(19) United States
`(12) Patent Application Publication (10) Pub. No.: US 2006/0085419 A1
`(43) Pub. Date:
`Apr. 20, 2006
`Rosen
`
`US 20060085419A1
`
`(54) SYSTEM AND METHOD FOR LOCATION
`BASED SOCIAL NETWORKING
`
`(76) Inventor: James S. Rosen, New York, NY (US)
`
`Correspondence Address:
`WILSON SONSINI GOODRICH & ROSATI
`650 PAGE MILL ROAD
`PALO ALTO, CA 94304-1050 (US)
`
`(21) Appl. No.:
`
`11/253,848
`
`(22) Filed:
`
`Oct. 18, 2005
`
`Related US. Application Data
`
`(60) Provisional application No. 60/ 620,456, ?led on Oct.
`19, 2004.
`
`Publication Classi?cation
`
`(51) Int. Cl.
`(2006.01)
`G06F 17/30
`(52) US. Cl. ................................................................ .. 707/9
`
`(57)
`
`ABSTRACT
`
`Systems and methods for social networking. Location-re
`lated data and other behavioral and exogenously generated
`characteristics are used to replace or supplement self-gen
`erated pro?les in order to enhance the quality and trustWor
`thiness of the matches made using the system and facilitate
`the inputting of pro?le information.
`
`I 602
`
`Provide
`Location
`Information
`
`ll
`Detect Users
`
`5 604
`
`User
`Profiles
`Available
`
`606
`
`I 608
`
`Apply Filters
`Compare Target Pro?les I610
`
`V
`
`No
`
`612
`
`I614
`k616
`
`Yes
`
`Check Settings
`
`ll
`Send Notices and Alerts
`Based on Settings
`
`V
`End
`
`ll
`
`618
`
`1
`
`Google Inc., Nest Labs, Inc., and Dropcam, Inc.
`GOOG 1012
`IPR of US Pat. No. 8,311,524
`
`

`

`Patent Application Publication Apr. 20, 2006 Sheet 1 0f 4
`
`US 2006/0085419 A1
`
`100
`
`102
`
`CPU
`
`104 1
`
`Bus
`
`106
`
`Memory
`
`Profile \/*\ 108
`
`NIC —>
`V“ 110
`
`GPS
`
`\_/'—\ 112
`
`FIG. 1
`
`202
`
`204
`
`<- ------ --->
`
`FIG. 2
`
`2
`
`

`

`Patent Application Publication Apr. 20, 2006 Sheet 2 0f 4
`
`US 2006/0085419 A1
`
`3&
`
`322
`
`Database
`
`System
`
`300
`
`306
`
`Internet/
`World Wide Web
`
`3181
`$5.0m
`“:65
`
`PC
`
`312
`r‘)
`Wireless
`Se
`We’
`I ‘
`/
`\
`/_
`\
`
`310
`
`i
`
`314
`
`316
`
`322
`320
`(J
`L3
`Mobile
`Mobile
`Telecom . . . Telecom
`Switching
`Switching
`Office
`Office
`I
`I
`I
`I
`'
`
`\
`\
`\
`\
`
`332
`
`r‘)
`330
`
`>
`324
`
`32a
`
`(\J
`328
`
`FIG. 3
`
`3
`
`

`

`Patent Application Publication Apr. 20, 2006 Sheet 3 0f 4
`
`US 2006/0085419 A1
`
`402
`
`(
`
`l
`Data
`
`45°
`
`l
`
`'
`
`User Pro?le
`
`.
`
`400
`[,1
`
`4 '
`User Type
`
`f
`
`Quamy
`A
`
`t
`\
`
`Type
`Value
`Field
`L404 @406 @408
`
`Relevance
`L»41o
`
`Con?dence
`@412
`
`\
`
`Settings
`@414
`
`Settings w 416
`
`User Settings
`L418
`
`Availability
`@420
`
`Aggregation
`v422
`
`Other Privacy Settings
`L-424
`
`Default and Automated Settings \_/—\ 426
`
`FIG. 4
`
`Target Profile
`
`500
`
`/
`
`502
`
`8
`
`Data Ranges
`
`.
`
`Value
`
`Relevance
`Range Type
`Field
`K4504 L4,06 L»5O8 L»51o
`
`_
`
`Con?dence
`@512
`
`.
`
`Other \
`
`Filters
`Weight
`L»514 L516
`
`Programmable Filters \f\ 518
`
`Location
`
`Time
`
`Context
`
`Associations
`
`L» 520
`
`k» 522
`
`E» 524
`
`L» 526
`
`Other
`
`L» 528
`
`FIG. 5
`
`4
`
`

