the crowds  76  through  80  are set to the initial optimal inclusion distance. As discussed above, the initial optimal inclusion distance is computed by the crowd analyzer  56  based on a density of users within the new bounding box  72 . 
     The crowd analyzer  56  then identifies the two closest crowds  76  and  78  in the new bounding box  72  and determines a distance between the two closest crowds  76  and  78 . In this example, the distance between the two closest crowds  76  and  78  is less than the optimal inclusion distance. As such, the two closest crowds  76  and  78  are merged and a new crowd center and new optimal inclusion distance are computed, as illustrated in  FIG. 7C . The crowd analyzer  56  then repeats the process such that the two closest crowds  76  and  80  in the new bounding box  72  are again merged, as illustrated in  FIG. 7D . At this point, the distance between the two closest crowds  74  and  76  is greater than the appropriate optimal inclusion distance. As such, the crowd formation process is complete. 
       FIGS. 8A through 8F  graphically illustrate the crowd formation process of  FIGS. 6A through 6D  for a scenario where the new and old bounding boxes overlap. As illustrated in  FIG. 8A , a user moves from an old location to a new location, as indicated by an arrow. The crowd analyzer  56  receives a location update for the user giving the new location of the user. In response, the crowd analyzer  56  creates an old bounding box  82  for the old location of the user and a new bounding box  84  for the new location of the user. Crowd  86  exists in the old bounding box  82 , and crowd  88  exists in the new bounding box  84 . 
     Since the old bounding box  82  and the new bounding box  84  overlap, the crowd analyzer  56  creates a bounding box  90  that encompasses both the old bounding box  82  and the new bounding box  84 , as illustrated in  FIG. 8B . In addition, the crowd analyzer  56  creates crowds  92  through  98  for individual users currently located within the bounding box  90 . The optimal inclusion distances of the crowds  92  through  98  are set to the initial optimal inclusion distance computed by the crowd analyzer  56  based on the density of users in the bounding box  90 . 
     Next, the crowd analyzer  56  analyzes the crowds  86 ,  88 , and  92  through  98  to determine whether any members of the crowds  86 ,  88 , and  92  through  98  violate the optimal inclusion distances of the crowds  86 ,  88 , and  92  through  98 . In this example, as a result of the user leaving the crowd  86  and moving to his new location, both of the remaining members of the crowd  86  violate the optimal inclusion distance of the crowd  86 . As such, the crowd analyzer  56  removes the remaining users from the crowd  86  and creates crowds  100  and  102  of one user each for those users, as illustrated in  FIG. 8C . 
     The crowd analyzer  56  then identifies the two closest crowds in the bounding box  90 , which in this example are the crowds  96  and  98 . Next, the crowd analyzer  56  computes a distance between the two crowds  96  and  98 . In this example, the distance between the two crowds  96  and  98  is less than the initial optimal inclusion distance and, as such, the two crowds  96  and  98  are combined. In this example, crowds are combined by merging the smaller crowd into the larger crowd. Since the two crowds  96  and  98  are of the same size, the crowd analyzer  56  merges the crowd  98  into the crowd  96 , as illustrated in  FIG. 8D . A new crowd center and new optimal inclusion distance are then computed for the crowd  96 . 
     At this point, the crowd analyzer  56  repeats the process and determines that the crowds  88  and  94  are now the two closest crowds. In this example, the distance between the two crowds  88  and  94  is less than the optimal inclusion distance of the larger of the two crowds  88  and  94 , which is the crowd  88 . As such, the crowd  94  is merged into the crowd  88  and a new crowd center and optimal inclusion distance are computed for the crowd  88 , as illustrated in  FIG. 8E . At this point, there are no two crowds closer than the optimal inclusion distance of the larger of the two crowds. As such, the crowd analyzer  56  discards any crowds having less than three members, as illustrated in  FIG. 8F . In this example, the crowds  92 ,  96 ,  100 , and  102  have less than three members and are therefore removed. The crowd  88  has three or more members and, as such, is not removed. At this point, the crowd formation process is complete. 
       FIGS. 9A through 9E  graphically illustrate the crowd formation process of  FIGS. 6A through 6D  in a scenario where the new and old bounding boxes do not overlap. As illustrated in  FIG. 9A , in this example, a user moves from an old location to a new location. The crowd analyzer  56  creates an old bounding box  104  for the old location of the user and a new bounding box  106  for the new location of the user. Crowds  108  and  110  exist in the old bounding box  104 , and crowd  112  exists in the new bounding box  106 . In this example, since the old and new bounding boxes  104  and  106  do not overlap, the crowd analyzer  56  processes the old and new bounding boxes  104  and  106  separately. 
     More specifically, as illustrated in  FIG. 9B , as a result of the movement of the user from the old location to the new location, the remaining users in the crowd  108  no longer satisfy the optimal inclusion distance for the crowd  108 . As such, the remaining users in the crowd  108  are removed from the crowd  108 , and crowds  114  and  116  of one user each are created for the removed users as shown in  FIG. 9B . In this example, no two crowds in the old bounding box  104  are close enough to be combined. As such, crowds having less than three users are removed as shown in  FIG. 9C , and processing of the old bounding box  104  is complete, and the crowd analyzer  56  proceeds to process the new bounding box  106 . 
