US20120047143A1

US20120047143A1 - Sparse profile augmentation using a mobile aggregate profiling system

Info

Publication number: US20120047143A1
Application number: US12/722,564
Authority: US
Inventors: Steven L. Petersen; Ravi Reddy Katpelly
Original assignee: Waldeck Technology LLC
Current assignee: Uber Technologies Inc
Priority date: 2009-03-25
Filing date: 2010-03-12
Publication date: 2012-02-23
Also published as: US20120047102A1; US8620532B2; US20120047087A1; US20120046860A1; US20140129502A1; US8589330B2; US9140566B1; US9410814B2; US20120042046A1; US9082077B2; US20160003634A1

Abstract

Systems and methods are provided for augmenting a user profile of a subject user. In general, the user profile of the subject user is augmented based on aggregate profile data for a group of users relevant to a current location of the subject user. In one embodiment, the group of users is a crowd of users currently located at a location that is relevant to the current location of the subject user. In another embodiment, the group of users is a number of users historically, or previously, located at locations relevant to the current location of the subject user.

Description

RELATED APPLICATIONS

This application claims the benefit of provisional patent application Ser. No. 61/163,091, filed Mar. 25, 2009, the disclosure of which is hereby incorporated by reference in its entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates to augmenting a sparse user profile.

BACKGROUND

Many systems and services rely on user profiles of their users. However, oftentimes, users fail to adequately complete their user profiles, thereby resulting in incomplete or sparse user profiles. In order to increase the effectiveness of systems and services that rely on user profiles, there is a need for a system and method for augmenting sparse user profiles.

SUMMARY

Systems and methods are provided for augmenting a user profile of a subject user. In general, the user profile of the subject user is augmented based on aggregate profile data for a group of users relevant to a current location of the subject user. In one embodiment, the group of users is a crowd of users currently located at a location that is relevant to the current location of the subject user. In another embodiment, the group of users is a number of users historically, or previously, located at locations relevant to the current location of the subject user.
In one embodiment, a profile augmentation function obtains an aggregate profile of a crowd of users currently located at or near the current location of the subject user. The aggregate profile of the crowd includes a number of keywords and a number of user matches, or occurrences, of each of the keywords in user profiles of the users in the crowd. The profile augmentation function then augments the user profile of the subject user based on the keywords and the number of user matches for the keywords in the aggregate profile of the crowd.
In another embodiment, the profile augmentation function obtains a historical aggregate profile for users previously, or historically, located at or near the current location of the subject user. In one embodiment, the historical aggregate profile includes a number of keywords and a number of user matches, or occurrences, of each of the keywords in user profiles of the users historically located at or near the current location of the subject user. The profile augmentation function then augments the user profile of the subject user based on the keywords and the number of user matches for the keywords in the historical aggregate profile.
Those skilled in the art will appreciate the scope of the present invention and realize additional aspects thereof after reading the following detailed description in association with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings incorporated in and forming a part of this specification illustrate several aspects of the invention, and together with the description serve to explain the principles of the invention.

FIG. 1A illustrates a system providing sparse profile augmentation according to a first embodiment of the present disclosure;

FIG. 1B illustrates a system providing sparse profile augmentation according to a second exemplary embodiment of the present disclosure;

FIG. 2 is a block diagram of the MAP server of FIGS. 1A and 1B according to one embodiment of the present disclosure;

FIG. 3 is a block diagram of the MAP client of one of the mobile devices of FIGS. 1A and 1B according to one embodiment of the present disclosure;

FIG. 4 illustrates the operation of the system of FIGS. 1A and 1B 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 FIGS. 1A and 1B to provide user profiles and current locations of the users of the mobile devices to the MAP server according to another embodiment of the present disclosure;

FIGS. 6 and 7 graphically illustrate bucketization of users according to location for purposes of maintaining a historical record of anonymized user profile data by location according to one embodiment of the present disclosure;

FIG. 8 is a flow chart illustrating the operation of a foreground bucketization process performed by the MAP server to maintain the lists of users for location buckets for purposes of maintaining a historical record of anonymized user profile data by location according to one embodiment of the present disclosure;

FIG. 9 is a flow chart illustrating the anonymization and storage process performed by the MAP server for the location buckets in order to maintain a historical record of anonymized user profile data by location according to one embodiment of the present disclosure;

FIG. 10 graphically illustrates anonymization of a user record according to one embodiment of the present disclosure;

FIG. 11 is a flow chart for a quadtree based storage process that may be used to store anonymized user profile data for location buckets according to one embodiment of the present disclosure;

FIG. 12 is a flow chart illustrating a quadtree algorithm that may be used to process the location buckets for storage of the anonymized user profile data according to one embodiment of the present disclosure;

FIGS. 13A through 13E graphically illustrate the process of FIG. 12 for the generation of a quadtree data structure for one exemplary base quadtree region;

FIG. 14 illustrates the operation of the system of FIGS. 1A and 1B wherein a mobile device is enabled to request and receive historical data from the MAP server according to one embodiment of the present disclosure;

FIG. 15 is a flow chart for a spatial crowd formation process according to one embodiment of the present disclosure;

FIGS. 16A through 16D graphically illustrate the crowd formation process of FIG. 15 for an exemplary bounding box;

FIGS. 17A through 17D illustrate a flow chart for a spatial crowd formation process according to another embodiment of the present disclosure;

FIGS. 18A through 18D graphically illustrate the crowd formation process of FIGS. 17A through 17D for a scenario where the crowd formation process is triggered by a location update for a user having no old location;

FIGS. 19A through 19F graphically illustrate the crowd formation process of FIGS. 17A through 17D for a scenario where the new and old bounding boxes overlap;

FIGS. 20A through 20E graphically illustrate the crowd formation process of FIGS. 17A through 17D in a scenario where the new and old bounding boxes do not overlap;

FIG. 21 illustrates the operation of the MAP server to process a crowd request according to one embodiment of the present disclosure;

FIG. 22 is a flow chart illustrating the operation of the profile augmentation function of FIGS. 1A and 1B to augment a user profile of a subject user based on an aggregate profile of a crowd at or near a current location of the subject user according to one embodiment of the present disclosure;

FIG. 23 is a more detailed illustration of the augmenting step of FIG. 22 according to one embodiment of the present disclosure;

FIG. 24 is a flow chart illustrating the operation of the profile augmentation to update a user profile of a subject user according to one embodiment of the present disclosure;

FIG. 25 is a more detailed illustration of the augmenting step of FIG. 22 according to another embodiment of the present disclosure;

FIG. 26 is a flow chart illustrating the operation of the profile augmentation function of FIG. 1A to augment a user profile of a subject user based on an aggregate profile of a crowd at or near a current location of the subject user according to one embodiment of the present disclosure;

FIG. 27 is a flow chart illustrating the operation of the profile augmentation function of FIG. 1B to augment a user profile of a subject user based on an aggregate profile of a crowd at or near a current location of the subject user according to one embodiment of the present disclosure;

FIG. 28 is a flow chart illustrating the operation of the profile augmentation function of FIGS. 1A and 1B to augment a user profile of a subject user based on a historical aggregate profile for a current location of the subject user according to another embodiment of the present disclosure;

FIG. 29 is more detailed illustration of the augmenting step of FIG. 28 according to one embodiment of the present disclosure;

FIG. 30 is more detailed illustration of the augmenting step of FIG. 28 according to another embodiment of the present disclosure;

FIG. 31 is a flow chart illustrating the operation of the profile augmentation function of FIG. 1A to augment a user profile of a subject user based on a historical aggregate profile for a current location of the subject user according to one embodiment of the present disclosure;

FIG. 32 is a flow chart illustrating the operation of the profile augmentation function of FIG. 1B to augment a user profile of a subject user based on a historical aggregate profile for a current location of the subject user according to another embodiment of the present disclosure;

FIG. 33 is a flow chart illustrating the operation of the MAP server to generate a historical aggregate profile for a current location of a subject user in response to a request from the profile augmentation function according to one embodiment of the present disclosure;

FIG. 34 is a block diagram of the MAP server of FIG. 1 according to one embodiment of the present disclosure;

FIG. 35 is a block diagram of one of the mobile devices of FIG. 1 according to one embodiment of the present disclosure;

FIG. 36 is a block diagram of the subscriber device of FIG. 1 according to one embodiment of the present disclosure;

FIG. 37 is a block diagram of the third-party server of FIG. 1 according to one embodiment of the present disclosure; and

