US20120066614A1 - Methods and systems for following crowds - Google Patents

Methods and systems for following crowds Download PDF

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US20120066614A1
US20120066614A1 US13/032,744 US201113032744A US2012066614A1 US 20120066614 A1 US20120066614 A1 US 20120066614A1 US 201113032744 A US201113032744 A US 201113032744A US 2012066614 A1 US2012066614 A1 US 2012066614A1
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crowd
users
requestor
crowds
user
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Christopher M. Amidon
Scott Curtis
Steven L. Petersen
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Waldeck Technology LLC
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Waldeck Technology LLC
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L51/00Arrangements for user-to-user messaging in packet-switching networks, e.g. e-mail or instant messages
    • H04L51/20Messaging using geographical location information
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10File systems; File servers
    • G06F16/17Details of further file system functions
    • G06F16/1734Details of monitoring file system events, e.g. by the use of hooks, filter drivers, logs
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10File systems; File servers
    • G06F16/18File system types
    • G06F16/1805Append-only file systems, e.g. using logs or journals to store data
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10File systems; File servers
    • G06F16/18File system types
    • G06F16/182Distributed file systems
    • G06F16/1834Distributed file systems implemented based on peer-to-peer networks, e.g. gnutella
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/40Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90Details of database functions independent of the retrieved data types
    • G06F16/95Retrieval from the web
    • G06F16/953Querying, e.g. by the use of web search engines
    • G06F16/9537Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L51/00Arrangements for user-to-user messaging in packet-switching networks, e.g. e-mail or instant messages
    • H04L51/32Messaging within social networks
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network-specific arrangements or communication protocols supporting networked applications
    • H04L67/22Tracking the activity of the user
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network-specific arrangements or communication protocols supporting networked applications
    • H04L67/30Network-specific arrangements or communication protocols supporting networked applications involving profiles
    • H04L67/306User profiles
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/01Social networking

Abstract

Systems and methods are disclosed for following status updates sent by users in crowds of users. In one embodiment, a requestor is enabled to follow status updates sent by users in a crowd of users even after the users have dispersed from the crowd. More specifically, in one embodiment, a requestor selects a crowd to follow. Subsequently, after one or more users have left the crowd, status updates from the users are obtained and sent to the requestor. In another embodiment, a requestor selects a crowd to follow. Subsequently, after some or all of the users in the crowd have dispersed, status updates from users in new crowds in which those users are located are obtained and sent to the requestor. In another embodiment, a requestor is enabled to follow a user such that the requestor receives status updates from crowds of users in which the user is located.

Description

    RELATED APPLICATIONS
  • This application claims the benefit of provisional patent application Ser. No. 61/309,903, filed Mar. 3, 2010, the disclosure of which is hereby incorporated herein by reference in its entirety.
  • FIELD OF THE DISCLOSURE
  • The present disclosure relates to status updates from users in crowds of users.
  • BACKGROUND
  • Status update services, such as the Twitter® micro-blogging and social networking service, provide the ability to follow status updates (e.g., tweets) of other users. However, in many instances, it may be desirable to follow the status updates by an entire crowd of users. For example, a person may desire to follow status updates made by users in a crowd of users located at a sporting event. Thus, there is a need for a system and method of following status updates made by users in a crowd of users.
  • SUMMARY
  • Systems and methods are disclosed for following status updates sent by users in crowds of users. In one embodiment, a requestor is enabled to follow status updates sent by users in a crowd of users even after the users have dispersed from the crowd. More specifically, in one embodiment, a requestor selects a crowd to follow. Subsequently, after one or more users have left the crowd, status updates from the one or more users are obtained and sent to the requestor. In another embodiment, a requestor selects a crowd to follow. Subsequently, after some or all of the users in the crowd have dispersed, status updates from users in new crowds in which those users are located are obtained and sent to the requestor.
  • In another embodiment, a requestor is enabled to follow a user such that the requestor receives status updates from crowds of users in which the user is located. More specifically, the requestor selects a user to follow. In response, the requestor is recorded as a follower of a crowd in which the user is currently located, and status updates sent by users in the crowd are obtained and sent to the requestor as a follower of the crowd. Thereafter, when the user is located in a new crowd, the requestor is recorded as a follower of the new crowd. Status updates from users in the new crowd followed by the requestor are then obtained and sent to the requestor.
  • 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 system that enables crowd following in order to receive status updates from users in followed crowds according to one embodiment of the present disclosure;
  • FIG. 2 is a more detailed illustration of the Mobile Aggregate Profile (MAP) server of FIG. 1 according to one embodiment of the present disclosure;
  • FIG. 3 is a more detailed illustration of the MAP application of one of the mobile devices of FIG. 1 according to one embodiment of the present disclosure;
  • 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;
  • FIG. 6 illustrates exemplary data records that may be used to represent crowds, users, crowd snapshots, and anonymous users according to one embodiment of the present disclosure;
  • FIGS. 7A through 7D illustrate one embodiment of a spatial crowd formation process that may be used to enable crowd tracking according to one embodiment of the present disclosure;
  • FIGS. 8A through 8D graphically illustrate the crowd formation process of FIGS. 7A through 7D for a scenario where the crowd formation process is triggered by a location update for a user having no old location;
  • FIGS. 9A through 9F graphically illustrate the crowd formation process of FIGS. 7A through 7D for a scenario where the new and old bounding boxes overlap;
  • FIGS. 10A through 10E graphically illustrate the crowd formation process of FIGS. 7A through 7D in a scenario where the new and old bounding boxes do not overlap;
  • FIG. 11 illustrates a process for creating crowd snapshots according to one embodiment of the present disclosure;
  • FIG. 12 illustrates a process that may be used to re-establish crowds and detect crowd splits according to one embodiment of the present disclosure;
  • FIG. 13 graphically illustrates the process of re-establishing a crowd for an exemplary crowd according to one embodiment of the present disclosure;
  • FIG. 14 graphically illustrates the process for capturing a crowd split for an exemplary crowd according to one embodiment of the present disclosure;
  • FIG. 15 graphically illustrates the merging of two exemplary pre-existing crowds according to one embodiment of the present disclosure;
  • FIG. 16 illustrates the operation of the status update processor of the MAP server of FIG. 1 to enable a requestor to follow status updates from users in a select crowd even after the users in the select crowd disperse according to one embodiment of the present disclosure;
  • FIG. 17 illustrates the operation of the status update processor of the MAP server of FIG. 1 to receive and distribute status updates sent by users to followers of corresponding crowds of users according to one embodiment of the present disclosure;
  • FIG. 18 illustrates the operation of the status update processor to automatically record a requestor as a follower of crowds of users including users dispersing from a select crowd of users according to one embodiment of the present disclosure;
  • FIG. 19 illustrates the operation of the status update processor of the MAP server according to another embodiment of the present disclosure;
  • FIG. 20 illustrates the operation of the status update processor of the MAP server according to yet another embodiment of the present disclosure in which a requestor is automatically added as a follower of crowds split from a select crowd;
  • FIG. 21 illustrates the operation of the status update processor of the MAP server according to yet another embodiment of the present disclosure in which a requestor is enabled to follow crowds of users in which a select user is located;
  • FIG. 22 illustrates the operation of the status update processor of the MAP server according to yet another embodiment of the present disclosure in which a requestor is enabled to receive status updates from users in a select crowd of users even after those users have dispersed from the select crowd of users;
  • FIG. 23 is a block diagram of the MAP server of FIG. 1 according to one embodiment of the present disclosure;
  • FIG. 24 is a block diagram of one of the mobile devices of FIG. 1 according to one embodiment of the present disclosure;
  • FIG. 25 is a block diagram of the subscriber device of FIG. 1 according to one embodiment of the present disclosure; and
  • FIG. 26 is a block diagram of a computing device operating to host the status update service of FIG. 1 according to one embodiment of the present disclosure.
  • DETAILED DESCRIPTION
  • 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.
  • The present disclosure relates to following status updates from users in crowds of users. As used herein, a status update is a message provided by a user for publication via a status update, or micro-blogging, service such as, for example, the Twitter® micro-blogging and social networking service or the Facebook® social networking service. The status update may include a text-based status update, an audio status update, a video status update, an image status update, or any combination thereof. As an example, a status update may be a tweet provided by a user of the Twitter® micro-blogging and social networking service, which is referred to herein as one example of a status updating service. As another example, a status update may be a status update posted by a user of the Facebook® social networking service. Note, however, that status updates are not limited to Twitter® tweets or Facebook® status updates. Other types of status updates may additionally or alternatively be used.
  • FIG. 1 illustrates a Mobile Aggregate Profiling (MAP) system 10 (hereinafter “system 10”) that operates to enable crowd following in order to receive status updates from followed crowds according to one embodiment of the present disclosure. Note that the system 10 is exemplary and is not intended to limit the scope of the present disclosure. In this embodiment, the system 10 includes a MAP server 12, one or more profile servers 14, a location server 16, a number of mobile devices 18-1 through 18-N (generally referred to herein collectively as mobile devices 18 and individually as mobile device 18) having associated users 20-1 through 20-N (generally referred to herein collectively as users 20 and individually as user 20), a subscriber device 22 having an associated subscriber 24, a third-party service 26, and a status update service 28 communicatively coupled via a network 30. The network 30 may be any type of network or any combination of networks. Specifically, the network 30 may include wired components, wireless components, or both wired and wireless components. In one exemplary embodiment, the network 30 is a distributed public network such as the Internet, where the mobile devices 18 are enabled to connect to the network 30 via local wireless connections (e.g., Wi-Fi® 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, tracking crowds of users, and distributing status updates from the users 20 obtained from the status update service 28. Note that while the MAP server 12 is illustrated as a single server for simplicity and ease of discussion, it should be appreciated that the MAP server 12 may be implemented as a single physical server or multiple physical servers operating in a collaborative manner for purposes of redundancy, load sharing, and/or the like.
  • 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's Fire Eagle® service.
  • The mobile devices 18 may be mobile smart phones, portable media player devices, mobile gaming devices, mobile computers (e.g., laptop computers), or the like. Some exemplary mobile devices that may be programmed or otherwise configured to operate as the mobile devices 18 are the Apple® iPhone®, the Palm Pre®, the Samsung Rogue™, the Blackberry Storm™, the Motorola DROID or similar phone running Google's Android™ Operating System, an Apple® iPad®, and the Apple® iPod Touch® device. However, this list of exemplary mobile devices is not exhaustive and is not intended to limit the scope of the present disclosure.
  • The mobile devices 18-1 through 18-N include MAP clients 32-1 through 32-N (generally referred to herein collectively as MAP clients 32 or individually as MAP client 32), MAP applications 34-1 through 34-N (generally referred to herein collectively as MAP applications 34 or individually as MAP application 34), third-party applications 36-1 through 36-N (generally referred to herein collectively as third-party applications 36 or individually as third-party application 36), and location functions 38-1 through 38-N (generally referred to herein collectively as location functions 38 or individually as location function 38), respectively. The MAP client 32 is preferably implemented in software. In general, in the preferred embodiment, the MAP client 32 is a middleware layer operating to interface an application layer (i.e., the MAP application 34 and the third-party applications 36) to the MAP server 12. More specifically, the MAP client 32 enables the MAP application 34 and the third-party applications 36 to request and receive data from the MAP server 12. In addition, the MAP client 32 enables applications, such as the MAP application 34 and the third-party applications 36, to access data from the MAP server 12.
  • The MAP application 34 is also preferably implemented in software. The MAP application 34 generally provides a user interface component between the user 20 and the MAP server 12. For example, the MAP application 34 may enable the user 20 to initiate crowd search requests or requests for crowd data from the MAP server 12 and presents corresponding data returned by the MAP server 12 to the user 20. As another example, as described below in detail, the MAP application 34 may enable the user 20 to follow status updates of crowds of users, which is more generally referred to herein as following crowds of users. The MAP application 34 also enables the user 20 to configure various settings.
  • For example, the MAP application 34 may enable the user 20 to select a desired social networking service (e.g., Facebook®, MySpace®, LinkedlN®, 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 36 are preferably implemented in software. The third-party applications 36 operate to access the MAP server 12 via the MAP client 32. The third-party applications 36 may utilize data obtained from the MAP server 12 in any desired manner. As an example, one of the third-party applications 36 may be a gaming application that utilizes crowd data to notify the user 20 of Points of Interest (POIs) or Areas of Interest (AOIs) where crowds of interest are currently located. It should be noted that while the MAP client 32 is illustrated as being separate from the MAP application 34 and the third-party applications 36, the present disclosure is not limited thereto. The functionality of the MAP client 32 may alternatively be incorporated into the MAP application 34 and the third-party applications 36.
  • The location function 38 may be implemented in hardware, software, or a combination thereof. In general, the location function 38 operates to determine or otherwise obtain the location of the mobile device 18. For example, the location function 38 may be or include a Global Positioning System (GPS) receiver. In addition or alternatively, the location function 38 may include hardware and/or software that enables improved location tracking in indoor environments such as, for example, shopping malls. For example, the location function 38 may be part of or compatible with the InvisiTrack Location System provided by InvisiTrack and described in U.S. Pat. No. 7,423,580 entitled “Method and System of Three-Dimensional Positional Finding” which issued on Sep. 9, 2008, U.S. Pat. No. 7,787,886 entitled “System and Method for Locating a Target using RFID” which issued on Aug. 31, 2010, and U.S. Patent Application Publication No. 2007/0075898 entitled “Method and System for Positional Finding Using RF, Continuous and/or Combined Movement” which published on Apr. 5, 2007, all of which are hereby incorporated herein by reference for their teachings regarding location tracking.