     As illustrated in  FIG. 9D , processing of the new bounding box  106  begins by the crowd analyzer  56  creating a crowd  118  of one user for the user. The crowd analyzer  56  then identifies the crowds  112  and  118  as the two closest crowds in the new bounding box  106  and determines a distance between the two crowds  112  and  118 . In this example, the distance between the two crowds  112  and  118  is less than the optimal inclusion distance of the larger crowd, which is the crowd  112 . As such, the crowd analyzer  56  combines the crowds  112  and  118  by merging the crowd  118  into the crowd  112 , as illustrated in  FIG. 9E . A new crowd center and new optimal inclusion distance are then computed for the crowd  112 . At this point, the crowd formation process is complete. Note that the crowd formation processes described above with respect to  FIGS. 6A  through  9 E are exemplary. The present disclosure is not limited thereto. Any type of crowd formation process may be used. 
       FIG. 10  illustrates a process for tagging micro-blog posts with crowd IDs of the crowds in which the corresponding users are located and distributing the micro-blog posts with the crowd ID tags according to one embodiment of the present disclosure. Note that, as discussed below in detail, this process may be performed by the micro-blog function  60  of the MAP server  12 , the MAP applications  32  of the mobile devices  18 , the micro-blogging service  26 , or a combination thereof. First, a micro-blog post of one of the users  20  is obtained (step  1300 ). A crowd in which the user  20  is located is then determined (step  1302 ). Notably, the crowd in which the user  20  is located is the crowd in which the user  20  is located at the time of creating the micro-blog post. The micro-blog post is then tagged with a crowd ID of the crowd in which the user  20  is located, thereby providing a crowd ID tag for the micro-blog post (step  1304 ). In one embodiment, the micro-blog post is a tweet for the Twitter® micro-blogging and social networking service, and the crowd ID tag is implemented as a hash tag (e.g., #{Crowd ID}) appended or otherwise inserted into the tweet. Thus, if the tweet is “Having a great time!” and the crowd ID of the crowd is A942S, then the tweet may be tagged with the crowd ID by modifying the tweet to be “Having a great time! #A942S”. Note, however, that the present disclosure is not limited to Twitter® tweets and hash tags. The micro-blog post may be any type of micro-blog post. Further, in one embodiment, the micro-blog post is tagged with the crowd ID by appending or otherwise inserting the crowd ID into a body of the micro-blog post. In another embodiment, the micro-blog post is tagged with the crowd ID by including the crowd ID as metadata for the micro-blog post. For example, the micro-blog post may be stored as an Extensible Markup Language (XML) file that includes a micro-blog post body (e.g., text message) and metadata (e.g., timestamp that defines a time at which the micro-blog post was made by the user  20 , etc.), where the crowd ID tag is included in the metadata. Lastly, distribution of the micro-blog post including the crowd ID tag is effected (step  1306 ). The manner in which the micro-blog post is distributed varies depending on the particular embodiment, as described below. The process then returns to step  1300  and is repeated for each new micro-blog post received. 
     Note that while the embodiment of the crowd ID tag primarily discussed herein is one where the crowd ID tag is the actual crowd ID of the crowd, the crowd ID tag is not limited thereto. In another embodiment, the crowd ID tag may not be the crowd ID itself, but may be a unique string generated from the crowd ID, or a string that uniquely maps to the crowd ID. This may be desirable in embodiments where the crowd ID tag has to be inserted within the message body of the micro-blog post. This would have multiple benefits, including reducing the number of characters occupied by the tag; avoiding generating unsightly strings of characters meaningless to users; and meeting character length restrictions imposed by the micro-blogging service  26  (for instance, Twitter® enforces a 140-character limit on all blog posts). As an example, a crowd ID may be a 32-bit or even a 64-bit numeric value, or variable length strings, which may result in a very long string when converted to text. Furthermore, this string would be relevant to the MAP application  32 , the MAP server  12 , and the micro-blogging service  26 , but would have no relevance to a user. Hence, to reduce the number of characters required by the tag a shorter alphanumeric string may be generated, associated with the crowd ID, and then used to tag the micro-blog post. In one embodiment, the shorter string is generated from the crowd ID itself, using a method that can be easily reversed to obtain the original crowd ID. One possible method may be to apply a hash function to the crowd ID to generate a string to be used as the crowd ID tag. Another method may be to generate a random string using a random number generator, and associating that string with the crowd ID at the MAP application  32 , the MAP server  12 , and the micro-blogging service  26  or any combination of these, such that the crowd ID can be obtained given the generated string. As an additional precaution, previous randomly generated strings may be persisted in a database, and newly generated strings may be checked against these to ensure that a string does not get re-used. Yet another method may be to convert a numeric crowd ID value from base-2 (binary format) to base-N, assign a unique alphanumeric character to each of the N base-N digits, and represent the crowd ID tag as the resulting string of alphanumeric characters, where N is greater than 2. The alphanumeric characters may utilize any of a number of possible character encoding schemes, such as ASCII or Unicode. As an example, a base-64 encoding could be adopted, where the 64 unique characters include, in order, the 10 digits (0 through 9), 26 lower-case letters (a through z), 26 upper-case letters (A through Z), a hyphen (-) and an underscore (_). Hence, a 32-bit numeric crowd ID having decimal value 606355638 and binary representation 00100100001001000100000010110110 would translate to a text string ‘A942S,’ which is shorter than either binary or decimal representation. This method can be trivially reversed to obtain the original crowd ID from the resulting string. Note that this is a simplified example, and more sophisticated methods may be used. Furthermore, methods resulting in strings that resemble real words may be used, such that the resulting tag appears relatively less nonsensical to users. Note that the tag may be specifically formatted to identify it as being generated by the MAP application  32  or the MAP server  12 , and to differentiate it from other tags that may be added, for instance, by the user. As an example, all MAP-generated hash tags may begin with the string “CL,” e.g. “#CLA942S.” Thus, as used herein, a “crowd ID tag” is to be understood as being either the actual crowd ID of the corresponding crowd or a unique string that is either derived from or maps to the actual crowd ID of the crowd. 