FIG. 38 is a block diagram of the profile server 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 invention and illustrate the best mode of practicing the invention. Upon reading the following description in light of the accompanying drawings, those skilled in the art will understand the concepts of the invention and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure and the accompanying claims.
FIG. 1A illustrates a system 10 providing sparse profile augmentation according to a first exemplary embodiment 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 having associated users 20-1 through 20-N, a subscriber device 22 having an associated subscriber 24, and a third-party server 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 18-1 through 18-N are enabled to connect to the network 28 via local wireless connections (e.g., WiFi or IEEE 802.11 connections) or wireless telecommunications connections (e.g., 3G or 4G telecommunications connections such as GSM, LTE, W-CDMA, or WiMAX connections).
As discussed below in detail, the MAP server 12 operates to obtain current locations, including location updates, and user profiles of the users 20-1 through 20-N of the mobile devices 18-1 through 18-N. The current locations of the users 20-1 through 20-N 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 20-1 through 20-N, the MAP server 12 is enabled to provide a number of features such as, but not limited to, maintaining a historical record of anonymized user profile data by location, generating aggregate profile data over time for a Point of Interest (POI) or Area of Interest (AOI) using the historical record of anonymized user profile data, identifying crowds of users using current locations and/or user profiles of the users 20-1 through 20-N, and generating aggregate profiles for crowds of users at a POI or in an AOI using the current user profiles of users in the crowds. While not essential, for additional information regarding the MAP server 12, the interested reader is directed to 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, U.S. patent application Ser. No. 12/645,532 entitled FORMING CROWDS AND PROVIDING ACCESS TO CROWD DATA IN A MOBILE ENVIRONMENT, U.S. patent application Ser. No. 12/645,539 entitled ANONYMOUS CROWD TRACKING, 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, U.S. patent application Ser. No. 12/645,546 entitled CROWD FORMATION FOR MOBILE DEVICE USERS, 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, and U.S. patent application Ser. No. 12/645,560 entitled HANDLING CROWD REQUESTS FOR LARGE GEOGRAPHIC AREAS, all of which were filed on Dec. 23, 2009 and are hereby incorporated herein by reference in their entireties. 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 20-1 through 20-N of the mobile devices 18-1 through 18-N. 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, and/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 20-1 through 20-N of the mobile devices 18-1 through 18-N. The location server 16 generally operates to receive location updates from the mobile devices 18-1 through 18-N and make the location updates available to entities such as, for instance, the MAP server 12. In one exemplary embodiment, the location server 16 is a server operating to provide Yahoo!'s FireEagle service. Before proceeding, it should be noted that while the system 10 of FIG. 1A 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.
The mobile devices 18-1 through 18-N may be mobile smart phones, portable media player devices, mobile gaming devices, or the like. Some exemplary mobile devices that may be programmed or otherwise configured to operate as the mobile devices 18-1 through 18-N are the Apple® iPhone, the Palm Pre, the Samsung Rogue, the Blackberry Storm, 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, MAP applications 32-1 through 32-N, third-party applications 34-1 through 34-N, and location functions 36-1 through 36-N, respectively. Using the mobile device 18-1 as an example, the MAP client 30-1 is preferably implemented in software. In general, in the preferred embodiment, the MAP client 30-1 is a middleware layer operating to interface an application layer (i.e., the MAP application 32-1 and the third-party applications 34-1) to the MAP server 12. More specifically, the MAP client 30-1 enables the MAP application 32-1 and the third-party applications 34-1 to request and receive data from the MAP server 12. In addition, the MAP client 30-1 enables applications, such as the MAP application 32-1 and the third-party applications 34-1, to access data from the MAP server 12.
The MAP application 32-1 is also preferably implemented in software. The MAP application 32-1 generally provides a user interface component between the user 20-1 and the MAP server 12. More specifically, among other things, the MAP application 32-1 enables the user 20-1 to initiate historical requests for historical data (e.g., historical aggregate profile data) or crowd requests for crowd data (e.g., aggregate profile data and/or crowd characteristics data) from the MAP server 12 for a POI or AOI. The MAP application 32-1 also enables the user 20-1 to configure various settings. For example, the MAP application 32-1 may enable the user 20-1 to select a desired social networking service (e.g., Facebook, MySpace, LinkedIN, etc.) from which to obtain the user profile of the user 20-1 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 34-1 are preferably implemented in software. The third-party applications 34-1 operate to access the MAP server 12 via the MAP client 30-1. The third-party applications 34-1 may utilize data obtained from the MAP server 12 in any desired manner. As an example, one of the third party applications 34-1 may be a gaming application that utilizes historical aggregate profile data to notify the user 20-1 of POIs or AOIs where persons having an interest in the game have historically congregated.
The location function 36-1 may be implemented in hardware, software, or a combination thereof. In general, the location function 36-1 operates to determine or otherwise obtain the location of the mobile device 18-1. For example, the location function 36-1 may be or include a Global Positioning System (GPS) receiver.
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 historical aggregate profile data for one or more POIs and/or one or more AOIs, 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.
The third-party server 26 is a physical server that has access to data from the MAP server 12 such as historical aggregate profile data for one or more POIs or one or more AOIs or crowd data such as aggregate profiles for one or more crowds at one or more POIs or within one or more AOIs. Based on the data from the MAP server 12, the third-party server 26 operates to provide a service such as, for example, targeted advertising. For example, the third-party server 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 service provided by the third-party server 26, other types of services may additionally or alternatively be provided. Other types of services that may be provided by the third-party server 26 will be apparent to one of ordinary skill in the art upon reading this disclosure.
Lastly, in this embodiment, the MAP server 12 includes a profile augmentation function 40. The profile augmentation function 40 is preferably implemented in software, but is not limited thereto. As discussed below in detail, the profile augmentation function 40 operates to augment user profiles of users, such as but not limited to the users 20-1 through 20-N, based on aggregate profile data for crowds of users and/or historical aggregate profile data. Using the user 20-1 as an example, the profile augmentation function 40 operates to augment the user profile of the user 20-1 based on aggregate profiles of crowds of users in which the user 20-1 is included, aggregate profiles of crowds of users 20-1 that are nearby to the user 20-1, and/or historical aggregate profile data for locations visited by the user 20-1.
FIG. 1B illustrates the system 10 providing sparse profile augmentation according to a second exemplary embodiment 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 having associated users 20-1 through 20-N, a subscriber device 22 having an associated subscriber 24, and a third-party server 26 communicatively coupled via a network 28. However, in this embodiment, the profile augmentation function 40 is implemented apart from the MAP server 12. Specifically, the profile augmentation function 40 may be implemented on any network device that is enabled to communicate with the MAP server 12 via the network 28. For example, the profile augmentation function 40 may be implemented on the profile server 14, one or more of the mobile devices 18-1 through 18-N, the subscriber device 22, and/or the third-party server 26.
As discussed below in detail, in this embodiment, the profile augmentation function 40 operates to augment user profiles of system users (i.e., one or more of the users 20-1 through 20-N), third-party users (e.g., one or more users associated with the profile server 14 other than the users 20-1 through 20-N, the subscriber 24, and/or one or more users associated with the third-party server 26), or a combination thereof. More specifically, for a particular user, the profile augmentation function 40 augments the user profile of the user based on aggregate profile data for crowds located at or near the current location of the user and/or historical aggregate profile data for the current location of the user.
Before describing the operation of the profile augmentation function 40 in detail, FIGS. 2 through 22 provide a description of some of the features of the MAP server 12 that may be utilized directly or indirectly by the profile augmentation function 40. FIG. 2 is a block diagram of the MAP server 12 of FIGS. 1A and 1B according to one embodiment of the present disclosure. As illustrated, the MAP server 12 includes an application layer 42, a business logic layer 44, and a persistence layer 46. The application layer 42 includes a user web application 48, a mobile client/server protocol component 50, and one or more data Application Programming Interfaces (APIs) 52. The user web application 48 is preferably implemented in software and operates to provide a web interface for users, such as the subscriber 24, to access the MAP server 12 via a web browser. The mobile client/server protocol component 50 is preferably implemented in software and operates to provide an interface between the MAP server 12 and the MAP clients 30-1 through 30-N hosted by the mobile devices 18-1 through 18-N. The data APIs 52 enable third-party services, such as the third-party server 26, to access the MAP server 12.
The business logic layer 44 includes a profile manager 54, a location manager 56, a history manager 58, a crowd analyzer 60, and an aggregation engine 62, each of which is preferably implemented in software. In addition, in the embodiment of FIG. 1A, the business logic layer 44 also includes the profile augmentation function 40. Note, however, that in the embodiment of FIG. 1B, the business logic layer 44 does not include the profile augmentation function 40. The profile manager 54 generally operates to obtain the user profiles of the users 20-1 through 20-N directly or indirectly from the one or more profile servers 14 and store the user profiles in the persistence layer 46. The location manager 56 operates to obtain the current locations of the users 20-1 through 20-N including location updates. As discussed below, the current locations of the users 20-1 through 20-N may be obtained directly from the mobile devices 18-1 through 18-N and/or obtained from the location server 16.
The history manager 58 generally operates to maintain a historical record of anonymized user profile data by location. The crowd analyzer 60 operates to form crowds of users. In one embodiment, the crowd analyzer 60 utilizes a spatial crowd formation algorithm. However, the present disclosure is not limited thereto. In addition, the crowd analyzer 60 may further characterize crowds to reflect degree of fragmentation, best-case and worst-case degree of separation (DOS), and/or degree of bi-directionality of relationships. Still further, the crowd analyzer 60 may also operate to track crowds. The aggregation engine 62 generally operates to provide aggregate profile data in response to requests from the mobile devices 18-1 through 18-N, the subscriber device 22, the profile server 14, and/or the third-party server 26. The aggregate profile data may be historical aggregate profile data for one or more geographic locations (e.g., one or more POIs) or one or more geographic areas (e.g., one or more AOIs) or aggregate profile data for crowd(s) currently at one or more geographic locations or in one or more geographic areas.
The persistence layer 46 includes an object mapping layer 64 and a datastore 66. The object mapping layer 64 is preferably implemented in software. The datastore 66 is preferably a relational database, which is implemented in a combination of hardware (i.e., physical data storage hardware) and software (i.e., relational database software). In this embodiment, the business logic layer 44 is implemented in an object-oriented programming language such as, for example, Java. As such, the object mapping layer 64 operates to map objects used in the business logic layer 44 to relational database entities stored in the datastore 66. Note that, in one embodiment, data is stored in the datastore 66 in a Resource Description Framework (RDF) compatible format.
In an alternative embodiment, rather than being a relational database, the datastore 66 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 20-1 through 20-N as a proprietary extension of the FOAF vocabulary that includes additional properties desired for the system 10.
FIG. 3 illustrates the MAP client 30-1 of FIGS. 1A and 1B in more detail according to one embodiment of the present disclosure. This discussion is equally applicable to the other MAP clients 30-2 through 30-N. As illustrated, in this embodiment, the MAP client 30-1 includes a MAP access API 68, a MAP middleware component 70, and a mobile client/server protocol component 72. The MAP access API 68 is implemented in software and provides an interface by which the MAP client 30-1 and the third-party applications 34-1 are enabled to access the MAP server 12. The MAP middleware component 70 is implemented in software and performs the operations needed for the MAP client 30-1 to operate as an interface between the MAP application 32-1 and the third-party applications 34-1 at the mobile device 18-1 and the MAP server 12. The mobile client/server protocol component 72 enables communication between the MAP client 30-1 and the MAP server 12 via a defined protocol.
FIG. 4 illustrates the operation of the system 10 of FIGS. 1A and 1B to provide the user profile of the user 20-1 of the mobile device 18-1 to the MAP server 12 according to one embodiment of the present disclosure. This discussion is equally applicable to user profiles of the other users 20-2 through 20-N of the other mobile devices 18-2 through 18-N. First, an authentication process is performed (step 1000). For authentication, in this embodiment, the mobile device 18-1 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 20-1 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 30-1 of the mobile device 18-1 (step 1000E).
At some point after authentication is complete, a user profile process is performed such that a user profile of the user 20-1 is obtained from the profile server 14 and delivered to the MAP server 12 (step 1002). In this embodiment, the MAP client 30-1 of the mobile device 18-1 sends a profile request to the profile server 14 (step 1002A). In response, the profile server 14 returns the user profile of the user 20-1 to the mobile device 18-1 (step 1002B). The MAP client 30-1 of the mobile device 18-1 then sends the user profile of the user 20-1 to the MAP server 12 (step 1002C). Note that while in this embodiment the MAP client 30-1 sends the complete user profile of the user 20-1 to the MAP server 12, in an alternative embodiment, the MAP client 30-1 may filter the user profile of the user 20-1 according to criteria specified by the user 20-1. For example, the user profile of the user 20-1 may include demographic information, general interests, music interests, and movie interests, and the user 20-1 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 20-1 from the MAP client 30-1 of the mobile device 18-1, the profile manager 54 of the MAP server 12 processes the user profile (step 1002D). More specifically, in the preferred embodiment, the profile manager 54 includes social network handlers for the social network services supported by the MAP server 12. Thus, for example, if the MAP server 12 supports user profiles from Facebook®, MySpace®, and LinkedIN®, the profile manager 54 may include a Facebook handler, a MySpace handler, and a LinkedIN handler. The social network handlers process user profiles to generate user profiles for the MAP server 12 that include lists of keywords for each of a number of profile categories. The profile categories may be the same for each of the social network handlers or different for each of the social network handlers. Thus, for this example assume that the user profile of the user 20-1 is from Facebook. The profile manager 54 uses a Facebook handler to process the user profile of the user 20-1 to map the user profile of the user 20-1 from Facebook to a user profile for the MAP server 12 including lists of keywords for a number of predefined profile categories. For example, for the Facebook handler, the profile categories may be 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 20-1 from Facebook may be processed by the Facebook handler of the profile manager 54 to create a list of keywords such as, for example, liberal, High School Graduate, 35-44, College Graduate, etc. for the demographic profile category, a list of keywords such as Seeking Friendship for the social interaction profile category, a list of keywords such as politics, technology, photography, books, etc. for the general interests profile category, a list of keywords including music genres, artist names, album names, or the like for the music interests profile category, and a list of keywords including movie titles, actor or actress names, director names, movie genres, or the like for the movie interests profile category. In one embodiment, the profile manager 54 may use natural language processing or semantic analysis. For example, if the Facebook user profile of the user 20-1 states that the user 20-1 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 20-1 for the MAP server 12.
After processing the user profile of the user 20-1, the profile manager 54 of the MAP server 12 stores the resulting user profile for the user 20-1 (step 1002E). More specifically, in one embodiment, the MAP server 12 stores user records for the users 20-1 through 20-N in the datastore 66 (FIG. 2). The user profile of the user 20-1 is stored in the user record of the user 20-1. The user record of the user 20-1 includes a unique identifier of the user 20-1, the user profile of the user 20-1, and, as discussed below, a current location of the user 20-1. Note that the user profile of the user 20-1 may be updated as desired. For example, in one embodiment, the user profile of the user 20-1 is updated by repeating step 1002 each time the user 20-1 activates the MAP application 32-1.
Note that the while the discussion herein focuses on an embodiment where the user profiles of the users 20-1 through 20-N are obtained from the one or more profile servers 14, the user profiles of the users 20-1 through 20-N may be obtained in any desired manner. For example, in one alternative embodiment, the user 20-1 may identify one or more favorite websites. The profile manager 54 of the MAP server 12 may then crawl the one or more favorite websites of the user 20-1 to obtain keywords appearing in the one or more favorite websites of the user 20-1. These keywords may then be stored as the user profile of the user 20-1.
At some point, a process is performed such that a current location of the mobile device 18-1 and thus a current location of the user 20-1 is obtained by the MAP server 12 (step 1004). In this embodiment, the MAP application 32-1 of the mobile device 18-1 obtains the current location of the mobile device 18-1 from the location function 36-1 of the mobile device 18-1. The MAP application 32-1 then provides the current location of the mobile device 18-1 to the MAP client 30-1, and the MAP client 30-1 then provides the current location of the mobile device 18-1 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 18-1 in order for the MAP application 32-1 to provide location updates for the user 20-1 to the MAP server 12.
In response to receiving the current location of the mobile device 18-1, the location manager 56 of the MAP server 12 stores the current location of the mobile device 18-1 as the current location of the user 20-1 (step 1004B). More specifically, in one embodiment, the current location of the user 20-1 is stored in the user record of the user 20-1 maintained in the datastore 66 of the MAP server 12. Note that in the preferred embodiment only the current location of the user 20-1 is stored in the user record of the user 20-1. In this manner, the MAP server 12 maintains privacy for the user 20-1 since the MAP server 12 does not maintain a historical record of the location of the user 20-1. As discussed below in detail, historical data maintained by the MAP server 12 is anonymized in order to maintain the privacy of the users 20-1 through 20-N.
In addition to storing the current location of the user 20-1, the location manager 56 sends the current location of the user 20-1 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 20-1 from the location server 16. This is particularly beneficial when the mobile device 18-1 does not permit background processes, which is the case for the Apple® iPhone. As such, if the mobile device 18-1 is an Apple® iPhone or similar device that does not permit background processes, the MAP application 32-1 will not be able to provide location updates for the user 20-1 to the MAP server 12 unless the MAP application 32-1 is active.
Therefore, when the MAP application 32-1 is not active, other applications running on the mobile device 18-1 (or some other device of the user 20-1) may directly or indirectly provide location updates to the location server 16 for the user 20-1. This is illustrated in step 1006 where the location server 16 receives a location update for the user 20-1 directly or indirectly from another application running on the mobile device 18-1 or an application running on another device of the user 20-1 (step 1006A). The location server 16 then provides the location update for the user 20-1 to the MAP server 12 (step 1006B). In response, the location manager 56 updates and stores the current location of the user 20-1 in the user record of the user 20-1 (step 1006C). In this manner, the MAP server 12 is enabled to obtain location updates for the user 20-1 even when the MAP application 32-1 is not active at the mobile device 18-1.
FIG. 5 illustrates the operation of the system 10 of FIGS. 1A and 1B to provide the user profile of the user 20-1 of the mobile device 18-1 according to another embodiment of the present disclosure. This discussion is equally applicable to user profiles of the other users 20-2 through 20-N of the other mobile devices 18-2 through 18-N. First, an authentication process is performed (step 1100). For authentication, in this embodiment, the mobile device 18-1 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 20-1 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 30-1 of the mobile device 18-1 (step 1100D).