`

`Patent Application Publication Apr. 20, 2006 Sheet 4 0f 4
`
`US 2006/0085419 A1
`
`Provide I 602
`Location
`Information
`
`l
`Detect Users
`
`User
`Profiles
`Available
`
`. Apply Filters
`
`l
`
`Compare Target Profiles
`
`Y
`No
`
`604
`I
`
`606
`
`608
`I
`
`j‘ 610
`
`612
`
`614
`I
`
`616
`Send Notices and Alerts f
`Based on Settings
`
`Yes
`Check Settings
`l
`
`l
`
`618
`> End I
`
`FIG. 6
`
`5
`
`

`

`US 2006/0085419 A1
`
`Apr. 20, 2006
`
`SYSTEM AND METHOD FOR LOCATION BASED
`SOCIAL NETWORKING
`
`CROSS REFERENCE
`
`[0001] This application claims the bene?t of US. Provi
`sional Application No. 60/620,456, ?led Oct. 19, 2004, and
`of US. Provisional Application ?led Oct. 17, 2005, for
`System and Method for Location Based Social Networking,
`invented by James S. Rosen [Attorney Docket No.
`30456701 .102], Which are incorporated herein by reference
`in their entirety.
`[0002] This application is related to copending US. patent
`application Ser. No.
`, ?led Oct. 18, 2005, for System
`And Method For Location Based Matching and Promotion,
`invented by James S. Rosen [Attorney Docket No.
`30456701202].
`
`FIELD OF THE INVENTION
`
`[0003] The ?eld of the present invention relates generally
`to a system and method for generating and collecting pro?le
`information regarding people and entities and matching or
`?ltering said people and entities based on that pro?le infor
`mation.
`
`BACKGROUND OF THE INVENTION
`
`[0004] Social netWorking systems may use pro?les to
`connect people Who might like to meet each other. The idea
`of connecting strangers or friends Who might not otherWise
`meet is poWerful. HoWever, the value of these systems may
`be limited by the rudimentary methods used to make
`matches: basic preference characteristics such as a common
`business relationship, social relationship, family relation
`ship, compatible physical characteristics, or self-declared
`preferences for food, clothing, leisure activities, sports,
`entertainment, music, art, etc.
`[0005] A key problem With such basic social netWorking
`systems is a lack of veri?ability and authenticity of match
`criteria, leading to a surfeit of loW quality matches. Too
`many loW quality matches can lead to a loss of faith in the
`entire system, poor usability overall, and questions of trust
`When you meet people (or connect to entities) through such
`matching criteria.
`[0006] Another problem is that such systems force users to
`do the tedious Work of creating self-generated pro?les by
`inputting personal information, akin to ?lling out a ques
`tionnaire. This creates tWo problems: inconvenience for
`participants and a lack of standards that everyone can trust.
`First, many people are busy, or laZy. Any system that relies
`on its users creating and updating multivariable pro?les is
`inherently ?aWed. Too many people Will let their pro?les
`become stale. Second, people have different standards When
`it comes to self-declared information. I may think I am a
`connoisseur of Wine, Whereas by someone else’s de?nition
`I am a novice. In addition, the information I supply in
`creating my pro?le may not be useful for distinguishing me
`from other users in the system. For instance, I may mention
`that I am a Red Sox fan in my self-generated pro?le.
`HoWever, this information may not useful for distinguishing
`among the thousands of other Red Sox fans in the Boston
`area. Subtleties are lost. For example, I may be a diehard fan
`and Want to meet others Who, like me, have season tickets.
`
`In other Words, gradation information can be important and
`is sometimes either lost or mischaracteriZed With self
`generated pro?ling.
`[0007] What is desired therefore is an improved system
`and method that adds accountability and standards to user
`pro?les, ideally one Which does not burden the user With the
`cumbersome task of building and maintaining a pro?le.
`What is also desired is an improved system and method for
`location- and context-based matching and ?ltering of users.
`What is also desired is an improved system that alloWs not
`only people to be matched With other people but also people
`to be matched With “entities”, such as restaurants, bars,
`organizations, parties, stores, and even cities.
`
`SUMMARY OF THE INVENTION
`[0008] Aspects of the present invention relate to systems
`and methods for social netWorking. Location-related data
`and other behavioral and exogenously generated character
`istics are used to replace or supplement self-generated
`pro?les in order to enhance the quality and trustworthiness
`of the matches made using the system and facilitate the
`inputting of pro?le information.
`
`INCORPORATION BY REFERENCE
`[0009] All publications and patent applications mentioned
`in this speci?cation are herein incorporated by reference to
`the same extent as if each individual publication or patent
`application Was speci?cally and individually indicated to be
`incorporated by reference.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`[0010] The novel features of the invention are set forth
`With particularity in the appended claims. A better under
`standing of the features and advantages of the present
`invention Will be obtained by reference to the folloWing
`detailed description that sets forth illustrative embodiments,
`in Which the principles of the invention are utiliZed, and the
`accompanying draWings of Which:
`
`[0011] FIG. 1 is a block diagram of a mobile device that
`may be used in connection With embodiments of the present
`invention.
`
`[0012] FIG. 2 is a block diagram illustrating the direct
`exchange of information betWeen mobile devices according
`to an example embodiment of the present invention.
`
`[0013] FIG. 3 is a diagram illustrating a netWork system
`according to an example embodiment of the present inven
`tion.
`
`[0014] FIG. 4 is a logical diagram of a user pro?le
`according to an example embodiment of the present inven
`tion.
`
`[0015] FIG. 5 is a logical diagram of a target pro?le
`according to an example embodiment of the present inven
`tion.
`
`[0016] FIG. 6 is a How chart illustrating a method for
`matching users according to an example embodiment of the
`present invention.
`
`DETAILED DESCRIPTION OF THE
`INVENTION
`[0017] An example embodiment of the present invention
`provides a system and method for collecting and generating
`
`6
`
`