       FIG. 11  illustrates the operation of the system  10  to tag micro-blog posts with crowd IDs of the crowds in which the corresponding users are located and distribute the micro-blog posts with the crowd ID tags according to one embodiment of the present disclosure. In this embodiment, the micro-blog function  60  of the MAP server  12  registers with the micro-blogging service  26  as a “follower” of each of the users  20  (step  1400 ). In other words, the micro-blogging service  26  subscribes to all micro-blog posts published by the users  20  that it “follows.” Some time thereafter, a micro-blog post is sent from the mobile device  18  of one of the users  20  to the micro-blogging service  26  (step  1402 ). More specifically, in one embodiment, the MAP application  32  of the mobile device  18  includes a micro-blog feature that enables the user  20  to create the micro-blog post and initiate the sending of the micro-blog post to the micro-blogging service  26 . Upon receiving the micro-blog post, the micro-blogging service  26  sends the micro-blog post to the micro-blog function  60  of the MAP server  12  as a follower of the user  20  (step  1404 ). Note that the micro-blogging service  26  may proactively publish the post to the MAP server  12  using a push mechanism, or the MAP server  12  may request newly published micro-blog posts from the micro-blogging service  26  using a pull mechanism, such as polling. The micro-blog post may be sent to the micro-blog function  60  of the MAP server  12  as part of publication of the micro-blog post to all followers of the user  20 . 
     Upon receiving the micro-blog post, the micro-blog function  60  of the MAP server  12  determines a crowd in which the user  20  is located (step  1406 ). More specifically, the micro-blog function  60  determines the crowd in which the user  20  is located at the current time, which is substantially the same as the time at which the user  20  made the micro-blog post. In one embodiment, the MAP server  12  forms crowds using the process of  FIGS. 6A through 6D . In this case, the micro-blog function  60  determines the crowd of the user  20  by querying the datastore  64  of the MAP server  12  for the crowd ID of the crowd in which the user  20  is located. The micro-blog function  60  then tags the micro-blog post with the crowd ID of the crowd (step  1408 ). As discussed above, the micro-blog post may be tagged with the crowd ID by appending the crowd ID to or otherwise inserting the crowd ID into the body of the micro-blog post (e.g., a hash tag for a Twitter® tweet) or including the crowd ID in metadata of the micro-blog post. 
     Next, the micro-blog function  60  sends the micro-blog post including the crowd ID tag to the micro-blogging service  26  (step  1410 ). In this embodiment, the micro-blog function  60  of the MAP server  12  is registered with the micro-blogging service  26  such that the micro-blog function  60  is enabled to make micro-blog posts. Thus, in step  1410 , the micro-blog function  60  sends the micro-blog post including the crowd ID tag to the micro-blogging service  26  as a post of the micro-blog function  60  of the MAP server  12 . For example, in one embodiment, the micro-blogging service  26  is the Twitter® micro-blogging and social networking service, and the micro-blog function  60  of the MAP server  12  has a Twitter® account. Then, in step  1410 , the micro-blog post is sent to the Twitter® service as a tweet from the micro-blog function  60  of the MAP server  12 . In this manner, the micro-blog post including the crowd ID tag is anonymized in that the micro-blog post sent in step  1410  does not identify the user  20  as the originator of the micro-blog post. Upon receiving the micro-blog post in step  1410 , the micro-blogging service  26  publishes the micro-blog post including the crowd ID tag (step  1412 ). Notably, steps  1402  through  1412  are repeated for additional micro-blog posts made by the user  20  as well as micro-blog posts made by other users  20 . 
       FIG. 12  illustrates a process for publishing the micro-blog post of  FIG. 11  according to one embodiment of the present disclosure. In this embodiment, the mobile devices  18 - 1  through  18 -N register the users  20 - 1  through  20 -N with the micro-blogging service  26  as followers of the MAP server  12  (steps  1500 - 1  through  1500 -N). The micro-blogging service  26  receives a micro-blog post including a crowd ID tag from the micro-blog function  60  of the MAP server  12  (step  1502 ). Because the users  20 - 1  through  20 -N are followers of the MAP server  12 , the micro-blogging service  26  sends the micro-blog post to the mobile devices  18 - 1  through  18 -N (steps  1504 - 1  through  1504 -N). 