At some point after authentication is complete, a user profile process is performed such that a user profile of the user 20-1 is obtained from the profile server 14 and delivered to the MAP server 12 (step 1102). In this embodiment, the profile manager 54 of the MAP server 12 sends a profile request to the profile server 14 (step 1102A). In response, the profile server 14 returns the user profile of the user 20-1 to the profile manager 54 of the MAP server 12 (step 1102B). Note that while in this embodiment the profile server 14 returns the complete user profile of the user 20-1 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 20-1 to the MAP server 12. The profile server 14 may filter the user profile of the user 20-1 according to criteria specified by the user 20-1. For example, the user profile of the user 20-1 may include demographic information, general interests, music interests, and movie interests, and the user 20-1 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 20-1, the profile manager 54 of the MAP server 12 processes the user profile (step 1102C). More specifically, as discussed above, in the preferred embodiment, the profile manager 54 includes social network handlers for the social network services supported by the MAP server 12. The social network handlers process user profiles to generate user profiles for the MAP server 12 that include lists of keywords for each of a number of profile categories. The profile categories may be the same for each of the social network handlers or different for each of the social network handlers.
After processing the user profile of the user 20-1, the profile manager 54 of the MAP server 12 stores the resulting user profile for the user 20-1 (step 1102D). More specifically, in one embodiment, the MAP server 12 stores user records for the users 20-1 through 20-N in the datastore 66 (FIG. 2). The user profile of the user 20-1 is stored in the user record of the user 20-1. The user record of the user 20-1 includes a unique identifier of the user 20-1, the user profile of the user 20-1, and, as discussed below, a current location of the user 20-1. Note that the user profile of the user 20-1 may be updated as desired. For example, in one embodiment, the user profile of the user 20-1 is updated by repeating step 1102 each time the user 20-1 activates the MAP application 32-1.
Note that while the discussion herein focuses on an embodiment where the user profiles of the users 20-1 through 20-N are obtained from the one or more profile servers 14, the user profiles of the users 20-1 through 20-N may be obtained in any desired manner. For example, in one alternative embodiment, the user 20-1 may identify one or more favorite websites. The profile manager 54 of the MAP server 12 may then crawl the one or more favorite websites of the user 20-1 to obtain keywords appearing in the one or more favorite websites of the user 20-1. These keywords may then be stored as the user profile of the user 20-1.
At some point, a process is performed such that a current location of the mobile device 18-1 and thus a current location of the user 20-1 is obtained by the MAP server 12 (step 1104). In this embodiment, the MAP application 32-1 of the mobile device 18-1 obtains the current location of the mobile device 18-1 from the location function 36-1 of the mobile device 18-1. The MAP application 32-1 then provides the current location of the user 20-1 of the mobile device 18-1 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 18-1 in order to provide location updates for the user 20-1 to the MAP server 12. The location server 16 then provides the current location of the user 20-1 to the MAP server 12 (step 1104B). The location server 16 may provide the current location of the user 20-1 to the MAP server 12 automatically in response to receiving the current location of the user 20-1 from the mobile device 18-1 or in response to a request from the MAP server 12.
In response to receiving the current location of the mobile device 18-1, the location manager 56 of the MAP server 12 stores the current location of the mobile device 18-1 as the current location of the user 20-1 (step 1104C). More specifically, in one embodiment, the current location of the user 20-1 is stored in the user record of the user 20-1 maintained in the datastore 66 of the MAP server 12. Note that in the preferred embodiment only the current location of the user 20-1 is stored in the user record of the user 20-1. In this manner, the MAP server 12 maintains privacy for the user 20-1 since the MAP server 12 does not maintain a historical record of the location of the user 20-1. As discussed below in detail, historical data maintained by the MAP server 12 is anonymized in order to maintain the privacy of the users 20-1 through 20-N.
As discussed above, the use of the location server 16 is particularly beneficial when the mobile device 18-1 does not permit background processes, which is the case for the Apple® iPhone. As such, if the mobile device 18-1 is an Apple® iPhone or similar device that does not permit background processes, the MAP application 32-1 will not provide location updates for the user 20-1 to the location server 16 unless the MAP application 32-1 is active. However, other applications running on the mobile device 18-1 (or some other device of the user 20-1) may provide location updates to the location server 16 for the user 20-1 when the MAP application 32-1 is not active. This is illustrated in step 1106 where the location server 16 receives a location update for the user 20-1 from another application running on the mobile device 18-1 or an application running on another device of the user 20-1 (step 1106A). The location server 16 then provides the location update for the user 20-1 to the MAP server 12 (step 1106B). In response, the location manager 56 updates and stores the current location of the user 20-1 in the user record of the user 20-1 (step 1106C). In this manner, the MAP server 12 is enabled to obtain location updates for the user 20-1 even when the MAP application 32-1 is not active at the mobile device 18-1.
Using the current locations of the users 20-1 through 20-N and the user profiles of the users 20-1 through 20-N, the MAP server 12 can provide a number of features. A first feature that may be provided by the MAP server 12 is historical storage of anonymized user profile data by location. This historical storage of anonymized user profile data by location is performed by the history manager 58 of the MAP server 12. More specifically, as illustrated in FIG. 6, in the preferred embodiment, the history manager 58 maintains lists of users located in a number of geographic regions, or “location buckets.” Preferably, the location buckets are defined by floor(latitude, longitude) to a desired resolution. The higher the resolution, the smaller the size of the location buckets. For example, in one embodiment, the location buckets are defined by floor(latitude, longitude) to a resolution of 1/10,000^thof a degree such that the lower left-hand corners of the squares illustrated in FIG. 6 are defined by the floor(latitude, longitude) values at a resolution of 1/10,000^thof a degree. In the example of FIG. 6, users are represented as dots, and location buckets 74 through 90 have lists of 1, 3, 2, 1, 1, 2, 1, 2, and 3 users, respectively.
As discussed below in detail, at a predetermined time interval such as, for example, 15 minutes, the history manager 58 makes a copy of the lists of users in the location buckets, anonymizes the user profiles of the users in the lists to provide anonymized user profile data for the corresponding location buckets, and stores the anonymized user profile data in a number of history objects. In one embodiment, a history object is stored for each location bucket having at least one user. In another embodiment, a quadtree algorithm is used to efficiently create history objects for geographic regions (i.e., groups of one or more adjoining location buckets).
FIG. 7 graphically illustrates a scenario where a user moves from one location bucket to another, namely, from the location bucket 76 to the location bucket 78. As discussed below in detail, assuming that the movement occurs during the time interval between persistence of the historical data by the history manager 58, the user is included on both the list for the location bucket 76 and the list for the location bucket 78. However, the user is flagged or otherwise marked as inactive for the location bucket 76 and active for the location bucket 78. As discussed below, after making a copy of the lists for the location buckets to be used to persist the historical data, users flagged as inactive are removed from the lists of users for the location buckets. Thus, in sum, once a user moves from the location bucket 76 to the location bucket 78, the user remains in the list for the location bucket 76 until the predetermined time interval has expired and the anonymized user profile data is persisted. The user is then removed from the list for the location bucket 76.
FIG. 8 is a flow chart illustrating the operation of a foreground “bucketization” process performed by the history manager 58 to maintain the lists of users for location buckets according to one embodiment of the present disclosure. First, the history manager 58 receives a location update for a user (step 1200). For this discussion, assume that the location update is received for the user 20-1. The history manager 58 then determines a location bucket corresponding to the updated location (i.e., the current location) of the user 20-1 (step 1202). In the preferred embodiment, the location of the user 20-1 is expressed as latitude and longitude coordinates, and the history manager 58 determines the location bucket by determining floor values of the latitude and longitude coordinates, which can be written as floor(latitude, longitude) at a desired resolution. As an example, if the latitude and longitude coordinates for the location of the user 20-1 are 32.24267381553987 and −111.9249213502935, respectively, and the floor values are to be computed to a resolution of 1/10,000^thof a degree, then the floor values for the latitude and longitude coordinates are 32.2426 and −111.9249. The floor values for the latitude and longitude coordinates correspond to a particular location bucket.
After determining the location bucket for the location of the user 20-1, the history manager 58 determines whether the user 20-1 is new to the location bucket (step 1204). In other words, the history manager 58 determines whether the user 20-1 is already on the list of users for the location bucket. If the user 20-1 is new to the location bucket, the history manager 58 creates an entry for the user 20-1 in the list of users for the location bucket (step 1206). Returning to step 1204, if the user 20-1 is not new to the location bucket, the history manager 58 updates the entry for the user 20-1 in the list of users for the location bucket (step 1208). At this point, whether proceeding from step 1206 or 1208, the user 20-1 is flagged as active in the list of users for the location bucket (step 1210).
The history manager 58 then determines whether the user 20-1 has moved from another location bucket (step 1212). More specifically, the history manager 58 determines whether the user 20-1 is included in the list of users for another location bucket and is currently flagged as active in that list. If the user 20-1 has not moved from another location bucket, the process proceeds to step 1216. If the user 20-1 has moved from another location bucket, the history manager 58 flags the user 20-1 as inactive in the list of users for the other location bucket from which the user 20-1 has moved (step 1214).
At this point, whether proceeding from step 1212 or 1214, the history manager 58 determines whether it is time to persist (step 1216). More specifically, as mentioned above, the history manager 58 operates to persist history objects at a predetermined time interval such as, for example, every 15 minutes. Thus, the history manager 58 determines that it is time to persist if the predetermined time interval has expired. If it is not time to persist, the process returns to step 1200 and is repeated for a next received location update, which will typically be for another user. If it is time to persist, the history manager 58 creates a copy of the lists of users for the location buckets and passes the copy of the lists to an anonymization and storage process (step 1218). In this embodiment, the anonymization and storage process is a separate process performed by the history manager 58. The history manager 58 then removes inactive users from the lists of users for the location buckets (step 1220). The process then returns to step 1200 and is repeated for a next received location update, which will typically be for another user.
FIG. 9 is a flow chart illustrating the anonymization and storage process performed by the history manager 58 at the predetermined time interval according to one embodiment of the present disclosure. First, the anonymization and storage process receives the copy of the lists of users for the location buckets passed to the anonymization and storage process by the bucketization process of FIG. 8 (step 1300). Next, anonymization is performed for each of the location buckets having at least one user in order to provide anonymized user profile data for the location buckets (step 1302). Anonymization prevents connecting information stored in the history objects stored by the history manager 58 back to the users 20-1 through 20-N or at least substantially increases a difficulty of connecting information stored in the history objects stored by the history manager 58 back to the users 20-1 through 20-N. Lastly, the anonymized user profile data for the location buckets is stored in a number of history objects (step 1304). In one embodiment, a separate history object is stored for each of the location buckets, where the history object of a location bucket includes the anonymized user profile data for the location bucket. In another embodiment, as discussed below, a quadtree algorithm is used to efficiently store the anonymized user profile data in a number of history objects such that each history object stores the anonymized user profile data for one or more location buckets.
FIG. 10 graphically illustrates one embodiment of the anonymization process of step 1302 of FIG. 9. In this embodiment, anonymization is performed by creating anonymous user records for the users in the lists of users for the location buckets. The anonymous user records are not connected back to the users 20-1 through 20-N. More specifically, as illustrated in FIG. 10, each user in the lists of users for the location buckets has a corresponding user record 92. The user record 92 includes a unique user identifier (ID) for the user, the current location of the user, and the user profile of the user. The user profile includes keywords for each of a number of profile categories, which are stored in corresponding profile category records 94-1 through 94-M. Each of the profile category records 94-1 through 94-M includes a user ID for the corresponding user which may be the same user ID used in the user record 92, a category ID, and a list of keywords for the profile category.
For anonymization, an anonymous user record 96 is created from the user record 92. In the anonymous user record 96, the user ID is replaced with a new user ID that is not connected back to the user, which is also referred to herein as an anonymous user ID. This new user ID is different than any other user ID used for anonymous user records created from the user record of the user for any previous or subsequent time periods. In this manner, anonymous user records for a single user created over time cannot be linked to one another.
In addition, anonymous profile category records 98-1 through 98-M are created for the profile category records 94-1 through 94-M. In the anonymous profile category records 98-1 through 98-M, the user ID is replaced with a new user ID, which may be the same new user ID included in the anonymous user record 96. The anonymous profile category records 98-1 through 98-M include the same category IDs and lists of keywords as the corresponding profile category records 94-1 through 94-M. Note that the location of the user is not stored in the anonymous user record 96. With respect to location, it is sufficient that the anonymous user record 96 is linked to a location bucket.
In another embodiment, the history manager 58 performs anonymization in a manner similar to that described above with respect to FIG. 10. However, in this embodiment, the profile category records for the group of users in a location bucket, or the group of users in a number of location buckets representing a node in a quadtree data structure (see below), may be selectively randomized among the anonymous user records of those users. In other words, each anonymous user record would have a user profile including a selectively randomized set of profile category records (including keywords) from a cumulative list of profile category records for all of the users in the group.
In yet another embodiment, rather than creating anonymous user records 96 for the users in the lists maintained for the location buckets, the history manager 58 may perform anonymization by storing an aggregate user profile for each location bucket, or each group of location buckets representing a node in a quadtree data structure (see below). The aggregate user profile may include a list of all keywords and potentially the number of occurrences of each keyword in the user profiles of the corresponding group of users. In this manner, the data stored by the history manager 58 is not connected back to the users 20-1 through 20-N.
FIG. 11 is a flow chart illustrating the storing step (step 1304) of FIG. 9 in more detail according to one embodiment of the present disclosure. First, the history manager 58 processes the location buckets using a quadtree algorithm to produce a quadtree data structure, where each node of the quadtree data structure includes one or more of the location buckets having a combined number of users that is at most a predefined maximum number of users (step 1400). The history manager 58 then stores a history object for each node in the quadtree data structure having at least one user (step 1402).
Each history object includes location information, timing information, data, and quadtree data structure information. The location information included in the history object defines a combined geographic area of the location bucket(s) forming the corresponding node of the quadtree data structure. For example, the location information may be latitude and longitude coordinates for a northeast corner of the combined geographic area of the node of the quadtree data structure and a southwest corner of the combined geographic area for the node of the quadtree data structure. The timing information includes information defining a time window for the history object, which may be, for example, a start time for the corresponding time interval and an end time for the corresponding time interval. The data includes the anonymized user profile data for the users in the list(s) maintained for the location bucket(s) forming the node of the quadtree data structure for which the history object is stored. In addition, the data may include a total number of users in the location bucket(s) forming the node of the quadtree data structure. Lastly, the quadtree data structure information includes information defining a quadtree depth of the node in the quadtree data structure.
FIG. 12 is a flow chart illustrating a quadtree algorithm that may be used to process the location buckets to form the quadtree data structure in step 1400 of FIG. 11 according to one embodiment of the present disclosure. Initially, a geographic area served by the MAP server 12 is divided into a number of geographic regions, each including multiple location buckets. These geographic regions are also referred to herein as base quadtree regions. The geographic area served by the MAP server 12 may be, for example, a city, a state, a country, or the like. Further, the geographic area may be the only geographic area served by the MAP server 12 or one of a number of geographic areas served by the MAP server 12. Preferably, the base quadtree regions have a size of 2ⁿ×2ⁿlocation buckets, where n is an integer greater than or equal to 1.
In order to form the quadtree data structure, the history manager 58 determines whether there are any more base quadtree regions to process (step 1500). If there are more base quadtree regions to process, the history manager 58 sets a current node to the next base quadtree region to process, which for the first iteration is the first base quadtree region (step 1502). The history manager 58 then determines whether the number of users in the current node is greater than a predefined maximum number of users and whether a current quadtree depth is less than a maximum quadtree depth (step 1504). In one embodiment, the maximum quadtree depth may be reached when the current node corresponds to a single location bucket. However, the maximum quadtree depth may be set such that the maximum quadtree depth is reached before the current node reaches a single location bucket.
If the number of users in the current node is greater than the predefined maximum number of users and the current quadtree depth is less than a maximum quadtree depth, the history manager 58 creates a number of child nodes for the current node (step 1506). More specifically, the history manager 58 creates a child node for each quadrant of the current node. The users in the current node are then assigned to the appropriate child nodes based on the location buckets in which the users are located (step 1508), and the current node is then set to the first child node (step 1510). At this point, the process returns to step 1504 and is repeated.
Once the number of users in the current node is not greater than the predefined maximum number of users or the maximum quadtree depth has been reached, the history manager 58 determines whether the current node has any more sibling nodes (step 1512). Sibling nodes are child nodes of the same parent node. If so, the history manager 58 sets the current node to the next sibling node of the current node (step 1514), and the process returns to step 1504 and is repeated. Once there are no more sibling nodes to process, the history manager 58 determines whether the current node has a parent node (step 1516). If so, since the parent node has already been processed, the history manager 58 determines whether the parent node has any sibling nodes that need to be processed (step 1518). If the parent node has any sibling nodes that need to be processed, the history manager 58 sets the next sibling node of the parent node to be processed as the current node (step 1520). From this point, the process returns to step 1504 and is repeated. Returning to step 1516, if the current node does not have a parent node, the process returns to step 1500 and is repeated until there are no more base quadtree regions to process. Once there are no more base quadtree regions to process, the finished quadtree data structure is returned to the process of FIG. 11 such that the history manager 58 can then store the history objects for nodes in the quadtree data structure having at least one user (step 1522).
FIGS. 13A through 13E graphically illustrate the process of FIG. 12 for the generation of the quadtree data structure for one exemplary base quadtree region 100. FIG. 13A illustrates the base quadtree region 100. As illustrated, the base quadtree region 100 is an 8×8 square of location buckets, where each of the small squares represents a location bucket. First, the history manager 58 determines whether the number of users in the base quadtree region 100 is greater than the predetermined maximum number of users. In this example, the predetermined maximum number of users is 3. Since the number of users in the base quadtree region 100 is greater than 3, the history manager 58 divides the base quadtree region 100 into four child nodes 102-1 through 102-4, as illustrated in FIG. 13B.
Next, the history manager 58 determines whether the number of users in the child node 102-1 is greater than the predetermined maximum, which again for this example is 3. Since the number of users in the child node 102-1 is greater than 3, the history manager 58 divides the child node 102-1 into four child nodes 104-1 through 104-4, as illustrated in FIG. 13C. The child nodes 104-1 through 104-4 are children of the child node 102-1. The history manager 58 then determines whether the number of users in the child node 104-1 is greater than the predetermined maximum number of users, which again is 3. Since there are more than 3 users in the child node 104-1, the history manager 58 further divides the child node 104-1 into four child nodes 106-1 through 106-N, as illustrated in FIG. 13D.