  • The subscriber device 22 is a physical device such as a personal computer, a mobile computer (e.g., a notebook computer, a netbook computer, a tablet computer, etc.), a mobile smart phone, or the like. The subscriber 24 associated with the subscriber device 22 is a person or entity. In general, the subscriber device 22 enables the subscriber 24 to access the MAP server 12 via a web browser 40 to obtain various types of data, preferably for a fee. For example, the subscriber 24 may pay a fee to have access to crowd data such as aggregate profiles for crowds located at one or more POIs and/or located in one or more AOIs, pay a fee to track crowds, or the like. As another example, the subscriber 24 may be enabled to follow status updates from crowds of users. Note that the web browser 40 is exemplary. In another embodiment, the subscriber device 22 is enabled to access the MAP server 12 via a custom application.
  • The third-party service 26 is a service that has access to data from the MAP server 12 such as, for example, aggregate profiles for one or more crowds at one or more POIs or within one or more AOIs. Based on the data from the MAP server 12, the third-party service 26 operates to provide a service such as, for example, targeted advertising. For example, the third-party service 26 may obtain anonymous aggregate profile data for one or more crowds located at a POI and then provide targeted advertising to known users located at the POI based on the anonymous aggregate profile data. Note that while targeted advertising is mentioned as an exemplary third-party service 26, other types of third-party services 26 may additionally or alternatively be provided. Other types of third-party services 26 that may be provided will be apparent to one of ordinary skill in the art upon reading this disclosure.
  • Lastly, the status update service 28 is preferably implemented in software and hosted by a physical server or a number of physical servers operating in a collaborative manner for purposes of load sharing, redundancy, or the like. Note that while only one status update service 28 is illustrated, there may be multiple status update services 28. The status update service 28 enables users, such as the users 20, to register with the status update service 28. In response, corresponding user accounts are created by the status update service 28. For each of the users 20 that is registered with the status update service 28, the user account of the user 20 may include a user identifier (ID) of the user 20 such as a screen name or username of the user 20 for the status update service 28 and, in some embodiments, an indicator such as a flag that indicates whether status updates from the user 20 are to be shared with the MAP server 12. In some embodiments, the user account of the user 20 may also include a user profile of the user 20 that defines one or more interests of the user 20.
  • As discussed below in detail, the status update service 28 receives status updates from the users 20 that are registered with the status update service 28 via the mobile devices 18 of the users 20 over the network 30. Each status update preferably includes the user ID of the user 20 from which the status update originated and a body of the status update. Each status update may also include information such as, for example, a timestamp defining a time and date on which the status update was sent from the mobile device 18 of the user 20 to the status update service 28, a location of the user 20 at the time the status update was sent from the mobile device 18 to the status update service 28, or the like. Upon receiving status updates from the mobile devices 18 of the users 20, the status update service 28 stores the status updates and/or delivers the status updates to the MAP server 12 if the corresponding users 20 have chosen to share their status updates with the MAP server 12. The status updates may be sent to the MAP server 12 as they are received, in a batch process, or the like.
  • Before proceeding, it should be noted that while the system 10 of FIG. 1 illustrates an embodiment where the one or more profile servers 14 and the location server 16 are separate from the MAP server 12, the present disclosure is not limited thereto. In an alternative embodiment, the functionality of the one or more profile servers 14 and/or the location server 16 may be implemented within the MAP server 12. In addition, while the system 10 of FIG. 1 illustrates an embodiment where the status update service 28 is separate from the MAP server 12, the one or more profile servers 14, and the location server 16, the present disclosure is not limited thereto. The status update service 28 may alternatively be implemented with the MAP server 12, the one or more profile servers 14, or the location server 16. For example, a social networking service such as the Facebook® social networking service may function as both the profile server 14 and the status update service 28.
  • FIG. 2 is a block diagram of the MAP server 12 of FIG. 1 according to one embodiment of the present disclosure. As illustrated, the MAP server 12 includes an application layer 42, a business logic layer 44, and a persistence layer 46. The application layer 42 includes a user web application 48, a mobile client/server protocol component 50, and one or more data Application Programming Interfaces (APIs) 52. The user web application 48 is preferably implemented in software and operates to provide a web interface for users, such as the subscriber 24, to access the MAP server 12 via a web browser. The mobile client/server protocol component 50 is preferably implemented in software and operates to provide an interface between the MAP server 12 and the MAP clients 32 hosted by the mobile devices 18. The data APIs 52 enable third-party services, such as the third-party service 26, to access the MAP server 12.
  • The business logic layer 44 includes a profile manager 54, a location manager 56, a crowd analyzer 58, an aggregation engine 60, and a status update processor 62 each of which is preferably implemented in software. The profile manager 54 generally operates to obtain the user profiles of the users 20 directly or indirectly from the one or more profile servers 14 and store the user profiles in the persistence layer 46. The location manager 56 operates to obtain the current locations of the users 20 including location updates. As discussed below, the current locations of the users 20 may be obtained directly from the mobile devices 18 and/or obtained from the location server 16.
  • The crowd analyzer 58 operates to form crowds of users. In one embodiment, the crowd analyzer 58 utilizes a spatial crowd formation algorithm. However, the present disclosure is not limited thereto. In addition, the crowd analyzer 58 may further characterize crowds to reflect degree of fragmentation, best-case and worst-case degree of separation (DOS), and/or degree of bi-directionality. Still further, the crowd analyzer 58 may also operate to track crowds. The aggregation engine 60 generally operates to provide aggregate profile data in response to requests from the mobile devices 18, the subscriber device 22, and the third-party service 26. The aggregate profile data may be historical aggregate profile data for one or more POIs or one or more AOIs or aggregate profile data for crowd(s) currently at one or more POIs or within one or more AOIs. As discussed below in detail, the status update processor 62 operates to obtain status updates sent by the users 20 from the status update service 28 and deliver the status updates to followers of the corresponding crowds of the users 20.
  • For additional information regarding the operation of the profile manager 54, the location manager 56, the crowd analyzer 58, and the aggregation engine 60, the interested reader is directed to U.S. Patent Application Publication No. 2010/0198828, entitled “Forming Crowds And Providing Access To Crowd Data In A Mobile Environment,” which was filed Dec. 23, 2009 and published Aug. 5, 2010; U.S. Patent Application Publication No. 2010/0197318, entitled “Anonymous Crowd Tracking,” which was filed Dec. 23, 2009 and published Aug. 5, 2010; U.S. Patent Application Publication No. 2010/0198826, entitled “Maintaining A Historical Record Of Anonymized User Profile Data By Location For Users In A Mobile Environment,” which was filed Dec. 23, 2009 and published Aug. 5, 2010; U.S. Patent Application Publication No. 2010/0198917, entitled “Crowd Formation For Mobile Device Users,” which was filed Dec. 23, 2009 and published Aug. 5, 2010; U.S. Patent Application Publication No. 2010/0198870, entitled “Serving A Request For Data From A Historical Record Of Anonymized User Profile Data In A Mobile Environment,” which was filed Dec. 23, 2009 and published Aug. 5, 2010; U.S. Patent Application Publication No. 2010/0198862, entitled “Handling Crowd Requests For Large Geographic Areas,” which was filed Dec. 23, 2009 and published Aug. 5, 2010; and U.S. Patent Application Publication No. 2010/0197319, entitled “Modifying A User's Contribution To An Aggregate Profile Based On Time Between Location Updates And External Events,” which was filed Dec. 23, 2009 and published Aug. 5, 2010; all of which are hereby incorporated herein by reference in their entireties.
  • The persistence layer 46 includes an object mapping layer 63 and a datastore 64. The object mapping layer 63 is preferably implemented in software. The datastore 64 is preferably a relational database, which is implemented in a combination of hardware (i.e., physical data storage hardware) and software (i.e., relational database software). In this embodiment, the business logic layer 44 is implemented in an object-oriented programming language such as, for example, Java. As such, the object mapping layer 63 operates to map objects used in the business logic layer 44 to relational database entities stored in the datastore 64. Note that, in one embodiment, data is stored in the datastore 64 in a Resource Description Framework (RDF) compatible format.
  • 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 32 of FIG. 1 in more detail according to one embodiment of the present disclosure. As illustrated, in this embodiment, the MAP client 32 includes a MAP access API 66, a MAP middleware component 68, and a mobile client/server protocol component 70. The MAP access API 66 is implemented in software and provides an interface by which the MAP application 34 and the third-party applications 36 are enabled to access the MAP client 32. The MAP middleware component 68 is implemented in software and performs the operations needed for the MAP client 32 to operate as an interface between the MAP application 34 and the third-party applications 36 at the mobile device 18 and the MAP server 12. The mobile client/server protocol component 70 enables communication between the MAP client 32 and the MAP server 12 via a defined protocol.
  • The present disclosure is primarily focused on obtaining and distributing status updates to followers of corresponding crowds of users. However, before discussing this in detail, it is beneficial to discuss other features of the MAP server 12, namely, the operation of the MAP server 12 to obtain user profiles and location updates and to create and track crowds of users. As described below, the crowds of users are utilized for distributing status updates from users in the crowds to followers of the crowds.
  • FIG. 4 illustrates the operation of the system 10 of FIG. 1 to provide the user profile of one of the users 20 of one of the mobile devices 18 to the MAP server 12 according to one embodiment of the present disclosure. This discussion is equally applicable to the other users 20 of the other mobile devices 18. First, an authentication process is performed (step 1000). For authentication, in this embodiment, the mobile device 18 authenticates with the profile server 14 (step 1000A) and the MAP server 12 (step 1000B). In addition, the MAP server 12 authenticates with the profile server 14 (step 1000C). Preferably, authentication is performed using OpenID or similar technology. However, authentication may alternatively be performed using separate credentials (e.g., username and password) of the user 20 for access to the MAP server 12 and the profile server 14. Assuming that authentication is successful, the profile server 14 returns an authentication succeeded message to the MAP server 12 (step 1000D), and the profile server 14 returns an authentication succeeded message to the MAP client 32 of the mobile device 18 (step 1000E).
  • At some point after authentication is complete, a user profile process is performed such that a user profile of the user 20 is obtained from the profile server 14 and delivered to the MAP server 12 (step 1002). In this embodiment, the MAP client 32 of the mobile device 18 sends a profile request to the profile server 14 (step 1002A). In response, the profile server 14 returns the user profile of the user 20 to the mobile device 18 (step 1002B). The MAP client 32 of the mobile device 18 then sends the user profile of the user 20 to the MAP server 12 (step 1002C). Note that while in this embodiment the MAP client 32 sends the complete user profile of the user 20 to the MAP server 12, in an alternative embodiment, the MAP client 32 may filter the user profile of the user 20 according to criteria specified by the user 20. For example, the user profile of the user 20 may include demographic information, general interests, music interests, and movie interests, and the user 20 may specify that the demographic information or some subset thereof is to be filtered, or removed, before sending the user profile to the MAP server 12.
  • Upon receiving the user profile of the user 20 from the MAP client 32 of the mobile device 18, the profile manager 54 of the MAP server 12 processes the user profile (step 1002D). More specifically, in the preferred embodiment, the profile manager 54 includes social network handlers for the social network services supported by the MAP server 12 that operate to map the user profiles of the users 20 obtained from the social network services to a common format utilized by the MAP server 12. This common format includes a number of user profile categories, or user profile slices, such as, for example, a demographic profile category, a social interaction profile category, a general interests category, a music interests profile category, and a movie interests profile category.
  • For example, if the MAP server 12 supports user profiles from Facebook®, MySpace®, and LinkedIN®, the profile manager 54 may include a Facebook handler, a MySpace handler, and a LinkedIN handler. The social network handlers process user profiles from the corresponding social network services to generate user profiles for the users 20 in the common format used by the MAP server 12. For this example assume that the user profile of the user 20 is from Facebook®. The profile manager 54 uses a Facebook handler to process the user profile of the user 20 to map the user profile of the user 20 from Facebook® 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 54 to create a list of keywords such as, for example, liberal, High School Graduate, 35-44, College Graduate, etc. for the demographic profile category; a list of keywords such as Seeking Friendship for the social interaction profile category; a list of keywords such as politics, technology, photography, books, etc. for the general interests profile category; a list of keywords including music genres, artist names, album names, or the like for the music interests profile category; and a list of keywords including movie titles, actor or actress names, director names, movie genres, or the like for the movie interests profile category. In one embodiment, the profile manager 54 may use natural language processing or semantic analysis. For example, if the Facebook® 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 54 of the MAP server 12 stores the resulting user profile for the user 20 (step 1002E). More specifically, in one embodiment, the MAP server 12 stores user records for the users 20 in the datastore 64 (FIG. 2). The user profile of the user 20 is stored in the user record of the user 20. The user record of the user 20 includes a unique identifier of the user 20, the user profile of the user 20, and, as discussed below, a current location of the user 20. Note that the user profile of the user 20 may be updated as desired. For example, in one embodiment, the user profile of the user 20 is updated by repeating step 1002 each time the user 20 activates the MAP application 34.