     In response, the MAP applications  32 - 1  through  32 -N of the mobile devices  18 - 1  through  18 -N filter the micro-blog post based on the crowd ID tag (steps  1506 - 1  through  1506 -N). More specifically, in one embodiment, the MAP applications  32 - 1  through  32 -N filter micro-blog posts made by the MAP server  12  such that only micro-blog posts tagged with the crowd IDs of desired crowds are presented to the users  20 - 1  through  20 -N. Using the MAP application  32 - 1  as an example, in the preferred embodiment, the MAP application  32 - 1  filters micro-blog posts made by the MAP server  12  such that only the micro-blog posts tagged with the crowd ID of the crowd in which the user  20 - 1  is located pass through the filter to be presented to the user  20 - 1 . In this manner, the users  20  in a crowd form an ad-hoc micro-blogging group, where micro-blog posts made by the users  20  in the ad-hoc micro-blogging group are published to the other users  20  in the ad-hoc micro-blogging group. However, in an alternative embodiment, the MAP application  32 - 1  filters micro-blog posts made by the MAP server  12  such that only the micro-blog posts tagged with the crowd ID(s) of a desired crowd(s) selected by the user  20 - 1  pass through the filter to be presented to the user  20 - 1 . Lastly, the MAP applications  32 - 1  through  32 -N present the micro-blog post to the users  20 - 1  through  20 -N if the micro-blog post passes through the corresponding filters applied by the MAP applications  32 - 1  through  32 -N (steps  1508 - 1  through  1508 -N). Notably, steps  1502  through  1508  are repeated for additional micro-blog posts sent to the micro-blogging service  26  from the MAP server  12 . 
       FIG. 13  illustrates a process for publishing the micro-blog post of  FIG. 11  according to another embodiment of the present disclosure. In general, in this embodiment, rather than registering each of the users  20  as a follower of the MAP server  12 , the MAP applications  32  of the mobile devices  18  of the users  20  query the micro-blogging service  26  for micro-blog posts tagged with desired crowd IDs. More specifically, the micro-blogging service  26  receives micro-blog posts including crowd ID tags from the MAP server  12  (step  1600 ). At some point in time, the MAP application  32  of one of the mobile devices  18  sends a search request to the micro-blogging service  26  for micro-blog posts tagged with a desired crowd ID (step  1602 ). The MAP application  32  may send the search request automatically or in response to a request from the user  20  of the mobile device  18 . The desired crowd ID is the ID of a desired crowd. In the preferred embodiment, the desired crowd is a crowd in which the user  20  is currently located. In this manner, the crowd operates as an ad-hoc micro-blogging group, where the mobile device  18  obtains micro-blog posts made by the users  20  in the ad-hoc micro-blogging group. In an alternative embodiment, the desired crowd is a crowd selected by the user  20  of the mobile device  18 . For instance, the MAP application  32  may present a map to the user  20  where crowds are displayed on the map at corresponding geographical locations. The user  20  may then select the desired crowd from the map to initiate the search request. Notably, the search request may include one or more additional search criteria such as, for example, a desired time range such that micro-blog posts returned in response to the search request have timestamps that fall within the desired time range. 
     In response to the search request, the micro-blogging service  26  performs a search for micro-blog posts tagged with the desired crowd ID (step  1604 ). More specifically, in this embodiment, the micro-blogging service  26  stores a repository of micro-blog posts. The micro-blogging service  26  searches the repository for micro-blog posts tagged with the desired crowd ID. Note that if any additional search criteria are defined in the search request, the additional search criteria are also used when performing the search. The micro-blogging service  26  then returns the micro-blog posts resulting from the search (i.e., the micro-blog posts tagged with the desired crowd ID and that satisfy any additional search criteria included in the search request) to the mobile device  18  (step  1606 ). The MAP application  32  of the mobile device  18  then presents the micro-blog posts to the user  20  (step  1608 ). Notably, using the process of  FIG. 13 , the user  20  is enabled to obtain micro-blog posts made by the users  20  in the desired crowd without necessarily being registered with the micro-blogging service  26  as a follower of the users  20  in the desired crowd. Note that since crowd IDs may be incorporated as text or hash tags in the micro-blog message body itself, the micro-blogging service  26  only needs to provide basic text indexing and searching services, or search services adapted for indexing and searching of hash tags, to be able to perform step  1604 . As such, ad-hoc micro-blogging groups may be successfully established even if the micro-blogging service  26  is not aware of the existence of crowd IDs or configured to provide search capabilities specific to crowd IDs. 
       FIG. 14  illustrates the operation of the system  10  to tag micro-blog posts with crowd IDs of the crowds in which the corresponding users are located and distribute the micro-blog posts with the crowd ID tags according to another embodiment of the present disclosure. This embodiment is similar to that of  FIG. 11 . However, in this embodiment, rather than being published by the micro-blogging service  26 , the micro-blog posts including the crowd ID tags are published by the MAP server  12 . More specifically, in this embodiment, the micro-blog function  60  of the MAP server  12  registers with the micro-blogging service  26  as a “follower” of each of the users  20  (step  1700 ). Some time thereafter, a micro-blog post is sent from the mobile device  18  of one of the users  20  to the micro-blogging service  26  (step  1702 ). More specifically, in one embodiment, the MAP application  32  of the mobile device  18  includes a micro-blog feature that enables the user  20  to create the micro-blog post and initiate the sending of the micro-blog post to the micro-blogging service  26 . Upon receiving the micro-blog post, the micro-blogging service  26  sends the micro-blog post to the micro-blog function  60  of the MAP server  12  as a follower of the user  20  (step  1704 ). The micro-blog post may be sent to the micro-blog function  60  of the MAP server  12  as part of publication of the micro-blog post to all followers of the user  20 . 