`

`US 2006/0085419 A1
`
`Apr. 20, 2006
`
`pro?le information for users of mobile devices and for
`matching users based on those pro?les. In particular, the
`mobile device may generate or receive location based infor
`mation to augment both the generation of pro?le information
`and the use of that information to match users in different
`contexts. In particular, both the pro?les and the Way that
`they are used may change based on a user’s location and
`programmable ?lters and settings established by the user.
`For instance, a user at a night club on a Saturday night may
`be noti?ed of friends Who are nearby or of other users Who
`have common friends or other characteristics that Would
`indicate a match or facilitate a social meeting. On the other
`hand, different pro?le information and ?lters may be used
`When the user is at a trade shoW on a Work day. Context may
`be set easily by the user (by manually selecting “Work
`mode”, “socializing mode”, etc.) and/or set by the device
`automatically based on time, place, or other parameters. To
`this end, devices may use certain programmable logic to
`determine context automatically, including use of statistics
`to guess, for example, that given a combination of place,
`time, and other factors, the user is most likely in “socializing
`mode,” thus saving the user the trouble of periodically
`having to adjust the settings of his device.
`[0018] Pro?le information may be generated from behav
`ioral characteristics of the user, exogenously generated
`characteristics, and user-speci?ed information. In addition,
`the pro?le may include a unique identi?er as Well as one or
`more pseudonyms or temporary identi?ers for privacy pur
`poses. Pro?les may then be used to propose matches
`betWeen users or provide users With icebreakers (topics of
`conversation suitable for initiating a conversation).
`[0019] The data ?elds in the user pro?le may be tagged to
`indicate the type of data (behavioral, exogenous, user
`speci?ed, or other type) as Well as the quality of the data for
`matching purposes. Quality factors may include an indica
`tion of relevance of the particular data to the user and
`con?dence in the accuracy of the information. For example,
`data may be collected based on the locations visited by the
`user. HoWever, if location tracking has been disabled on
`most days, the data regarding a small set of locations visited
`by the user may not provide meaningful information regard
`ing the user’s behavior. In addition, con?dence in the data
`may depend upon the source of the data (e.g., Whether it is
`user-speci?ed or obtained from an exogenous source) or
`Whether the data has been veri?ed by a reliable source. For
`instance, my user pro?le may indicate that I am friends With
`Joe Smith. This information may provide a useful Way to
`match people Who have common friends. HoWever, Joe may
`not consider me to be a friend at all. Thus, a bona ?de friend
`of his may be misled into meeting me, believing me to be a
`friend of Joe’s, only to ?nd out later that Joe hardly knoWs
`me. This kind of con?dence problem may arise frequently
`Without some means of qualifying the reliability of data used
`for matching. Without other indicia of con?dence in the
`data, the usefulness of the information may be undermined,
`calling into question the entire matching system. Given that
`the matching system is intended to instill trust in connec
`tions, this could lead to abuses Where people end up feeling
`tricked, having trusted the system, only to ?nd that they have
`made associations based on false pretenses. On the other
`hand, the con?dence in this information (and in the system
`generally) may be enhanced if Joe has acknoWledged, veri
`?ed, or even rated our relationship, or (another Way of
`accomplishing the same thing) if my behavioral pro?le
`
`indicates (through GPS tracking or other means) that I spend
`a lot of time physically With Joe, or if I regularly commu
`nicate With him by phone or email. Supplementing user
`submitted information With behavioral information obtained
`from observed behavior can thus signi?cantly increase the
`credibility of data used to make matches, and thus improve
`the overall user experience.
`
`[0020] FIG. 1 is a block diagram of an example mobile
`device 100 that may be used in connection With embodi
`ments of the present invention. The mobile device may be,
`for example, a personal digital assistant (PDA), cellular
`telephone, laptop computer, pager or other communications
`device. The example device 100 includes a central process
`ing unit 102, memory 106, netWork interface card (NIC)
`110, global positioning system (GPS) 112 and a bus 104 for
`communications among these components. The memory
`106 may store pro?le information 108, including pro?le
`information regarding the user of the mobile device 100,
`target pro?les for matching other users and settings and
`?lters for the use of those pro?les. The memory 106 may
`store the pro?les in a relational database, ?at ?le system or
`other database or ?le format. The storage of this information
`on device 100 alloWs matching to occur on an ad hoc basis
`When other devices are encountered (e. g., through Bluetooth
`or other communications interfaces), Whether or not there is
`a connection to a particular netWork or server. For instance,
`FIG. 2 illustrates tWo mobile devices 202 and 204 using
`direct communication to exchange selected pro?le informa
`tion (Which may include, for example, target pro?les indi
`cating potential matches). Either device may determine
`Whether there is a match and send a noti?cation to the other
`user.
`
`[0021] The pro?le information 108 may be used by appli
`cation softWare stored in memory 106 and processed on
`CPU 102. All or part of this information may also be stored
`on a separate netWork server that may generate, maintain
`and process user pro?les. NIC 110 or other netWork interface
`provides access to an external netWork to alloW communi
`cations With the netWork server and/or other mobile devices.
`
`[0022] In some embodiments, the user may participate
`through use of a passive identi?er, such as a card in his
`Wallet. And if the system can track the user by his physical
`properties alone, such as With casino-style cameras con
`nected to computers running face recognition softWare or
`through other biometric identi?ers, the user may in fact
`participate Without having to do or carry anything; his
`pro?le gets updated and retrieved as necessary by the system
`as it “Watches” him. In some embodiments, the devices and
`equipment contemplated to use this system may be replaced
`by chips and devices embedded into people’s bodies, bio
`metric identi?ers, and other tracking technologies. Accord
`ingly, embodiments of the present invention are not limited
`to smart phones and other mobile devices and may be
`adapted to encompass other enabling technologies.
`
`[0023] GPS 112 generates location data that can be used
`for generating pro?le information as Well as matching users.
`Other location based information may be generated or
`received instead of, or in addition to, position information
`generated from a global positioning system. In some
`embodiments, location information may be generated based
`on a cellular telephone netWork (e.g., based on the cell
`Where the phone is located or through more sophisticated
`
`7
`
`