The history manager 58 then determines whether the number of users in the child node 106-1 is greater than the predetermined maximum number of users, which again is 3. Since the number of users in the child node 106-1 is not greater than the predetermined maximum number of users, the child node 106-1 is identified as a node for the finished quadtree data structure, and the history manager 58 proceeds to process the sibling nodes of the child node 106-1, which are the child nodes 106-2 through 106-4. Since the number of users in each of the child nodes 106-2 through 106-4 is less than the predetermined maximum number of users, the child nodes 106-2 through 106-4 are also identified as nodes for the finished quadtree data structure.
Once the history manager 58 has finished processing the child nodes 106-1 through 106-4, the history manager 58 identifies the parent node of the child nodes 106-1 through 106-4, which in this case is the child node 104-1. The history manager 58 then processes the sibling nodes of the child node 104-1, which are the child nodes 104-2 through 104-4. In this example, the number of users in each of the child nodes 104-2 through 104-4 is less than the predetermined maximum number of users. As such, the child nodes 104-2 through 104-4 are identified as nodes for the finished quadtree data structure.
Once the history manager 58 has finished processing the child nodes 104-1 through 104-4, the history manager 58 identifies the parent node of the child nodes 104-1 through 104-4, which in this case is the child node 102-1. The history manager 58 then processes the sibling nodes of the child node 102-1, which are the child nodes 102-2 through 102-4. More specifically, the history manager 58 determines that the child node 102-2 includes more than the predetermined maximum number of users and, as such, divides the child node 102-2 into four child nodes 108-1 through 108-4, as illustrated in FIG. 13E. Because the number of users in each of the child nodes 108-1 through 108-4 is not greater than the predetermined maximum number of users, the child nodes 108-1 through 108-4 are identified as nodes for the finished quadtree data structure. Then, the history manager 58 proceeds to process the child nodes 102-3 and 102-4. Since the number of users in each of the child nodes 102-3 and 102-4 is not greater than the predetermined maximum number of users, the child nodes 102-3 and 102-4 are identified as nodes for the finished quadtree data structure. Thus, at completion, the quadtree data structure for the base quadtree region 100 includes the child nodes 106-1 through 106-4, the child nodes 104-2 through 104-4, the child nodes 108-1 through 108-4, and the child nodes 102-3 and 102-4, as illustrated in FIG. 13E.
As discussed above, the history manager 58 stores a history object for each of the nodes in the quadtree data structure including at least one user. As such, in this example, the history manager 58 stores history objects for the child nodes 106-2 and 106-3, the child nodes 104-2 and 104-4, the child nodes 108-1 and 108-4, and the child node 102-3. However, no history objects are stored for the nodes that do not have any users (i.e., the child nodes 106-1 and 106-4, the child node 104-3, the child nodes 108-2 and 108-3, and the child node 102-4).
FIG. 14 illustrates the operation of the system 10 of FIGS. 1A and 1B wherein a mobile device is enabled to request and receive historical data from the MAP server 12 according to one embodiment of the present disclosure. Note that the MAP server 12 may operate in a similar manner to process requests for historical data from other devices such as the subscriber device 22, the third-party server 26, and/or the profile server 14. As illustrated, in this embodiment, the MAP application 32-1 of the mobile device 18-1 sends a historical request to the MAP client 30-1 of the mobile device 18-1 (step 1600). In one embodiment, the historical request identifies either a POI or an AOI and a time window. A POI is a geographic point whereas an AOI is a geographic area. In one embodiment, the historical request is for a POI and a time window, where the POI is a POI corresponding to the current location of the user 20-1, a POI selected from a list of POIs defined by the user 20-1 of the mobile device 18-1, a POI selected from a list of POIs defined by the MAP application 32-1 or the MAP server 12, a POI selected by the user 20-1 from a map, a POI implicitly defined via a separate application (e.g., POI is implicitly defined as the location of the nearest Starbucks coffee house in response to the user 20-1 performing a Google search for “Starbucks”), or the like. If the POI is selected from a list of POIs, the list of POIs may include static POIs which may be defined by street addresses or latitude and longitude coordinates, dynamic POIs which may be defined as the current locations of one or more friends of the user 20-1, or both.
In another embodiment, the historical request is for an AOI and a time window, where the AOI may be an AOI of a geographic area of a predefined shape and size centered at the current location of the user 20-1, an AOI selected from a list of AOIs defined by the user 20-1, an AOI selected from a list of AOIs defined by the MAP application 32-1 or the MAP server 12, an AOI selected by the user 20-1 from a map, an AOI implicitly defined via a separate application (e.g., AOI is implicitly defined as an area of a predefined shape and size centered at the location of the nearest Starbucks coffee house in response to the user 20-1 performing a Google search for “Starbucks”), or the like. If the AOI is selected from a list of AOIs, the list of AOIs may include static AOIs, dynamic AOIs which may be defined as areas of a predefined shape and size centered at the current locations of one or more friends of the user 20-1, or both. Note that the POI or AOI of the historical request may be selected by the user 20-1 via the MAP application 32-1. In yet another embodiment, the MAP application 32-1 automatically uses the current location of the user 20-1 as the POI or as a center point for an AOI of a predefined shape and size.
The time window for the historical request may be relative to the current time. For example, the time window may be the last hour, the last day, the last week, the last month, or the like. Alternatively, the time window may be an arbitrary time window selected by the user 20-1 such as, for example, yesterday from 7 pm-9 pm, last Friday, last week, or the like. Note that while in this example the historical request includes a single POI or AOI and a single time window, the historical request may include multiple POIs or AOIs and/or multiple time windows.
In one embodiment, the historical request is made in response to user input from the user 20-1 of the mobile device 18-1. For instance, in one embodiment, the user 20-1 selects either a POI or an AOI and a time window and then instructs the MAP application 32-1 to make the historical request by, for example, selecting a corresponding button on a graphical user interface. In another embodiment, the historical request is made automatically in response to some event such as, for example, opening the MAP application 32-1.
Upon receiving the historical request from the MAP application 32-1, the MAP client 30-1 forwards the historical request to the MAP server 12 (step 1602). Note that the MAP client 30-1 may, in some cases, process the historical request from the MAP application 32-1 before forwarding the historical request to the MAP server 12. For example, if the historical request from the MAP application 32-1 is for multiple POIs/AOIs and/or for multiple time windows, the MAP client 30-1 may process the historical request from the MAP application 32-1 to produce multiple historical requests to be sent to the MAP server 12. For instance, a separate historical request may be produced for each POI/AOI and time window combination. However, for this discussion, the historical request is for a single POI or AOI for a single time window.
Upon receiving the historical request from the MAP client 30-1, the MAP server 12 processes the historical request (step 1604). More specifically, the historical request is processed by the history manager 58 of the MAP server 12. First, the history manager 58 obtains history objects that are relevant to the historical request from the datastore 66 of the MAP server 12. The relevant history objects are those recorded for locations relevant to the POI or AOI and the time window for the historical request. The history manager 58 then processes the relevant history objects to provide historical aggregate profile data for the POI or AOI. In this embodiment, the historical aggregate profile data is based on the user profiles of the anonymous user records in the relevant history objects as compared to the user profile of the user 20-1 or a select subset thereof. In another embodiment, the historical aggregate profile data is based on the user profiles of the anonymous user records in the relevant history objects as compared to a target user profile defined or otherwise specified by the user 20-1 or as compared to one another. Once the MAP server 12 has processed the historical request, the MAP server 12 returns the resulting historical aggregate profile data to the MAP client 30-1 (step 1606). Upon receiving the historical aggregate profile data, the MAP client 30-1 passes the historical aggregate profile data to the MAP application 32-1 (step 1608). The MAP application 32-1 then presents the historical aggregate profile data to the user 20-1 (step 1610).
FIG. 15 begins a discussion of the operation of the crowd analyzer 60 to form crowds of users according to one embodiment of the present disclosure. Specifically, FIG. 15 is a flow chart for a spatial crowd formation process according to one embodiment of the present disclosure. Note that, in one embodiment, this process is performed in response to a request for crowd data for a POI or an AOI. In another embodiment, this process may be performed proactively by the crowd analyzer 60 as, for example, a background process.
First, the crowd analyzer 60 establishes a bounding box for the crowd formation process (step 1700). Note that while a bounding box is used in this example, other geographic shapes may be used to define a bounding region for the crowd formation process (e.g., a bounding circle). In one embodiment, if crowd formation is performed in response to a specific request, the bounding box is established based on the POI or the AOI of the request. If the request is for a POI, then the bounding box is a geographic area of a predetermined size centered at the POI. If the request is for an AOI, the bounding box is the AOI. Alternatively, if the crowd formation process is performed proactively, the bounding box is a bounding box of a predefined size.
The crowd analyzer 60 then creates a crowd for each individual user in the bounding box (step 1702). More specifically, the crowd analyzer 60 queries the datastore 66 of the MAP server 12 to identify users currently located within the bounding box. Then, a crowd of one user is created for each user currently located within the bounding box. Next, the crowd analyzer 60 determines the two closest crowds in the bounding box (step 1704) and determines a distance between the two crowds (step 1706). The distance between the two crowds is a distance between crowd centers of the two crowds. Note that the crowd center of a crowd of one is the current location of the user in the crowd. The crowd analyzer 60 then determines whether the distance between the two crowds is less than an optimal inclusion distance (step 1708). In this embodiment, the optimal inclusion distance is a predefined static distance. If the distance between the two crowds is less than the optimal inclusion distance, the crowd analyzer 60 combines the two crowds (step 1710) and computes a new crowd center for the resulting crowd (step 1712). The crowd center may be computed based on the current locations of the users in the crowd using a center of mass algorithm. At this point the process returns to step 1704 and is repeated until the distance between the two closest crowds is not less than the optimal inclusion distance. At that point, the crowd analyzer 60 discards any crowds with less than three users (step 1714). Note that throughout this disclosure crowds are only maintained if the crowds include three or more users. However, while three users is the preferred minimum number of users in a crowd, the present disclosure is not limited thereto. The minimum number of users in a crowd may be defined as any number greater than or equal to two users.
FIGS. 16A through 16D graphically illustrate the crowd formation process of FIG. 15 for an exemplary bounding box 110. In FIGS. 16A through 16D, crowds are noted by dashed circles, and the crowd centers are noted by cross-hairs (+). As illustrated in FIG. 16A, initially, the crowd analyzer 60 creates crowds 112 through 120 for the users in the geographic area, where, at this point, each of the crowds 112 through 120 includes one user. The current locations of the users are the crowd centers of the crowds 112 through 120. Next, the crowd analyzer 60 determines the two closest crowds and a distance between the two closest crowds. In this example, at this point, the two closest crowds are crowds 114 and 116, and the distance between the two closest crowds 114 and 116 is less than the optimal inclusion distance. As such, the two closest crowds 114 and 116 are combined by merging crowd 116 into crowd 114, and a new crowd center (+) is computed for the crowd 114, as illustrated in FIG. 16B. Next, the crowd analyzer 60 again determines the two closest crowds, which are now crowds 112 and 114. The crowd analyzer 60 then determines a distance between the crowds 112 and 114. Since the distance is less than the optimal inclusion distance, the crowd analyzer 60 combines the two crowds 112 and 114 by merging the crowd 112 into the crowd 114, and a new crowd center (+) is computed for the crowd 114, as illustrated in FIG. 16C. At this point, there are no more crowds separated by less than the optimal inclusion distance. As such, the crowd analyzer 60 discards crowds having less than three users, which in this example are crowds 118 and 120. As a result, at the end of the crowd formation process, the crowd 114 has been formed with three users, as illustrated in FIG. 16D.
FIGS. 17A through 17D illustrate a flow chart for a spatial crowd formation process according to another embodiment of the present disclosure. In this embodiment, the spatial crowd formation process is triggered in response to receiving a location update for one of the users 20-1 through 20-N and is preferably repeated for each location update received for the users 20-1 through 20-N. As such, first, the crowd analyzer 60 receives a location update, or a new location, for a user (step 1800). Assume that, for this example, the location update is received for the user 20-1. In response, the crowd analyzer 60 retrieves an old location of the user 20-1, if any (step 1802). The old location is the current location of the user 20-1 prior to receiving the new location. The crowd analyzer 60 then creates a new bounding box of a predetermined size centered at the new location of the user 20-1 (step 1804) and an old bounding box of a predetermined size centered at the old location of the user 20-1, if any (step 1806). The predetermined size of the new and old bounding boxes may be any desired size. As one example, the predetermined size of the new and old bounding boxes is 40 meters by 40 meters. Note that if the user 20-1 does not have an old location (i.e., the location received in step 1800 is the first location received for the user 20-1), then the old bounding box is essentially null. Also note that while bounding “boxes” are used in this example, the bounding areas may be of any desired shape.
Next, the crowd analyzer 60 determines whether the new and old bounding boxes overlap (step 1808). If so, the crowd analyzer 60 creates a bounding box encompassing the new and old bounding boxes (step 1810). 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 60 may create a 79×79 meter square bounding box encompassing both the new and old bounding boxes.
The crowd analyzer 60 then determines the individual users and crowds relevant to the bounding box created in step 1810 (step 1812). The crowds relevant to the bounding box are crowds that are within or overlap the bounding box (e.g., have at least one user located within the bounding box). The individual users relevant to the bounding box are users that are currently located within the bounding box and not already part of a crowd. Next, the crowd analyzer 60 computes an optimal inclusion distance for individual users based on user density within the bounding box (step 1814). More specifically, in one embodiment, the optimal inclusion distance for individuals, which is also referred to herein as an initial optimal inclusion distance, is set according to the following equation:
$initial_optimal_inclusion_dist = a \cdot \sqrt{\frac{A_{BoundingBox}}{number_of_users}},$
where a is a number between 0 and 1, A_BoundingBoxis an area of the bounding box, and number_of_users is the total number of users in the bounding box. The total number of users in the bounding box includes both individual users that are not already in a crowd and users that are already in a crowd. In one embodiment, a is ⅔.
The crowd analyzer 60 then creates a crowd for each individual user within the bounding box that is not already included in a crowd and sets the optimal inclusion distance for the crowds to the initial optimal inclusion distance (step 1816). At this point, the process proceeds to FIG. 17B where the crowd analyzer 60 analyzes the crowds relevant to the bounding box to determine whether any of the crowd members (i.e., users in the crowds) violate the optimal inclusion distance of their crowds (step 1818). Any crowd member that violates the optimal inclusion distance of his or her crowd is then removed from that crowd (step 1820). The crowd analyzer 60 then creates a crowd of one user for each of the users removed from their crowds in step 1820 and sets the optimal inclusion distance for the newly created crowds to the initial optimal inclusion distance (step 1822).
Next, the crowd analyzer 60 determines the two closest crowds for the bounding box (step 1824) and a distance between the two closest crowds (step 1826). The distance between the two closest crowds is the distance between the crowd centers of the two closest crowds. The crowd analyzer 60 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 1828). 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 60 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 60 may compare the distance between the two closest crowds to an average of the optimal inclusion distances of the two closest crowds.
If the distance between the two closest crowds is not less than the optimal inclusion distance, the process proceeds to step 1838. If the distance between the two closest crowds is less than the optimal inclusion distance, the two closest crowds are combined or merged (step 1830), and a new crowd center for the resulting crowd is computed (step 1832). Again, a center of mass algorithm may be used to compute the crowd center of a crowd. In addition, a new optimal inclusion distance for the resulting crowd is computed (step 1834). In one embodiment, the new optimal inclusion distance for the resulting crowd is computed as:
$average = \frac{1}{n + 1} \cdot (initial_optimal_inclusion_dist + \sum_{i = 1}^{n} d_{i}), optimal_inclusion_dist = average + \sqrt{(\frac{1}{n} \cdot \sum_{i = 1}^{n} {(d_{i} - average)}^{2})},$
where n is the number of users in the crowd and d_iis a distance between the ith user and the crowd center. In other words, the new optimal inclusion distance is computed as the average of the initial optimal inclusion distance and the distances between the users in the crowd and the crowd center plus one standard deviation.
At this point, the crowd analyzer 60 determines whether a maximum number of iterations have been performed (step 1836). The maximum number of iterations is a predefined number that ensures that the crowd formation process does not indefinitely loop over steps 1818 through 1834 or loop over steps 1818 through 1834 more than a desired maximum number of times. If the maximum number of iterations has not been reached, the process returns to step 1818 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 60 discards crowds with less than three users, or members (step 1838) and the process ends.
Returning to step 1808 in FIG. 17A, if the new and old bounding boxes do not overlap, the process proceeds to FIG. 17C and the bounding box to be processed is set to the old bounding box (step 1840). In general, the crowd analyzer 60 then processes the old bounding box in much the same manner as described above with respect to steps 1812 through 1838. More specifically, the crowd analyzer 60 determines the individual users and crowds relevant to the bounding box (step 1842). The crowds relevant to the bounding box are crowds that are within or overlap the bounding box (e.g., have at least one user located within the bounding box). The individual users relevant to the bounding box are users that are currently located within the bounding box and not already part of a crowd. Next, the crowd analyzer 60 computes an optimal inclusion distance for individual users based on user density within the bounding box (step 1844). More specifically, in one embodiment, the optimal inclusion distance for individuals, which is also referred to herein as an initial optimal inclusion distance, is set according to the following equation:
$initial_optimal_inclusion_dist = a \cdot \sqrt{\frac{A_{BoundingBox}}{number_of_users}},$
where a is a number between 0 and 1, A_BoundingBoxis an area of the bounding box, and number_of_users is the total number of users in the bounding box. The total number of users in the bounding box includes both individual users that are not already in a crowd and users that are already in a crowd. In one embodiment, a is ⅔.
The crowd analyzer 60 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 1846). At this point, the crowd analyzer 60 analyzes the crowds for the bounding box to determine whether any crowd members (i.e., users in the crowds) violate the optimal inclusion distance of their crowds (step 1848). Any crowd member that violates the optimal inclusion distance of his or her crowd is then removed from that crowd (step 1850). The crowd analyzer 60 then creates a crowd of one user for each of the users removed from their crowds in step 1850 and sets the optimal inclusion distance for the newly created crowds to the initial optimal inclusion distance (step 1852).
Next, the crowd analyzer 60 determines the two closest crowds in the bounding box (step 1854) and a distance between the two closest crowds (step 1856). The distance between the two closest crowds is the distance between the crowd centers of the two closest crowds. The crowd analyzer 60 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 1858). 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 60 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 60 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 less than the optimal inclusion distance, the two closest crowds are combined or merged (step 1860), and a new crowd center for the resulting crowd is computed (step 1862). Again, a center of mass algorithm may be used to compute the crowd center of a crowd. In addition, a new optimal inclusion distance for the resulting crowd is computed (step 1864). As discussed above, in one embodiment, the new optimal inclusion distance for the resulting crowd is computed as:
$average = \frac{1}{n + 1} \cdot (initial_optimal_inclusion_dist + \sum_{i = 1}^{n} d_{i}), optimal_inclusion_dist = average + \sqrt{(\frac{1}{n} \cdot \sum_{i = 1}^{n} {(d_{i} - average)}^{2})},$
where n is the number of users in the crowd and d_iis a distance between the ith user and the crowd center. In other words, the new optimal inclusion distance is computed as the average of the initial optimal inclusion distance and the distances between the users in the crowd and the crowd center plus one standard deviation.