     Upon receiving the micro-blog post, the micro-blog function  60  of the MAP server  12  determines a crowd in which the user  20  is located (step  1706 ). More specifically, the micro-blog function  60  determines the crowd in which the user  20  is located at the current time, which in this embodiment is substantially the same as the time at which the user  20  sent the micro-blog post. In one embodiment, the MAP server  12  forms crowds using the process of  FIGS. 6A through 6D . In this case, the micro-blog function  60  determines the crowd of the user  20  by querying the datastore  64  of the MAP server  12  for the crowd ID of the crowd in which the user  20  is located. The micro-blog function  60  then tags the micro-blog post with the crowd ID of the crowd (step  1708 ). As discussed above, the micro-blog post may be tagged with the crowd ID by appending the crowd ID to or otherwise inserting the crowd ID into the body of the micro-blog post (e.g., a hash tag for a Twitter® tweet) or including the crowd ID in metadata of the micro-blog post. Lastly, the micro-blog function  60  publishes the micro-blog post including the crowd ID tag (step  1710 ). The manner in which the micro-blog post is published may vary depending on the particular embodiment, as described below. Notably, steps  1702  through  1710  are repeated for additional micro-blog posts made by the user  20  as well as micro-blog posts made by other users  20 . 
       FIG. 15  illustrates a process for publishing the micro-blog post of  FIG. 14  according to one embodiment of the present disclosure. In this embodiment, the micro-blog function  60  of the MAP server  12  receives micro-blog posts from the micro-blogging service  26  and tags the micro-blog posts with crowd IDs (step  1800 ). In order to publish the micro-blog posts, the micro-blog function  60  of the MAP server  12  sends the micro-blog posts including the crowd ID tags to the MAP applications  32  of the mobile devices  18  (steps  1802 - 1  through  1802 -N). Notably, in this embodiment, all of the micro-blog posts received and tagged by the micro-blog function  60  are sent to all of the mobile devices  18 . 
     The MAP applications  32  of the mobile devices  18  filter the micro-blog posts to pass only the micro-blog posts of desired crowds for presentation to the users  20  (steps  1804 - 1  through  1804 -N). More specifically, for each mobile device  18 , the MAP application  32  of the mobile device  18  filters the micro-blog posts to pass only those micro-blog posts tagged with a desired crowd ID. The desired crowd ID is the crowd ID of a desired crowd. In the preferred embodiment, the desired crowd is the crowd in which the user  20  of the mobile device  18  is located. In this manner, the users  20  in a crowd form an ad-hoc micro-blogging group, where micro-blog posts by the users  20  in the ad-hoc micro-blogging group are published to the other users  20  in the ad-hoc micro-blogging group. In an alternative embodiment, the desired crowd is a crowd selected by the user  20  of the mobile device  18 . Lastly, the MAP applications  32  of the mobile devices  18  present the filtered micro-blog posts to the users  20  (steps  1806 - 1  through  1806 -N). 
       FIG. 16  illustrates a process for publishing the micro-blog post of  FIG. 14  according to another embodiment of the present disclosure. In this embodiment, the micro-blog function  60  of the MAP server  12  receives micro-blog posts from the micro-blogging service  26  and tags the micro-blog posts with crowd IDs (step  1900 ). At some point in time, the MAP application  32  of one of the mobile devices  18  sends a search request to the micro-blogging service  26  for micro-blog posts tagged with a desired crowd ID (step  1902 ). The MAP application  32  may send the search request automatically or in response to a request from the user  20  of the mobile device  18 . The desired crowd ID is the ID of a desired crowd. In the preferred embodiment, the desired crowd is a crowd in which the user  20  is currently located. In this manner, the crowd operates as an ad-hoc micro-blogging group, where the mobile device  18  obtains micro-blog posts made by the users  20  in the ad-hoc micro-blogging group. In an alternative embodiment, the desired crowd is a crowd selected by the user  20  of the mobile device  18 . Notably, the search request may include one or more additional search criteria such as, for example, a desired time range such that micro-blog posts returned in response to the search request have timestamps that fall within the desired time range. 
     In response to the search request, the micro-blog function  60  of the MAP server  12  performs a search for micro-blog posts tagged with the desired crowd ID (step  1904 ). More specifically, in this embodiment, the MAP server  12  stores a repository of micro-blog posts received from the micro-blogging service  26  and tagged with corresponding crowd IDs. The micro-blog function  60  searches the repository for micro-blog posts tagged with the desired crowd ID. Note that if any additional search criteria are defined in the search request, the additional search criteria are also used when performing the search. The micro-blog function  60  then returns the micro-blog posts resulting from the search (i.e., the micro-blog posts tagged with the desired crowd ID and that satisfy any additional search criteria included in the search request) to the mobile device  18  (step  1906 ). The MAP application  32  of the mobile device  18  then presents the micro-blog posts to the user  20  (step  1908 ). 
