FRAGMENTED ADVERTISEMENTS FOR CO-LOCATED SOCIAL GROUPS

Abstract

Systems and methods are disclosed for delivering a fragmented advertisement to a group of users identified as a group of participants for the fragmented advertisement. As used herein, a fragmented advertisement is an advertisement that includes two or more advertisement fragments each to be delivered to a different participant in a group of participants identified for the fragmented advertisement, where the two or more advertisement fragments encourage interaction between participants to achieve a predefined goal of the fragmented advertisement in order to obtain a corresponding advertisement benefit. For example, the predefined goal of a fragmented advertisement may be correctly answering a question (e.g., a trivia question), solving a puzzle, or the like. The advertisement benefit is preferably a coupon for a business, product, or service being advertised by the fragmented advertisement.

Description

RELATED APPLICATIONS

This application claims the benefit of provisional patent application Ser. No. 61/289,107, filed Dec. 22, 2009, the disclosure of which is hereby incorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates to delivering advertisements to a group of users and more specifically relates to delivering a fragmented advertisement to a group of users.

BACKGROUND

The current generation of location-based targeted advertisements tend to be simple, static advertisements that are targeted at individuals. However, in many situations, people travel in groups. For example, in many situations, a person goes to shopping malls with groups of friends. As such, there is a need for a system and method of delivering targeted advertisements to groups of users.

SUMMARY

The present disclosure relates to delivering a fragmented advertisement to a group of users identified as a group of participants for the fragmented advertisement. As used herein, a fragmented advertisement is an advertisement that includes two or more advertisement fragments each to be delivered to a different participant in a group of participants identified for the fragmented advertisement, where the two or more advertisement fragments encourage interaction between participants to achieve a predefined goal of the fragmented advertisement in order to obtain a corresponding advertisement benefit. For example, the predefined goal of a fragmented advertisement may be correctly answering a question (e.g., a trivia question), solving a puzzle, or the like. The advertisement benefit is preferably a coupon for a business, product, or service being advertised by the fragmented advertisement.

In general, a group of participants is identified. The group of participants is preferably a group of co-located users. A fragmented advertisement is then determined for the group of participants. In one embodiment, the fragmented advertisement is selected for the group of participants based on one or more selection criteria such as, for example, an aggregate profile of the group of participants, user profiles of the participants in the group of participants, a location of the group of co-located users, time of day, nearby businesses or other Points of Interest (POIs), advertisement value, or the like. In another embodiment, the fragmented advertisement is generated for the group of participants based on one or more characteristics of the group of participants such as, for example, a number of participants in the group of participants, an aggregate profile of the group of participants, user profiles of the participants in the group of participants, a location of the group of participants, or the like. The fragmented advertisement is then delivered to the group of participants. Thereafter, in one embodiment, a response is received that is indicative of whether the group of participants achieved the predefined goal of the fragmented advertisement. If the group of participants achieved the predefined goal of the fragmented advertisement, then an advertisement benefit for the fragmented advertisement is delivered to the group of participants.

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 for delivering fragmented advertisements 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 the operation of the system to deliver a fragmented advertisement to a group of participants according to one embodiment of the present disclosure;

FIGS. 13A and 13B illustrate the operation of a fragmented advertisement delivery function to deliver a fragmented advertisement to a group of participants and the operation of a master device of one of the participants to receive and distribute the fragmented advertisement, respectively, according to one embodiment of the present disclosure;

FIGS. 14A through 14D graphically illustrate an exemplary fragmented advertisement and a corresponding advertisement benefit according to one embodiment of the present disclosure;

FIG. 15 is a block diagram of the MAP server of FIG. 1 according to one embodiment of the present disclosure; and

FIG. 16 is a block diagram of one of the mobile devices of FIG. 1 according to one embodiment of the present disclosure.

DETAILED DESCRIPTION

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 delivering a fragmented advertisement to a group of users identified as a group of participants for the fragmented advertisement. As used herein, a fragmented advertisement is an advertisement that includes two or more advertisement fragments each to be delivered to a different participant in a group of participants identified for the fragmented advertisement, where the two or more advertisement fragments encourage interaction between participants to achieve a predefined goal of the fragmented advertisement in order to obtain a corresponding advertisement benefit. For example, the predefined goal of a fragmented advertisement may be correctly answering a question (e.g., a trivia question), solving a puzzle, or the like. The advertisement benefit is preferably a coupon for a business, product, or service being advertised by the fragmented advertisement.

FIG. 1 illustrates a Mobile Aggregate Profiling (MAP) system 10 (hereinafter “system 10”) that operates to deliver fragmented advertisements to groups of participants according to one embodiment of the present disclosure. Note that the system 10 is exemplary and is not intended to limit the scope of the present disclosure. In this embodiment, the system 10 includes a MAP server 12, one or more profile servers 14, a location server 16, a number of mobile devices 18-1 through 18-N (generally referred to herein collectively as mobile devices 18 and individually as mobile device 18) having associated users 20-1 through 20-N (generally referred to herein collectively as users 20 and individually as user 20), a subscriber device 22 having an associated subscriber 24, and a third-party service 26 communicatively coupled via a network 28. The network 28 may be any type of network or any combination of networks. Specifically, the network 28 may include wired components, wireless components, or both wired and wireless components. In one exemplary embodiment, the network 28 is a distributed public network such as the Internet, where the mobile devices 18 are enabled to connect to the network 28 via local wireless connections (e.g., Wi-Fi® or IEEE 802.11 connections) or wireless telecommunications connections (e.g., 3 G or 4 G 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 delivering fragmented advertisements. Note that while the MAP server 12 is illustrated as a single server for simplicity and ease of discussion, it should be appreciated that the MAP server 12 may be implemented as a single physical server or multiple physical servers operating in a collaborative manner for purposes of redundancy and/or load sharing.

In general, the one or more profile servers 14 operate to store user profiles for a number of persons including the users 20 of the mobile devices 18. For example, the one or more profile servers 14 may be servers providing social network services such as the Facebook® social networking service, the MySpace® social networking service, the LinkedIN® social networking service, or the like. As discussed below, using the one or more profile servers 14, the MAP server 12 is enabled to directly or indirectly obtain the user profiles of the users 20 of the mobile devices 18. The location server 16 generally operates to receive location updates from the mobile devices 18 and make the location updates available to entities such as, for instance, the MAP server 12. In one exemplary embodiment, the location server 16 is a server operating to provide Yahoo!'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 30-1 through 30-N (generally referred to herein collectively as MAP clients 30 or individually as MAP client 30), MAP applications 32-1 through 32-N (generally referred to herein collectively as MAP applications 32 or individually as MAP application 32), third-party applications 34-1 through 34-N (generally referred to herein collectively as third-party applications 34 or individually as third-party application 34), and location functions 36-1 through 36-N (generally referred to herein collectively as location functions 36 or individually as location function 36), respectively. The MAP client 30 is preferably implemented in software. In general, in the preferred embodiment, the MAP client 30 is a middleware layer operating to interface an application layer (i.e., the MAP application 32 and the third-party applications 34) to the MAP server 12. More specifically, the MAP client 30 enables the MAP application 32 and the third-party applications 34 to request and receive data from the MAP server 12. In addition, the MAP client 30 enables applications, such as the MAP application 32 and the third-party applications 34, to access data from the MAP server 12.

The MAP application 32 is also preferably implemented in software. The MAP application 32 generally provides a user interface component between the user 20 and the MAP server 12. For example, the MAP application 32 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. The MAP application 32 also enables the user 20 to configure various settings. For example, the MAP application 32 may enable the user 20 to select a desired social networking service (e.g., Facebook®, MySpace®, LinkedIN®, etc.) from which to obtain the user profile of the user 20 and provide any necessary credentials (e.g., username and password) needed to access the user profile from the social networking service.

The third-party applications 34 are preferably implemented in software. The third-party applications 34 operate to access the MAP server 12 via the MAP client 30. The third-party applications 34 may utilize data obtained from the MAP server 12 in any desired manner. As an example, one of the third-party applications 34 may be a gaming application that utilizes crowd data to notify the user 20 of Points of Interest (POIs) or Areas of Interest (AOIs) where crowds of interest are currently located. It should be noted that while the MAP client 30 is illustrated as being separate from the MAP application 32 and the third-party applications 34, the present disclosure is not limited thereto. The functionality of the MAP client 30 may alternatively be incorporated into the MAP application 32 and the third-party applications 34.