At this point, the crowd analyzer 60 determines whether a maximum number of iterations have been performed (step 1866). If the maximum number of iterations has not been reached, the process returns to step 1848 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 60 discards crowds with less than three users, or members (step 1868). The crowd analyzer 60 then determines whether the crowd formation process for the new and old bounding boxes is done (step 1870). In other words, the crowd analyzer 60 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 1872), and the process returns to step 1842 and is repeated for the new bounding box. Once both the new and old bounding box have been processed, the crowd formation process ends.
FIGS. 18A through 18D graphically illustrate the crowd formation process of FIGS. 17A through 17D for a scenario where the crowd formation process is triggered by a location update for a user having no old location. In this scenario, the crowd analyzer 60 creates a new bounding box 122 for the new location of the user, and the new bounding box 122 is set as the bounding box to be processed for crowd formation. Then, as illustrated in FIG. 18A, the crowd analyzer 60 identifies all individual users currently located within the bounding box 122 and all crowds located within or overlapping the bounding box. In this example, crowd 124 is an existing crowd relevant to the bounding box 122. Crowds are indicated by dashed circles, crowd centers are indicated by cross-hairs (+), and users are indicated as dots. Next, as illustrated in FIG. 18B, the crowd analyzer 60 creates crowds 126 through 130 of one user for the individual users, and the optional inclusion distances of the crowds 126 through 130 are set to the initial optimal inclusion distance. As discussed above, the initial optimal inclusion distance is computed by the crowd analyzer 60 based on a density of users within the bounding box 122.
The crowd analyzer 60 then identifies the two closest crowds 126 and 128 in the bounding box 122 and determines a distance between the two closest crowds 126 and 128. In this example, the distance between the two closest crowds 126 and 128 is less than the optimal inclusion distance. As such, the two closest crowds 126 and 128 are merged and a new crowd center and new optimal inclusion distance are computed, as illustrated in FIG. 18C. The crowd analyzer 60 then repeats the process such that the two closest crowds 126 and 130 in the bounding box 122 are again merged, as illustrated in FIG. 18D. At this point, the distance between the two closest crowds 124 and 126 is greater than the appropriate optimal inclusion distance. As such, the crowd formation process is complete.
FIGS. 19A through 19F graphically illustrate the crowd formation process of FIGS. 17A through 17D for a scenario where the new and old bounding boxes overlap. As illustrated in FIG. 19A, a user moves from an old location to a new location, as indicated by an arrow. The crowd analyzer 60 receives a location update for the user giving the new location of the user. In response, the crowd analyzer 60 creates an old bounding box 132 for the old location of the user and a new bounding box 134 for the new location of the user. Crowd 136 exists in the old bounding box 132, and crowd 138 exists in the new bounding box 134.
Since the old bounding box 132 and the new bounding box 134 overlap, the crowd analyzer 60 creates a bounding box 140 that encompasses both the old bounding box 132 and the new bounding box 134, as illustrated in FIG. 19B. In addition, the crowd analyzer 60 creates crowds 142 through 148 for individual users currently located within the bounding box 140. The optimal inclusion distances of the crowds 142 through 148 are set to the initial optimal inclusion distance computed by the crowd analyzer 60 based on the density of users in the bounding box 140.
Next, the crowd analyzer 60 analyzes the crowds 136, 138, and 142 through 148 to determine whether any members of the crowds 136, 138, and 142 through 148 violate the optimal inclusion distances of the crowds 136, 138, and 142 through 148. In this example, as a result of the user leaving the crowd 136 and moving to his new location, both of the remaining members of the crowd 136 violate the optimal inclusion distance of the crowd 136. As such, the crowd analyzer 60 removes the remaining users from the crowd 136 and creates crowds 150 and 152 of one user each for those users, as illustrated in FIG. 19C.
The crowd analyzer 60 then identifies the two closest crowds in the bounding box 140, which in this example are the crowds 146 and 148. Next, the crowd analyzer 60 computes a distance between the two crowds 146 and 148. In this example, the distance between the two crowds 146 and 148 is less than the initial optimal inclusion distance and, as such, the two crowds 146 and 148 are combined. In this example, crowds are combined by merging the smaller crowd into the larger crowd. Since the two crowds 146 and 148 are of the same size, the crowd analyzer 60 merges the crowd 148 into the crowd 146, as illustrated in FIG. 19D. A new crowd center and new optimal inclusion distance are then computed for the crowd 146.
At this point, the crowd analyzer 60 repeats the process and determines that the crowds 138 and 144 are now the two closest crowds. In this example, the distance between the two crowds 138 and 144 is less than the optimal inclusion distance of the larger of the two crowds 138 and 144, which is the crowd 138. As such, the crowd 144 is merged into the crowd 138 and a new crowd center and optimal inclusion distance are computed for the crowd 138, as illustrated in FIG. 19E. 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 60 discards any crowds having less than three members, as illustrated in FIG. 19F. In this example, the crowds 142, 146, 150, and 152 have less than three members and are therefore removed. The crowd 138 has three or more members and, as such, is not removed. At this point, the crowd formation process is complete.
FIGS. 20A through 20E graphically illustrate the crowd formation process of FIGS. 17A through 17D in a scenario where the new and old bounding boxes do not overlap. As illustrated in FIG. 20A, in this example, the user moves from an old location to a new location. The crowd analyzer 60 creates an old bounding box 154 for the old location of the user and a new bounding box 156 for the new location of the user. Crowds 158 and 160 exist in the old bounding box 154, and crowd 162 exists in the new bounding box 156. In this example, since the old and new bounding boxes 154 and 156 do not overlap, the crowd analyzer 60 processes the old and new bounding boxes 154 and 156 separately.
More specifically, as illustrated in FIG. 20B, as a result of the movement of the user from the old location to the new location, the remaining users in the crowd 158 no longer satisfy the optimal inclusion distance for the crowd 158. As such, the remaining users in the crowd 158 are removed from the crowd 158, and crowds 164 and 166 of one user each are created for the removed users as shown in FIG. 20C. In this example, no two crowds in the old bounding box 154 are close enough to be combined. As such, processing of the old bounding box 154 is complete, and the crowd analyzer 60 proceeds to process the new bounding box 156.
As illustrated in FIG. 20D, processing of the new bounding box 156 begins by the crowd analyzer 60 creating a crowd 168 of one user for the user. The crowd analyzer 60 then identifies the crowds 162 and 168 as the two closest crowds in the new bounding box 156 and determines a distance between the two crowds 162 and 168. In this example, the distance between the two crowds 162 and 168 is less than the optimal inclusion distance of the larger crowd, which is the crowd 162. As such, the crowd analyzer 60 combines the crowds 162 and 168 by merging the crowd 168 into the crowd 162, as illustrated in FIG. 20E. A new crowd center and new optimal inclusion distance are then computed for the crowd 162. At this point, the crowd formation process is complete.
Before proceeding, a variation of the spatial formation process discussed above with respect to FIGS. 17A through 17D, 18A through 18D, 19A through 19F, and 20A through 20E will be described. In this alternative embodiment, a location accuracy of the location update from the user received in step 1800 is considered. More specifically, in step 1800, the location update received by the MAP server 12 includes the updated location of the user 20-1 as well as a location accuracy for the location of the user 20-1, which may be expressed as, for example, a radius in meters from the location of the user 20-1. In the embodiment where the location of the user 20-1 is obtained from a GPS receiver of the mobile device 18-1, the location accuracy of the location of the user 20-1 may be provided by the GPS receiver or derived from data from the GPS receiver as will be appreciated by one having ordinary skill in the art.
Then, in steps 1802 and 1804, sizes of the new and old bounding boxes centered at the new and old locations of the user 20-1 are set as a function of the location accuracy of the new and old locations of the user 20-1. If the new location of the user 20-1 is inaccurate, then the new bounding box will be large. If the new location of the user 20-1 is accurate, then the new bounding box will be small. For example, the length and width of the new bounding box may be set to M times the location accuracy of the new location of the user 20-1, where the location accuracy is expressed as a radius in meters from the new location of the user 20-1. The number M may be any desired number. For example, the number M may be 5. In a similar manner, the location accuracy of the old location of the user 20-1 may be used to set the length and width of the old bounding box.
In addition, the location accuracy may be considered when computing the initial optimal inclusion distances used for crowds of one user in steps 1814 and 1844. As discussed above, the initial optimal inclusion distance is computed based on the following equation:
$initial_optimal_inclusion_dist = a \cdot \sqrt{\frac{A_{BoundingBox}}{number_of_users}},$
where a is a number between 0 and 1, A_BoundingBoxis an area of the bounding box, and number_of_users is the total number of users in the bounding box. The total number of users in the bounding box includes both individual users that are not already in a crowd and users that are already in a crowd. In one embodiment, a is ⅔. However, if the computed initial optimal inclusion distance is less than the location accuracy of the current location of the individual user in a crowd, then the location accuracy, rather than the computed value, is used for the initial optimal inclusion distance for that crowd. As such, as location accuracy decreases, crowds become larger and more inclusive. In contrast, as location accuracy increases, crowds become smaller and less inclusive. In other words, the granularity with which crowds are formed is a function of the location accuracy.
Likewise, when new optimal inclusion distances for crowds are recomputed in steps 1834 and 1864, location accuracy may also be considered. As discussed above, the new optimal inclusion distance may first be computed based on the following equation:
$average = \frac{1}{n + 1} \cdot (initial_optimal_inclusion_dist + \sum_{i = 1}^{n} d_{i}), optimial_inclusion_dist = average + \sqrt{(\frac{1}{n} \cdot \sum_{i = 1}^{n} {(d_{i} - average)}^{2})},$
where n is the number of users in the crowd and d_iis a distance between the ith user and the crowd center. In other words, the new optimal inclusion distance is computed as the average of the initial optimal inclusion distance and the distances between the users in the crowd and the crowd center plus one standard deviation. However, if the computed value for the new optimal inclusion distance is less than an average location accuracy of the users in the crowd, the average location accuracy of the users in the crowd, rather than the computed value, is used as the new optimal inclusion distance.
FIG. 21 illustrates the operation of the system 10 of FIGS. 1A and 1B to enable the mobile devices 18-1 through 18-N to request crowd data for currently formed crowds according to one embodiment of the present disclosure. Note that while in this example the request is initiated by the MAP application 32-1 of the mobile device 18-1, this discussion is equally applicable to the MAP applications 32-2 through 32-N of the other mobile devices 18-2 through 18-N. In addition, in a similar manner, requests may be received from the third-party applications 34-1 through 34-N. Still further, the MAP server 12 may process crowd requests from other devices such as the subscriber device 22, the third-party server 26, and/or the profile server 14.
First, the MAP application 32-1 sends a crowd request to the MAP client 30-1 (step 1900). The crowd request is a request for crowd data for crowds currently formed near a specified POI or within a specified AOI. The crowd request may be initiated by the user 20-1 of the mobile device 18-1 via the MAP application 32-1 or may be initiated automatically by the MAP application 32-1 in response to an event such as, for example, start-up of the MAP application 32-1, movement of the user 20-1, or the like. In one embodiment, the crowd request is for a POI, where the POI is a POI corresponding to the current location of the user 20-1, a POI selected from a list of POIs defined by the user 20-1, a POI selected from a list of POIs defined by the MAP application 32-1 or the MAP server 12, a POI selected by the user 20-1 from a map, a POI implicitly defined via a separate application (e.g., POI is implicitly defined as the location of the nearest Starbucks coffee house in response to the user 20-1 performing a Google search for “Starbucks”), or the like. If the POI is selected from a list of POIs, the list of POIs may include static POIs which may be defined by street addresses or latitude and longitude coordinates, dynamic POIs which may be defined as the current locations of one or more friends of the user 20-1, or both. Note that in some embodiments, the user 20-1 may be enabled to define a POI by selecting a crowd center of a crowd as a POI, where the POI would thereafter remain static at that point and would not follow the crowd.
In another embodiment, the crowd request is for an AOI, where the AOI may be an AOI of a predefined shape and size centered at the current location of the user 20-1, an AOI selected from a list of AOIs defined by the user 20-1, an AOI selected from a list of AOIs defined by the MAP application 32-1 or the MAP server 12, an AOI selected by the user 20-1 from a map, an AOI implicitly defined via a separate application (e.g., AOI is implicitly defined as an area of a predefined shape and size centered at the location of the nearest Starbucks coffee house in response to the user 20-1 performing a Google search for “Starbucks”), or the like. If the AOI is selected from a list of AOIs, the list of AOIs may include static AOIs, dynamic AOIs which may be defined as areas of a predefined shape and size centered at the current locations of one or more friends of the user 20-1, or both. Note that in some embodiments, the user 20-1 may be enabled to define an AOI by selecting a crowd such that an AOI is created of a predefined shape and size centered at the crowd center of the selected crowd. The AOI would thereafter remain static and would not follow the crowd. The POI or the AOI of the crowd request may be selected by the user 20-1 via the MAP application 32-1. In yet another embodiment, the MAP application 32-1 automatically uses the current location of the user 20-1 as the POI or as a center point for an AOI of a predefined shape and size.
Upon receiving the crowd request, the MAP client 30-1 forwards the crowd request to the MAP server 12 (step 1902). Note that in some embodiments, the MAP client 30-1 may process the crowd request before forwarding the crowd request to the MAP server 12. For example, in some embodiments, the crowd request may include more than one POI or more than one AOI. As such, the MAP client 30-1 may generate a separate crowd request for each POI or each AOI.
In response to receiving the crowd request from the MAP client 30-1, the MAP server 12 identifies one or more crowds relevant to the crowd request (step 1904). More specifically, in one embodiment, the crowd analyzer 60 performs a crowd formation process such as that described above in FIG. 15 to form one or more crowds relevant to the POI or the AOI of the crowd request. In another embodiment, the crowd analyzer 60 proactively forms crowds using a process such as that described above in FIGS. 17A through 17D and stores corresponding crowd records in the datastore 66 of the MAP server 12. Then, rather than forming the relevant crowds in response to the crowd request, the crowd analyzer 60 queries the datastore 66 to identify the crowds that are relevant to the crowd request. The crowds relevant to the crowd request may be those crowds within or intersecting a bounding region, such as a bounding box, for the crowd request. If the crowd request is for a POI, the bounding region is a geographic region of a predefined shape and size centered at the POI. If the crowd request is for an AOI, the bounding region is the AOI.
Once the crowd analyzer 60 has identified the crowds relevant to the crowd request, the MAP server 12 generates crowd data for the identified crowds (step 1906). As discussed below in detail, the crowd data for the identified crowds may include aggregate profiles for the crowds, information characterizing the crowds, or both. In addition, the crowd data may include spatial information defining the locations of the crowds, the number of users in the crowds, the amount of time the crowds have been located at or near the POI or within the AOI of the crowd request, or the like. The MAP server 12 then returns the crowd data to the MAP client 30-1 (step 1908).
Upon receiving the crowd data, the MAP client 30-1 forwards the crowd data to the MAP application 32-1 (step 1910). Note that in some embodiments the MAP client 30-1 may process the crowd data before sending the crowd data to the MAP application 32-1. The MAP application 32-1 then presents the crowd data to the user 20-1 (step 1912). The manner in which the crowd data is presented depends on the particular implementation of the MAP application 32-1. In one embodiment, the crowd data is overlaid upon a map. For example, the crowds may be represented by corresponding indicators overlaid on a map. The user 20-1 may then select a crowd in order to view additional crowd data regarding that crowd such as, for example, the aggregate profile of that crowd, characteristics of that crowd, or the like.
Note that in one embodiment, the MAP application 32-1 may operate to roll-up the aggregate profiles for multiple crowds into a rolled-up aggregate profile for those crowds. The rolled-up aggregate profile may be the average of the aggregate profiles of the crowds. For example, the MAP application 32-1 may roll-up the aggregate profiles for multiple crowds at a POI and present the rolled-up aggregate profile for the multiple crowds at the POI to the user 20-1. In a similar manner, the MAP application 32-1 may provide a rolled-up aggregate profile for an AOI. In another embodiment, the MAP server 12 may roll-up crowds for a POI or an AOI and provide the rolled-up aggregate profile in addition to or as an alternative to the aggregate profiles for the individual crowds.
FIGS. 22 through 31 describe the operation of the profile augmentation function 40 of FIGS. 1A and 1B according to several different embodiments of the present disclosure. More specifically, FIG. 22 illustrates the operation of the profile augmentation function 40 to augment a user profile of a subject user based on aggregate profile data for a crowd of users located at or near a current location of the subject user according to one embodiment of the present disclosure. First, the profile augmentation function 40 gets, or obtains, a current location of a subject user (step 2000). Depending on the particular embodiment, the subject user may be a system-user, which is one of the users 20-1 through 20-N of the mobile devices 18-1 through 18-N or the subscriber 24 of the subscriber device 22, or a third-party user, which may be a user associated with the profile server 14 or the third-party server 26 that is not one of the system-users. The current location of the subject user may be obtained using any suitable technique. For example, if the subject user is user 20-1, the current location of the user 20-1 is preferably obtained from the mobile device 18-1 or the location server 16. As another example, if the subject user is a third-party user, the current location of the third-party user may be obtained from a location-aware device of the third-party user.
Next, the profile augmentation function 40 obtains an aggregate profile for a crowd of users currently located at or near the current location of the subject user (step 2002). Crowds of users currently located at or near the current location of the subject user are referred to herein as crowds that are currently located at locations that are relevant to the current location of the subject user. More specifically, depending on the particular implementation, the crowd currently located at or near the current location of the subject user is a crowd in which the subject user is included, a crowd closest to the current location of the subject user, or a crowd that is within a geographic region of a predefined shape and size encompassing (e.g., centered at) the current location of the subject user. The aggregate profile of the crowd is preferably generated by comparing the user profiles of the users in the crowd to one another, and the aggregate profile of the crowd preferably includes a number of keywords and, for each keyword, a number of user matches, or occurrences, for that keyword in the user profiles of the users in the crowd. The profile augmentation function 40 then augments a user profile of the subject user based on the aggregate profile of the crowd (step 2004).
FIG. 23 illustrates step 2004 of FIG. 22 in more detail according to one embodiment of the present disclosure. In this embodiment, the user profile of the subject user includes a number of keywords and a probability assigned to each of the keywords. The probability assigned to a keyword in the user profile of the subject user is a probability that the subject user has an interest in the keyword. Initially, the user profile of the subject user includes zero or more keywords entered by the subject user, where each of these keywords is assigned a fixed probability of 100%. Over time, as a number of iterations of the profile augmentation process of FIG. 22 are performed, additional keywords are added to the user profile of the subject user and probabilities are assigned to those keywords. As discussed below, the probabilities of these additional keywords are determined based on questions asked of the subject user and/or the number of user matches, or occurrences, for the keywords from one or more aggregate profiles of crowds located at or near the subject user.
More specifically, in order to augment the user profile of the subject user based on the aggregate profile of the crowd at or near the current location of the subject user, the profile augmentation function 40 identifies, or selects, a predetermined number (N_MAX) of keywords from the aggregate profile of the crowd having the highest number of user matches and not having a fixed probability in the user profile of the subject user (step 2100). Keywords in the user profile of the subject user having fixed probabilities are keywords that have been entered by the subject user into the user profile. In addition, as discussed below, the keywords having fixed probabilities may also include keywords in which the subject user has expressed an interest or disinterest in response to questions from the profile augmentation function 40.
For example, assume that the user profile of the subject user is:
Keyword Probability (%)