       FIG. 17  illustrates the operation of the system  10  to tag micro-blog posts with crowd IDs of the crowds in which the corresponding users are located and distribute the micro-blog posts with the crowd ID tags according to another embodiment of the present disclosure. In this embodiment, the mobile device  18  of one of the users  20  sends a micro-blog post to the MAP server  12  (step  2000 ). More specifically, in one embodiment, the MAP application  32  of the mobile device  18  includes a micro-blog feature that enables the user  20  to create the micro-blog post and initiate the sending of the micro-blog post to the MAP server  12  either via a communication channel provided by the MAP application  32  directly to the MAP server  12  or via a direct messaging scheme provided by the micro-blogging service  26 , depending on the particular implementation. Upon receiving the micro-blog post, the micro-blog function  60  of the MAP server  12  determines a crowd in which the user  20  of the mobile device  18  is located (step  2002 ). More specifically, the micro-blog function  60  determines the crowd in which the user  20  is located at the current time, which in this embodiment is substantially the same as the time at which the user  20  made the micro-blog post. In one embodiment, the MAP server  12  forms crowds using the process of  FIGS. 6A through 6D . In this case, the micro-blog function  60  determines the crowd of the user  20  by querying the datastore  64  of the MAP server  12  for the crowd ID of the crowd in which the user  20  is located. The micro-blog function  60  then tags the micro-blog post with the crowd ID of the crowd (step  2004 ). As discussed above, the micro-blog post may be tagged with the crowd ID by appending the crowd ID to or otherwise inserting the crowd ID into the body of the micro-blog post (e.g., a hash tag for a Twitter® tweet) or including the crowd ID in metadata of the micro-blog post. 
     Next, the micro-blog function  60  of the MAP server  12  sends the micro-blog post including the crowd ID tag to the micro-blogging service  26  (step  2006 ). In the preferred embodiment, the micro-blog post is sent as a micro-blog post of the MAP server  12 . In this manner, the micro-blog post is anonymized such that the user  20  is not identified as the sender, or originator, of the micro-blog post. However, in an alternative embodiment, the micro-blog function  60  sends the micro-blog post to the micro-blogging service  26  on behalf of the user  20 . In this manner, the micro-blog post is not anonymous and may be published to followers of the user  20 . Upon receiving the micro-blog post, the micro-blogging service  26  publishes the micro-blog post including the crowd ID tag (step  2008 ). The manner in which the micro-blog post is published may vary depending on the particular implementation. In one embodiment, the micro-blogging service  26  publishes the micro-blog post using the process of  FIG. 12 . In another embodiment, the micro-blogging service  26  publishes the micro-blog post using the process of  FIG. 13 . Notably, the processes of  FIGS. 12 and 13  are exemplary publication processes and neither the embodiment of  FIG. 11  nor the embodiment of  FIG. 17  is limited thereto. 
       FIG. 18  illustrates the operation of the system  10  to tag micro-blog posts with crowd IDs of the crowds in which the corresponding users are located and distribute the micro-blog posts with the crowd ID tags according to another embodiment of the present disclosure. In the embodiments described above, tagging is performed by the MAP server  12 . However, in this embodiment, tagging is performed by the MAP application  32  of the mobile device  18  of the sender of the micro-blog post. More specifically, the MAP application  32  generates a micro-blog post (step  2100 ). In one embodiment, the MAP application  32  enables the user  20  to define a body (e.g., a text message) of the micro-blog post and then generates the micro-blog post with the defined body. Next, the MAP application  32  requests a crowd ID of the current crowd of the user  20  from the MAP server  12  (step  2102 ). In response to the request, the micro-blog function  60  of the MAP server  12  determines the crowd in which the user  20  is located (step  2104 ) and returns the crowd ID of the crowd to the mobile device  18  (step  2106 ). Notably, while in this embodiment the crowd ID is obtained after generating the micro-blog post, the crowd ID may alternatively be obtained prior to generating the micro-blog post. In one embodiment, once the crowd ID is obtained by the mobile device  18 , the crowd ID may be cached and re-used for future micro-blog posts by the user  20  until a threshold amount of time passes or until it is detected that the user&#39;s crowd has changed. 
     Next, the MAP application  32  tags the micro-blog post with the crowd ID of the crowd in which the user  20  is located (step  2108 ) and sends the micro-blog post including the crowd ID tag to the micro-blogging service  26  (step  2110 ). The micro-blogging service  26  then publishes the micro-blog post including the crowd ID tag (step  2112 ). In this embodiment, the micro-blog post is published as a micro-blog post of the user  20 . However, in an alternative embodiment, the MAP application  32  may send the post as a micro-blog post of the MAP server  12  such that the MAP server  12 , rather than the user  20 , is identified as the sender of the micro-blog post (i.e., the micro-blog post is anonymized). Also, for this embodiment, the micro-blogging service  26  preferably publishes the micro-blog post using the process of  FIG. 13  where the MAP applications  32  of the other users  20  search for micro-blog posts tagged with the desired crowd IDs. Notably, the process of  FIG. 13  is an exemplary publication process and the embodiment of  FIG. 18  is not limited thereto. 
       FIG. 19  illustrates the operation of the system  10  to tag micro-blog posts with crowd IDs of the crowds in which the corresponding users are located and distribute the micro-blog posts with the crowd ID tags according to another embodiment of the present disclosure. In this embodiment, tagging is performed by the MAP application  32  of the mobile device  18  of the sender of the micro-blog post. More specifically, the MAP application  32  generates a micro-blog post (step  2200 ). In one embodiment, the MAP application  32  enables the user  20  to define a body (e.g., a text message) of the micro-blog post and then generates the micro-blog post with the defined body. Next, the MAP application  32  requests a crowd ID of the current crowd of the user  20  from the MAP server  12  (step  2202 ). In response to the request, the micro-blog function  60  of the MAP server  12  determines the crowd in which the user  20  is located (step  2204 ) and returns the crowd ID of the crowd to the mobile device  18  (step  2206 ). Notably, while in this embodiment the crowd ID is obtained after generating the micro-blog post, the crowd ID may alternatively be obtained prior to generating the micro-blog post. 