`

`US 2006/0085419 A1
`
`Apr. 20, 2006
`
`triangulation techniques), or by determining proximity to a
`device or network access point for Which location data is
`available. In other Words, a device Without location data can
`knoW its location, at least approximately, if it can commu
`nicate, either directly or indirectly, With a device that does
`knoW its location (for example, devices that rely on GPS but
`are indoors and unable to see the sky can be daisy-chained
`to pass along location information from a nearby device that
`can see the sky). In addition, a particular venue may broad
`cast or otherWise provide information about a particular
`location, event or activity (e.g., through bluetooth, Wi-Fi or
`other mechanism). When a user enters or leaves a nightclub,
`amusement park, sports arena, concert hall or other venue a
`message may be sent to the mobile communication device
`indicating the location or other information about the event
`or activity. Different messages may be sent for different parts
`of the venue, so that, for example, a device of a user at a
`multi-screen movie theatre can knoW Which movie the user
`sees. A local triangulation system or mechanism to deter
`mine relative locations of users may also be used to identify
`speci?c “micro-locations” or help users ?nd each other
`Within a venue, Which can be useful especially When the
`users don’t knoW each other, for instance, if a match occurs
`betWeen strangers in a croWded bar (micro-location methods
`might include the strength and direction of the signal
`received betWeen tWo users and thus Work like a compass,
`With an arroW pointing toWards the target user and a “hot
`cold” meter to indicate distance). The identi?cation of other
`users and devices Within a speci?ed range may be collected
`and time stamped for use in the user’s pro?le, incorporating
`safeguards for privacy (such as the recording and time
`stamping of certain meta-information, instead of names,
`about people in your vicinity). In addition, if other users and
`devices contain location information (such as a GPS posi
`tion, user entered location, etc.) this information can also be
`associated With the pro?le even if the user’s device is not
`able to generate this information.
`[0024] Data relating to people at the same venue can be
`used to enhance the user’s pro?le. For example, if a user
`goes to a bar populated by people in their 20s and 30s Who
`Work in ?nancial sector jobs, this can be used to “teach” the
`system about that user, Which can inform matching choices.
`Similarly, information that is knoWn about people Who visit
`a given location can be used to build the pro?le of the place
`itself; thus, a bar may develop an “entity pro?le” as a
`hangout for motorcycle enthusiasts, Red Sox fans, or both,
`depending on the time of day or Whether there is a game
`being played that day at FenWay Park. When an entity has
`pro?le information associated With it, this information can
`then be accessed in advance by anyone Who is thinking of
`going to that bar. Pro?le matching algorithms could be used
`to predict Whether you Would be compatible With the regular
`customers of that bar. A feedback system could be
`employed, using behavioral information (such as Whether a
`user repeatedly frequents a particular bar or “type” of bar
`and/or user-declared information (such as When a user
`explicitly evaluates his experience at a given place)). An
`entity’s pro?le and its patrons’ pro?les can interact dynami
`cally, building on each other and evolving over time as
`Warranted.
`[0025] This process of choosing places to spend time, such
`as bars, nightclubs, schools, restaurants, country clubs,
`vacation resorts, companies (Where you might be