  • Note that while the discussion herein focuses on an embodiment where the user profiles of the users 20 are obtained from the one or more profile servers 14, the user profiles of the users 20 may be obtained in any desired manner. For example, in one alternative embodiment, the user 20 may identify one or more favorite websites. The profile manager 54 of the MAP server 12 may then crawl the one or more favorite websites of the user 20 to obtain keywords appearing in the one or more favorite websites of the user 20. These keywords may then be stored as the user profile of the user 20.
  • At some point, a process is performed such that a current location of the mobile device 18 and thus a current location of the user 20 is obtained by the MAP server 12 (step 1004). In this embodiment, the MAP application 34 of the mobile device 18 obtains the current location of the mobile device 18 from the location function 38 of the mobile device 18. The MAP application 34 then provides the current location of the mobile device 18 to the MAP client 32, and the MAP client 32 then provides the current location of the mobile device 18 to the MAP server 12 (step 1004A). Note that step 1004A may be repeated periodically or in response to a change in the current location of the mobile device 18 in order for the MAP application 34 to provide location updates for the user 20 to the MAP server 12.
  • In response to receiving the current location of the mobile device 18, the location manager 56 of the MAP server 12 stores the current location of the mobile device 18 as the current location of the user 20 (step 1004B). More specifically, in one embodiment, the current location of the user 20 is stored in the user record of the user 20 maintained in the datastore 64 of the MAP server 12.
  • In addition to storing the current location of the user 20, the location manager 56 sends the current location of the user 20 to the location server 16 (step 1004C). In this embodiment, by providing location updates to the location server 16, the MAP server 12 in return receives location updates for the user 20 from the location server 16. This is particularly beneficial when the mobile device 18 does not permit background processes. If the mobile device 18 does not permit background processes, the MAP application 34 will not be able to provide location updates for the user 20 to the MAP server 12 unless the MAP application 34 is active. Therefore, when the MAP application 34 is not active, other applications running on the mobile device 18 (or some other device of the user 20) may directly or indirectly provide location updates to the location server 16 for the user 20. This is illustrated in step 1006 where the location server 16 receives a location update for the user 20 directly or indirectly from another application running on the mobile device 18 or an application running on another device of the user 20 (step 1006A). The location server 16 then provides the location update for the user 20 to the MAP server 12 (step 1006B). In response, the location manager 56 updates and stores the current location of the user 20 in the user record of the user 20 (step 1006C). In this manner, the MAP server 12 is enabled to obtain location updates for the user 20 even when the MAP application 34 is not active at the mobile device 18.
  • FIG. 5 illustrates the operation of the system 10 of FIG. 1 to provide the user profile of the user 20 of one of the mobile devices 18 to the MAP server 12 according to another embodiment of the present disclosure. This discussion is equally applicable to user profiles of the users 20 of the other mobile devices 18. First, an authentication process is performed (step 1100). For authentication, in this embodiment, the mobile device 18 authenticates with the MAP server 12 (step 1100A), and the MAP server 12 authenticates with the profile server 14 (step 1100B). Preferably, authentication is performed using OpenID or similar technology. However, authentication may alternatively be performed using separate credentials (e.g., username and password) of the user 20 for access to the MAP server 12 and the profile server 14. Assuming that authentication is successful, the profile server 14 returns an authentication succeeded message to the MAP server 12 (step 1100C), and the MAP server 12 returns an authentication succeeded message to the MAP client 32 of the mobile device 18 (step 1100D).
  • At some point after authentication is complete, a user profile process is performed such that a user profile of the user 20 is obtained from the profile server 14 and delivered to the MAP server 12 (step 1102). In this embodiment, the profile manager 54 of the MAP server 12 sends a profile request to the profile server 14 (step 1102A). In response, the profile server 14 returns the user profile of the user 20 to the profile manager 54 of the MAP server 12 (step 1102B). Note that while in this embodiment the profile server 14 returns the complete user profile of the user 20 to the MAP server 12, in an alternative embodiment, the profile server 14 may return a filtered version of the user profile of the user 20 to the MAP server 12. The profile server 14 may filter the user profile of the user 20 according to criteria specified by the user 20. For example, the user profile of the user 20 may include demographic information, general interests, music interests, and movie interests, and the user 20 may specify that the demographic information or some subset thereof is to be filtered, or removed, before sending the user profile to the MAP server 12.
  • Upon receiving the user profile of the user 20, the profile manager 54 of the MAP server 12 processes the user profile (step 1102C). More specifically, as discussed above, in the preferred embodiment, the profile manager 54 includes social network handlers for the social network services supported by the MAP server 12. The social network handlers process user profiles to generate user profiles for the MAP server 12 that include lists of keywords for each of a number of profile categories, or profile slices.
  • After processing the user profile of the user 20, the profile manager 54 of the MAP server 12 stores the resulting user profile for the user 20 (step 1102D). More specifically, in one embodiment, the MAP server 12 stores user records for the users 20 in the datastore 64 (FIG. 2). The user profile of the user 20 is stored in the user record of the user 20. The user record of the user 20 includes a unique identifier of the user 20, the user profile of the user 20, and, as discussed below, a current location of the user 20. Note that the user profile of the user 20 may be updated as desired. For example, in one embodiment, the user profile of the user 20 is updated by repeating step 1102 each time the user 20 activates the MAP application 34.
  • Note that while the discussion herein focuses on an embodiment where the user profiles of the users 20 are obtained from the one or more profile servers 14, the user profiles of the users 20 may be obtained in any desired manner. For example, in one alternative embodiment, the user 20 may identify one or more favorite websites. The profile manager 54 of the MAP server 12 may then crawl the one or more favorite websites of the user 20 to obtain keywords appearing in the one or more favorite websites of the user 20. These keywords may then be stored as the user profile of the user 20.
  • At some point, a process is performed such that a current location of the mobile device 18 and thus a current location of the user 20 is obtained by the MAP server 12 (step 1104). In this embodiment, the MAP application 34 of the mobile device 18 obtains the current location of the mobile device 18 from the location function 38 of the mobile device 18. The MAP application 34 then provides the current location of the user 20 of the mobile device 18 to the location server 16 (step 1104A). Note that step 1104A may be repeated periodically or in response to changes in the location of the mobile device 18 in order to provide location updates for the user 20 to the MAP server 12. The location server 16 then provides the current location of the user 20 to the MAP server 12 (step 1104B). The location server 16 may provide the current location of the user 20 to the MAP server 12 automatically in response to receiving the current location of the user 20 from the mobile device 18 or in response to a request from the MAP server 12. In response to receiving the current location of the mobile device 18, the location manager 56 of the MAP server 12 stores the current location of the mobile device 18 as the current location of the user 20 (step 1104C). More specifically, in one embodiment, the current location of the user 20 is stored in the user record of the user 20 maintained in the datastore 64 of the MAP server 12.
  • As discussed above, the use of the location server 16 is particularly beneficial when the mobile device 18 does not permit background processes. As such, if the mobile device 18 does not permit background processes, the MAP application 34 will not provide location updates for the user 20 to the location server 16 unless the MAP application 34 is active. However, other applications running on the mobile device 18 (or some other device of the user 20) may provide location updates to the location server 16 for the user 20 when the MAP application 34 is not active. This is illustrated in step 1106 where the location server 16 receives a location update for the user 20 from another application running on the mobile device 18 or an application running on another device of the user 20 (step 1106A). The location server 16 then provides the location update for the user 20 to the MAP server 12 (step 1106B). In response, the location manager 56 updates and stores the current location of the user 20 in the user record of the user 20 (step 1106C). In this manner, the MAP server 12 is enabled to obtain location updates for the user 20 even when the MAP application 34 is not active at the mobile device 18.
  • FIG. 6 begins a discussion of the operation of the crowd analyzer 58 to form crowds of users according to one embodiment of the present disclosure. Specifically, FIG. 6 illustrates exemplary data records that may be used to represent crowds, users, crowd snapshots used for crowd tracking, and anonymous users according to one embodiment of the present disclosure. As illustrated, for each crowd created by the crowd analyzer 58 of the MAP server 12, a corresponding crowd record 72 is created and stored in the datastore 64 of the MAP server 12. The crowd record 72 for a crowd includes a users field, a North-East (NE) corner field, a South-West (SW) corner field, a center field, a crowd snapshots field, a split from field, and a combined into field. The users field stores a set or list of user records 74 corresponding to a subset of the users 20 that are currently in the crowd. The NE corner field stores a location corresponding to a NE corner of a bounding box for the crowd. The NE corner may be defined by latitude and longitude coordinates and optionally an altitude. Similarly, the SW corner field stores a location of a SW corner of the bounding box for the crowd. Like the NE corner, the SW corner may be defined by latitude and longitude coordinates and optionally an altitude. Together, the NE corner and the SW corner define a bounding box for the crowd, where the edges of the bounding box pass through the current locations of the outermost users 20 in the crowd. The center field stores a location corresponding to a center of the crowd. The center of the crowd may be defined by latitude and longitude coordinates and optionally an altitude. The center of the crowd may be computed based on the current locations of the users 20 in the crowd using a center of mass algorithm. Together, the NE corner, the SW corner, and the center of the crowd form spatial information defining the location of the crowd. Note, however, that the spatial information defining the location of the crowd may include additional or alternative information depending on the particular implementation. The crowd snapshots field stores a list of crowd snapshot records 76 corresponding to crowd snapshots for the crowd created and stored over time. As discussed below in detail, the split from field may be used to store a reference to a crowd record corresponding to another crowd from which the crowd split, and the combined into field may be used to store a reference to a crowd record corresponding to another crowd into which the crowd has been merged.
  • Each of the user records 74 includes an ID field, a location field, a profile field, a crowd field, and a previous crowd field. The ID field stores a unique ID for the user 20 represented by the user record 74. The location field stores the current location of the user 20, which may be defined by latitude and longitude coordinates and optionally an altitude. The profile field stores the user profile of the user 20, which may be defined as a list of keywords for one or more profile categories. The crowd field is used to store a reference to a crowd record of a crowd of which the user 20 is currently a member. The previous crowd field may be used to store a reference to a crowd record of a crowd of which the user 20 was previously a member.
  • Each of the crowd snapshot records 76 includes an anonymous users field, a NE corner field, a SW corner field, a center field, a sample time field, and a vertices field. The anonymous users field stores a set or list of anonymous user records 78, which are anonymized versions of user records for the users 20 that are in the crowd at a time the crowd snapshot was created. The NE corner field stores a location corresponding to a NE corner of a bounding box for the crowd at the time the crowd snapshot was created. The NE corner may be defined by latitude and longitude coordinates and optionally an altitude. Similarly, the SW corner field stores a location of a SW corner of the bounding box for the crowd at the time the crowd snapshot was created. Like the NE corner, the SW corner may be defined by latitude and longitude coordinates and optionally an altitude. The center field stores a location corresponding to a center of the crowd at the time the crowd snapshot was created. The center of the crowd may be defined by latitude and longitude coordinates and optionally an altitude. Together, the NE corner, the SW corner, and the center of the crowd form spatial information defining the location of the crowd at the time the crowd snapshot was created. Note, however, that the spatial information defining the location of the crowd at the time the crowd snapshot was created may include additional or alternative information depending on the particular implementation. The sample time field stores a timestamp indicating a time at which the crowd snapshot was created. The timestamp preferably includes a date and a time of day at which the crowd snapshot was created. The vertices field stores locations of a number of the users 20 in the crowd at the time the crowd snapshot was created that define an actual outer boundary of the crowd (e.g., as a polygon) at the time the crowd snapshot was created. Note that the actual outer boundary of a crowd may be used to show the location of the crowd when displayed to a user.
  • Each of the anonymous user records 78 includes an anonymous ID field and a profile field. The anonymous ID field stores an anonymous user ID, which is preferably a unique user ID that is not tied, or linked, back to any of the users 20 and particularly not tied back to the user 20 or the user record 74 for which the anonymous user record 78 has been created. In one embodiment, the anonymous user records 78 for a crowd snapshot record 76 are anonymized versions of the user records 74 of the users in the crowd at the time the crowd snapshot was created. The profile field stores an anonymized user profile of the anonymous user, which may be defined as a list of keywords for one or more profile categories.
  • FIGS. 7A through 7D illustrate one embodiment of a spatial crowd formation process that may be performed by the crowd analyzer 58 to enable a crowd tracking feature according to one embodiment of the present disclosure. In this embodiment, the spatial crowd formation process is triggered in response to receiving a location update for one of the users 20 and is preferably repeated for each location update received for any one of the users 20. As such, first, the crowd analyzer 58 receives a location update, or a new location, for one of the users 20 (step 1200). In response, the crowd analyzer 58 retrieves an old location of the user 20, if any (step 1202). The old location is the current location of the user 20 prior to receiving the new location of the user 20. The crowd analyzer 58 then creates a new bounding box of a predetermined size centered at or otherwise encompassing the new location of the user 20 (step 1204) and an old bounding box of a predetermined size centered at or otherwise encompassing the old location of the user 20, if any (step 1206). The predetermined size of the new and old bounding boxes may be any desired size.
  • As one example, the predetermined size of the new and old bounding boxes is 40 meters by 40 meters. Note that if the user 20 does not have an old location (i.e., the location received in step 1200 is the first location received for the user 20), then the old bounding box is essentially null. Also note that while bounding “boxes” are used in this example, the bounding regions may be of any desired shape.