     Next, the MAP application  32  tags the micro-blog post with the crowd ID of the crowd in which the user  20  is located (step  2208 ) and sends the micro-blog post including the crowd ID tag to the MAP server  12  (step  2210 ). The MAP application  32  may send the micro-blog post to the MAP server  12  via a direct communication channel between the MAP application  32  and the MAP server  12  or a direct messaging scheme provided by the micro-blogging service  26 . The micro-blog function  60  of the MAP server  12  then sends the micro-blog post including the crowd ID tag to the micro-blogging service  26  (step  2212 ). Preferably, the micro-blog function  60  sends the micro-blog post as a micro-blog post of the MAP server  12  such that the user  20  is anonymized (i.e., the MAP server  12 , rather than the user  20 , is identified as the sender of the micro-blog post). The micro-blogging service  26  then publishes the micro-blog post including the crowd ID tag (step  2214 ). The manner in which the micro-blog post is published may vary depending on the particular implementation. In one embodiment, the micro-blogging service  26  publishes the micro-blog post using the process of  FIG. 12 . In another embodiment, the micro-blogging service  26  publishes the micro-blog post using the process of  FIG. 13 . Notably, the process of  FIGS. 12 and 13  are exemplary publication processes and the embodiment of  FIG. 19  is not limited thereto. 
       FIG. 20  is a block diagram of the MAP server  12  according to one embodiment of the present disclosure. As illustrated, the MAP server  12  includes a controller  120  connected to memory  122 , one or more secondary storage devices  124 , and a communication interface  126  by a bus  128  or similar mechanism. The controller  120  is a microprocessor, digital Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), or similar hardware component. In this embodiment, the controller  120  is a microprocessor, and the application layer  40 , the business logic layer  42 , and the object mapping layer  62  ( FIG. 2 ) are implemented in software and stored in the memory  122  for execution by the controller  120 . Further, the datastore  64  ( FIG. 2 ) may be implemented in the one or more secondary storage devices  124 . The secondary storage devices  124  are digital data storage devices such as, for example, one or more hard disk drives. The communication interface  126  is a wired or wireless communication interface that communicatively couples the MAP server  12  to the network  28  ( FIG. 1 ). For example, the communication interface  126  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. 21  is a block diagram of one of the mobile devices  18  according to one embodiment of the present disclosure. This discussion is equally applicable to the other mobile devices  18 . As illustrated, the mobile device  18  includes a controller  130  connected to memory  132 , a communication interface  134 , one or more user interface components  136 , and the location function  36  by a bus  138  or similar mechanism. The controller  130  is a microprocessor, digital ASIC, FPGA, or similar hardware component. In this embodiment, the controller  130  is a microprocessor, and the MAP client  30 , the MAP application  32 , and the third-party applications  34  are implemented in software and stored in the memory  132  for execution by the controller  130 . In this embodiment, the location function  36  is a hardware component such as, for example, a GPS receiver. The communication interface  134  is a wireless communication interface that communicatively couples the mobile device  18  to the network  28  ( FIG. 1 ). For example, the communication interface  134  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., a 3G interface such as a Global System for Mobile Communications (GSM) interface, a 4G interface such as a Long Term Evolution (LTE) interface, or the like), or the like. The one or more user interface components  136  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. 
       FIG. 22  is a block diagram of the subscriber device  22  according to one embodiment of the present disclosure. As illustrated, the subscriber device  22  includes a controller  140  connected to memory  142 , one or more secondary storage devices  144 , a communication interface  146 , and one or more user interface components  148  by a bus  150  or similar mechanism. The controller  140  is a microprocessor, digital ASIC, FPGA, or similar hardware component. In this embodiment, the controller  140  is a microprocessor, and the web browser  38  ( FIG. 1 ) is implemented in software and stored in the memory  142  for execution by the controller  140 . The one or more secondary storage devices  144  are digital data storage devices such as, for example, one or more hard disk drives. The communication interface  146  is a wired or wireless communication interface that communicatively couples the subscriber device  22  to the network  28  ( FIG. 1 ). For example, the communication interface  146  may be an Ethernet interface, local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, a mobile communications interface such as a cellular telecommunications interface, or the like. The one or more user interface components  148  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. 
       FIG. 23  is a block diagram of a server computer  152  operating to host the micro-blogging service  26  according to one embodiment of the present disclosure. The server computer  152  may be, for example, a server computer. As illustrated, the server computer  152  includes a controller  154  connected to memory  156 , one or more secondary storage devices  158 , a communication interface  160 , and one or more user interface components  162  by a bus  164  or similar mechanism. The controller  154  is a microprocessor, digital ASIC, FPGA, or similar hardware component. In this embodiment, the controller  154  is a microprocessor, and the micro-blogging service  26  is implemented in software and stored in the memory  156  for execution by the controller  154 . The one or more secondary storage devices  158  are digital data storage devices such as, for example, one or more hard disk drives. The communication interface  160  is a wired or wireless communication interface that communicatively couples the server computer  152  to the network  28  ( FIG. 1 ). For example, the communication interface  160  may be an Ethernet interface, local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, a mobile communications interface such as a cellular telecommunications interface, or the like. The one or more user interface components  162  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. 