The location function 36 may be implemented in hardware, software, or a combination thereof. In general, the location function 36 operates to determine or otherwise obtain the location of the mobile device 18. For example, the location function 36 may be or include a Global Positioning System (GPS) receiver. In addition or alternatively, the location function 36 may include hardware and/or software that enables improved location tracking in indoor environments such as, for example, shopping malls. For example, the location function 36 may be part of or compatible with the InvisiTrack Location System provided by InvisiTrack and described in U.S. Pat. No. 7,423,580 entitled “Method and System of Three-Dimensional Positional Finding” which issued on Sep. 9, 2008, U.S. Pat. No. 7,787,886 entitled “System and Method for Locating a Target using RFID” which issued on Aug. 31, 2010, and U.S. Patent Application Publication No. 2007/0075898 entitled “Method and System for Positional Finding Using RF, Continuous and/or Combined Movement” which published on Apr. 5, 2007, all of which are hereby incorporated herein by reference for their teachings regarding location tracking.

The subscriber device 22 is a physical device such as a personal computer, a mobile computer (e.g., a notebook computer, a netbook computer, a tablet computer, etc.), a mobile smart phone, or the like. The subscriber 24 associated with the subscriber device 22 is a person or entity. In general, the subscriber device 22 enables the subscriber 24 to access the MAP server 12 via a web browser 38 to obtain various types of data, preferably for a fee. For example, the subscriber 24 may pay a fee to have access to crowd data such as aggregate profiles for crowds located at one or more POIs and/or located in one or more AOIs, pay a fee to track crowds, or the like. Note that the web browser 38 is exemplary. In another embodiment, the subscriber device 22 is enabled to access the MAP server 12 via a custom application.

Lastly, the 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.

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.

FIG. 2 is a block diagram of the MAP server 12 of FIG. 1 according to one embodiment of the present disclosure. As illustrated, the MAP server 12 includes an application layer 40, a business logic layer 42, and a persistence layer 44. The application layer 40 includes a user web application 46, a mobile client/server protocol component 48, and one or more data Application Programming Interfaces (APIs) 50. The user web application 46 is preferably implemented in software and operates to provide a web interface for users, such as the subscriber 24, to access the MAP server 12 via a web browser. The mobile client/server protocol component 48 is preferably implemented in software and operates to provide an interface between the MAP server 12 and the MAP clients 30 hosted by the mobile devices 18. The data APIs 50 enable third-party services, such as the third-party service 26, to access the MAP server 12.

The business logic layer 42 includes a profile manager 52, a location manager 54, a history manager 56, a crowd analyzer 58, an aggregation engine 60, and a fragmented advertisement (“ad”) function 62 each of which is preferably implemented in software. The profile manager 52 generally operates to obtain the user profiles of the users 20 directly or indirectly from the one or more profile servers 14 and store the user profiles in the persistence layer 44. The location manager 54 operates to obtain the current locations of the users 20 including location updates. As discussed below, the current locations of the users 20 may be obtained directly from the mobile devices 18 and/or obtained from the location server 16.

The history manager 56 generally operates to maintain a historical record of anonymized user profile data by location. Note that while the user profile data stored in the historical record is preferably anonymized, it is not limited thereto. The crowd analyzer 58 operates to form crowds of users. In one embodiment, the crowd analyzer 58 utilizes a spatial crowd formation algorithm. However, the present disclosure is not limited thereto. In addition, the crowd analyzer 58 may further characterize crowds to reflect degree of fragmentation, best-case and worst-case degree of separation (DOS), and/or degree of bi-directionality. Still further, the crowd analyzer 58 may also operate to track crowds. The aggregation engine 60 generally operates to provide aggregate profile data in response to requests from the mobile devices 18, the subscriber device 22, and the third-party service 26. The aggregate profile data may be historical aggregate profile data for one or more POIs or one or more AOIs or aggregate profile data for crowd(s) currently at one or more POIs or within one or more AOIs. As discussed below in detail, the fragmented ad function 62 operates to deliver fragmented advertisements to groups of participants. Preferably, the groups of participants are groups of co-located users. More specifically, in the preferred embodiment described herein, the groups of participants are crowds of users. However, the present disclosure is not limited thereto.

For additional information regarding the operation of the profile manager 52, the location manager 54, the history manager 56, the crowd analyzer 58, and the aggregation engine 60, the interested reader is directed to U.S. patent application Ser. No. 12/645,532, entitled FORMING CROWDS AND PROVIDING ACCESS TO CROWD DATA IN A MOBILE ENVIRONMENT, which was filed Dec. 23, 2009; U.S. patent application Ser. No. 12/645,539, entitled ANONYMOUS CROWD TRACKING, which was filed Dec. 23, 2009; U.S. patent application Ser. No. 12/645,535, entitled MAINTAINING A HISTORICAL RECORD OF ANONYMIZED USER PROFILE DATA BY LOCATION FOR USERS IN A MOBILE ENVIRONMENT, which was filed Dec. 23, 2009; U.S. patent application Ser. No. 12/645,546, entitled CROWD FORMATION FOR MOBILE DEVICE USERS, which was filed Dec. 23, 2009; U.S. patent application Ser. No. 12/645,556, entitled SERVING A REQUEST FOR DATA FROM A HISTORICAL RECORD OF ANONYMIZED USER PROFILE DATA IN A MOBILE ENVIRONMENT, which was filed Dec. 23, 2009; U.S. patent application Ser. No. 12/645,560, entitled HANDLING CROWD REQUESTS FOR LARGE GEOGRAPHIC AREAS, which was filed Dec. 23, 2009; and U.S. patent application Ser. No. 12/645,544, entitled MODIFYING A USER'S CONTRIBUTION TO AN AGGREGATE PROFILE BASED ON TIME BETWEEN LOCATION UPDATES AND EXTERNAL EVENTS, which was filed Dec. 23, 2009; all of which are hereby incorporated herein by reference in their entireties.

The persistence layer 44 includes an object mapping layer 63 and a datastore 64. The object mapping layer 63 is preferably implemented in software. The datastore 64 is preferably a relational database, which is implemented in a combination of hardware (i.e., physical data storage hardware) and software (i.e., relational database software). In this embodiment, the business logic layer 42 is implemented in an object-oriented programming language such as, for example, Java. As such, the object mapping layer 63 operates to map objects used in the business logic layer 42 to relational database entities stored in the datastore 64. Note that, in one embodiment, data is stored in the datastore 64 in a Resource Description Framework (RDF) compatible format.

In an alternative embodiment, rather than being a relational database, the datastore 64 may be implemented as an RDF datastore. More specifically, the RDF datastore may be compatible with RDF technology adopted by Semantic Web activities. Namely, the RDF datastore may use the Friend-Of-A-Friend (FOAF) vocabulary for describing people, their social networks, and their interests. In this embodiment, the MAP server 12 may be designed to accept raw FOAF files describing persons, their friends, and their interests. These FOAF files are currently output by some social networking services such as LiveJournal® and Facebook®. The MAP server 12 may then persist RDF descriptions of the users 20 as a proprietary extension of the FOAF vocabulary that includes additional properties desired for the system 10.

FIG. 3 illustrates the MAP client 30 of FIG. 1 in more detail according to one embodiment of the present disclosure. As illustrated, in this embodiment, the MAP client 30 includes a MAP access API 66, a MAP middleware component 68, and a mobile client/server protocol component 70. The MAP access API 66 is implemented in software and provides an interface by which the MAP client 30 and the third-party applications 34 are enabled to access the MAP client 30. The MAP middleware component 68 is implemented in software and performs the operations needed for the MAP client 30 to operate as an interface between the MAP application 32 and the third-party applications 34 at the mobile device 18 and the MAP server 12. The mobile client/server protocol component 70 enables communication between the MAP client 30 and the MAP server 12 via a defined protocol.

The present disclosure is primarily focused on the delivery of fragmented advertisements. However, before discussing the delivery of fragmented advertisements 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 by the fragmented ad function 62 to identify groups of participants to which to deliver fragmented advertisements.