`employed), etc. is something that happens today Without any
`
`help from technology: people go to places and return if they
`like them. Or they go to places based on recommendations
`from friends. Embodiments of the present invention serve to
`remove some of the time and effort, and simultaneously
`introduce an element of statistical analysis, into a human
`process that is inef?cient and characterized by trial-and
`error. This is not intended to eliminate the serendipitous
`nature of human discovery or trump subjective recommen
`dations; rather, it is intended to be helpful as a supplemental
`guiding system.
`[0026] FIG. 3 is a diagram illustrating a netWork 300
`according to an example embodiment of the present inven
`tion. The netWork 300 includes a netWork server 302 and a
`database storage system 304. The database storage system
`304 stores pro?le information regarding users of the system.
`The database storage system 304 may store the pro?les in a
`relational database, ?at ?le system or other database or ?le
`format. NetWork server 302 collects information from the
`mobile devices and other sources to generate pro?le infor
`mation. For instance, netWork server 302 may collect infor
`mation regarding the use of email accounts to communicate
`With other users of the system. NetWork server 302 also
`collects information about the locations of users and pro
`cesses pro?les to match users based on this information.
`While some or all of this information may be stored on
`individual devices and alloW for ad hoc matching, the use of
`a netWork server alloWs information about venues to be
`collected Whether or not a particular user is at that location.
`For instance, the server may determine the number of users
`at a particular night club and the number of users Who are
`potential matches or Whether speci?ed friends are at that
`location. A user may request this information in advance to
`decide Whether to go to a particular location. Aggregated (or
`individual) pro?le information may also be provided for
`purposes of evaluating advertising opportunities at a par
`ticular venue (e.g., determining Which ads should be dis
`played on a monitor at a sporting event depending upon the
`pro?les of the users at that event) or for other purposes. The
`netWork server 302 can also be used to collect historical
`information about venues and the people Who go there. Such
`information can be useful to people interested in knoWing
`patterns, such as Who frequents the venue and When. A user
`might Want to knoW, for example, Whether a given club tends
`to be frequented by people Who have friends in common
`With him. Similarly, if he is planning an illicit rendeZvous,
`he may Want to verify that nobody he knoWs is at the venue
`noW, or a regular patron, or even likely to appear there
`(likeliness being determined by statistical analysis and
`cross-matching of his acquaintances’ pro?les With the enti
`ty’s pro?le).
`[0027] In an example embodiment, the server 302 is
`connected to the Internet 306 for communications With other
`devices. In other embodiments, the server may be connected
`directly to a Wireless netWork, cellular telephone system or
`other netWork. In the example embodiment, mobile devices
`310, 314, 316, 324, 326 and 328 may be connected to the
`Internet (or other netWorks) through a variety of methods to
`alloW communication With server 302 and other mobile
`devices. For instance, device 310 may be synchroniZed With
`a personal computer 308 that provides a connection to the
`Internet. Data may be exchanged With the server through the
`personal computer 308. In addition, data for a user pro?le
`may be entered through the personal computer 308 Whether
`or not it is connected to particular mobile device. Similarly,
`
`8
`
`