     The systems and methods described herein have substantial opportunity for variation without departing from the spirit and scope of the present disclosure. For example,  FIGS. 11 through 19  describe several exemplary embodiments for tagging and publishing micro-blog posts. However, one of ordinary skill in the art will appreciate that the system and processes described in  FIGS. 11 through 19  may vary depending on the particular implementation. For example, while tagging is performed by either the MAP server  12  or the MAP applications  32  of the mobile devices  18  in the embodiments described above, the present disclosure is not limited thereto. In another embodiment, tagging may be performed by the micro-blogging service  26 . For example, upon receiving a micro-blog post from one of the users  20 , the micro-blogging service  26  may query the MAP server  12  for the crowd ID of the crowd in which the user  20  is located and then tag the micro-blog post with the crowd ID prior to publication. The MAP applications of other users  20  in the crowd may then submit search requests to the micro-blogging service  26  for micro-blog posts tagged with the crowd ID of the crowd to thereby obtain micro-blog posts made by the users  20  in the crowd without registering the users  20  in the crowd as followers of one another. As another example, while the discussion herein focuses on micro-blog posts, the systems and methods described herein may additionally or alternatively be used to tag other types of communications (e.g., RSS feeds) with crowd IDs of the crowds in which the originators of the communications are located. As a final example, as an alternative to or in addition to tagging micro-blog posts with crowd IDs, Uniform Resource Locators (URLs) may be inserted into the micro-blog posts. The URL inserted into a micro-blog post may link to a website for the MAP server  12 , link to a web application provided by the MAP server  12 , or link to a download of the MAP application  32 . In this manner, the URLs may be used to virally introduce the MAP application  32 . Furthermore, the URLs may include the crowd IDs of the crowds in which the corresponding users  20  are located as a parameter (e.g., an HTTP GET parameter) so that the MAP application  32  (or a MAP web-client) can process the URL to display relevant information. 
     The following use cases illustrate some, but not necessarily all, of the aspects discussed above with respect to tagging micro-blog posts with crowd IDs. Note that these use cases are exemplary and are not intended to limit the scope of the concepts described herein. 
     Use Case #1:
         1. Users Alex, Bob, and Cathy (i.e., three of the users  20 ) are watching the big game at Carolina Sports Bar.   2. The MAP server  12  forms a crowd for Alex, Bob, and Cathy and assigns the crowd a crowd ID, “CL3QW5”.   3. The MAP server  12  notifies the MAP applications  32  of the mobile devices  18  of Alex, Bob, and Cathy that the crowd ID of their crowd is “CL3QW5”.   4. Alex creates a tweet of “Go Saints! Woohoo!” via his MAP application  32 , which is configured to post it anonymously via the Twitter® micro-blogging and social networking service (which is operating as the micro-blogging service  26 ) using a Twitter® account of the MAP server  12 .   5. The MAP application  32  automatically inserts a hash tag into the tweet before posting it to the Twitter® account of the MAP server  12  such that the tweet now reads “Go Saints! Woohoo! #CL3QW5”.   6. Cathy then opens her MAP application  32  to see what the crowd is saying.   7. Cathy&#39;s MAP application  32  determines her crowd ID and sends a search request to the Twitter® service for tweets containing the crowd ID tag “#CL3QW5” (which could be a simple search for the string “#CL3QW5”).   8. The Twitter® service returns all tweets containing the string “#CL3QW5”, which includes Alex&#39;s tweet.   9. Cathy posts a tweet saying “Colts gonna murder the Saints!” which is posted as “Colts gonna murder the Saints! #CL3QW5”.   10. The next time Alex checks his MAP application  32  he sees Cathy&#39;s tweet (which is anonymous) posted on the account of the MAP server  12 .   11. Alex wonders which of the many Colts fans posted that tweet.       

     Use Case #2:
         1. Users Alex, Bob, and Cathy (i.e., three of the users  20 ) are watching the big game at Carolina Sports Bar.   2. The MAP server  12  forms a crowd for Alex, Bob, and Cathy and assigns the crowd a crowd ID, “CL3QW5”.   3. The MAP server  12  notifies the MAP applications  32  of the mobile devices  18  of Alex, Bob, and Cathy that the crowd ID of their crowd is “CL3QW5”.   4. Alex creates a tweet of “Go Saints! Woohoo!” via his MAP application  32 , which is configured to post it to the Twitter® micro-blogging and social networking service (which is operating as the micro-blogging service  26 ) using Alex&#39;s Twitter® account.   5. The MAP application  32  automatically inserts a hash tag into the tweet before posting it to Alex&#39;s Twitter® account such that the tweet now reads “Go Saints! Woohoo! #CL3QW5”.   6. Cathy then opens her MAP application  32  to see what the crowd is saying.   7. Cathy&#39;s MAP application  32  determines her crowd ID and sends a search request to the Twitter® service for tweets containing the crowd ID tag “#CL3QW5” (which could be a simple search for the string “#CL3QW5”).   8. The Twitter® service returns all tweets containing the string “#CL3QW5”, which includes Alex&#39;s tweet.   9. Since Cathy is not following Alex on the Twitter® service, Cathy&#39;s MAP application  32  anonymizes the tweet to hide Alex&#39;s user information by replacing it with a random string.   10. Cathy posts a tweet saying “Colts gonna murder the Saints!” which is posted as “Colts gonna murder the Saints! #CL3QW5”.   11. The next time Alex checks his MAP application  32  he sees Cathy&#39;s anonymized tweet.   12. Bob also checks his MAP application  32 , but since he is following Cathy on the Twitter® service, he sees the post unanonymized.   13. Bob, also a Colts fan, high-fives Cathy.       

     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.