FIG. 4 illustrates the operation of the system 10 of FIG. 1 to provide the user profile of one of the users 20 of one of the mobile devices 18 to the MAP server 12 according to one embodiment of the present disclosure. This discussion is equally applicable to the other users 20 of the other mobile devices 18. First, an authentication process is performed (step 1000). For authentication, in this embodiment, the mobile device 18 authenticates with the profile server 14 (step 1000A) and the MAP server 12 (step 1000B). In addition, the MAP server 12 authenticates with the profile server 14 (step 1000C). Preferably, authentication is performed using OpenID or similar technology. However, authentication may alternatively be performed using separate credentials (e.g., username and password) of the user 20 for access to the MAP server 12 and the profile server 14. Assuming that authentication is successful, the profile server 14 returns an authentication succeeded message to the MAP server 12 (step 1000D), and the profile server 14 returns an authentication succeeded message to the MAP client 30 of the mobile device 18 (step 1000E).

At some point after authentication is complete, a user profile process is performed such that a user profile of the user 20 is obtained from the profile server 14 and delivered to the MAP server 12 (step 1002). In this embodiment, the MAP client 30 of the mobile device 18 sends a profile request to the profile server 14 (step 1002A). In response, the profile server 14 returns the user profile of the user 20 to the mobile device 18 (step 1002B). The MAP client 30 of the mobile device 18 then sends the user profile of the user 20 to the MAP server 12 (step 1002C). Note that while in this embodiment the MAP client 30 sends the complete user profile of the user 20 to the MAP server 12, in an alternative embodiment, the MAP client 30 may filter the user profile of the user 20 according to criteria specified by the user 20. For example, the user profile of the user 20 may include demographic information, general interests, music interests, and movie interests, and the user 20 may specify that the demographic information or some subset thereof is to be filtered, or removed, before sending the user profile to the MAP server 12.

Upon receiving the user profile of the user 20 from the MAP client 30 of the mobile device 18, the profile manager 52 of the MAP server 12 processes the user profile (step 1002D). More specifically, in the preferred embodiment, the profile manager 52 includes social network handlers for the social network services supported by the MAP server 12 that operate to map the user profiles of the users 20 obtained from the social network services to a common format utilized by the MAP server 12. This common format includes a number of user profile categories, or user profile slices, such as, for example, a demographic profile category, a social interaction profile category, a general interests category, a music interests profile category, and a movie interests profile category.

For example, if the MAP server 12 supports user profiles from Facebook®, MySpace®, and LinkedIN®, the profile manager 52 may include a Facebook handler, a MySpace handler, and a LinkedIN handler. The social network handlers process user profiles from the corresponding social network services to generate user profiles for the users 20 in the common format used by the MAP server 12. For this example assume that the user profile of the user 20 is from Facebook®. The profile manager 52 uses a Facebook handler to process the user profile of the user 20 to map the user profile of the user 20 from Facebook® to a user profile for the user 20 for the MAP server 12 that includes lists of keywords for a number of predefined profile categories, or profile slices, such as, for example, a demographic profile category, a social interaction profile category, a general interests profile category, a music interests profile category, and a movie interests profile category. As such, the user profile of the user 20 from Facebook® may be processed by the Facebook handler of the profile manager 52 to create a list of keywords such as, for example, liberal, High School Graduate, 35-44, College Graduate, etc. for the demographic profile category; a list of keywords such as Seeking Friendship for the social interaction profile category; a list of keywords such as politics, technology, photography, books, etc. for the general interests profile category; a list of keywords including music genres, artist names, album names, or the like for the music interests profile category; and a list of keywords including movie titles, actor or actress names, director names, movie genres, or the like for the movie interests profile category. In one embodiment, the profile manager 52 may use natural language processing or semantic analysis. For example, if the Facebook® user profile of the user 20 states that the user 20 is 20 years old, semantic analysis may result in the keyword of 18-24 years old being stored in the user profile of the user 20 for the MAP server 12.

After processing the user profile of the user 20, the profile manager 52 of the MAP server 12 stores the resulting user profile for the user 20 (step 1002E). More specifically, in one embodiment, the MAP server 12 stores user records for the users 20 in the datastore 64 (FIG. 2). The user profile of the user 20 is stored in the user record of the user 20. The user record of the user 20 includes a unique identifier of the user 20, the user profile of the user 20, and, as discussed below, a current location of the user 20. Note that the user profile of the user 20 may be updated as desired. For example, in one embodiment, the user profile of the user 20 is updated by repeating step 1002 each time the user 20 activates the MAP application 32.

Note that while the discussion herein focuses on an embodiment where the user profiles of the users 20 are obtained from the one or more profile servers 14, the user profiles of the users 20 may be obtained in any desired manner. For example, in one alternative embodiment, the user 20 may identify one or more favorite websites. The profile manager 52 of the MAP server 12 may then crawl the one or more favorite websites of the user 20 to obtain keywords appearing in the one or more favorite websites of the user 20. These keywords may then be stored as the user profile of the user 20.

At some point, a process is performed such that a current location of the mobile device 18 and thus a current location of the user 20 is obtained by the MAP server 12 (step 1004). In this embodiment, the MAP application 32 of the mobile device 18 obtains the current location of the mobile device 18 from the location function 36 of the mobile device 18. The MAP application 32 then provides the current location of the mobile device 18 to the MAP client 30, and the MAP client 30 then provides the current location of the mobile device 18 to the MAP server 12 (step 1004A). Note that step 1004A may be repeated periodically or in response to a change in the current location of the mobile device 18 in order for the MAP application 32 to provide location updates for the user 20 to the MAP server 12.

In response to receiving the current location of the mobile device 18, the location manager 54 of the MAP server 12 stores the current location of the mobile device 18 as the current location of the user 20 (step 1004B). More specifically, in one embodiment, the current location of the user 20 is stored in the user record of the user 20 maintained in the datastore 64 of the MAP server 12. Note that, in the preferred embodiment, only the current location of the user 20 is stored in the user record of the user 20. In this manner, the MAP server 12 maintains privacy for the user 20 since the MAP server 12 does not maintain a historical record of the location of the user 20. Any historical data maintained by the MAP server 12 is preferably anonymized by the history manager 56 in order to maintain the privacy of the users 20.

In addition to storing the current location of the user 20, the location manager 54 sends the current location of the user 20 to the location server 16 (step 1004C). In this embodiment, by providing location updates to the location server 16, the MAP server 12 in return receives location updates for the user 20 from the location server 16. This is particularly beneficial when the mobile device 18 does not permit background processes. If the mobile device 18 does not permit background processes, the MAP application 32 will not be able to provide location updates for the user 20 to the MAP server 12 unless the MAP application 32 is active. Therefore, when the MAP application 32 is not active, other applications running on the mobile device 18 (or some other device of the user 20) may directly or indirectly provide location updates to the location server 16 for the user 20. This is illustrated in step 1006 where the location server 16 receives a location update for the user 20 directly or indirectly from another application running on the mobile device 18 or an application running on another device of the user 20 (step 1006A). The location server 16 then provides the location update for the user 20 to the MAP server 12 (step 1006B). In response, the location manager 54 updates and stores the current location of the user 20 in the user record of the user 20 (step 1006C). In this manner, the MAP server 12 is enabled to obtain location updates for the user 20 even when the MAP application 32 is not active at the mobile device 18.

FIG. 5 illustrates the operation of the system 10 of FIG. 1 to provide the user profile of the user 20 of one of the mobile devices 18 to the MAP server 12 according to another embodiment of the present disclosure. This discussion is equally applicable to user profiles of the users 20 of the other mobile devices 18. First, an authentication process is performed (step 1100). For authentication, in this embodiment, the mobile device 18 authenticates with the MAP server 12 (step 1100A), and the MAP server 12 authenticates with the profile server 14 (step 1100B). Preferably, authentication is performed using OpenID or similar technology. However, authentication may alternatively be performed using separate credentials (e.g., username and password) of the user 20 for access to the MAP server 12 and the profile server 14. Assuming that authentication is successful, the profile server 14 returns an authentication succeeded message to the MAP server 12 (step 1100C), and the MAP server 12 returns an authentication succeeded message to the MAP client 30 of the mobile device 18 (step 1100D).

At some point after authentication is complete, a user profile process is performed such that a user profile of the user 20 is obtained from the profile server 14 and delivered to the MAP server 12 (step 1102). In this embodiment, the profile manager 52 of the MAP server 12 sends a profile request to the profile server 14 (step 1102A). In response, the profile server 14 returns the user profile of the user 20 to the profile manager 52 of the MAP server 12 (step 1102B). Note that while in this embodiment the profile server 14 returns the complete user profile of the user 20 to the MAP server 12, in an alternative embodiment, the profile server 14 may return a filtered version of the user profile of the user 20 to the MAP server 12. The profile server 14 may filter the user profile of the user 20 according to criteria specified by the user 20. For example, the user profile of the user 20 may include demographic information, general interests, music interests, and movie interests, and the user 20 may specify that the demographic information or some subset thereof is to be filtered, or removed, before sending the user profile to the MAP server 12.