`

`US 2006/0085419 A1
`
`Apr. 20, 2006
`
`data for a user pro?le may come from a (networked)
`appliance (330, 332), such as a DVR, smart card, digital
`book reader, etc. Mobile devices 314 and 316 may also be
`connected to the netWork through a Wireless server 312 that
`provides a connection to the Internet. For instance, Wireless
`“hot spots” are increasingly being provided at coffee shops,
`libraries and other locations and may be provided at night
`clubs, sporting events or other venues visited by users of the
`system. Mobile devices 324, 326 and 328 may also be
`connected to the server 302 through a cellular telephone
`network. Mobile telecommunication sWitching of?ces 320
`and 322 may use cellular telecommunications protocols
`(such as CDMA, GSM, TDMA or other protocol) to com
`municate With mobile devices 324, 326 and 328. The mobile
`telecommunication sWitching o?ices 320 and 322 may, in
`turn, be connected to the Internet 306 through local of?ces
`318.
`
`[0028] FIG. 4 is a block diagram of a user pro?le 400
`according to an example embodiment of the present inven
`tion. While FIG. 4 illustrates a data structure for an example
`user pro?le, it is understood that other databases, data
`structures and formats may be used to store and associate the
`desired data in the user pro?le. A mobile device 100 and/or
`netWork server 302 may be used to generate and update a
`user pro?le 400. User pro?les may also be imported from
`other systems.
`
`[0029] The example user pro?le 400 may be stored in a
`relational database and may have associated tables for
`storing pro?le data 402 and settings 416. The data table 402
`may also include entries for various characteristics to be
`stored as part of the pro?le. Each characteristic may be
`stored as a roW in the data table 402. The data table 402 may
`contain an entry, user type 450, to indicate Whether the
`pro?le is for a person or entity (such as a restaurant,
`entertainment venue or the like). For instance, an entry may
`include a ?eld identi?er 404 to identify the entry, a data
`value 406, the type 408 of the data, quality indicators such
`as relevance 410 and con?dence 412 and a pointer or other
`link to applicable settings 414 in the settings table 416. The
`settings table 416 includes settings and parameters that
`control hoW entries in the data table 402 are used for
`matching and other purposes. The settings table 416 includes
`both user settings 418 and default and automated settings
`426 that are established by the system (e.g., application
`softWare on a mobile device 100 or netWork server 302). The
`user may provide settings that control availability 420 of a
`data entry for various uses, Whether a data entry may be used
`alone or must be aggregated 422 With other speci?ed infor
`mation before it can be used and other privacy settings 424.
`Default settings for each of these options may be established
`and stored in table 426. In addition, automated settings may
`be stored by the system in table 426 for options that cannot
`be changed by the user.
`
`[0030] The folloWing is a more detailed description of the
`data table 402 and examples of the types of entries that may
`be stored in the table. The data entries include a type 408
`indicating hoW the data Was collected or generated. Data
`types may include behavioral characteristics collected by the
`system based on the actions of the user, exogenous infor
`mation collected from sources other than the user, and
`user-provided information. The data entry also includes
`indications of the quality and usefulness of the information.
`In the example shoWn in FIG. 4, both relevance 410 and
`
`con?dence 412 can be associated With the data stored in the
`table. Relevance 410 and con?dence 412 may be indicated
`by a numerical rating based on hoW the information Was
`collected, generated and/or veri?ed. For instance, relevance
`410 may indicate Whether behavioral characteristics or com
`posite data Was generated from a large sample siZe. For
`instance, if the data entry is based on the location of the user,
`it may only be generated When the user’s location can be
`tracked (Which can be controlled by the settings 416 for the
`device or may be limited by the range and availability of the
`positioning system). If a user’s location is tracked exten
`sively, this data entry may have more relevance as an