Tennis 100 (fixed)

Politics 100 (fixed)

Also, assume that the aggregate profile for the crowd is:
Keyword User Matches

Tennis 5

Sports 3

China 2

Coffee 2

Photography 1

As such, the profile augmentation function 40 selects, at most, the predetermined number (N_MAX) of the keywords from the aggregate profile having the highest number of user matches and not having a fixed probability in the user profile of the subject user. In this example, assume that N_MAXis three (3). Here, the three keywords having the highest number of user matches and not having fixed probabilities in the user profile of the subject user are the keywords Sports, China, and Coffee. Note that the keyword Tennis has a fixed probability in the user profile of the subject user and as such is not selected by the profile augmentation function 40.
Next, the profile augmentation function 40 generates a question for each of the keywords identified in step 2100 (step 2102). In general, the questions are automatically generated in order to determine whether the subject user is interested in the identified keywords. Returning to our example, if the identified keywords are Sports, China, and Coffee, the profile augmentation function 40 may generate the following questions: “Do you have an interest in sports?,” “Do you have an interest in China?,” and “Do you like coffee?.” Alternatively, the questions for the identified keywords may be a list of the identified keywords where the subject user will be enabled to select “Yes” or “No” for each of the identified keywords, or the like. The profile augmentation function 40 then provides the questions for the identified keywords to the subject user (step 2104), and receives answers to the questions from the subject user (2106). Note that the subject user may choose to answer all, some, or none of the questions. Lastly, the profile augmentation function 40 updates the user profile of the subject user based on any answers received from the subject user and, in this embodiment, the number of user matches for the keywords in the aggregate profile of the crowd (step 2108).
FIG. 24 illustrates step 2108 of FIG. 23 in more detail according to one embodiment of the present disclosure. Specifically, FIG. 24 illustrates the operation of the profile augmentation function 40 to update the user profile of the subject user based on any answers received from the subject user and the number of user matches for the keywords in the aggregate profile of the crowd. First, the profile augmentation function 40 gets the next keyword from the aggregate profile (step 2200). Note that for the first iteration, the next keyword is the first keyword in the aggregate profile. Next, the profile augmentation function 40 determines whether a question was asked for the keyword and whether an answer to the question was received from the subject user (step 2202). If not, the profile augmentation function 40 determines whether the keyword is already in the user profile of the subject user (step 2204). The keyword may already be in the user profile if the subject user entered the keyword into the user profile or if the keyword was added to the user profile of the subject user during a previous iteration of the profile augmentation process. If the keyword is not already in the user profile of the subject user, the profile augmentation function 40 computes a probability for the keyword based on the number of user matches for the keyword in the aggregate profile (step 2206). In one embodiment, the probability score is computed based on the following equation:
$PROBABILITY = \frac{USER_MATCHES}{TOTAL_USER_MATCHES} \times 100,$
where PROBABILITY is the probability of the keyword, USER_MATCHES is the number of user matches for the keyword from the aggregate profile, and TOTAL_USER_MATCHES is the sum of the number of user matches for all of the keywords in the aggregate profile. Note that the equation above is exemplary and is not intended to limit the scope of the present disclosure. The profile augmentation function 40 then adds the keyword and the probability computed for the keyword to the user profile of the subject user (step 2208). At this point, the process proceeds to step 2222, which is described below.
Returning to step 2204, if the keyword is already in the user profile of the subject user, the profile augmentation function 40 determines whether the keyword has a fixed probability in the user profile of the subject user (step 2210).
In this embodiment, the keyword will have a fixed probability if the subject user added the keyword to the user profile or if the subject user previously answered a question for the keyword. More specifically, in this embodiment, the keyword will have a fixed probability of 100% if the subject user previously added the keyword to the user profile, a fixed probability of 100% if the subject user answered a question for the keyword in a previous iteration of the profile augmentation process in a manner that indicated that the subject user has an interest in the keyword, or a fixed probability of 0% if the subject user answered a question for the keyword in a previous iteration of the profile augmentation process in a manner that indicated that the subject user does not have an interest in the keyword. If the keyword does not have a fixed probability, the profile augmentation function 40 computes a new probability for the keyword based on the probability for the keyword in the user profile (i.e., the old probability for the keyword) and the number of user matches for the keyword in the aggregate profile (step 2212). In one embodiment, the new probability is computed based on the following equation:
PROBABILITY_NEW=AVG(PROBABILITY_OLD,PROBABILITY_TEMP),
where
${PROBABILITY}_{TEMP} = \frac{USER_MATCHES}{TOTAL_USER_MATCHES} \times 100,$
and PROBABILITY_NEWis the new probability for the keyword, PROBABILITY_OLDis the old probability for the keyword, PROBABILITY_TEMPis a temporary probability used for purposes of this exemplary calculation, USER_MATCHES is the number of user matches for the keyword from the aggregate profile, and TOTAL_USER_MATCHES is the sum of the number of user matches for all of the keywords in the aggregate profile. The function AVG(x,y) is the average of the values x and y. Note that other techniques may be used to combine the old probability and the temporary probability to provide the new probability (e.g., summing, weighted averaging, or the like). The profile augmentation function 40 then updates the user profile of the subject user to include the new probability for the keyword (step 2214). At this point, the process proceeds to step 2222, which is described below.
Returning to step 2202, if a question was asked for the keyword and an answer was received from the subject user for the question asked for the keyword, the profile augmentation function 40 determines whether the keyword is already in the user profile of the subject user (step 2216). If not, the profile augmentation function 40 adds the keyword to the user profile of the subject user along with a fixed probability for the keyword that reflects the answer given by the subject user to the corresponding question (step 2218). In this example, the fixed probability for the keyword is 100% if the subject user gave a positive answer indicating that the subject user has an interest in the keyword. In contrast, the fixed probability for the keyword is 0% if the subject user gave a negative answer indicating that the subject user does not have an interest in the keyword. At this point, the process proceeds to step 2222, which is described below.
Returning to step 2216, if the keyword is already in the user profile of the subject user, then the profile augmentation function 40 updates the user profile of the subject user with a fixed probability for the keyword that reflects the answer given by the subject user to the corresponding question (step 2220). Again, in this example, the fixed probability for the keyword is 100% if the subject user gave a positive answer indicating that the subject user has an interest in the keyword. In contrast, the fixed probability for the keyword is 0% if the subject user gave a negative answer indicating that the subject user does not have an interest in the keyword. The process then proceeds to step 2222, which is described below.
At this point, whether proceeding from step 2208, step 2210, step 2214, step 2218, or step 2220, the profile augmentation function 40 determines whether that last keyword in the aggregate profile has been processed (step 2222). If not, the process returns to step 2200 and is repeated for the next keyword in the aggregate profile. Once all of the keywords in the aggregate profile have been processed, the process is complete.
Again, as an example, assume that the user profile of the subject user is:
Keyword Probability (%)

Tennis 100 (fixed)

Politics 100 (fixed)

Also, assume that the aggregate profile is:
Keyword User Matches

Tennis 5

Sports 3

China 2

Coffee 2

Photography 1

and that questions were asked for the keywords Sports, China, and Coffee from the aggregate profile. Further assume, that the subject user answered the question for Sports in a manner that indicated that the subject user has an interest in sports, answered the question for Coffee in a manner that indicated that the subject user is not interested in Coffee, and did not answer the question for China. As such, using the process described above, the user profile of the subject user is updated as follows:
Keyword Probability

Tennis 100 (fixed)

Politics 100 (fixed)

Sports 100 (fixed)

China 15

Photography 8

Coffee 0 (fixed)

Note that future iterations of the profile augmentation process may be performed in order to further augment the user profile of the subject user.
For instance, if the subject user thereafter moves to a new location and the process is repeated, an aggregate profile for a new crowd that is at or near the new location of the subject user may be:
Keyword User Matches

Sports 5

Photography 3

Hiking 2

Technology 2

Farming 1

In this example, assuming again that N_MAXis 3, then Photography, Hiking, and Technology are selected and corresponding questions are provided to the subject user. For this example, assume that the subject user does not respond to any of the questions. As such, since the keyword Photography is already in the user profile of the subject user, a new probability is computed for the keyword Photography based on the old probability which in this case is 8 and the number of user matches for the keyword Photography in the aggregate profile for the new crowd. The keywords Hiking and Technology are not already in the user profile of the subject user. As such, the keywords Hiking and Technology are added to the user profile of the subject user along with corresponding probabilities computed based on the number of user matches for those keywords in the aggregate profile of the new crowd. As such, the updated user profile of the subject user may then be:


	Keyword	Probability

	Tennis	100 (fixed)
	Politics	100 (fixed)
	Sports	100 (fixed)
	China	15
	Photography	15
	Hiking	15
	Technology	15
	Farming	8
	Coffee	0 (fixed)

FIG. 25 illustrates step 2004 of FIG. 22 in more detail according to another embodiment of the present disclosure. In this embodiment, the user profile of the subject user includes a number of keywords and a probability assigned to each of the keywords. Initially, the user profile of the subject user includes zero or more keywords entered by the subject user, where each of these keywords is assigned a fixed probability of 100%. Over time, as a number of iterations of the profile augmentation process of FIG. 22 are performed, additional keywords are added to the user profile of the subject user. As discussed below, the probabilities of these additional keywords are determined based on questions asked of the subject user and/or the number of user matches for the keywords for aggregate profiles of crowds located at or near the subject user.
More specifically, in order to augment the user profile of the subject user based on the aggregate profile of the crowd at or near the current location of the subject user, the profile augmentation function 40 first provides a preliminary question to the subject user (step 2300). The preliminary question is preferably an open-ended question regarding the interests of the subject user. For example, the preliminary question may be “What is one of your current topics of interest?.” The profile augmentation function 40 then receives an answer from the subject user (step 2302). The answer from the subject user is referred to herein as a topic of interest. The profile augmentation function 40 then selects a desired number of keywords from the aggregate profile that have the highest number of user matches, do not have fixed probabilities in the user profile of the subject user, and have the highest affinity to the topic of interest provided by the subject user (step 2304). Affinity between the topic of interest and the keywords in the aggregate profile may be determined using any suitable technique for determining the affinity between two keywords. For example, an ontology or similar data structure that defines relationships between words and topics may be used to determine the degree of relationship between two keywords (e.g., degrees of separation of the two keywords in the ontology), where the degree of relationship is utilized as the affinity of the two keywords.
For example, assume that the user profile of the subject user is:
Keyword Probability (%)

Tennis 100 (fixed)

Politics 100 (fixed)