Upon receiving the user profile of the user 20, the profile manager 52 of the MAP server 12 processes the user profile (step 1102C). More specifically, as discussed above, in the preferred embodiment, the profile manager 52 includes social network handlers for the social network services supported by the MAP server 12. The social network handlers process user profiles to generate user profiles for the MAP server 12 that include lists of keywords for each of a number of profile categories, or profile slices.

After processing the user profile of the user 20, the profile manager 52 of the MAP server 12 stores the resulting user profile for the user 20 (step 1102D). More specifically, in one embodiment, the MAP server 12 stores user records for the users 20 in the datastore 64 (FIG. 2). The user profile of the user 20 is stored in the user record of the user 20. The user record of the user 20 includes a unique identifier of the user 20, the user profile of the user 20, and, as discussed below, a current location of the user 20. Note that the user profile of the user 20 may be updated as desired. For example, in one embodiment, the user profile of the user 20 is updated by repeating step 1102 each time the user 20 activates the MAP application 32.

Note that while the discussion herein focuses on an embodiment where the user profiles of the users 20 are obtained from the one or more profile servers 14, the user profiles of the users 20 may be obtained in any desired manner. For example, in one alternative embodiment, the user 20 may identify one or more favorite websites. The profile manager 52 of the MAP server 12 may then crawl the one or more favorite websites of the user 20 to obtain keywords appearing in the one or more favorite websites of the user 20. These keywords may then be stored as the user profile of the user 20.

At some point, a process is performed such that a current location of the mobile device 18 and thus a current location of the user 20 is obtained by the MAP server 12 (step 1104). In this embodiment, the MAP application 32 of the mobile device 18 obtains the current location of the mobile device 18 from the location function 36 of the mobile device 18. The MAP application 32 then provides the current location of the user 20 of the mobile device 18 to the location server 16 (step 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 54 of the MAP server 12 stores the current location of the mobile device 18 as the current location of the user 20 (step 1104C). More specifically, in one embodiment, the current location of the user 20 is stored in the user record of the user 20 maintained in the datastore 64 of the MAP server 12. Note that, in the preferred embodiment, only the current location of the user 20 is stored in the user record of the user 20. In this manner, the MAP server 12 maintains privacy for the user 20 since the MAP server 12 does not maintain a historical record of the location of the user 20. As discussed below in detail, historical data maintained by the MAP server 12 is preferably anonymized in order to maintain the privacy of the users 20.

As discussed above, the use of the location server 16 is particularly beneficial when the mobile device 18 does not permit background processes. As such, if the mobile device 18 does not permit background processes, the MAP application 32 will not provide location updates for the user 20 to the location server 16 unless the MAP application 32 is active. However, other applications running on the mobile device 18 (or some other device of the user 20) may provide location updates to the location server 16 for the user 20 when the MAP application 32 is not active. This is illustrated in step 1106 where the location server 16 receives a location update for the user 20 from another application running on the mobile device 18 or an application running on another device of the user 20 (step 1106A). The location server 16 then provides the location update for the user 20 to the MAP server 12 (step 1106B). In response, the location manager 54 updates and stores the current location of the user 20 in the user record of the user 20 (step 1106C). In this manner, the MAP server 12 is enabled to obtain location updates for the user 20 even when the MAP application 32 is not active at the mobile device 18.

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 the 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 northeast corner of the new bounding box overlaps a 1×1 meter square at the southwest corner of the old bounding box, the crowd analyzer 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:

$\mathrm{initial\_optimal}\mathrm{\_inclusion}\mathrm{\_dist}=a·\sqrt{\frac{A_{\mathrm{Bounding}\mathrm{Box}}}{\mathrm{number\_of}\mathrm{\_users}}},$

where a is a number between 0 and 1, A_BoundingBox is an area of the bounding box, and number_of_users is the total number of users in the bounding box. The total number of users in the bounding box includes both individual users that are not already in a crowd and users that are already in a crowd. In one embodiment, a is ⅔.

The crowd analyzer 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 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 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 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 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:

$\begin{array}{cc}\mathrm{average}=\frac{1}{n+1}·\left(\mathrm{initial\_optimal}\mathrm{\_inclusion}\mathrm{\_dist}+\sum _{i=1}^nd_i\right),& \\ \mathrm{optimal\_inclusion}\mathrm{\_dist}=\mathrm{average}+\sqrt{\left(\frac{1}{n}·\sum _{i=1}^n{\left(d_i-\mathrm{average}\right)}^2\right)},& \end{array}$

where n is the number of users in the crowd and d, 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 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 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. 10C. 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, 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.

FIGS. 12 through 14D describe the operation of the fragmented ad function 62 of the MAP server 12. Specifically, FIG. 12 illustrates the delivery and utilization of a fragmented advertisement according to one embodiment of the present disclosure. As illustrated, the fragmented ad function 62 of the MAP server 12 first identifies a crowd of users for fragmented advertisement delivery (step 1400). The crowd may be identified based on one or more criteria such as, but not limited to, the location of the crowd, historical information regarding the crowd, historical information regarding the users 20 currently in the crowd, or the like. The historical information regarding the crowd may include an amount of time that the crowd has existed and/or an amount of time that the crowd has been located at its current location or near its current location as determined by, for example, the crowd snapshots stored for the crowd. The historical information regarding the users 20 currently in the crowd may include information indicating whether the users 20 in the crowd have previously participated in a fragmented advertisement. In this embodiment, the criteria used to identify the crowd may be system-defined criteria that are used for all advertisers or advertiser-defined criteria defined for a particular advertiser. Note that if the criteria are advertiser-defined criteria and there are multiple advertisers, then the process of FIG. 12 may be performed for each advertiser.

As an example, the criteria used to identify the crowd in step 1400 may be system-defined criteria stating that any crowd that is located within a defined distance (e.g., 1 mile) from any one of a number of defined POIs (e.g., a list of POIs for which the fragmented ad function 62 has corresponding fragmented advertisements) and has existed for at least a defined threshold amount of time (e.g., 30 minutes) is identified as a crowd for delivery of a fragmented advertisement. Using these criteria, the fragmented ad function 62 queries the datastore 64 of the MAP server 12 for a crowd that has corresponding crowd record 72 that indicates that the crowd is located within the defined distance from any of the defined POIs and has existed for at least the defined threshold amount of time. The resulting crowd is identified as the crowd for fragmented advertisement delivery. Note however that if the query results in more than one crowd that satisfies the criteria, then the following steps of FIG. 12 (i.e., steps 1402-1426) may be performed for each of those crowds. Alternatively, one of the crowds may be selected using any suitable technique. For example, the crowd having the most users that have previously participated in a fragmented advertisement may be selected.

Once the crowd for the fragmented advertisement delivery process is identified in step 1400, the fragmented ad function 62 of the MAP server 12 selects one of the mobile devices 18 of the users 20 currently in the identified crowd as a master device (step 1402). The master device may be selected based on historical information regarding the users 20 in the crowd, device capabilities of the mobile devices 18 of the users 20 in the crowd, a degree of similarity between the user profiles of the users 20 in the crowd to an aggregate profile of the crowd, or the like. The historical information regarding the users 20 in the crowd may include, for each of the users 20 in the crowd:

• • information indicating whether the user 20 has previously participated in a fragmented advertisement such as, for example, information indicating whether the mobile device 18 of the user 20 has previously been selected as a master device for delivery of a fragmented advertisement and, if so, whether the user 20 accepted or rejected the fragmented advertisement, or
  • the amount of time that the user 20 has been in the crowd.
    Note that the amount of time that the users 20 have been in the crowd would require non-anonymously recording the users 20 in the crowd in each crowd snapshot for the crowd or non-anonymously recording location histories for the users 20. The device capabilities of the mobile devices 18 of the users 20 in the crowd may include, for each of the mobile devices 18 of the users 20 in the crowd, information indicating whether the mobile device 18 has wireless LAN or wireless PAN capabilities (e.g., IEEE 802.11x or Bluetooth® capabilities) that can be used to distribute advertisement fragments (see below).