  • Next, the crowd analyzer 58 determines whether the new and old bounding boxes overlap (step 1208). If so, the crowd analyzer 58 creates a bounding box encompassing the new and old bounding boxes (step 1210). For example, if the new and old bounding boxes are 40×40 meter regions and a 1×1 meter square at the North-East corner of the new bounding box overlaps a 1×1 meter square at the South-West corner of the old bounding box, the crowd analyzer 58 may create a 79×79 meter square bounding box encompassing both the new and old bounding boxes.
  • The crowd analyzer 58 then determines the individual users and crowds relevant to the bounding box created in step 1210 (step 1212). Note that the crowds relevant to the bounding box are pre-existing crowds resulting from previous iterations of the spatial crowd formation process. In this embodiment, the crowds relevant to the bounding box are crowds having crowd bounding boxes that are within or overlap the bounding box established in step 1210. Alternatively, the crowds relevant to the bounding box may be crowds having crowd centers located within the bounding box or crowds having at least one user currently located within the bounding box. In order to determine the relevant crowds, the crowd analyzer 58 queries the datastore 64 of the MAP server 12 to obtain crowd records for crowds that are within or overlap the bounding box established in step 1210. The individual users relevant to the bounding box are any of the users 20 that are currently located within the bounding box and are not already members of a crowd. In order to identify the relevant individual users, the crowd analyzer 58 queries the datastore 64 of the MAP server 12 for the user records 74 of the users 20 that are currently located in the bounding box created in step 1210 and are not already members of a crowd. Next, the crowd analyzer 58 computes an optimal inclusion distance for individual users based on user density within the bounding box (step 1214). More specifically, in one embodiment, the optimal inclusion distance for individuals, which is also referred to herein as an initial optimal inclusion distance, is set according to the following equation:
  • initial_optimal _inclusion _dist = a · A BoundingBox number_of _users ,
  • where a is a number between 0 and 1, ABoundingBox 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 58 then creates a crowd of one user for each individual user within the bounding box established in step 1210 that is not already included in a crowd and sets the optimal inclusion distance for those crowds to the initial optimal inclusion distance (step 1216). The crowds created for the individual users are temporary crowds created for purposes of performing the crowd formation process. At this point, the process proceeds to FIG. 7B where the crowd analyzer 58 analyzes the crowds in the bounding box established in step 1210 to determine whether any of the crowd members (i.e., users in the crowds) violate the optimal inclusion distance of their crowds (step 1218). Any crowd member that violates the optimal inclusion distance of his or her crowd is then removed from that crowd and the previous crowd fields in the corresponding user records 74 are set (step 1220). More specifically, in this embodiment, a member is removed from a crowd by removing the user record 74 of the member from the set or list of user records in the crowd record 72 of the crowd and setting the previous crowd stored in the user record 74 of the member to the crowd from which the member has been removed. The crowd analyzer 58 then creates a crowd of one user for each of the users 20 removed from their crowds in step 1220 and sets the optimal inclusion distance for the newly created crowds to the initial optimal inclusion distance (step 1222).
  • Next, the crowd analyzer 58 determines the two closest crowds in the bounding box (step 1224) and a distance between the two closest crowds (step 1226). The distance between the two closest crowds is the distance between the crowd centers of the two closest crowds, which are stored in the crowd records for the two closest crowds. The crowd analyzer 58 then determines whether the distance between the two closest crowds is less than the optimal inclusion distance of a larger of the two closest crowds (step 1228). If the two closest crowds are of the same size (i.e., have the same number of users), then the optimal inclusion distance of either of the two closest crowds may be used. Alternatively, if the two closest crowds are of the same size, the optimal inclusion distances of both of the two closest crowds may be used such that the crowd analyzer 58 determines whether the distance between the two closest crowds is less than the optimal inclusion distances of both of the crowds. As another alternative, if the two closest crowds are of the same size, the crowd analyzer 58 may compare the distance between the two closest crowds to an average of the optimal inclusion distances of the two crowds.
  • If the distance between the two closest crowds is greater than the optimal inclusion distance, the process proceeds to step 1240. However, if the distance between the two closest crowds is less than the optimal inclusion distance, the two crowds are merged (step 1230). The manner in which the two crowds are merged differs depending on whether the two crowds are pre-existing crowds or temporary crowds created for the spatial crowd formation process. If both crowds are pre-existing crowds, one of the two crowds is selected as a non-surviving crowd and the other is selected as the surviving crowd. If one crowd is larger than the other, the smaller crowd is selected as the non-surviving crowd and the larger crowd is selected as the surviving crowd. If the two crowds are of the same size, one of the crowds is selected as the surviving crowd and the other crowd is selected as the non-surviving crowd using any desired technique. The non-surviving crowd is then merged into the surviving crowd by adding the set or list of user records for the non-surviving crowd to the set or list of user records for the surviving crowd and setting the merged into field of the non-surviving crowd to a reference to the crowd record of the surviving crowd. In addition, the crowd analyzer 58 sets the previous crowd fields of the user records 74 in the set or list of user records from the non-surviving crowd to a reference to the crowd record 72 of the non-surviving crowd.
  • If one of the crowds is a temporary crowd and the other crowd is a pre-existing crowd, the temporary crowd is selected as the non-surviving crowd, and the pre-existing crowd is selected as the surviving crowd. The non-surviving crowd is then merged into the surviving crowd by adding the set or list of user records from the crowd record 72 of the non-surviving crowd to the set or list of user records in the crowd record 72 of the surviving crowd. However, since the non-surviving crowd is a temporary crowd, the previous crowd field(s) of the user record(s) 74 of the user(s) 20 in the non-surviving crowd are not set to a reference to the crowd record 72 of the non-surviving crowd. Similarly, the crowd record 72 of the temporary crowd may not have a merged into field, but, if it does, the merged into field is not set to a reference to the surviving crowd.
  • If both the crowds are temporary crowds, one of the two crowds is selected as a non-surviving crowd and the other is selected as a surviving crowd. If one crowd is larger than the other, the smaller crowd is selected as the non-surviving crowd and the larger crowd is selected as the surviving crowd. If the two crowds are of the same size, one of the crowds is selected as the surviving crowd and the other crowd is selected as the non-surviving crowd using any desired technique. The non-surviving crowd is then merged into the surviving crowd by adding the set or list of user records for the non-surviving crowd to the set or list of user records for the surviving crowd. However, since the non-surviving crowd is a temporary crowd, the previous crowd field(s) of the user record(s) 74 of the user(s) 20 in the non-surviving crowd are not set to a reference to the crowd record 72 of the non-surviving crowd. Similarly, the crowd record 72 of the temporary crowd may not have a merged into field, but, if it does, the merged into field is not set to a reference to the surviving crowd.
  • Next, the crowd analyzer 58 removes the non-surviving crowd (step 1232). In this embodiment, the manner in which the non-surviving crowd is removed depends on whether the non-surviving crowd is a pre-existing crowd or a temporary crowd. If the non-surviving crowd is a pre-existing crowd, the removal process is performed by removing or nulling the users field, the NE corner field, the SW corner field, and the center field of the crowd record 72 of the non-surviving crowd. In this manner, the spatial information for the non-surviving crowd is removed from the corresponding crowd record such that the non-surviving or removed crowd will no longer be found in response to spatial-based queries on the datastore 64. However, the crowd snapshots for the non-surviving crowd are still available via the crowd record 72 for the non-surviving crowd. In contrast, if the non-surviving crowd is a temporary crowd, the crowd analyzer 58 may remove the crowd by deleting the corresponding crowd record 72.
  • The crowd analyzer 58 also computes a new crowd center for the surviving crowd (step 1234). Again, a center of mass algorithm may be used to compute the crowd center of a crowd. In addition, a new optimal inclusion distance for the surviving crowd is computed (step 1236). In one embodiment, the new optimal inclusion distance for the resulting crowd is computed as:
  • average = 1 n + 1 · ( initial_optimal _inclusion _dist + i = 1 n d i ) , optimal_inclusion _dist = average + ( 1 n · i = 1 n ( d i - average ) 2 ) ,
  • where n is the number of users in the crowd and di 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 58 determines whether a maximum number of iterations have been performed (step 1238). The maximum number of iterations is a predefined number that ensures that the crowd formation process does not indefinitely loop over steps 1218 through 1236 or loop over steps 1218 through 1236 more than a desired maximum number of times. If the maximum number of iterations has not been reached, the process returns to step 1218 and is repeated until either the distance between the two closest crowds is not less than the optimal inclusion distance of the larger crowd or the maximum number of iterations has been reached. At that point, the crowd analyzer 58 removes crowds with less than three users, or members (step 1240), and the process ends. As discussed above, in this embodiment, the manner in which a crowd is removed depends on whether the crowd is a pre-existing crowd or a temporary crowd. If the crowd is a pre-existing crowd, a removal process is performed by removing or nulling the users field, the NE corner field, the SW corner field, and the center field of the crowd record 72 of the crowd. In this manner, the spatial information for the crowd is removed from the corresponding crowd record 72 such that the crowd will no longer be found in response to spatial-based queries on the datastore 64. However, the crowd snapshots for the crowd are still available via the crowd record 72 for the crowd. In contrast, if the crowd is a temporary crowd, the crowd analyzer 58 may remove the crowd by deleting the corresponding crowd record 72. In this manner, crowds having less than three members are removed in order to maintain privacy of individuals as well as groups of two users (e.g., a couple). Note that in this example, the minimum number of users required for a crowd is 3. However, the present disclosure is not limited thereto. The minimum number of users for a crowd may be any desired number greater than or equal to 2.
  • Returning to step 1208 in FIG. 7A, if the new and old bounding boxes do not overlap, the process proceeds to FIG. 7C and the bounding box to be processed is set to the old bounding box (step 1242). In general, the crowd analyzer 58 then processes the old bounding box in much that same manner as described above with respect to steps 1212 through 1240. More specifically, the crowd analyzer 58 determines the individual users and crowds relevant to the bounding box (step 1244). Next, the crowd analyzer 58 computes an optimal inclusion distance for individual users based on user density within the bounding box (step 1246). The optimal inclusion distance may be computed as described above with respect to step 1214.
  • The crowd analyzer 58 then creates a crowd of one user for each individual user within the bounding box that is not already included in a crowd and sets the optimal inclusion distance for the crowds to the initial optimal inclusion distance (step 1248). The crowds created for the individual users are temporary crowds created for purposes of performing the crowd formation process. At this point, the crowd analyzer 58 analyzes the crowds in the bounding box to determine whether any crowd members (i.e., users in the crowds) violate the optimal inclusion distance of their crowds (step 1250). Any crowd member that violates the optimal inclusion distance of his or her crowd is then removed from that crowd and the previous crowd fields in the corresponding user records 74 are set (step 1252). More specifically, in this embodiment, a member is removed from a crowd by removing the user record 74 of the member from the set or list of user records in the crowd record 72 of the crowd and setting the previous crowd stored in the user record 74 of the member to the crowd from which the member has been removed. The crowd analyzer 58 then creates a crowd for each of the users 20 removed from their crowds in step 1252 and sets the optimal inclusion distance for the newly created crowds to the initial optimal inclusion distance (step 1254).
  • Next, the crowd analyzer 58 determines the two closest crowds in the bounding box (step 1256) and a distance between the two closest crowds (step 1258). The distance between the two closest crowds is the distance between the crowd centers of the two closest crowds. The crowd analyzer 58 then determines whether the distance between the two closest crowds is less than the optimal inclusion distance of a larger of the two closest crowds (step 1260). If the two closest crowds are of the same size (i.e., have the same number of users), then the optimal inclusion distance of either of the two closest crowds may be used. Alternatively, if the two closest crowds are of the same size, the optimal inclusion distances of both of the two closest crowds may be used such that the crowd analyzer 58 determines whether the distance between the two closest crowds is less than the optimal inclusion distances of both of the two closest crowds. As another alternative, if the two closest crowds are of the same size, the crowd analyzer 58 may compare the distance between the two closest crowds to an average of the optimal inclusion distances of the two closest crowds.
  • If the distance between the two closest crowds is greater than the optimal inclusion distance, the process proceeds to step 1272. However, if the distance between the two closest crowds is less than the optimal inclusion distance, the two crowds are merged (step 1262). The manner in which the two crowds are merged differs depending on whether the two crowds are pre-existing crowds or temporary crowds created for the spatial crowd formation process. If both crowds are pre-existing crowds, one of the two crowds is selected as a non-surviving crowd and the other is selected as a surviving crowd. If one crowd is larger than the other, the smaller crowd is selected as the non-surviving crowd and the larger crowd is selected as the surviving crowd. If the two crowds are of the same size, one of the crowds is selected as the surviving crowd and the other crowd is selected as the non-surviving crowd using any desired technique. The non-surviving crowd is then merged into the surviving crowd by adding the set or list of user records for the non-surviving crowd to the set or list of user records for the surviving crowd and setting the merged into field of the non-surviving crowd to a reference to the crowd record 72 of the surviving crowd. In addition, the crowd analyzer 58 sets the previous crowd fields of the set or list of user records from the non-surviving crowd to a reference to the crowd record 72 of the non-surviving crowd.