Also, assume that the aggregate profile is:
Keyword User Matches

Tennis 5

Sports 3

China 2

Coffee 2

Europe 2

Photography 1

Further assume that topic of interest provided by the subject user is History and that the desired number of keywords to be selected is three (3). As such, the profile augmentation function 40 selects the Sports keyword because it has the highest number of user matches and is not already included in the user profile of the subject user with a fixed probability. The profile augmentation function 40 then selects two more keywords from the keywords China, Coffee, and Europe based on the affinities of those keywords to the topic of interest, which is History. As such, in this example, the profile augmentation function 40 selects the keywords China and Europe because they have higher affinities with the topic of interest than the keyword Coffee.
Next, the profile augmentation function 40 generates a question for each of the selected keywords (step 2306). In general, the questions are automatically generated in order to determine whether the subject user is interested in the identified keywords. Returning to our example, if the identified keywords are Sports, China, and Europe, the profile augmentation function 40 may generate the following questions: “Do you have an interest in sports?,” “Do you have an interest in China?,” and “Do you have an interest in Europe?.” Alternatively, the questions for the identified keywords may be a list of the identified keywords where the subject user will be enabled to select “Yes” or “No” for each of the identified keywords, or the like. The profile augmentation function 40 then provides the questions for the identified keywords to the subject user (step 2308), and receives answers to the questions from the subject user (2310). Note that the subject user may choose to answer all, some, or none of the questions. Lastly, the profile augmentation function 40 updates the user profile of the subject user based on any answers received from the subject user and, in this embodiment, the number of user matches for the keywords in the aggregate profile of the crowd (step 2312). More specifically, in one embodiment, the profile augmentation function 40 updates the user profile of the subject user using the process of FIG. 24 described above.
FIG. 26 illustrates the operation of the profile augmentation function 40 to augment a user profile of a user based on an aggregate profile of a crowd for the embodiment of FIG. 1A wherein the profile augmentation function 40 is implemented on the MAP server 12. First, the MAP server 12 obtains a user profile of a system user (step 2400). The system user is one of the users 20-1 through 20-N. As discussed above, the MAP server 12 may obtain the user profile of the system user from the profile server 14, but is not limited thereto. Next, the MAP server 12 obtains the current location of the system user in the manner described above and identifies a crowd at or near the current location of the system user (steps 2402 and 2404). The MAP server 12 may identify the crowd by reactively performing the crowd formation process of FIG. 15 for a geographic region encompassing the current location of the system user. Alternatively, the MAP server 12 may proactively form crowds using the process of FIG. 15 or FIGS. 17A through 17D. In this case, the MAP server 12 identifies the crowd at or near the current location of the system user by querying the datastore 66 of the MAP server 12. For example, the crowd at or near the current location of the system user may be a crowd in which the system user is currently included or a crowd that is closest to the current location of the system user.
Next, the MAP server 12 obtains an aggregate profile for the crowd (step 2406). More specifically, the MAP server 12 compares the user profiles of the users in the crowd to one another to generate the aggregate profile of the crowd. As discussed above, in this embodiment, the aggregate profile of the crowd preferably includes a number of keywords and, for each keyword, a number of user matches for the keyword in the user profiles of the users in the crowd. The profile augmentation function 40, which for this embodiment is implemented on the MAP server 12, then augments the user profile of the system user based on the aggregate profile of the crowd in the manner described above (step 2408). Preferably, the process returns to step 2402 and is periodically or otherwise repeated to further augment the user profile of the system user over time.
FIG. 27 illustrates the operation of the profile augmentation function 40 for the embodiment of FIG. 1B. In this embodiment, the profile augmentation function 40 is implemented on the profile server 14, one of the mobile devices 18-1 through 18-N, the subscriber device 22, or the third-party server 26. First, the profile augmentation function 40 sends an aggregate profile request to the MAP server 12 that includes the current location of the subject user (step 2500). Alternatively, if the subject user is a system user (i.e., one of the users 20-1 through 20-N), the aggregate profile request may identify the subject user, where the MAP server 12 then obtains the current location of the subject user. In this embodiment, the subject user may be a system user or a third-party user depending on the particular implementation. In response, the MAP server 12 identifies a crowd that is at or near the current location of the subject user (step 2502). Again, the crowd may be formed proactively or reactively, as described above. The crowd that is at or near the current location of the subject user is preferably a crowd that is at or closest to the current location of the subject user. If the subject user is a system user, the crowd that is at or near the current location of the subject user may be a crowd in which the subject user is included. The MAP server 12 then obtains an aggregate profile for the crowd (step 2504). More specifically, the MAP server 12 compares the user profiles of the users in the crowd to one another to generate the aggregate profile of the crowd. As discussed above, in this embodiment, the aggregate profile of the crowd preferably includes a number of keywords and, for each keyword, a number of user matches for the keyword in the user profiles of the users in the crowd. Next, the MAP server returns the aggregate profile of the crowd to the profile augmentation function 40 (step 2506). The profile augmentation function 40 then augments the user profile of the subject user based on the aggregate profile of the crowd in the manner described above (step 2508).
FIGS. 28 through 32 illustrate the operation of the profile augmentation function 40 according to another embodiment of the present disclosure. In this embodiment, the profile augmentation function 40 augments a user profile of a subject user based on a historical aggregate profile for users historically located at locations relevant to the current location of the subject user. The locations relevant to the current location of the subject user are locations that are at or near the current location of the subject user. Note that while described separately herein, the profile augmentation function 40 may augment the user profile of a subject user based on both an aggregate profile of a crowd at or near the current location of the subject user and a historical aggregate profile for users historically located at or near the current location of the subject user.
FIG. 28 illustrates the operation of the profile augmentation function 40 to augment a user profile of a subject user based on a historical aggregate profile data for users historically, or previously, located at or near a current location of the subject user according to another embodiment of the present disclosure. First, the profile augmentation function 40 gets, or obtains, a current location of a subject user (step 2600). Depending on the particular embodiment, the subject user may be a system-user, which is one of the users 20-1 through 20-N of the mobile devices 18-1 through 18-N or the subscriber 24 of the subscriber device 22, or a third-party user, which may be a user associated with the profile server 14 or the third-party server 26 that is not one of the system-users. The current location of the subject user may be obtained using any suitable technique. For example, if the subject user is user 20-1, the current location of the user 20-1 is preferably obtained from the mobile device 18-1 or the location server 16. As another example, if the subject user is a third-party user, the current location of the third-party user may be obtained from a location-aware device of the third-party user.
Next, the profile augmentation function 40 obtains a historical aggregate profile for users historically located at or near the current location of the subject user (step 2602). In one embodiment, the users historically located at or near the current location of the subject user are users that were located at or near the current location of the subject user within one or more defined time windows. Each time window may be a relative time window that is relative to the current time (e.g., past 6 months, past 2 years, or the like) or an absolute time window (e.g., Friday, Feb. 12, 2010 or the like). In general, the users historically located at or near the current location of the subject user are users that were historically located within a defined geographic region encompassing (e.g., centered at) the current location of the subject user. In the preferred embodiment, the historical aggregate profile includes a number of keywords and, for each keyword, a number of user matches, or occurrences, of the keyword in the user profiles of the users historically located at or near the current location of the subject user. The profile augmentation function 40 then augments a user profile of the subject user based on the historical aggregate profile (step 2604).
FIG. 29 illustrates step 2604 of FIG. 28 in more detail according to one embodiment of the present disclosure. In this embodiment, the user profile of the subject user includes a number of keywords and a probability assigned to each of the keywords. Initially, the user profile of the subject user includes zero or more keywords entered by the subject user, where each of these keywords is assigned a fixed probability of 100%. Over time, as a number of iterations of the profile augmentation process of FIG. 28 are performed, additional keywords are added to the user profile of the subject user. As discussed below, the probabilities of these additional keywords are determined based on questions asked of the subject user and/or the number of user matches for the keywords from a historical aggregate profile obtained for the current location of the subject user.
More specifically, in order to augment the user profile of the subject user based on the historical aggregate profile for the current location of the subject user, the profile augmentation function 40 identifies, or selects, a predetermined number (N_MAX) of keywords from the historical aggregate profile having the highest number of user matches and not having a fixed probability in the user profile of the subject user (step 2700). Keywords in the user profile of the subject user having fixed probabilities are keywords that have been entered by the subject user into the user profile. In addition, as discussed below, the keywords having fixed probabilities may also include keywords in which the subject user has expressed an interest or disinterest in response to questions from the profile augmentation function 40.
For example, assume that the user profile of the subject user is:
Keyword Probability (%)

Tennis 100 (fixed)

Politics 100 (fixed)

Also, assume that the historical aggregate profile is:
Keyword User Matches

Tennis 19

Books 8

Photography 7

Fishing 4

Sports 3

China 3

Coffee 2

Skiing 2

Europe 1

Farm Animals 1

As such, the profile augmentation function 40 selects, at most, the predetermined number (N_MAX) of the keywords from the historical aggregate profile having the highest number of user matches and not having a fixed probability in the user profile of the subject user. In this example, assume that N_MAXis three (3). Here, the three keywords having the highest number of user matches and not having fixed probabilities in the user profile of the subject user are the keywords Books, Photography, and Fishing. Note that the keyword Tennis has a fixed probability in the user profile of the subject user.
Next, the profile augmentation function 40 generates a question for each of the identified keywords (step 2702). In general, the questions are automatically generated in order to determine whether the subject user is interested in the identified keywords. Returning to our example, if the identified keywords are Books, Photography, and Fishing, the profile augmentation function 40 may generate the following questions: “Do you have an interest in books?,” “Do you have an interest in photography?,” and “Do you like fishing?.” Alternatively, the questions for the identified keywords may be a list of the identified keywords where the subject user will be enabled to select “Yes” or “No” for each of the identified keywords, or the like. The profile augmentation function 40 then provides the questions for the identified keywords to the subject user (step 2704), and receives answers to the questions from the subject user (2706). Note that the subject user may choose to answer all, some, or none of the questions. Lastly, the profile augmentation function 40 updates the user profile of the subject user based on any answers received from the subject user and, in this embodiment, the number of user matches for the keywords in the historical aggregate profile (step 2708). In one embodiment, the profile augmentation function 40 updates the user profile of the subject user using the process of FIG. 24 described above.
Continuing our example, assume that the subject user responds positively to the question for the keyword Books, responds negatively to the question for the keyword Photography, and does not respond to the question for the keyword Fishing. After the process of FIG. 24 has been performed, the updated user profile of the subject user would be:


	Keyword	Probability

	Tennis	100 (fixed)
	Politics	100 (fixed)
	Books	100 (fixed)
	Fishing	8
	Sports	6
	China	6
	Coffee	4
	Skiing	4
	Europe	2
	Farm Animals	2
	Photography	0 (fixed)

Note that future iterations of the profile augmentation process may be performed in order to further augment the user profile of the subject user.

FIG. 30 illustrates step 2604 of FIG. 28 in more detail according to another embodiment of the present disclosure. In this embodiment, the user profile of the subject user includes a number of keywords and a probability assigned to each of the keywords. Initially, the user profile of the subject user includes zero or more keywords entered by the subject user, where each of these keywords is assigned a fixed probability of 100%. Over time, as a number of iterations of the profile augmentation process of FIG. 28 are performed, additional keywords are added to the user profile of the subject user. As discussed below, the probabilities of these additional keywords are determined based on questions asked of the subject user and/or the number of user matches for the keywords in historical aggregate profiles.
More specifically, in order to augment the user profile of the subject user based on the historical aggregate profile of users historically located at or near the current location of the subject user, the profile augmentation function 40 first provides a preliminary question to the subject user (step 2800). The preliminary question is preferably an open-ended question regarding the interests of the subject user. For example, the preliminary question may be “What is one of your current topics of interest?.” The profile augmentation function 40 then receives an answer from the subject user (step 2802). The answer from the subject user is referred to herein as a topic of interest. The profile augmentation function 40 then selects a desired number of keywords from the historical aggregate profile that have the highest number of user matches, do not have fixed probabilities in the user profile of the subject user, and have the highest affinity to the topic of interest provided by the subject user (step 2804). Affinity between the topic of interest and the keywords in the historical aggregate profile may be determined using any suitable technique for determining the affinity between two keywords. For example, an ontology or similar data structure that defines relationships between words and topics may be used to determine the degree of relationship between two keywords (e.g., degrees of separation of the two keywords in the ontology), where the degree of relationship is utilized as the affinity of the two keywords.
For example, assume that the user profile of the subject user is:
Keyword Probability (%)

Sports 100 (fixed)

Politics 100 (fixed)