It should also be noted that in some cases one or more of the users 20 may have more than one mobile device 18. In this case, one mobile device 18 of one of the users 20 is selected as the master device based on historical information regarding the users 20 in the crowd, device capabilities of the mobile devices 18 of the users 20 in the crowd, a degree of similarity between the user profiles of the users 20 in the crowd to an aggregate profile of the crowd, or the like. Alternatively, a master user may be selected from the users 20 in the crowd based on historical information regarding the users 20 in the crowd. The user device 18 of the master user that is to serve as the master device may then be selected by the master user or selected automatically based on the device capabilities of the mobile devices 20 of the master user.

The aggregate profile for the crowd is preferably generated by the aggregation engine 60 of the MAP server 12 based on the user profiles of the users 20 in the crowd. The aggregate profile of the crowd generally indicates the aggregate interests of the users 20 in the crowd. For example, the aggregate profile for the crowd may include a list of keywords appearing in the user profiles of the users 20 in the crowd and a number of user matches for each keyword in the list or a ratio of the number of user matches to the total number of users in the crowd for each keyword in the list. Thus, if the keyword “politics” appears in the user profiles of three of the users 20 in the crowd, then the aggregate profile of the crowd may include the keyword “politics” and the value of 3 for the number of user matches for the keyword “politics.” For each of the users 20 in the crowd, the degree of similarity between the user profile of the user 20 and the aggregate profile of the crowd may then be computed using the number of user matches for the keywords in the aggregate profile of the crowd as weighting factors. For example, the degree of similarity between the user profile of the user 20 and the aggregate profile of the crowd may be computed as:

$\mathrm{Similiarity}=\frac{\sum _{i=1}^M\left({\mathrm{user\_matches}}_i\times {\mathrm{matching\_keyword}}_i\right)}{\sum _{i=1}^M{\mathrm{user\_matches}}_i},$

where Similarity is a value representing the degree of similarity between the user profile of the user 20 and the aggregate profile of the crowd, user_matches_i is the number of user matches for the i-th keyword in the aggregate profile of the crowd, matching_keyword_i is 0 if the user profile of the user 20 does not include a keyword that matches the i-th keyword in the aggregate profile of the crowd or 1 if the user profile of the user 20 does include a keyword that matches the i-th keyword in the aggregate profile of the crowd, and M is the number of keywords in the aggregate profile of the crowd.

In one exemplary embodiment, the master device may be selected as follows. If none of the mobile devices 18 of the users 20 in the crowd has previously been selected as a master device for another fragmented advertisement and accepted that fragmented advertisement, then the mobile device 18 selected as the master device is the mobile device 18 that: (1) is the mobile device 18 of the user 20 that has been in the crowd the longest, (2) has wireless Local Area Network (LAN) and/or wireless Personal Area Network (PAN) capabilities, and (3) is the mobile device 18 of the user 20 having a degree of similarity between the user profile of the user 20 and the aggregate profile of the crowd that is at least a predefined minimum similarity (e.g., 0.5). If one or more of the mobile devices 18 of the users 20 in the crowd has been previously selected as a master device for another fragmented advertisement and accepted that fragmented advertisement, then the mobile device 18 selected as the master device is one of those mobile devices 18 that: (1) is the mobile device 18 of the user 20 that has been in the crowd the longest, (2) has wireless LAN and/or wireless PAN capabilities, and (3) is the mobile device 18 of the user 20 having a degree of similarity between the user profile of the user 20 and the aggregate profile of the crowd that is at least a predefined minimum similarity (e.g., 0.5). If only one of the mobile devices 18 of the users 20 in the crowd has been previously selected as a master device for another fragmented advertisement and accepted that fragmented advertisement, then that mobile device 18 is selected as the master device. Again, note that this exemplary process for selecting the master device is only one example of many processes that may be used to select the master device. Numerous variations will be apparent to one of ordinary skill in the art upon reading this disclosure.

Once the master device is selected, the fragmented ad function 62 of the MAP server 12 determines a fragmented advertisement to be delivered to the crowd of users identified in step 1400 (step 1404). In one embodiment, the fragmented advertisement is selected from a number of fragmented advertisements accessible to the fragmented ad function 62 based on one or more advertisement selection criteria. The one or more advertisement selection criteria include one or more of the following:

• • the aggregate profile of the crowd,
  • the user profiles of the users 20 in the crowd,
  • the location of the crowd,
  • time of day,
  • businesses or other POIs in spatial proximity to the crowd,
  • historical information regarding fragmented advertisements previously delivered to the crowd (e.g., information identifying any fragmented advertisements previously delivered to the crowd),
  • advertisement value, and
  • device capabilities of the mobile devices 18 of the users 20 in the crowd.
    Again, the aggregate profile is generally an aggregation of the user profiles of the users 20 in the crowd. For example, the aggregate profile of the crowd may include a list of keywords found in the user profiles of the users 20 in the crowd and a number of user matches for each keyword in the list of keywords or a ratio of the number of user matches to total number of users in the crowd for each keyword in the list of keywords. The one or more advertisement selection criteria may be system-defined, advertiser-specific, or advertisement-specific.

In one embodiment, the one or more advertisement selection criteria are utilized by the fragmented ad function 62 to select the fragmented advertisement that best matches the crowd. More specifically, in this embodiment, each fragmented advertisement accessible to the fragmented ad function 62 has corresponding metadata that describes the fragmented advertisement and, optionally, target crowds for the fragmented advertisement. The metadata that describes the fragmented advertisement may include, for example, information that describes the advertised business, good, or service (e.g., business name and hours of operation) and information that defines the number of advertisement fragments included in the fragmented advertisement. The metadata that describes the target crowds for the fragmented advertisement may include any information that can be matched against the aggregate profile of a crowd or the user profiles of the users 20 in a crowd such as, for example, demographic information (e.g., target age range, target income level, etc.), interests (e.g., music), or the like. In addition, the metadata that describes the target crowds for the fragmented advertisement may include a minimum crowd size (i.e., minimum number of users) and/or a maximum crowd size (i.e., maximum number of users). The fragmented advertisement for the crowd may then be selected based on the metadata for the fragmented advertisements accessible to the fragmented ad function 62 and the one or more advertisement selection criteria.

In another embodiment, rather than selecting the fragmented advertisement, the fragmented ad function 62 generates the fragmented advertisement based on one or more characteristics of the crowd. More specifically, the fragmented ad function 62 may have access to a number of traditional advertisements having defined advertisement benefits (e.g., advertisement for a particular business with a buy one get two free coupon). The fragmented ad function 62 first selects the traditional advertisement that best matches the crowd based on metadata that describes the traditional advertisement, the advertisement benefits of the traditional advertisements, and data that describes the crowd. Again, the metadata that describes a traditional advertisement may include, for example, information that describes the advertised business, good, or service (e.g., business name and hours of operation). The data that describes the crowd may be an aggregate profile of the crowd, the user profiles of the users 20 in the crowd, the location of the crowd, or the like. Additional factors such as time of day and advertisement value may also be considered. As an example, if the aggregate profile of the crowd indicates that the crowd has three users and all three users like coffee, then the fragmented ad function 62 may select a traditional advertisement for Starbucks® coffee that includes a buy one get two free coupon. Then, the fragmented ad function 62 generates the fragmented advertisement based on the selected traditional advertisement, where the fragmented advertisement includes a different advertisement fragment for each participant, which at this point is each of the users 20 in the crowd. As discussed above, the fragmented advertisement may include a first advertisement fragment that includes a question, puzzle, or other item defining a goal to be achieved by the participants and a number of additional advertisements that encourage interaction among the participants to achieve the goal (e.g., a number of hints).

Once the fragmented advertisement is determined, the fragmented ad function 62 of the MAP server 12 delivers the fragmented advertisement to the master device selected for the crowd, which in this example is the mobile device 18-1 (step 1406). The fragmented advertisement includes a number of advertisement fragments to be distributed to the different participants, which at this point are the different users in the crowd. In general, the advertisement fragments encourage interaction between the participants to achieve a predefined goal. The predefined goal may be answering a question, solving a puzzle, or the like. As an example, the fragmented advertisement may include a first advertisement fragment that provides a question to be answered by the crowd, a second advertisement fragment that provides a first hint for the question, a third advertisement fragment that provides a second hint for the question, and so on.