`indicator of the user’s behavior. A match might be made, for
`example, betWeen tWo people Who get coffee at the same
`cafe every morning. This information could at least be used
`to serve as an icebreaker. Con?dence 412 may also be
`indicated. Ahigh level of con?dence may be associated With
`data entries generated from behavior that Was tracked by the
`system, data provided in the form of a secure token, or
`information veri?ed or rated by additional sources, as
`opposed to user-declared data.
`[0031] The use of behavioral and exogenously generated
`information in the user pro?le can greatly enhance the
`quality of the matches and types of matches that can be made
`by the system. The folloWing are example behavioral and
`exogenously generated characteristics that can be used to
`enhance a location-dependant matching engine included as
`part of application softWare on server 302 and/or mobile
`devices in accordance With embodiments of the invention.
`[0032] Phone and Email Usage. Rather than relying on a
`?at contact list or requiring a user to categoriZe everyone in
`a contact list, the netWork server 302 or mobile device 100
`may monitor actual phone, SMS, and/ or email usage (and/or
`any other communications device or account or connected
`appliance, including PC) to infer Who the user really knoWs
`as Well as some information about the nature of the rela
`tionship (business vs. social); a system that collects all of
`this in an automated fashion Will be richer than any system
`that requires constant manual inputs. If I talk to John Burns
`every day for an hour, it can be inferred that I knoW him very
`Well. As a result, I Would Want to knoW if John Burns
`happens to be in the mall that I just entered (and vice versa),
`Whereas I do not necessarily care to meet up With someone
`Who happens to be in my contact list but Whom I talked to
`once, years ago, for ?ve minutes. (Alternatively, there may
`be situations Where a user seeks out looser and more remote
`connections as these sometimes offer more value as they
`tend to be more numerous than close relationships and they
`extend the user’s netWork farther a?eld, Which can particu
`larly be helpful When looking for a job, ?nding sales
`prospects, etc. There is a theory that many distant contacts
`are in fact more valuable, in a business sense, than a smaller
`group of very close contacts). This actual usage information,
`When used for matching, is richer than a ?at contact list. It
`also adds a degree of reciprocity: I cannot claim that
`GWeneth PaltroW is a friend, even if she is somehoW on my
`contact list, because I have never talked to her on the phone.
`Reciprocity and authenticity are important factors for second
`degree matching (i.e., tWo people connected via a mutual
`acquaintance) as it is important to connect people through a
`bona ?de intermediary if you are going to leverage the
`instant trust that arises When tWo strangers ?nd that they
`knoW someone in common. It is Worth noting that behavioral
`information can be combined With user-entered information
`
`9
`
`

`

`US 2006/0085419 A1
`
`Apr. 20, 2006
`
`for greater richness. For example, When a user speaks to
`someone on the phone, a record of such conversation is
`logged automatically. This record can then be enriched With
`comments from the user annotating or rating said conver
`sation and said contact.
`
`[0033] This data may be entered in a user pro?le 400 by
`either netWork server 302 or mobile device 100 (or a mobile
`device that connects to a PC). For instance mobile device
`100 may be a cell phone With a contact list in memory 106.
`The cell phone can track the frequency of calls, and amount
`of time spent on calls, to each person in the contact list. In
`addition, frequently dialed numbers (or the numbers of
`people Who call you frequently) can be automatically added
`to the contact list and the data can be associated With the
`phone number even if other contact information is not
`available. A laptop computer or mobile email device (Which
`may be the same or different device than the cell phone) can
`track the number of messages sent and received from
`various email addresses. This information can be tracked
`separately or associated With a contact list. The netWork
`server 302 and database storage system 304 may alloW
`multiple mobile (or non-mobile) devices to be registere