  • If one of the crowds is a temporary crowd and the other crowd is a pre-existing crowd, the temporary crowd is selected as the non-surviving crowd, and the pre-existing crowd is selected as the surviving crowd. The non-surviving crowd is then merged into the surviving crowd by adding the user records 74 from the set or list of user records from the crowd record 72 of the non-surviving crowd to the set or list of user records in the crowd record 72 of the surviving crowd. However, since the non-surviving crowd is a temporary crowd, the previous crowd field(s) of the user record(s) 74 of the user(s) 20 in the non-surviving crowd are not set to a reference to the crowd record 72 of the non-surviving crowd. Similarly, the crowd record 72 of the temporary crowd may not have a merged into field, but, if it does, the merged into field is not set to a reference to the surviving crowd.
  • If both the crowds are temporary crowds, one of the two crowds is selected as a non-surviving crowd and the other is selected as a surviving crowd. If one crowd is larger than the other, the smaller crowd is selected as the non-surviving crowd and the larger crowd is selected as a surviving crowd. If the two crowds are of the same size, one of the crowds is selected as the surviving crowd and the other crowd is selected as the non-surviving crowd using any desired technique. The non-surviving crowd is then merged into the surviving crowd by adding the set or list of user records for the non-surviving crowd to the set or list of user records for the surviving crowd. However, since the non-surviving crowd is a temporary crowd, the previous crowd field(s) of the user record(s) 74 of the user(s) 20 in the non-surviving crowd are not set to a reference to the crowd record 72 of the non-surviving crowd. Similarly, the crowd record 72 of the temporary crowd may not have a merged into field, but, if it does, the merged into field is not set to a reference to the surviving crowd.
  • Next, the crowd analyzer 58 removes the non-surviving crowd (step 1264). In this embodiment, the manner in which the non-surviving crowd is removed depends on whether the non-surviving crowd is a pre-existing crowd or a temporary crowd. If the non-surviving crowd is a pre-existing crowd, the removal process is performed by removing or nulling the users field, the NE corner field, the SW corner field, and the center field of the crowd record 72 of the non-surviving crowd. In this manner, the spatial information for the non-surviving crowd is removed from the corresponding crowd record 72 such that the non-surviving or removed crowd will no longer be found in response to spatial-based queries on the datastore 64. However, the crowd snapshots for the non-surviving crowd are still available via the crowd record 72 for the non-surviving crowd. In contrast, if the non-surviving crowd is a temporary crowd, the crowd analyzer 58 may remove the crowd by deleting the corresponding crowd record 72.
  • The crowd analyzer 58 also computes a new crowd center for the surviving crowd (step 1266). Again, a center of mass algorithm may be used to compute the crowd center of a crowd. In addition, a new optimal inclusion distance for the surviving crowd is computed (step 1268). In one embodiment, the new optimal inclusion distance for the surviving crowd is computed in the manner described above with respect to step 1234.
  • At this point, the crowd analyzer 58 determines whether a maximum number of iterations have been performed (step 1270). If the maximum number of iterations has not been reached, the process returns to step 1250 and is repeated until either the distance between the two closest crowds is not less than the optimal inclusion distance of the larger crowd or the maximum number of iterations has been reached. At that point, the crowd analyzer 58 removes crowds with less than three users, or members (step 1272). As discussed above, in this embodiment, the manner in which a crowd is removed depends on whether the crowd is a pre-existing crowd or a temporary crowd. If the crowd is a pre-existing crowd, a removal process is performed by removing or nulling the users field, the NE corner field, the SW corner field, and the center field of the crowd record 72 of the crowd. In this manner, the spatial information for the crowd is removed from the corresponding crowd record 72 such that the crowd will no longer be found in response to spatial-based queries on the datastore 64. However, the crowd snapshots for the crowd are still available via the crowd record 72 for the crowd. In contrast, if the crowd is a temporary crowd, the crowd analyzer 58 may remove the crowd by deleting the corresponding crowd record 72. In this manner, crowds having less than three members are removed in order to maintain privacy of individuals as well as groups of two users (e.g., a couple). Again, note that in this example the minimum number of users required for a crowd is 3. However, the present disclosure is not limited thereto. The minimum number of users for a crowd may be any desired number greater than or equal to 2.
  • The crowd analyzer 58 then determines whether the crowd formation process for the new and old bounding boxes is done (step 1274). In other words, the crowd analyzer 58 determines whether both the new and old bounding boxes have been processed. If not, the bounding box is set to the new bounding box (step 1276), and the process returns to step 1244 and is repeated for the new bounding box. Once both the new and old bounding boxes have been processed, the crowd formation process ends.
  • FIGS. 8A through 8D graphically illustrate the crowd formation process of FIGS. 7A through 7D for a scenario where the crowd formation process is triggered by a location update for one of the users 20 having no old location. In this scenario, the crowd analyzer 58 creates a new bounding box 80 for the new location of the user 20, and the new bounding box 80 is set as the bounding box to be processed for crowd formation. Then, as illustrated in FIG. 8A, the crowd analyzer 58 identifies all individual users currently located within the bounding box 80 and all crowds located within or overlapping the bounding box 80. In this example, crowd 82 is an existing crowd relevant to the bounding box 80. Crowds are indicated by dashed circles, crowd centers are indicated by cross-hairs (±), and users are indicated as dots. Next, as illustrated in FIG. 8B, the crowd analyzer 58 creates crowds 84 through 88 of one user for the individual users, and the optimal inclusion distances of the crowds 84 through 88 are set to the initial optimal inclusion distance. As discussed above, the initial optimal inclusion distance is computed by the crowd analyzer 58 based on a density of users within the bounding box 80.
  • The crowd analyzer 58 then identifies the two closest crowds 84 and 86 in the bounding box 80 and determines a distance between the two closest crowds 84 and 86. In this example, the distance between the two closest crowds 84 and 86 is less than the optimal inclusion distance. As such, the two closest crowds 84 and 86 are merged and a new crowd center and new optimal inclusion distance are computed, as illustrated in FIG. 8C. The crowd analyzer 58 then repeats the process such that the two closest crowds 84 and 88 in the bounding box 80 are merged, as illustrated in FIG. 8D. At this point, the distance between the two closest crowds 82 and 84 is greater than the appropriate optimal inclusion distance. As such, the crowd formation process is complete.
  • FIGS. 9A through 9F graphically illustrate the crowd formation process of FIGS. 7A through 7D for a scenario where the new and old bounding boxes overlap. As illustrated in FIG. 9A, one of the users 20 moves from an old location to a new location, as indicated by an arrow. The crowd analyzer 58 receives a location update for the user 20 giving the new location of the user 20. In response, the crowd analyzer 58 creates an old bounding box 90 for the old location of the user and a new bounding box 92 for the new location of the user. Crowd 94 exists in the old bounding box 90, and crowd 96 exists in the new bounding box 92.
  • Since the old bounding box 90 and the new bounding box 92 overlap, the crowd analyzer 58 creates a bounding box 98 that encompasses both the old bounding box 90 and the new bounding box 92, as illustrated in FIG. 9B. In addition, the crowd analyzer 58 creates crowds 100 through 106 for individual users currently located within the bounding box 98. The optimal inclusion distances of the crowds 100 through 106 are set to the initial optimal inclusion distance computed by the crowd analyzer 58 based on the density of users in the bounding box 98.
  • Next, the crowd analyzer 58 analyzes the crowds 94, 96, and 100 through 106 to determine whether any members of the crowds 94, 96, and 100 through 106 violate the optimal inclusion distances of the crowds 94, 96, and 100 through 106. In this example, as a result of the user leaving the crowd 94 and moving to his new location, both of the remaining members of the crowd 94 violate the optimal inclusion distance of the crowd 94. As such, the crowd analyzer 58 removes the remaining users 20 from the crowd 94 and creates crowds 108 and 110 of one user each for those users, as illustrated in FIG. 9C.
  • The crowd analyzer 58 then identifies the two closest crowds in the bounding box 98, which in this example are the crowds 104 and 106. Next, the crowd analyzer 58 computes a distance between the two crowds 104 and 106. In this example, the distance between the two crowds 104 and 106 is less than the initial optimal inclusion distance and, as such, the two crowds 104 and 106 are merged. In this example, the crowd analyzer 58 merges the crowd 106 into the crowd 104, as illustrated in FIG. 9D. A new crowd center and new optimal inclusion distance are then computed for the crowd 104.
  • At this point, the crowd analyzer 58 repeats the process and determines that the crowds 96 and 102 are now the two closest crowds. In this example, the distance between the two crowds 96 and 102 is less than the optimal inclusion distance of the larger of the two crowds 96 and 102, which is the crowd 96. As such, the crowd 102 is merged into the crowd 96 and a new crowd center and optimal inclusion distance are computed for the crowd 96, as illustrated in FIG. 9E. At this point, there are no two crowds closer than the optimal inclusion distance of the larger of the two crowds. As such, the crowd analyzer 58 discards any crowds having less than three members, as illustrated in FIG. 9F. In this example, the crowds 100, 104, 108, and 110 have less than three members and are therefore removed. The crowd 96 has three or more members and, as such, is not removed. At this point, the crowd formation process is complete.
  • FIGS. 10A through 10E graphically illustrate the crowd formation process of FIGS. 7A through 7D in a scenario where the new and old bounding boxes do not overlap. As illustrated in FIG. 10A, in this example, the user 20 moves from an old location to a new location. The crowd analyzer 58 creates an old bounding box 112 for the old location of the user 20 and a new bounding box 114 for the new location of the user 20. Crowds 116 and 118 exist in the old bounding box 112, and crowd 120 exists in the new bounding box 114. In this example, since the old and new bounding boxes 112 and 114 do not overlap, the crowd analyzer 58 processes the old and new bounding boxes 112 and 114 separately.
  • More specifically, as illustrated in FIG. 10B, as a result of the movement of the user 20 from the old location to the new location, the remaining users 20 in the crowd 116 no longer satisfy the optimal inclusion distance for the crowd 116. As such, the remaining users 20 in the crowd 116 are removed from the crowd 116, and crowds 122 and 124 of one user each are created for the removed users as shown in FIG. 10B. In this example, no two crowds in the old bounding box 112 are close enough to be combined. As such, since the crowds 122 and 124 do not have at least 3 users, the crowds 122 and 124 are discarded as shown in FIG. 10C, and processing of the old bounding box 112 is complete. The crowd analyzer 58 then proceeds to process the new bounding box 114.
  • As illustrated in FIG. 10D, processing of the new bounding box 114 begins by the crowd analyzer 58 creating a crowd 126 of one user for the user 20. The crowd analyzer 58 then identifies the crowds 120 and 126 as the two closest crowds in the new bounding box 114 and determines a distance between the two crowds 120 and 126. In this example, the distance between the two crowds 120 and 126 is less than the optimal inclusion distance of the larger crowd, which is the crowd 120. As such, the crowd analyzer 58 merges the crowd 126 into the crowd 120, as illustrated in FIG. 10E. A new crowd center and new optimal inclusion distance are then computed for the crowd 120. At this point, the crowd formation process is complete.
  • FIG. 11 illustrates a process for creating crowd snapshots according to one embodiment of the present disclosure. In this embodiment, after the spatial crowd formation process of FIGS. 7A through 7D is performed in response to a location update for one of the users 20, the crowd analyzer 58 detects crowd change events, if any, for the relevant crowds (step 1300). The relevant crowds are pre-existing crowds that are relevant to the bounding region(s) processed during the spatial crowd formation process in response to the location update for the user 20. The crowd analyzer 58 may detect crowd change events by comparing the crowd records 72 of the relevant crowds before and after performing the spatial crowd formation process in response to the location update for the user 20. The crowd change events may be a change in the users 20 in the crowd, a change to a location of one of the users 20 within the crowd, or a change in the spatial information for the crowd (e.g., the NE corner, the SW corner, or the crowd center). Note that if multiple crowd change events are detected for a single crowd, then those crowd change events are preferably consolidated into a single crowd change event.
  • Next, the crowd analyzer 58 determines whether there are any crowd change events (step 1302). If not, the process ends. Otherwise, the crowd analyzer 58 gets the next crowd change event (step 1304) and generates a crowd snapshot for a corresponding crowd (step 1306). More specifically, the crowd change event identifies the crowd record 72 stored for the crowd for which the crowd change event was detected. A crowd snapshot is then created for that crowd by creating a new crowd snapshot record 76 for the crowd and adding the new crowd snapshot record 76 to the list of crowd snapshots stored in the crowd record 72 for the crowd. As discussed above, in this embodiment, the crowd snapshot record 76 includes a set or list of anonymous user records 78, which are an anonymized version of the user records 74 for the users 20 in the crowd at the current time. In addition, the crowd snapshot record includes the NE corner, the SW corner, and the center of the crowd at the current time as well as a timestamp defining the current time as the sample time at which the crowd snapshot record 76 was created. Lastly, locations of the users 20 in the crowd that define the outer boundary of the crowd at the current time are stored in the crowd snapshot record 76 as the vertices of the crowd. After creating the crowd snapshot, the crowd analyzer 58 determines whether there are any more crowd change events (step 1308). If so, the process returns to step 1304 and is repeated for the next crowd change event. Once all of the crowd change events are processed, the process ends.