Next, the mobile device 18-1 distributes the advertisement fragments to the mobile devices 18 of the other users 20 in the crowd (steps 1408 and 1410). In this example, the other users 20 in the crowd are the users 20-2 and 20-3. As such, the mobile device 18-1 sends one advertisement fragment to the mobile device 18-2 of the user 20-2 and a different advertisement fragment to the mobile device 18-3 of the user 20-3. Preferably, the mobile device 18-1 delivers the advertisement fragments to the mobile devices 18-2 and 18-3 via corresponding wireless LAN or wireless PAN connections. Returning to the example given above, the mobile device 18-1 retains the first advertisement fragment (ad fragment X) which includes the question to be answered, delivers the second advertisement fragment (ad fragment Y) which includes the first hint to the mobile device 18-2, and delivers the third advertisement fragment (ad fragment Z) which includes the second hint to the mobile device 18-3.

The mobile device 18-1 presents the first advertisement fragment (ad fragment X) to the user 20-1 at the mobile device 18-1 (step 1412). Likewise, the mobile device 18-2 presents the second advertisement fragment (ad fragment Y) to the user 20-2 at the mobile device 18-2 (step 1414), and the mobile device 18-3 presents the third advertisement fragment (ad fragment Z) to the user 20-3 at the mobile device 18-3 (step 1416). Note that the advertisement fragments may be presented by the MAP applications 32 of the mobile devices 18-1, 18-2, and 18-3 or by corresponding third-party applications 34 depending on the particular implementation. The users 20-1, 20-2, and 20-3 may then interact in an attempt to achieve the predefined goal of the fragmented advertisement. The users 20-1, 20-2, and 20-3 may interact via, for example, direct verbal communication. Continuing the example above, the users 20-1, 20-2, and 20-3 may talk to one another to answer the question presented to the user 20-1 based on the hints presented to the users 20-2 and 20-3.

Next, the mobile device 18-1 receives user input from at least the user 20-1 of the mobile device 18-1 in an attempt to achieve the predefined goal of the fragmented advertisement (step 1418). In one embodiment, if the fragmented advertisement asks a question, then the user 20-1 provides user input to the mobile device 18-1 to provide an answer to the question. In an alternative embodiment, any of the users 20-1, 20-2, and 20-3 may be enabled to provide an answer to the question at their corresponding mobile devices 18-1, 18-2, and 18-3. In a similar manner, if the fragmented advertisement provides a puzzle, the user 20-1 or any of the users 20-1, 20-2, and 20-3 provides user input in an attempt to solve the puzzle.

After receiving the user input, the mobile device 18-1 sends a response to the fragmented ad function 62 of the MAP server 12 (step 1420). The response generally includes information indicating whether the users 20-1, 20-2, and 20-3 achieved the predefined goal of the fragmented advertisement. More specifically, in one embodiment, the response includes the user input received in step 1418. In another embodiment, the mobile device 18-1 may be enabled to determine whether the users 20-1, 20-2, and 20-3 achieved the predefined goal of the fragmented advertisement based on the user input received in step 1418, and the response includes an indicator that directly indicates whether the users 20-1, 20-2, and 20-3 achieved the predefined goal of the fragmented advertisement.

In this exemplary embodiment, based on the response, the fragmented ad function 62 determines that the users 20-1, 20-2, and 20-3 achieved the predefined goal of the fragmented advertisement (step 1422). As such, the fragmented ad function 62 delivers an advertisement benefit to the mobile device 18-1 for distribution to the users 20-1, 20-2, and 20-3 (step 1424). The advertisement benefit is generally any benefit conferred to the users 20-1, 20-2, and 20-3 for the advertised business, service, or product. Preferably, the advertisement benefit is a coupon. The advertisement benefit may be fragmented between the participants (e.g., a 15% off coupon to give each participant 5% off) or targeted to the group as a whole (e.g., a buy one get two free coupon for a group of three participants). In this embodiment, the mobile device 18-1 then presents the advertisement benefit to the user 20-1 (step 1426). While not illustrated, the mobile device 18-1 may distribute the advertisement benefit to the mobile devices 18-2 and 18-3 of the other users 20-2 and 20-3. In an alternative embodiment, the advertisement benefit is included in the fragmented advertisement and is released locally at the mobile device 18-1 if the participants achieve the predefined goal of the fragmented advertisement.

FIGS. 13A and 13B are flow charts that illustrate the operation of the fragmented ad function 62 and the master device, respectively, according to another embodiment of the present disclosure. As illustrated in FIG. 13A, the fragmented ad function 62 identifies a crowd for fragmented advertisement delivery (step 1500), selects one of the mobile devices 18 of the users 20 in the identified crowd as a master device (step 1502), determines a fragmented advertisement for the crowd (step 1504), and delivers the fragmented advertisement to the master device (step 1506) in the same manner as that described above with respect to steps 1400 through 1406 of FIG. 12.

As discussed below in detail, in this embodiment after receiving the fragmented advertisement, the master device either accepts or rejects the fragmented advertisement. If accepted, the master device verifies that the users 20 in the crowd are willing to participate in the fragmented advertisement and also determines whether there are additional users located nearby that are willing to participate in the fragmented advertisement. Any change in participants is communicated back to the fragmented ad function 62, which in turn updates the fragmented advertisement for the updated group of participants.

As such, in this embodiment after delivering the fragmented advertisement to the master device, the fragmented ad function 62 determines whether a response has been received from the master device (step 1508). If not, the fragmented ad function 62 continues to wait until a response has been received (step 1510). Once a response has been received, the fragmented ad function 62 determines whether the response indicates that the master device has rejected the fragmented advertisement (step 1512). If so, the user record 74 of the user 20 of the master device is updated to reflect that the fragmented advertisement was rejected (step 1514). Note that, in this case, the user record 74 includes one or more additional fields for storing such historical information regarding fragmented advertisements.

If the response is not a response indicating that the fragmented advertisement was rejected, the fragmented ad function 62 determines whether the response is a notification of a change in the participants for the fragmented advertisement (step 1516). Initially, the participants for the fragmented advertisement are all of the users 20 in the crowd. However, as discussed below, some of the users 20 in the crowd may choose not to participate. In addition, the master device may identify additional participants for the fragmented advertisement. If the response is a notification of a change in the participants, the process returns to step 1504 and is repeated for an updated group of participants as defined by the received notification. Note, however, that if there are additional participants that are not users 20 of the system 10, the notification may also include user profiles for the additional participants. When repeating step 1504, the fragmented advertisement is determined for the group of participants, which is now different than the users 20 in the crowd. The group of participants is still preferably a group of co-located users, but may include less than all of the users 20 in the crowd identified in step 1500 and/or may include additional users not in the crowd identified in step 1500. As such, when repeating step 1504, the fragmented advertisement is selected based on the group of participants (e.g., an aggregate profile for the group of participants) rather than the crowd identified in step 1500.

Returning to step 1516, if the response is not a notification of a change in participants, the fragmented ad function 62 determines whether the response indicates that the predefined goal of the fragmented advertisement has been achieved by the participants (step 1518). If not, in this embodiment, the fragmented ad function 62 delivers a consolation to the master device for distribution to the participants (step 1520). The consolation may simply be a message indicating that the predefined goal was not achieved. In addition or alternatively, the consolation may include a downgraded version of the advertisement benefit that would be delivered if the participants were to achieve the predefined goal of the fragmented advertisement or some other lesser advertisement benefit. Returning to step 1518, if the response indicates that the predefined goal of the fragmented advertisement has been achieved by the participants, the fragmented ad function 62 delivers the advertisement benefit for the fragmented advertisement to the master device for distribution to the participants (step 1522).

FIG. 13B illustrates the operation of the master device in response to receiving the fragmented advertisement from the fragmented ad function 62 in step 1506 of FIG. 13A according to one embodiment of the present disclosure. More specifically, this process may be performed by, for example, the MAP application 32 of the master device. As illustrated, the master device receives the fragmented advertisement (step 1600). Next, the master device determines whether to accept the fragmented advertisement (step 1602). The determination as to whether to accept the fragmented advertisement may be based on manual input from the user 20 of the master device in response to receiving the fragmented advertisement. Alternatively, the determination may be made automatically based on one or more predefined criteria, which may be configured by the user 20 of the master device prior to receiving the fragmented advertisement. These predefined criteria may include, for example, a block setting that may be set by the user 20 of the master device such that all fragmented advertisements are automatically blocked during a defined period of time (e.g., next hour, after 7 p.m., or the like), when located at a defined location (e.g., block all fragmented advertisements when at work), or the like. Other contextual data may be used in addition to or as an alternative to time and location.