Also, assume that the historical aggregate profile is:
Keyword User Matches

Sports 15

Books 10

Photography 7

Coffee 5

China 5

Skiing 5

Europe 5

Fishing 5

Further assume that the topic of interest provided by the subject user is the Winter Olympics and that the desired number of keywords to be selected is three (3). As such, the profile augmentation function 40 selects Books and Photography because they have the highest number of user matches for keywords that do not have fixed probabilities in the user profile of the subject user. The profile augmentation function 40 then selects one more keyword from the keywords Coffee, China, Skiing, Europe, and Fishing based on the affinities of those keywords to the topic of interest, which is the Winter Olympics. As such, in this example, the profile augmentation function 40 selects the keyword Skiing because it has a higher affinity with the topic of interest than the other keywords.
Next, the profile augmentation function 40 generates a question for each of the selected keywords (step 2806). In general, the questions are automatically generated in order to determine whether the subject user is interested in the identified keywords. Returning to our example, if the identified keywords are Books, Photography, and Skiing, the profile augmentation function 40 may generate the following questions: “Do you have an interest in books?,” “Do you have an interest in photography?,” and “Do you have an interest in skiing?.” Alternatively, the questions for the identified keywords may be a list of the identified keywords where the subject user will be enabled to select “Yes” or “No” for each of the identified keywords, or the like. The profile augmentation function 40 then provides the questions for the identified keywords to the subject user (step 2808), and receives answers to the questions from the subject user (step 2810). Note that the subject user may choose to answer all, some, or none of the questions. Lastly, the profile augmentation function 40 updates the user profile of the subject user based on any answers received from the subject user and, in this embodiment, the number of user matches for the keywords in the historical aggregate profile for the current location of the subject user (step 2812). More specifically, in one embodiment, the profile augmentation function 40 updates the user profile of the subject user using the process of FIG. 24 described above.
FIG. 31 illustrates the operation of the profile augmentation function 40 to augment a user profile of a subject user based on a historical aggregate profile for the embodiment of FIG. 1A wherein the profile augmentation function 40 is implemented on the MAP server 12. First, the MAP server 12 obtains a user profile of a system user (step 2900). The system user is one of the users 20-1 through 20-N. As discussed above, the MAP server 12 may obtain the user profile of the system user from the profile server 14, but is not limited thereto. Next, the MAP server 12 obtains the current location of the system user in the manner described above (step 2902). Next, the MAP server 12 obtains a historical aggregate profile for the current location of the system user (step 2904). The manner in which the historical aggregate profile is preferably obtained by the MAP server 12 is described below. In general, user profiles of users historically located at or near the current location of the system user during one or more defined time windows are combined to provide the historical aggregate profile. Again, the historical aggregate profile preferably includes a number of keywords and, for each keyword, a number of user matches for that keyword in the user profiles of the users historically located at or near the current location of the system user. The profile augmentation function 40, which for this embodiment is implemented on the MAP server 12, then augments the user profile of the system user based on the historical aggregate profile in the manner described above (step 2906). Preferably, the process returns to step 2902 and is periodically or otherwise repeated to further augment the user profile of the system user over time.
FIG. 32 illustrates the operation of the profile augmentation function 40 to perform the profile augmentation process of FIG. 28 for the embodiment of FIG. 1B. In this embodiment, the profile augmentation function 40 is implemented on the profile server 14, one of the mobile devices 18-1 through 18-N, the subscriber device 22, or the third-party server 26. First, the profile augmentation function 40 sends a historical aggregate profile request to the MAP server 12 that includes the current location of the subject user (step 3000). Alternatively, if the subject user is a system user (i.e., one of the users 20-1 through 20-N), the historical aggregate profile request may identify the subject user, where the MAP server 12 then obtains the current location of the subject user. In this embodiment, the subject user may be a system user or a third-party user depending on the particular implementation details. In response, the MAP server 12 generates or otherwise obtains a historical aggregate profile for the current location of the subject user (step 3002). Again, the manner in which the historical aggregate profile is preferably generated is described below. Next, the MAP server returns the historical aggregate profile to the profile augmentation function 40 (step 3004). The profile augmentation function 40 then augments the user profile of the subject user based on the historical aggregate profile in the manner described above (step 3006).
FIG. 33 illustrates the operation of the MAP server 12 to generate a historical aggregate profile for a current location of a subject user according to one embodiment of the present disclosure. First, the history manager 58 of the MAP server 12 establishes a bounding box for the historical aggregate profile request based on the current location of the subject user (step 3100). Note that while a bounding box is used in this example, other geographic shapes may be used to define a bounding region for the historical request (e.g., a bounding circle). In this embodiment, the bounding box is a bounding box of a predefined size centered at the current location of the subject user. In addition to establishing the bounding box, the history manager 58 establishes a time window for the historical aggregate profile request (step 3102). The time window is preferably identified in the historical aggregate profile request, but is not limited thereto. For example, if the historical aggregate profile request is for the last week and the current date and time are Sep. 17, 2009 at 10:00 pm, the history manager 58 may generate the time window as Sep. 10, 2009 at 10:00 pm through Sep. 17, 2009 at 10:00 pm. Note that while a single time window is used for this example, any number of one or more time windows may be defined and processed accordingly.
Next, the history manager 58 obtains history objects relevant to the bounding box and the time window for the historical aggregate profile request from the datastore 66 of the MAP server 12 (step 3104). The relevant history objects are history objects recorded for time periods within or intersecting the time window and for locations, or geographic areas, within or intersecting the bounding box for the historical aggregate profile request. At this point, the history manager 58 gets the next history object from the relevant history objects identified in step 3104 (step 3106). The history manager 58 then generates an aggregate profile for the history object (step 3108). In order to generate the aggregate profile for the history object, the history manager 58 compares the user profiles of the anonymous user records stored in the history object to one another. The resulting aggregate profile for the history object includes a number of keywords and, for each keyword, a number of user matches for that keyword. The history manager 58 then determines whether there are more history objects to be processed (step 3110). If so, the process returns to step 3106 and is repeated until all of the history objects that are relevant to the historical aggregate profile request have been processed. Once all of the history objects have been processed, the history manager 58 combines the aggregate profiles of the history objects to provide the historical aggregate profile for the current location of the subject user (step 3112). More specifically, in this embodiment, the history manager 58 combines the aggregate profiles of the history objects to provide a list of keywords and, for each keyword, a number of user matches for that keyword over all of the history objects.
Before proceeding, it should be noted that, in the embodiments described above, the profile augmentation function 40 utilizes questions asked of the subject user. However, the profile augmentation function 40 is not limited thereto. In another embodiment, no questions are asked of the subject user. Rather, the profile augmentation function 40 operates to update the user profile of the subject user based on the aggregate profile date (i.e., the aggregate profile of the crowd at or near the current location of the subject user or the historical aggregate profile for the current location of the subject user) according to steps 2200, 2204-2214, and 2222 of FIG. 24.
It should also be noted that the profile augmentation function 40 may utilize things in addition to the aggregate profile data when augmenting a user profile of a subject user. For example, in the embodiments of FIGS. 25 and 30, the topic of interest provided by the subject user may be used to identify other keywords identified as being of interest by other users that also provided the same topic of interest. The other users may be other users in general or other users currently or previously located at or near the current location of the subject user.
FIG. 34 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 170 connected to memory 172, one or more secondary storage devices 174, and a communication interface 176 by a bus 178 or similar mechanism. The controller 170 is a microprocessor, digital Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), or the like. In this embodiment, the controller 170 is a microprocessor, and the application layer 42, the business logic layer 44, and the object mapping layer 64 (FIG. 2) are implemented in software and stored in the memory 172 for execution by the controller 170. Further, the datastore 66 (FIG. 2) may be implemented in the one or more secondary storage devices 174. The secondary storage devices 174 are digital data storage devices such as, for example, one or more hard disk drives. The communication interface 176 is a wired or wireless communication interface that communicatively couples the MAP server 12 to the network 28 (FIGS. 1A and 1B). For example, the communication interface 176 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. 35 is a block diagram of the mobile device 18-1 according to one embodiment of the present disclosure. This discussion is equally applicable to the other mobile devices 18-2 through 18-N. As illustrated, the mobile device 18-1 includes a controller 180 connected to memory 182, a communication interface 184, one or more user interface components 186, and the location function 36-1 by a bus 188 or similar mechanism. The controller 180 is a microprocessor, digital ASIC, FPGA, or the like. In this embodiment, the controller 180 is a microprocessor, and the MAP client 30-1, the MAP application 32-1, and the third-party applications 34-1 are implemented in software and stored in the memory 182 for execution by the controller 180. In addition, if implemented on the mobile device 18-1, the profile augmentation function 40 is also preferably implemented in software and stored in the memory 182 for execution by the controller 180. In this embodiment, the location function 36-1 is a hardware component such as, for example, a GPS receiver. The communication interface 184 is a wireless communication interface that communicatively couples the mobile device 18-1 to the network 28 (FIGS. 1A and 1B). For example, the communication interface 184 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 186 include, for example, a touchscreen, a display, one or more user input components (e.g., a keypad), a speaker, or the like, or any combination thereof.
FIG. 36 is a block diagram of the subscriber device 22 according to one embodiment of the present disclosure. As illustrated, the subscriber device 22 includes a controller 190 connected to memory 192, one or more secondary storage devices 194, a communication interface 196, and one or more user interface components 198 by a bus 200 or similar mechanism. The controller 190 is a microprocessor, digital ASIC, FPGA, or the like. In this embodiment, the controller 190 is a microprocessor, and the web browser 38 (FIGS. 1A and 1B) is implemented in software and stored in the memory 192 for execution by the controller 190. In addition, if implemented on the subscriber device 22, the profile augmentation function 40 is also preferably implemented in software and stored in the memory 192 for execution by the controller 190. The one or more secondary storage devices 194 are digital storage devices such as, for example, one or more hard disk drives. The communication interface 196 is a wired or wireless communication interface that communicatively couples the subscriber device 22 to the network 28 (FIGS. 1A and 1B). For example, the communication interface 196 may be an Ethernet interface, local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, a mobile communications interface such as a cellular telecommunications interface, or the like. The one or more user interface components 198 include, for example, a touchscreen, a display, one or more user input components (e.g., a keypad), a speaker, or the like, or any combination thereof.
FIG. 37 is a block diagram of the third-party server 26 according to one embodiment of the present disclosure. As illustrated, the third-party server 26 includes a controller 202 connected to memory 204, one or more secondary storage devices 206, a communication interface 208, and one or more user interface components 210 by a bus 212 or similar mechanism. The controller 202 is a microprocessor, digital ASIC, FPGA, or the like. In this embodiment, the controller 202 is a microprocessor, and one or more services provided by the third-party server 26 are implemented in software and stored in the memory 204 for execution by the controller 202. For instance, if implemented on the third-party server 26, the profile augmentation function 40 is also preferably implemented in software and stored in the memory 204 for execution by the controller 202. The one or more secondary storage devices 206 are digital storage devices such as, for example, one or more hard disk drives. The communication interface 208 is a wired or wireless communication interface that communicatively couples the third-party server 26 to the network 28 (FIGS. 1A and 1B). For example, the communication interface 208 may be an Ethernet interface, local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, a mobile communications interface such as a cellular telecommunications interface, or the like. The one or more user interface components 210 include, for example, a touchscreen, a display, one or more user input components (e.g., a keypad), a speaker, or the like, or any combination thereof.
FIG. 38 is a block diagram of the profile server 14 according to one embodiment of the present disclosure. As illustrated, the profile server 14 includes a controller 214 connected to memory 216, one or more secondary storage devices 218, a communication interface 220, and one or more user interface components 222 by a bus 224 or similar mechanism. The controller 214 is a microprocessor, digital ASIC, FPGA, or the like. In this embodiment, the controller 214 is a microprocessor, and one or more services provided by the profile server 14 are implemented in software and stored in the memory 216 for execution by the controller 214. For instance, if implemented on the profile server 14, the profile augmentation function 40 is also preferably implemented in software and stored in the memory 216 for execution by the controller 214. The one or more secondary storage devices 218 are digital storage devices such as, for example, one or more hard disk drives. The communication interface 220 is a wired or wireless communication interface that communicatively couples the profile server 14 to the network 28 (FIGS. 1A and 1B). For example, the communication interface 220 may be an Ethernet interface, local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, a mobile communications interface such as a cellular telecommunications interface, or the like. The one or more user interface components 222 include, for example, a touchscreen, a display, one or more user input components (e.g., a keypad), a speaker, or the like, or any combination thereof.
Those skilled in the art will recognize improvements and modifications to the embodiments of the present invention. All such improvements and modifications are considered within the scope of the concepts disclosed herein and the claims that follow.

Claims

What is claimed is:

1. A computer-implemented method comprising:

obtaining aggregate profile data for a group of users relevant to a current location of a subject user; and

augmenting a user profile of the subject user based on the aggregate profile data for the group of users relevant to the current location of the subject user.

2. The method of claim 1 wherein the group of users is a crowd of users that is currently located at a location that is relevant to the current location of the subject user.

3. The method of claim 2 wherein the aggregate profile data for the group of users is an aggregate profile of the crowd of users that includes a plurality of keywords from user profiles of the users in the crowd and, for each keyword of the plurality of keywords, a number of occurrences of the keyword in the user profiles of the users in the crowd.

4. The method of claim 3 wherein augmenting the user profile of the subject user comprises augmenting the user profile of the subject user based on the aggregate profile of the crowd of users such that one or more keywords from the aggregate profile of the crowd of users are added to the user profile of the subject user.

5. The method of claim 4 wherein augmenting the user profile of the subject user further comprises, for each of the one or more keywords from the aggregate profile added to the user profile of the subject user:

computing a probability for the keyword as a function of the number of occurrences of the keyword in the user profiles of the users in the crowd; and

adding the probability for the keyword to the user profile of the subject user.

6. The method of claim 3 wherein augmenting the user profile of the subject user comprises:

selecting one or more keywords from the plurality of keywords in the aggregate profile of the crowd of users based on the number of occurrences of each of the plurality of keywords in the aggregate profile of the crowd of users;

generating a question for each of the one or more keywords;

providing the question for each of the one or more keywords to the subject user;

receiving an answer from the subject user for the question for at least one of the one or more keywords that is indicative of whether the subject user has an interest in the at least one of the one or more keywords; and

updating the user profile of the subject user based on the answer from the subject user for the question for the at least one of the one or more keywords.

7. The method of claim 6 wherein updating the user profile of the subject user comprises, for each keyword of the one or more keywords for which questions were provided to the subject user and for which an answer was received, updating the user profile of the subject user such that the user profile of the subject user includes the keyword and a fixed probability that reflects the answer from the subject user.

8. The method of claim 7 wherein updating the user profile of the subject user comprises, for each keyword of the plurality of keywords in the aggregate profile of the crowd of users for which a question was provided to the subject user but an answer was not received from the subject user:

determining whether the keyword is already included in the user profile of the subject user; and

if the keyword is not already included in the user profile of the subject user:

computing a probability for the keyword as a function of the number of occurrences for the keyword from the aggregate profile of the crowd of users; and

adding the keyword and the probability for the keyword to the user profile of the subject user.

9. The method of claim 8 wherein updating the user profile of the subject user further comprises, if the keyword is already included in the user profile of the subject user:

computing a new probability for the keyword as a function of an old probability for the keyword from the user profile of the subject user and the number of occurrences for the keyword from the aggregate profile of the crowd of users; and

updating the user profile of the subject user to include the new probability for the keyword.

10. The method of claim 7 wherein updating the user profile of the subject user comprises, for each keyword of the plurality of keywords in the aggregate profile of the crowd of users for which a question was not provided to the subject user:

if the keyword is not already included in the user profile of the subject user:

11. The method of claim 10 wherein updating the user profile of the subject user further comprises, if the keyword is already included in the user profile of the subject user:

12. The method of claim 6 wherein augmenting the user profile of the subject user further comprises:

providing a preliminary question to the subject user that asks the subject user for a topic of interest; and

receiving an answer to the preliminary question from the subject user that includes the topic of interest;

wherein selecting the one or more keywords from the plurality of keywords in the aggregate profile of the crowd of users comprises selecting the one or more keywords from the plurality of keywords in the aggregate profile of the crowd of users based on the number of occurrences of each of the plurality of keywords from the aggregate profile of the crowd of users and the topic of interest provided by the subject user.

13. The method of claim 1 wherein the group of users is a plurality of users historically located at locations that are relevant to the current location of the subject user.

14. The method of claim 13 wherein obtaining the aggregate profile data comprises obtaining a historical aggregate profile for the plurality of users historically located at locations that are relevant to the current location of the subject user during one or more previous time windows.

15. The method of claim 13 wherein the aggregate profile data for the group of users is a historical aggregate profile for the plurality of users historically located at locations that are relevant to the current location of the subject user, the historical aggregate profile comprising a plurality of keywords and, for each keyword of the plurality of keywords, a number of occurrences of the keyword in user profiles of the plurality of users.

16. The method of claim 15 wherein augmenting the user profile of the subject user comprises augmenting the user profile of the subject user based on the historical aggregate profile such that one or more keywords from the historical aggregate profile are added to the user profile of the subject user.

17. The method of claim 16 wherein augmenting the user profile of the subject user further comprises, for each keyword of the one or more keywords from the historical aggregate profile added to the user profile of the subject user:

computing a probability for the keyword as a function of the number of occurrences of the keyword in the user profiles of the plurality of users; and

adding the probability for the keyword to the user profile of the subject user.

18. The method of claim 15 wherein augmenting the user profile of the subject user comprises:

selecting one or more keywords from the plurality of keywords in the historical aggregate profile based on the number of occurrences of each of the plurality of keywords in the historical aggregate profile;

generating a question for each of the one or more keywords;

19. The method of claim 18 wherein updating the user profile of the subject user comprises, for each keyword of the one or more keywords for which questions were provided to the subject user and for which an answer was received, updating the user profile of the subject user such that the user profile of the subject user includes the keyword and a fixed probability that reflects the answer from the subject user.

20. The method of claim 19 wherein updating the user profile of the subject user comprises, for each keyword of the plurality of keywords in the historical aggregate profile for which a question was provided to the subject user but an answer was not received from the subject user:

if the keyword is not already included in the user profile of the subject user:

computing a probability for the keyword as a function of the number of occurrences for the keyword from the historical aggregate profile; and

21. The method of claim 20 wherein updating the user profile of the subject user further comprises, if the keyword is already included in the user profile of the subject user:

computing a new probability for the keyword as a function of an old probability for the keyword from the user profile of the subject user and the number of occurrences for the keyword from the historical aggregate profile; and

22. The method of claim 19 wherein updating the user profile of the subject user comprises, for each keyword of the plurality of keywords in the historical aggregate profile for which a question was provided to the subject user but an answer was not received from the subject user:

if the keyword is not already included in the user profile of the subject user:

23. The method of claim 22 wherein updating the user profile of the subject user further comprises, if the keyword is already included in the user profile of the subject user:

24. The method of claim 18 wherein augmenting the user profile of the subject user further comprises:

wherein selecting the one or more keywords from the plurality of keywords in the historical aggregate profile comprises selecting the one or more keywords from the plurality of keywords in the historical aggregate profile based on the number of occurrences of each of the plurality of keywords from the historical aggregate profile and the topic of interest provided by the subject user.

25. The method of claim 1 further comprising repeating the steps of obtaining aggregate profile data and augmenting the user profile of the subject user based on the aggregate profile data for each of one or more subsequent locations of the subject user.

26. A computing device comprising:

a communication interface communicatively coupling the computing device to a network; and

a controller associated with the communication interface and adapted to:

obtain aggregate profile data for a group of users relevant to a current location of a subject user; and

augment a user profile of the subject user based on the aggregate profile data for the group of users relevant to the current location of the subject user.

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