  • FIG. 12 illustrates a process that may be used to re-establish crowds and detect crowd splits according to one embodiment of the present disclosure. In general, in order to accurately track a crowd, it is preferable to enable crowds that have been removed to be re-established in the future. For example, a crowd may be removed as a result of users in the crowd deactivating their MAP applications (or powering down their mobile devices). If those users then move together to a different location and then reactivate their MAP applications (or power on their mobile devices), it is preferable for the resulting crowd to be identified as the same crowd that was previously removed. In other words, it is desirable to re-establish the crowd. In addition, in order to accurately track a crowd, it is desirable to capture when the crowd splits into two or more crowds.
  • Accordingly, in this embodiment, the spatial crowd formation process of FIGS. 7A through 7D is performed in response to a location update for one of the users 20. The crowd analyzer 58 then gets a next relevant crowd (step 1400). The relevant crowds are pre-existing and new crowds that are within the bounding region(s) processed during the spatial crowd formation process in response to the location update for the user 20. Note that, for the first iteration, the next relevant crowd is the first relevant crowd. The crowd analyzer 58 then determines a maximum number of users in the crowd from a common previous crowd (step 1402). More specifically, the crowd analyzer 58 examines the previous crowd fields of the user records of all of the users 20 in the crowd to identify users from a common previous crowd. For each previous crowd found in the user records of the users 20 in the crowd, the crowd analyzer 58 counts the number of users in the crowd that are from that previous crowd. The crowd analyzer 58 then selects the previous crowd having the highest number of users, and determines that the number of users counted for the selected previous crowd is the maximum number of users in the crowd from a common previous crowd.
  • The crowd analyzer 58 then determines whether the maximum number of users in the crowd from a common previous crowd is greater than a predefined threshold number of users (step 1404). In an alternative embodiment, rather than determining the maximum number of users from a common previous crowd and comparing that number to a predefined threshold number of users, a maximum percentage of users in the crowd from a common previous crowd may be determined and compared to a predefined threshold percentage. If the maximum number of users in the crowd from a common previous crowd is not greater than the predefined threshold number of users, the process proceeds to step 1410. Otherwise, the crowd analyzer 58 determines whether the common previous crowd has been removed (step 1406). If so, then the crowd is re-established as the common previous crowd (step 1408). More specifically, in this embodiment, the crowd is re-established as the common previous crowd by storing the set or list of user records, the NE corner, the SW corner, and the center from the crowd record of the crowd in the crowd record of the common previous crowd. The crowd record for the crowd may then be deleted. In addition, the previous crowd fields of the users from the common previous crowd may be set to null or otherwise cleared. Once the common previous crowd is re-established, the crowd analyzer 58 determines whether there are more relevant crowds to process (step 1410). If so, the process returns to step 1400 and is repeated until all relevant crowds are processed.
  • Returning to step 1406, if the common previous crowd has not been removed, the crowd analyzer 58 identifies the crowd as being split from the common previous crowd (step 1412). More specifically, in this embodiment, the crowd analyzer 58 stores a reference to the crowd record of the common previous crowd in the split from field of the crowd record of the crowd. At this point, the crowd analyzer 58 then determines whether there are more relevant crowds to process (step 1410). If so, the process returns to step 1400 and is repeated until all relevant crowds are processed, at which time the process ends.
  • FIG. 13 graphically illustrates the process of re-establishing a crowd for an exemplary crowd according to one embodiment of the present disclosure. As illustrated, at TIME 1, CROWD A has been formed and a corresponding crowd record has been created and stored. Between TIME 1 and TIME 2, three users from CROWD A have moved, thereby resulting in the removal of those three users from CROWD A as well as the removal of CROWD A. Again, CROWD A has been removed by removing the set or list of user records and spatial information from the crowd record for CROWD A. At TIME 2, a new crowd, CROWD B, has been formed for the three users that were previously in CROWD A. As such, the previous crowd fields for the three users now in CROWD B indicate that the three users are from CROWD A. Using the process of FIG. 12, the crowd analyzer 58 determines that the three users in CROWD B have a common previous crowd, namely, CROWD A. As a result, the crowd analyzer 58 re-establishes CROWD B as CROWD A, as shown at TIME 2′.
  • FIG. 14 graphically illustrates the process for capturing a crowd split for an exemplary crowd according to one embodiment of the present disclosure. As illustrated, at TIME 1, CROWD A has been formed and a corresponding crowd record has been created and stored. Between TIME 1 and TIME 2, four users from CROWD A have separated from the other three users of CROWD A. As a result, a new crowd, CROWD B, has been formed at TIME 2 for the four users from CROWD A. Using the process of FIG. 12, the crowd analyzer 58 determines that the four users in CROWD B are all from CROWD A and therefore identifies CROWD B as being split from CROWD A.
  • FIG. 15 graphically illustrates the merging of two exemplary pre-existing crowds according to one embodiment of the present disclosure. As discussed above, the merger of crowds is performed during the spatial crowd formation process of FIGS. 7A through 7D. As illustrated, at TIME 1, CROWD A and CROWD B have been formed and corresponding crowd records have been created and stored. Between TIME 1 and TIME 2, CROWD A and CROWD B move close to one another such that the distance between CROWD A and CROWD B is less than the optimal inclusion distance(s) at TIME 2. As such, the crowd analyzer 58 merges CROWD A into CROWD B at TIME 2′. As part of the merger, CROWD A is removed, and the merged into field of the crowd record for CROWD A is set to a reference to the crowd record for CROWD B. In addition, the previous crowd fields in the user records of the users from CROWD A are set to a reference to the crowd record of CROWD A.
  • Up until this point, the disclosure has primarily focused on the operation of the system 10 to form and track crowds of users. Now, the discussion will turn to processes by which the system 10 enables crowd following in order to receive status updates from the users 20 in the followed crowds according to various embodiments of the present disclosure. FIG. 16 illustrates the operation of the status update processor 62 of the MAP server 12 to enable a requestor to follow status updates from users in a select crowd of users even after users in the select crowd have dispersed according to one embodiment of the present disclosure. To give context, consider the scenario where a number of the users 20 form a crowd of users attending a college football game. The system 10 may enable a requestor, such as one of the users 20 in the crowd at the football game, to receive status updates from the users 20 in the crowd while at the football game. However, in this scenario, it is very likely that the users 20 will post status updates about the football game even after the football game has ended and the users 20 in the crowd have dispersed. The process of FIG. 16 enables a requestor, such as but not limited to one of the users 20 in the crowd, to receive status updates from other crowds of users in which the users 20 in the select crowd are located after the football game has ended and the crowd has dispersed. So, for example, if a number of the users 20 that were in the crowd at the football game move to a new crowd at a nearby sports bar after the football game has ended, the requestor will receive status updates from the users in the new crowd at the nearby sports bar. In this manner, the requestor will continue to receive relevant status updates regarding the football game even after the crowd at the football game has dispersed.
  • More specifically, first, the status update processor 62 of the MAP server 12 receives a crowd selection of a requestor (step 1500). The crowd selection identifies a crowd selected by the requestor. The requestor may be one of the users 20 in the selected crowd or one of the users 20 that is not in the selected crowd where the crowd selection is sent to the MAP server 12 via the MAP application 34 or alternatively one of the third-party applications 36 of the corresponding mobile device 18. Alternatively, the requestor may be the subscriber 24 at the subscriber device 22 where the crowd selection is sent to the MAP server 12 via, for example, the web browser 40 of the subscriber device 22. As yet another alternative, the requestor may be the third-party service 26.
  • Upon receiving the crowd selection of the requestor, the status update processor 62 of the MAP server 12 records a list of users currently in the selected crowd (step 1502). More specifically, as discussed above, the crowd record stored for the selected crowd includes a list of users that are currently in the crowd. As such, the status update processor 62 may store a copy of the list of users in the crowd record of the selected crowd in response to receiving the crowd selection of the requestor. Thereafter, the status update processor 62 records the requestor as a follower of new crowds in which the users 20 in the recorded list of users are located after leaving, or dispersing from, the selected crowd (step 1504). In this manner, the requestor is automatically added as a follower of any new crowds in which the users 20 in the recorded list of users are located after leaving the selected crowd of users. Notably, step 1504 may be performed even while the selected crowd still exists (i.e., before all of the users in the selected crowd have dispersed) or may be performed only after all of the users in the selected crowd have dispersed (i.e., performed only after the selected crowd has been removed or no longer exists).
  • FIG. 17 illustrates the operation of the status update processor 62 to distribute status updates sent by the users 20 to followers of the corresponding crowds in which the users 20 are located according to one embodiment of the present disclosure. In one embodiment, this process is performed in parallel with the process of FIG. 16. First, the status update processor 62 receives a status update sent by one of the users 20 from the status update service 28 (step 1600). In one embodiment, the status update service 28 automatically sends status updates sent by the users 20 to the MAP server 12 as they are received by the status update service 28 or in a batch process. In another embodiment, the status update processor 62 of the MAP server 12 periodically requests status updates sent by the users 20 from the status update service 28.
  • Upon receiving the status update sent by the user 20, the status update processor 62 determines whether there are any followers of the crowd in which the user 20 is currently located (step 1602). If not, the process returns to step 1600 and is repeated for the next status update received for one of the users 20. If there are one or more followers of the crowd in which the user 20 that sent the status update is currently located, the status update processor 62 sends the status update to each of the followers (step 1604). For example, if one of the followers is one of the other users 20, then the status update processor 62 preferably sends the status update to the mobile device 18 of the other user 20 for presentation to the other user 20 via, for instance, the MAP application 34 or one of the third-party applications 36 of the mobile device 18, depending on the particular implementation. If one of the followers is the subscriber 24, then the status update processor 62 preferably sends the status update for display to the subscriber 24 via the web browser 40 of the subscriber device 22. The process then returns to step 1600 and is repeated for the next status update received for one of the users 20.
  • Notably, in the scenario where the requestor is one of the users 20, the requestor may be provided with one or more features to assist the user 20 in viewing status updates from followed crowds. These features may be provided by the MAP application 34 or one of the third-party applications 36 depending on which is enabling the requestor to follow the crowds. For example, the requestor may be enabled to select a desired crowd from a list of crowds followed by that requestor. In response, the requestor may be presented with a consolidated list of status updates resulting from following the selected crowd. In addition or alternatively, the requestor may be presented with a map and/or list that illustrates how the selected crowd has dispersed. The requestor may then be enabled to select any crowd to which the users in the selected crowd have dispersed to view only the status updates from that particular crowd.
  • FIG. 18 illustrates step 1504 of FIG. 16 in more detail according to one embodiment of the present disclosure. More specifically, FIG. 18 is a more detailed illustration of a process by which the status update processor 62 of the MAP server 12 is enabled to automatically record, or add, the requestor as a follower of new crowds in which the users in the recorded list of users are located after leaving the crowd selected by the requestor. First, the status update processor 62 determines whether a predefined time limit has expired since the requestor selected the crowd in step 1500 (FIG. 16) (step 1700). The predefined time limit may be defined by the requestor or may be system-defined. For example, the status update processor 62 may use a system-defined time limit of 1 hour. Such a time limit may be particularly beneficial where the requestor is not automatically added as a follower of new crowds of the users in the recorded list of users for the crowd selected by the requestor until the crowd selected by the requestor has completely dispersed (i.e., has been removed or no longer exists). If the predefined time limit has expired, then the requestor is removed as a follower of the current crowds of the users in the recorded list of users for the crowd selected by the requestor (step 1702). Note that steps 1700 and 1702 are optional. If the predefined time limit has not expired, the status update processor 62 sets a counter i to 1 (step 1704). Next, the status update processor 62 determines whether the ith user (user i) in the recorded list of users has left his previous crowd (step 1706). If user i has not left his previous crowd (i.e., if user i remains in the same crowd as before), then the process proceeds to step 1712.
  • In this embodiment, if user i has left his previous crowd, the status update processor 62 determines whether less than a predefined threshold number of users from the recorded list of users remain in the previous crowd of user i (step 1708). The predefined threshold number of users may be expressed as an absolute number of users (e.g., 3 users) or a percentage of the number of users in the recorded list (e.g., 5% of the users in the recorded list of users for the crowd selected by the requestor). If more than the predefined threshold number of users from the recorded list of users remain in the previous crowd of user i, then the requestor remains a follower of that crowd and the process proceeds to step 1712. If less than the predefined threshold number of users from the recorded list of users remain in the previous crowd of user i, then the status update processor 62 removes the requestor as a follower of the previous crowd of user i (step 1710).
  • Next, whether proceeding from step 1706, 1708, or 1710, the status update processor 62 determines whether user i is located in a new crowd (step 1712). If not, the process proceeds to step 1720. If user i is located in a new crowd, in this embodiment, the status update processor 62 determines whether the new crowd of user i satisfies a predefined geographical limitation (step 1714). Note that step 1714 is optional. The predefined geographical limitation may be, for example, that the new crowd of user i is within a predefined geographical boundary that is centered at or otherwise encompasses the location of the crowd selected by the requestor at the time that the crowd was selected by the requestor (e.g., within a defined maximum distance from the location of the crowd selected by the requestor at the time the crowd was selected by the requestor). Alternatively, if the crowd selected by the requestor still exists, the predefined geographical limitation may be, for example, that the new crowd of user i is within a predefined geographical boundary that is centered at or otherwise encompasses a current location of the crowd selected by the requestor (e.g., within a defined maximum distance from the current location of the crowd selected by the requestor). If the new crowd of user i does not satisfy the predefined geographical limitation, then the process proceeds to step 1720.