If the fragmented advertisement is not accepted, the master device returns an advertisement rejection response to the fragmented ad function 62 (step 1604). If the fragmented advertisement is accepted, the master device verifies that the users identified as participants for the fragmented advertisement are willing to participate (step 1606). Initially, the users identified as participants are the users 20 in the crowd identified by the fragmented ad function 62 in step 1500 of FIG. 13A. Verification may be based on manual input from the user 20 of the master device after verifying the participants via a communication channel such as, for example, verbal communication. For example, the user 20 may verbally ask the other users 20 in the crowd, which may be identified by data from the MAP server provided in association with the fragmented advertisement, if they are willing to participate in the fragmented advertisement. Then, the user 20 may provide corresponding input to the master device to identify the users 20 that agreed to participate or that have chosen not to participate. Alternatively, the master device may prompt the user 20 of the master device for verification that the user 20 is willing to participate and send requests to the mobile devices 18 of the other users 20 in the crowd for verification that the other users 20 are willing to participate. The mobile devices 18 of the other users 20 in the crowd may then prompt the other users 20 for verification that they are willing to participate and then return the results to the master device. Alternatively, the mobile devices 18 of the other users 20 in the crowd may automatically determine whether to verify that the other users 20 are willing to participate based on one or more predefined criteria, which may be pre-configured by the other users 20. These predefined criteria may include, for example, a block setting that may be set by the other users 20 such that all verification requests are automatically denied (e.g., response sent indicating that the other users 20 are not willing to participate or no verification response sent) during a defined period of time (e.g., next hour, after 7 p.m., or the like), when located at a defined location (e.g., deny all verification requests when at work), or the like. Other contextual data may be used in addition to or as an alternative to time and location.

In addition to verifying the participants, in this embodiment, the master device attempts to find additional participants for the fragmented advertisement (step 1608). More specifically, in one embodiment, the master device utilizes a proximity based messaging system (e.g., messaging via a wireless LAN or wireless PAN connection) to query other users of nearby devices that are not users 20 of the mobile devices 18 if they are willing to participate. If so, user profiles of the users are preferably returned to the master device.

At this point, the master device determines whether there has been a change to the participants (step 1610). The change in participants may occur as a result of one or more of the users 20 in the crowd choosing not to participate or as a result of finding additional users that are nearby and willing to participate. If there is a change in participants, the master device returns a response including a notification of the change in participants to the fragmented ad function 62 (step 1612). Note that if the change in participants includes adding additional users that are not users of the system 10, then the response preferably includes user profiles of the additional users.

If there is no change in the participants, then the master device sends the advertisement fragments of the fragmented advertisement to the devices of the participants (step 1614). The master device then receives user input from one or more of the participants in an attempt for the participants to achieve the predefined goal of the fragmented advertisement (step 1616) and then sends a response to the fragmented ad function 62 that is indicative of whether the participants achieved the predefined goal of the fragmented advertisement based on the received user input (step 1618). Lastly, the master device receives the advertisement benefit or the consolation from the fragmented ad function 62 depending on whether the participants achieved the predefined goal of the fragmented advertisement (step 1620).

FIGS. 14A through 14D graphically illustrate an exemplary fragmented advertisement according to one embodiment of the present disclosure. In this example, the participants for the fragmented advertisement are three of the users 20, namely the users 20-1 through 20-3, and the advertisement components of the fragmented advertisement are presented to the users 20-1 through 20-3 via a Graphical User Interface (GUI) of the MAP applications 32-1 through 32-3 of the mobile devices 18-1 through 18-3. Specifically, FIG. 14A illustrates a first advertisement component of the fragmented advertisement presented to the user 20-1 via a Coupon screen 128 of the GUI of the MAP application 32-1 at the mobile device 18-1, which has been selected as the master device. In this example, the first advertisement component includes a question that reads “What Spanish conquistador led an expedition that caused the fall of the Aztec empire?” Also, in this example, the first advertisement component includes instructions that tell the user 20-1 that the participants will win a Starbucks® Buy One Get Two Free Coupon if the question is answered correctly in 60 seconds and a notification that nearby friends (participants) have some helpful clues. The user 20-1 can enter an answer to the question by selecting an “Answer this Question” button 130. Alternatively, in this example, the user 20-1 can choose to try another question by selecting a corresponding button 132. In this example, the fragmented advertisement has multiple questions, where any of the questions may be answered in order to receive the coupon. Thus, if the user 20-1 does not know the answer to the current question, the user 20-1 may select the button 132 to try another question included in the fragmented advertisement.

FIGS. 14B and 14C illustrate second and third advertisement fragments of the fragmented advertisement presented to the two other users 20-2 and 20-3 that are participants for the fragmented advertisement according to one embodiment of the present disclosure. More specifically, as illustrated in FIG. 14B, the second advertisement fragment is presented to the user 20-2 via a Coupon screen 134 of the GUI of the MAP application 32-2 of the mobile device 18-2. In this example, the second advertisement fragment includes the hint “For your friend's coupon question: The last name begins with a ‘C’.” In addition, the second advertisement component includes instructions that tell the user 20-2 that the participants will win a Starbucks® Buy One Get Two Free Coupon if the question is answered correctly in 60 seconds and a notification that nearby friends (participants) have some additional helpful clues.

Likewise, as illustrated in FIG. 14C, the third advertisement fragment is presented to the user 20-3 via a Coupon screen 136 of the GUI of the MAP application 32-3 of the mobile device 18-3. In this example, the third advertisement fragment includes the hint “For your friend's coupon question: The first name is ‘Hernan’.” In addition, the third advertisement component includes instructions that tell the user 20-3 that the participants will win a Starbucks® Buy One Get Two Free Coupon if the question is answered correctly in 60 seconds and a notification that nearby friends (participants) have some additional helpful clues. The users 20-1, 20-2, and 20-3 are thereby encouraged to interact with one another (speak with one another to share the question and the clues) in order to come up with the correct answer to the question. If the users 20-1, 20-2, and 20-3 provide the correct answer, then the Starbucks® Buy One Get Two Free Coupon is provided to the users 20-1, 20-2, and 20-3. In this example, the coupon is provided to the users 20-1, 20-2, and 20-3 by presenting the coupon in a Coupon display screen 138 of the MAP application 32-1 of the mobile device 18-1, as illustrated in FIG. 14D.

The following is an exemplary and non-limiting use case that utilizes the fragmented advertisement and corresponding advertisement benefit described above with respect to FIGS. 14A through 14D.

• • 1. Karen and two of her friends are walking in the Village.
  • 2. Karen has opted in to fragmented advertisements because she has heard that they are both fun and valuable.
  • 3. The system identifies Karen and her two friends as a group of co-located users to which a fragmented advertisement can be provided.
  • 4. The fragmented ad function 62 determines that the group is likely to respond to a fragmented advertisement for coffee based on an aggregate profile of the group.
  • 5. As such, the fragmented ad function 62 delivers a fragmented advertisement for Starbucks® coffee to the group, where the fragmented advertisement includes three advertisement fragments, namely, a question and two different hints for the answer to the question.
  • 6. The advertisement fragment that includes the following question is presented to Karen on her mobile device:
    • “Answer this question in 60 seconds and win a Buy One Get Two Free Coupon!”
    • “What Spanish conquistador led an expedition that caused the fall of the Aztec empire?” “Hint: Nearby friends have some helpful clues!”
  • 7. Karen also sees that a sixty second clock has started counting down for the offer. She is interested in the offer because it is very generous and she loves coffee. However, she can only think of a couple of conquistador names and she is unsure which might be the correct answer.
  • 8. She knows that she does not have time to perform an Internet search to find the answer to the question. However, she does have time to ask her nearby friends for help.
  • 9. Both friends have received advertisement fragments that provide different hints to the answer of the question. One friend has a hint that reads: “For your friend's coupon question: The last name begins with a ‘C’.” The other friend has a hint that reads: “For your friend's coupon question: The first name is ‘Hernan’.”
  • 10. As the clock ticks down, Karen and her friends pool their resources to come up with the right answer and win the coupon.

FIG. 15 is a block diagram of the MAP server 12 according to one embodiment of the present disclosure. As illustrated, the MAP server 12 includes a controller 140 connected to memory 142, one or more secondary storage devices 144, and a communication interface 146 by a bus 148 or similar mechanism. The controller 140 is a microprocessor, digital Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), or similar hardware device. In this embodiment, the controller 140 is a microprocessor, and the application layer 40, the business logic layer 42, and the object mapping layer 63 (FIG. 2) are implemented in software and stored in the memory 142 for execution by the controller 140. Further, the datastore 64 (FIG. 2) may be implemented in the one or more secondary storage devices 144. The secondary storage devices 144 are digital data storage devices such as, for example, one or more hard disk drives. The communication interface 146 is a wired or wireless communication interface that communicatively couples the MAP server 12 to the network 28 (FIG. 1). For example, the communication interface 146 may be an Ethernet interface, local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, or the like.

FIG. 16 is a block diagram of the mobile device 18 of one of the users 20 of FIG. 1 according to one embodiment of the present disclosure. As illustrated, the mobile device 18 includes a controller 150 connected to memory 152, a communication interface 154, one or more user interface components 156, and the location function 36 by a bus 158 or similar mechanism. The controller 150 is a microprocessor, digital ASIC, FPGA, or similar hardware device. In this embodiment, the controller 150 is a microprocessor, and the MAP client 30, the MAP application 32, and the third-party applications 34 are implemented in software and stored in the memory 152 for execution by the controller 150. In this embodiment, the location function 36 is a hardware component such as, for example, a GPS receiver. The communication interface 154 is a wireless communication interface that communicatively couples the mobile device 18 to the network 28 (FIG. 1). For example, the communication interface 154 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, 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.

The present disclosure provides substantial opportunity for variation without departing from the scope of the concepts disclosed herein. For example, in the embodiments of FIG. 12 and FIGS. 13A and 13B, the fragmented advertisement is delivered to the group of participants by delivering the fragmented advertisement to a master device, which then distributes the advertisement fragments to the participants. However, the present disclosure is not limited thereto. In an alternative embodiment, no master device is selected, and the fragmented ad function 62 delivers the advertisement fragments to the devices of the participants.

As another example, while fragmented advertisement delivery is primarily described herein with respect to the system 10, the present disclosure is not limited thereto. For instance, the present disclosure is not limited to delivering fragmented advertisements to crowds of users or to groups of participants that are based on crowds of users. Fragmented advertisements may be delivered to groups of co-located users formed or identified using any suitable technique.

As yet another example, while the fragmented ad function 62 has been described as being implemented in the MAP server 12, the present disclosure is not limited thereto. In one alternative embodiment, the fragmented ad function 62 may be implemented as, or as part of, the third-party service 26. More specifically, when implemented as the third-party service 26, the fragmented ad function 62 may query the MAP server 12 for crowd data (e.g., crowd locations, aggregate profiles, etc.), and then identify a crowd for fragmented advertisement delivery based on the crowd data. Then, the fragmented ad function 62 may deliver the fragmented advertisement to the crowd either directly using contact information obtained from the MAP server 12 or via the MAP server 12 as an intermediary.

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

1. A computer-implemented method comprising:

identifying a group of participants for fragmented advertisement delivery;

determining a fragmented advertisement for the group of participants, the fragmented advertisement comprising a plurality of advertisement fragments that encourage interaction between participants for the fragmented advertisement to achieve a predefined goal of the fragmented advertisement; and

delivering the fragmented advertisement to the group of participants.

2. The method of claim 1 wherein the group of participants is a group of co-located users.

3. The method of claim 1 wherein the group of participants is a crowd of users being formed via a spatial crowd formation process.

4. The method of claim 1 wherein identifying the group of participants comprises:

identifying an initial group of participants for fragmented advertisement delivery;

determining an initial fragmented advertisement for the initial group of participants;

delivering the initial fragmented advertisement to the initial group of participants; and

receiving a notification of a change in participants that defines at least one change to the initial group of participants to thereby identify the group of participants for fragmented advertisement delivery.

5. The method of claim 4 wherein the notification of the change in participants comprises a notification that one or more of the participants in the initial group of participants are to be removed as participants.

6. The method of claim 4 wherein the notification of the change in participants comprises a notification that one or more users are to be added as additional participants.

7. The method of claim 1 wherein selecting the fragmented advertisement for the group of participants comprises selecting the fragmented advertisement for the group of participants from a plurality of fragmented advertisements.

8. The method of claim 7 wherein selecting the fragmented advertisement comprises selecting the fragmented advertisement for the group of participants from the plurality of fragmented advertisements based on an aggregate profile of the group of participants.

9. The method of claim 7 wherein selecting the fragmented advertisement comprises selecting the fragmented advertisement for the group of participants from the plurality of fragmented advertisements based on user profiles of a plurality of users that form the group of participants.

10. The method of claim 7 wherein selecting the fragmented advertisement comprises selecting the fragmented advertisement for the group of participants from the plurality of fragmented advertisements based on a location of the group of participants.

11. The method of claim 7 wherein selecting the fragmented advertisement comprises selecting the fragmented advertisement for the group of participants from the plurality of fragmented advertisements based on historical information regarding one or more fragmented advertisements previously delivered to the group of participants.

12. The method of claim 7 wherein selecting the fragmented advertisement comprises selecting the fragmented advertisement for the group of participants from the plurality of fragmented advertisements based on device capabilities of a plurality of user devices of the group of participants, wherein each participant in the group of participants has a corresponding user device of the plurality of user devices.

13. The method of claim 1 wherein determining the fragmented advertisement for the group of participants comprises generating the fragmented advertisement based on one or more characteristics of the group of participants.

14. The method of claim 13 wherein the one or more characteristics of the group of participants comprise a number of participants in the group of participants.

15. The method of claim 14 wherein the one or more characteristics of the group of participants further comprise an aggregate profile of the group of participants.

16. The method of claim 1 wherein delivering the fragmented advertisement to the group of participants comprises:

selecting a user device of one of the group of participants as a master device; and

delivering the fragmented advertisement to the master device such that the master device distributes each of the plurality of advertisement fragments to a different participant in the group of participants.

17. The method of claim 16 wherein selecting the user device of the one of the group of participants as the master device comprises selecting the user device of the one of the group of participants as the master device based on historical information that indicates that the one of the group of participants was previously a participant for another fragmented advertisement.

18. The method of claim 16 wherein selecting the user device of the one of the group of participants as the master device comprises selecting the user device of the one of the group of participants as the master device based on historical information that indicates that the user device of the one of the group of participants was previously selected as a master device for delivery of another fragmented advertisement.

19. The method of claim 16 wherein selecting the user device of the one of the group of participants as the master device comprises selecting the user device of the one of the group of participants as the master device based on device capabilities.

20. The method of claim 16 wherein the group of participants is a crowd of users formed via a spatial crowd formation process, and selecting the user device of the one of the group of participants as the master device comprises selecting the user device of the one of the group of participants as the master device based on an amount of time that the one of the group of participants has been in the crowd of users.

21. The method of claim 1 further comprising:

receiving a response from at least one of the group of participants that is indicative of whether the group of participants achieved the predefined goal of the fragmented advertisement; and

delivering an advertisement benefit to the group of participants if the group of participants achieved the predefined goal of the fragmented advertisement.

22. The method of claim 21 wherein the predefined goal of the fragmented advertisement is answering a question, and the plurality of advertisement fragments of the fragmented advertisement comprise a first advertisement fragment that includes the question and one or more additional advertisement fragments that each include a different hint to a correct answer to the question.

23. The method of claim 21 wherein the predefined goal of the fragmented advertisement is solving a puzzle, and the plurality of advertisement fragments of the fragmented advertisement comprise a first advertisement fragment that includes the puzzle and one or more additional advertisement fragments that each include a different hint to solving the puzzle.

24. A server comprising:

a communication interface adapted to communicatively couple the server to a network; and

a controller associated with the communication interface and adapted to: identify a group of participants for fragmented advertisement delivery; determine a fragmented advertisement for the group of participants, the fragmented advertisement comprising a plurality of advertisement fragments that encourage interaction between participants for the fragmented advertisement to achieve a predefined goal of the fragmented advertisement; and deliver the fragmented advertisement to the group of participants via the network.

25. A computer readable medium storing software for instructing a controller of a computing device to:

identify a group of participants for fragmented advertisement delivery;

determine a fragmented advertisement for the group of participants, the fragmented advertisement comprising a plurality of advertisement fragments that encourage interaction between participants for the fragmented advertisement to achieve a predefined goal of the fragmented advertisement; and

deliver the fragmented advertisement to the group of participants.