  • If the new crowd of user i does satisfy the predefined geographical limitation, in this embodiment, the status update processor 62 determines whether a threshold number of users in the recorded list of users for the crowd selected by the requestor are in the new crowd of user i (step 1716). Notably, the thresholds in steps 1708 and 1716 may be the same threshold values or different threshold values. The threshold number of users is a predefined threshold and may be expressed as an absolute number of users (e.g., 3 users) or a percentage of the number of users in the recorded list (e.g., 5% of the users in the recorded list of users for the crowd selected by the requestor). If at least the threshold number of users in the recorded list of users for the crowd selected by the requestor is not included in the new crowd of user i, then the process proceeds to step 1720. Otherwise, the status update processor 62 adds the requestor as a follower of the new crowd of user i (step 1718). Note that step 1716 is optional. In another embodiment, the requestor is added as a follower of the new crowd of user i without first requiring that at least the threshold number of users in the recorded list of users for the crowd selected by the requestor (i.e., the original crowd) to be in the new crowd of user i.
  • At this point, whether proceeding from step 1712, 1714, 1716, or 1718, the status update processor 62 determines whether user i is the last user in the list of users recorded for the crowd of users selected by the requestor (step 1720). If not, the status update processor 62 increments the counter i (step 1722), and then the process returns to step 1706 and is repeated for the next user. Once the last user in the recorded list of users for the crowd selected by the requestor has been processed, the process returns to step 1700 and is repeated.
  • FIG. 19 illustrates the operation of the status update processor 62 of the MAP server 12 according to another embodiment of the present disclosure. First, the status update processor 62 of the MAP server 12 receives a crowd selection of a requestor (step 1800). The crowd selection identifies a crowd selected by the requestor. The requestor may be one of the users 20 in the selected crowd or one of the users 20 that is not in the selected crowd where the crowd selection is sent to the MAP server 12 via the MAP application 34 or alternatively one of the third-party applications 36 of the corresponding mobile device 18. Alternatively, the requestor may be the subscriber 24 at the subscriber device 22 where the crowd selection is sent to the MAP server 12 via, for example, the web browser 40 of the subscriber device 22. As yet another alternative, the requestor may be the third-party service 26. Upon receiving the crowd selection of the requestor, the status update processor 62 of the MAP server 12 records a list of users currently in the selected crowd (step 1802). More specifically, as discussed above, the crowd record stored for the selected crowd includes a list of users that are currently in the crowd. As such, the status update processor 62 may store a copy of the list of users in the crowd record of the selected crowd at the time of the crowd selection as the recorded list of users in the selected crowd.
  • Some time thereafter, the status update processor 62 receives a status update request from the requestor (step 1804). The status update request may be manually initiated by the requestor or automatically sent on behalf of the requestor. In response, the status update processor 62 identifies the crowds in which the users in the recorded list of users for the selected crowd are currently located (step 1806). The status update processor 62 then obtains status updates sent by the users 20 in the identified crowds (step 1808) and returns those status updates to the requestor (step 1810). More specifically, the requestor may be added as a follower of the identified crowds. Thereafter, as status updates are sent by users in the identified crowds, the status updates are returned to the requestor as a follower of those crowds. The requestor may remain a follower of the identified crowds indefinitely, for a predefined amount of time, or the like, depending on the particular implementation. Further, while not illustrated, the identified crowds may be filtered based on geographic limitations such that the requestor receives status updates only for those crowds that satisfy one or more predefined geographic limitations in a manner similar to that described above with respect to step 1714 of FIG. 18. Notably, steps 1804 through 1810 may be performed while the selected crowd still exists (i.e., before all of the users in the selected crowd have dispersed) or may be performed only after all of the users in the selected crowd have dispersed (i.e., performed only after the selected crowd has been removed or no longer exists). Also, steps 1804 through 1810 may be repeated.
  • FIG. 20 illustrates the operation of the status update processor 62 of the MAP server 12 according to yet another embodiment of the present disclosure. First, the status update processor 62 of the MAP server 12 receives a crowd selection of a requestor (step 1900). The crowd selection identifies a crowd selected by the requestor. The requestor may be one of the users 20 in the selected crowd or one of the users 20 that is not in the selected crowd where the crowd selection is sent to the MAP server 12 via the MAP application 34 or alternatively one of the third-party applications 36 of the corresponding mobile device 18. Alternatively, the requestor may be the subscriber 24 at the subscriber device 22 where the crowd selection is sent to the MAP server 12 via, for example, the web browser 40 of the subscriber device 22. As yet another alternative, the requestor may be the third-party service 26.
  • Sometime after receiving the crowd selection of the requestor, the status update processor 62 of the MAP server 12 detects a crowd split from the selected crowd (step 1902). In one embodiment, the detected crowd is a crowd that directly split from the selected crowd. In another embodiment, the detected crowd is either a crowd that directly split from the selected crowd or indirectly split from the selected crowd. As used herein, a crowd that indirectly split from the selected crowd is a crowd that split from a crowd that split from the selected crowd, a crowd that split from a crowd that split from a crowd that split from the selected crowd, or so on. Upon detecting a crowd that split from the selected crowd, the status update processor 62 adds the requestor as a follower of the detected crowd (step 1904). The process then returns to step 1902. While not illustrated, in another embodiment, the requestor is not added as a follower of the detected crowd if the detected crowd does not satisfy one or more predefined geographic limitations in a manner similar to that described above with respect to step 1714 of FIG. 18. In addition or alternatively, steps 1902 through 1904 may be repeated until a predefined time limit has expired. The predefined time limit may be defined by the requestor or may be system defined. After the predefined time limit has expired, the requestor is removed as a follower of the crowds that split from the selected crowd. Note that in the embodiment of FIG. 20, the status update processor 62 also performs the process of FIG. 17 in order to deliver status updates to the requestor as a follower of the crowds detected in step 1902.
  • FIG. 21 illustrates the operation of the status update processor 62 according to yet another embodiment of the present disclosure. In this embodiment, a requestor selects one of the users 20 to follow and, in response, is provided with status updates of the crowds in which the user 20 is located. Thus, as the user 20 moves from a first crowd to a second crowd, the requestor is removed as a follower of the first crowd and added as a follower of the second crowd. In this manner, as the user 20 moves among crowds over time, the requestor is enabled to follow the crowds in which the user 20 is located.
  • More specifically, first, the status update processor 62 of the MAP server 12 receives a user selection made by a requestor (step 2000). The user selection identifies one of the users 20 selected by the requestor. The requestor may be one of the users 20, where the selected user 20 is another one of the users 20. In this case, the user selection is sent to the MAP server 12 via the MAP application 34 or alternatively one of the third-party applications 36 of the corresponding mobile device 18. Alternatively, the requestor may be the subscriber 24 at the subscriber device 22 where the user selection is sent to the MAP server 12 via, for example, the web browser 40 of the subscriber device 22. As yet another alternative, the requestor may be the third-party service 26.
  • Upon receiving the user selection made by the requestor, the status update processor 62 of the MAP server 12 identifies the crowd in which the selected user is located (step 2002) and records the requestor as a follower of the identified crowd (step 2004). Next, the status update processor 62 determines whether the selected user has left the crowd previously identified as the crowd of the selected user (step 2006). If not, the process proceeds to step 2010. If so, the status update processor 62 removes the requestor as a follower of the previous crowd of the selected user (step 2008). In other words, once the selected user leaves a crowd, the requestor is removed as a follower of that crowd. The status update processor 62 then determines whether the selected user has joined a new crowd (step 2010). If not, the process returns to step 2006 and is repeated. If the selected user has joined a new crowd, the status update processor 62 records the requestor as a follower of the new crowd of the selected user (step 2012). The process then returns to step 2006 and is repeated. Note that in the embodiment of FIG. 21, the status update processor 62 also performs the process of FIG. 17 in order to deliver status updates sent by users in the crowd of the selected user to the requestor as a follower of the crowd of the selected user.
  • FIG. 22 illustrates the operation of the status update processor 62 of the MAP server 12 to enable a requestor to follow status updates sent by users in a select crowd even after those users have dispersed from the select crowd according to another embodiment of the present disclosure. First, the status update processor 62 of the MAP server 12 receives a crowd selection of a requestor (step 2100). The crowd selection identifies a crowd selected by the requestor. The requestor may be one of the users 20 in the selected crowd or one of the users 20 that is not in the selected crowd where the crowd selection is sent to the MAP server 12 via the MAP application 34 or alternatively one of the third-party applications 36 of the corresponding mobile device 18. Alternatively, the requestor may be the subscriber 24 at the subscriber device 22 where the crowd selection is sent to the MAP server 12 via, for example, the web browser 40 of the subscriber device 22. As yet another alternative, the requestor may be the third-party service 26. Upon receiving the crowd selection of the requestor, the status update processor 62 of the MAP server 12 records a list of users currently in the selected crowd (step 2102). More specifically, as discussed above, the crowd record stored for the selected crowd includes a list of users that are currently in the crowd. As such, the status update processor 62 may store a copy of the list of users in the crowd record of the selected crowd at the time of the crowd selection as the recorded list of users in the selected crowd.
  • Thereafter, the status update processor 62 obtains status updates from the users 20 in the recorded list of users for the select crowd and sends the status updates to the requestor even after the users 20 in the recorded list of users have dispersed, or left, the selected crowd (step 2104). Optionally, step 2104 may include time and/or geographic limitations. For example, status updates from the users 20 in the recorded list of users may be obtained and sent to the requestor only for a predefined amount of time after the users 20 leave the select crowd or, alternatively, for only a predefined amount of time after the select crowd has completely dispersed (i.e., no longer exists). The time limit may be per individual user 20 in the recorded list. For example, there may be a separate 1 hour time limit for each user 20 in the recorded list since the user 20 may leave the crowd at different times. The time limit may alternatively be a single time limit that is applicable to the entire list of users. For example, there may be a single 1 hour time limit that begins when the last user in the recorded list of users leaves the selected crowd. In addition or alternatively, status updates from the users 20 in the recorded list of users may be obtained and sent to the requestor only while the users 20 are subject to geographic limitations. For example, the status updates may be obtained and sent to the requestor as long as the users 20 in the recorded list remain in a predefined geographic boundary. This predefined geographic boundary may be a predefined geographic boundary that is centered at or otherwise encompasses the location of the selected crowd at the time the crowd was selected by the requestor, a predefined geographic boundary that is centered at or otherwise encompasses the current location of the selected crowd, or the like. The geographic limitation may be per user such that status updates for any one of the users 20 in the recorded list are obtained and sent to the requestor as long as the geographic limitation is satisfied. Alternatively, the geographic limitation may be for the recorded list of users as a whole where the status updates are obtained and sent to the requestor for all of the users 20 in the recorded list as long as any one of the users 20 in the recorded list satisfies the geographic limitation or, alternatively, all of the users 20 in the recorded list satisfy the geographic limitation.
  • FIG. 23 is a block diagram of the MAP server 12 according to one embodiment of the present disclosure. As illustrated, the MAP server 12 includes a controller 128 connected to memory 130, one or more secondary storage devices 132, and a communication interface 134 by a bus 136 or similar mechanism. The controller 128 is a microprocessor, digital Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), or similar hardware component. In this embodiment, the controller 128 is a microprocessor, and the application layer 42, the business logic layer 44, and the object mapping layer 63 (FIG. 2) are implemented in software and stored in the memory 130 for execution by the controller 128. Further, the datastore 64 (FIG. 2) may be implemented in the one or more secondary storage devices 132. The secondary storage devices 132 are digital data storage devices such as, for example, one or more hard disk drives. The communication interface 134 is a wired or wireless communication interface that communicatively couples the MAP server 12 to the network 30 (FIG. 1). For example, the communication interface 134 may be an Ethernet interface, local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, or the like.
  • FIG. 24 is a block diagram of one of the mobile devices 18 according to one embodiment of the present disclosure. This discussion is equally applicable to the other mobile devices 18. As illustrated, the mobile device 18 includes a controller 138 connected to memory 140, a communication interface 142, one or more user interface components 144, and the location function 38 by a bus 146 or similar mechanism. The controller 138 is a microprocessor, digital ASIC, FPGA, or similar hardware component. In this embodiment, the controller 138 is a microprocessor, and the MAP client 32, the MAP application 34, and the third-party applications 36 are implemented in software and stored in the memory 140 for execution by the controller 138. In this embodiment, the location function 38 is a hardware component such as, for example, a GPS receiver. The communication interface 142 is a wireless communication interface that communicatively couples the mobile device 18 to the network 30 (FIG. 1). For example, the communication interface 142 may be a local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, a mobile communications interface such as a cellular telecommunications interface (e.g., 3G telecommunications interface such as a Global System for Mobile communications (GSM) interface or the like, or a 4G telecommunications interface such as Long Term Evolution (LTE), or the like. The one or more user interface components 144 include, for example, a touchscreen, a display, one or more user input components (e.g., a keypad), a speaker, or the like, or any combination thereof.
  • FIG. 25 is a block diagram of the subscriber device 22 according to one embodiment of the present disclosure. As illustrated, the subscriber device 22 includes a controller 148 connected to memory 150, one or more secondary storage devices 152, a communication interface 154, and one or more user interface components 156 by a bus 158 or similar mechanism. The controller 148 is a microprocessor, digital ASIC, FPGA, or similar hardware component. In this embodiment, the controller 148 is a microprocessor, and the web browser 40 (FIG. 1) is implemented in software and stored in the memory 150 for execution by the controller 148. The one or more secondary storage devices 152 are digital storage devices such as, for example, one or more hard disk drives. The communication interface 154 is a wired or wireless communication interface that communicatively couples the subscriber device 22 to the network 30 (FIG. 1). For example, the communication interface 154 may be an Ethernet interface, local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, a mobile communications interface such as a cellular telecommunications interface, or the like. The one or more user interface components 156 include, for example, a touchscreen, a display, one or more user input components (e.g., a keypad), a speaker, or the like, or any combination thereof.
  • FIG. 26 is a block diagram of a computing device 160 operating to host the status update service 28 according to one embodiment of the present disclosure. The computing device 160 may be, for example, a physical server. As illustrated, the computing device 160 includes a controller 162 connected to memory 164, one or more secondary storage devices 166, a communication interface 168, and one or more user interface components 170 by a bus 172 or similar mechanism. The controller 162 is a microprocessor, digital ASIC, FPGA, or similar hardware component. In this embodiment, the controller 162 is a microprocessor, and the status update service 28 is implemented in software and stored in the memory 164 for execution by the controller 162. The one or more secondary storage devices 166 are digital storage devices such as, for example, one or more hard disk drives. The communication interface 168 is a wired or wireless communication interface that communicatively couples the computing device 160 to the network 30 (FIG. 1). For example, the communication interface 168 may be an Ethernet interface, local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, a mobile communications interface such as a cellular telecommunications interface, or the like. The one or more user interface components 170 include, for example, a touchscreen, a display, one or more user input components (e.g., a keypad), a speaker, or the like, or any combination thereof.
  • The following use cases illustrate some aspects of the systems and methods described above. However, these use cases are exemplary and are not intended to limit the scope of the present disclosure.
  • Use Case #1: Following a crowd in order to continue receiving status updates when the crowd disperses.
      • 1. Jim and his friend are in a crowd at the N.C. State football game.
      • 2. State ends up losing and the crowd begins to disperse.
      • 3. Jim wants to view status updates sent by users that are in the crowd at the game even after the crowd disperses so he selects to follow his current crowd.
      • 4. The MAP server 12 takes a snapshot of the users in the crowd.
      • 5. Everyone goes their own way, but several of the fans continue sending status updates about the game.
      • 6. Jim is able to see all of the status updates from the fans that are continuing to post. Jim joins in.
  • Use Case #1a: Creating and utilizing sub-groups from the original followed group. (Continuing from the previous use case).
      • 1. After the game, Jim and his friend decide to go hang out on Hillsborough Street.
      • 2. Jim opens up the MAP application 34 on his mobile device 18, and he selects the crowd that he is following (i.e., the “Game Crowd” that has now dispersed or that is continuing to disperse).
      • 3. The MAP server 12 detects that two new crowds have split from the “Game Crowd” and are located at neighboring bars on Hillsborough Street—Porter's Tavern and Mitch's Tavern.
      • 4. Jim is able to select each of the crowds using his MAP application 34 to view their ongoing status updates.
      • 5. One of the crowds (the one at Mitch's Tavern) is still harping on how bad the team played, how the coach should be fired, etc., while the other crowd (at Porter's Tavern) is discussing how much fun they had at the game and the (few) bright spots from the game.
      • 6. Additionally, the MAP application 34 allows Jim to quickly see various and useful crowd characteristics of each of the two crowds that he is monitoring.
      • 7. Jim can see that the more upbeat crowd also has a predominance of members who have a history of both gathering at and sending status updates from Porter's Tavern. The crowd at Mitch's Tavern on the other hand does not appear to be a “regular” crowd at that location.
      • 8. Jim and his friend decide to join the more upbeat and “regular” crowd for a few drinks at Porter's Tavern.
  • Use Case #2: Following a User.
      • 1. Amy enjoys the insightful comments of a mobile industry analyst and follows him on Twitter®.
      • 2. Amy sees that the analyst is also a user registered with the MAP server 12.
      • 3. Amy decides to follow the analyst via the MAP server 12 as well.
      • 4. A mobile conference is taking place in N.Y. and Amy isn't able to go.
      • 5. However, because Amy is following the analyst via the MAP server 12, Amy is able to follow the status updates from the users in the crowds in which the analyst is located.
      • 6. Amy occasionally checks the crowd status updates from the crowds in which the analyst is located in order to get the analyst's latest thoughts as well as perspectives and viewpoints of other users in the analyst's crowds.
  • The present disclosure provides substantial opportunity for variation without departing from the spirit or scope of the disclosure. For example, FIGS. 16 through 22 focus on embodiments where the MAP server 12 obtains status updates sent by the users 20 from the status update service 28 and then distributes the status updates to the followers of the corresponding crowds. However, the present disclosure is not limited thereto. In an alternative embodiment, the MAP server 12 may record followers of crowds in the manner described above with respect to FIGS. 16 and 18 through 22 and then communicate with the status update service 28 such that the status update service 28 distributes status updates sent by the users 20 to the followers of the corresponding crowds of users. Other variations will be apparent to one of ordinary skill in the art upon reading this disclosure and are to be considered to be within the scope of the present disclosure.
  • Those skilled in the art will recognize improvements and modifications to the preferred embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein and the claims that follow.

Claims (24)

What is claimed is:
1. A computer-implemented method comprising:
receiving a crowd selection of a requestor that identifies a first crowd of users selected by the requestor;
obtaining status updates sent by one or more users from the first crowd of users after the one or more users have left the first crowd of users; and
sending the status updates sent by the one or more users from the first crowd of users to the requestor.
2. The method of claim 1 wherein:
obtaining the status updates sent by the one or more users from the first crowd of users after the one or more users have left the first crowd of users comprises obtaining status updates sent by users, including the one or more users, in one or more second crowds of users in which the one or more users from the first crowd of users are located after the one or more users have left the first crowd of users; and
sending the status updates sent by the one or more users from the first crowd of users to the requestor comprises sending the status updates sent by the users in the one or more second crowds of users to the requestor.
3. The method of claim 2 further comprising:
recording a list of users currently in the first crowd of users in response to receiving the crowd selection of the requestor; and
automatically recording the requestor as a follower of the one or more second crowds of users, wherein the one or more second crowds of users are one or more crowds in which users in the list of users recorded for the first crowd of users are located after the users have left the first crowd of users.
4. The method of claim 3 wherein automatically recording the requestor as a follower of the one or more second crowds of users comprises:
detecting that a user from the list of users recorded for the first crowd of users is located in a new crowd; and
recording the requestor as a follower of the new crowd in response to detecting that the user from the list of users is located in the new crowd.
5. The method of claim 4 further comprising automatically removing the requestor as a follower of the new crowd after no users from the list of users recorded for the first crowd of users remain in the new crowd.
6. The method of claim 3 wherein automatically recording the requestor as a follower of the one or more second crowds of users comprises:
detecting when at least a predefined threshold number of users from the list of users recorded for the first crowd of users are located in a new crowd; and
recording the requestor as a follower of the new crowd in response to detecting that at least the predefined threshold number of users from the list of users recorded for the first crowd of users are located in the new crowd.
7. The method of claim 6 further comprising automatically removing the requestor as a follower of the new crowd after less than the at least the predefined threshold number of users from the list of users recorded for the first crowd of users remain in the new crowd.
8. The method of claim 2 further comprising:
recording a list of users currently in the first crowd of users in response to receiving the crowd selection of the requestor;
subsequently receiving a status update request from the requestor; and
in response to receiving the status update request from the requestor, identifying one or more crowds of users, other than the first crowd of users, in which users from the list of users are currently located as the one or more second crowds of users;
wherein obtaining the status updates sent by the users in the one or more second crowds of users and sending the status updates sent by the users in the one or more second crowds of users are in response to receiving the status update request from the requestor and identifying the one or more crowds of users, other than the first crowd of users, in which users from the list of users are currently located as the one or more second crowds of users.
9. The method of claim 2 further comprising detecting the one or more second crowds of users as one or more crowds of users that split from the first crowd of users subsequent to receiving the crowd selection of the requestor.
10. The method of claim 2 wherein the one or more second crowds of users are one or more crowds of users, other than the first crowd of users, in which one or more users from the first crowd of users are located after the one or more users have left the first crowd of users and that are located within a predefined geographic region.
11. The method of claim 10 wherein the predefined geographic region is a predefined geographic region that encompasses a location of the first crowd of users at a time of receiving the crowd selection of the requestor.
12. The method of claim 10 wherein the predefined geographic region is a predefined geographic region that encompasses a current location of the first crowd of users.
13. The method of claim 2 further comprising limiting the steps of obtaining the status updates and sending the status updates to the requestor to a predefined amount of time.
14. The method of claim 2 wherein obtaining the status updates sent by the users in the one or more second crowds of users and sending the status updates to the requestor are performed while the first crowd of users still exists.
15. The method of claim 2 wherein obtaining the status updates sent by the users in the one or more second crowds of users and sending the status updates to the requestor are performed only after all of the users in the first crowd of users have dispersed such that the first crowd of users no longer exists.
16. The method of claim 2 wherein the first crowd of users is a first group of spatially proximate users, and each of the one or more second crowds of users is a corresponding group of spatially proximate users.
17. The method of claim 1 wherein obtaining the status updates and sending the status updates are subject to one or more geographic limitations.
18. The method of claim 1 wherein sending the status updates to the requestor is subject to one or more time limitations.
19. A computing device comprising:
a communication interface adapted to communicatively couple the computing device to a network; and
a controller associated with the communication interface adapted to:
receive, via the communication interface, a crowd selection of a requestor that identifies a first crowd of users selected by the requestor;
obtain status updates sent by one or more users from the first crowd of users after the one or more users have left the first crowd of users; and
send, via the communication interface, the status updates sent by the one or more users from the first crowd of users to the requestor.
20. A non-transitory computer-readable medium storing software for instructing a controller of a computing device to:
receive a crowd selection of a requestor that identifies a first crowd of users selected by the requestor;
obtain status updates sent by one or more users from the first crowd of users after the one or more users have left the first crowd of users; and
send the status updates sent by the one or more users from the first crowd of users to the requestor.
21. A computer-implemented method comprising:
receiving a user selection made by a requestor that identifies a select user selected by the requestor;
identifying a first crowd of users in which the select user is located;
recording the requestor as a follower of the first crowd of users;
obtaining status updates sent by users in the first crowd of users;
sending the status updates sent by the users in the first crowd of users to the requestor as a follower of the first crowd of users;
identifying a second crowd of users in which the select user is located after the select user has left the first crowd of users;
recording the requestor as a follower of the second crowd of users;
obtaining status updates sent by users in the second crowd of users; and
sending the status updates sent by the users in the second crowd of users to the requestor as a follower of the second crowd of users.
22. The method of claim 21 further comprising:
detecting that the select user has left the first crowd of users; and
removing the requestor as a follower of the first crowd of users in response to detecting that the select user has left the first crowd of users.
23. The method of claim 21 wherein the first crowd of users is a first group of spatially proximate users, and each of the one or more second crowds of users is a corresponding group of spatially proximate users.
24. A computing device comprising:
a communication interface adapted to communicatively couple the computing device to a network; and
a controller associated with the communication interface adapted to:
receive, via the communication interface, a user selection made by a requestor that identifies a select user selected by the requestor;
identify a first crowd of users in which the select user is located;
record the requestor as a follower of the first crowd of users;
obtain status updates sent by users in the first crowd of users;
send, via the communication interface, the status updates sent by the users in the first crowd of users to the requestor as a follower of the first crowd of users;
identify a second crowd of users in which the select user is located after the select user has left the first crowd of users;
record the requestor as a follower of the second crowd of users;
obtain status updates sent by users in the second crowd of users; and
send, via the communication interface, the status updates sent by the users in the second crowd of users to the requestor as a follower of the second crowd of users.
US13/032,744 2010-03-03 2011-02-23 Methods and systems for following crowds Abandoned US20120066614A1 (en)

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US13/027,756 Abandoned US20120066303A1 (en) 2010-03-03 2011-02-15 Synchronized group location updates
US13/032,744 Abandoned US20120066614A1 (en) 2010-03-03 2011-02-23 Methods and systems for following crowds
US13/037,546 Active 2032-02-24 US9203793B2 (en) 2010-03-03 2011-03-01 Ad-hoc micro-blogging groups
US13/037,520 Active 2032-05-30 US8898288B2 (en) 2010-03-03 2011-03-01 Status update propagation based on crowd or POI similarity
US13/039,356 Expired - Fee Related US8566309B2 (en) 2010-03-03 2011-03-03 Monitoring hashtags in micro-blog posts to provide one or more crowd-based features
US14/028,866 Active 2031-04-05 US9407590B2 (en) 2010-03-03 2013-09-17 Monitoring hashtags in micro-blog posts to provide one or more crowd-based features
US14/546,580 Abandoned US20150074214A1 (en) 2010-03-03 2014-11-18 Status Update Propagation Based On Crowd Or POI Similarity
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US9407590B2 (en) 2016-08-02
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