EX1003
`EX1003
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`US 11,823,219 B2
`Page 2
`
`Related U.S. Application Data
`
`(60) Provisional application No. 63/208,275, filed on Jun.
`8, 2021.
`
`(51)
`
`Int. Cl.
`G06F 16100
`(2019.01)
`G06Q 3010204
`(2023.01)
`H04L 67152
`(2022.01)
`G06F 161955
`(2019.01)
`H04L 9/40
`(2022.01)
`( 58) Field of Classification Search
`USPC
`......................................................... 705/7.34
`See application file for complete search history.
`
`(56)
`
`References Cited
`
`U.S. PATENT DOCUMENTS
`
`2010/0094727 Al *
`
`4/2010
`
`2010/0212716 Al
`2012/0240151 Al*
`
`2013/0151645 Al
`2014/0143655 Al
`2016/0180376 Al*
`
`8/2010
`9/2012
`
`6/2013
`5/2014
`6/2016
`
`2017 /0064032 Al
`2019/0199774 Al
`2020/0233911 Al
`2020/0386565 Al
`
`3/2017
`6/2019
`7/2020
`12/2020
`
`* cited by examiner
`
`Shapiro .................. G06Q 20/12
`705/26.1
`
`Lerner et al.
`Tapper ............. H04N 21/44008
`725/32
`
`Siliski et al.
`Alon et al.
`Lu .. ... ... .... ... ... ... . G06Q 30/0244
`705/14.43
`
`Ulrich et al.
`Demsey et al.
`Siroker et al.
`Rao et al.
`
`Launch Labs EX1003 Page 2
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`U.S. Patent
`
`Nov. 21, 2023
`
`Sheet 3 of 16
`
`US 11,823,219 B2
`
`300
`---------------✓
`
`/
`Y-
`
`/-
`\
`
`,
`
`Start
`
`>----"
`) 302
`
`l/
`
`,
`
`/--z,
`304
`
`Receive data associated
`with a browser session
`accessing a URL
`
`/----,-, __
`
`306
`
`Determine location data
`associated with the
`browser session
`
`,-'"-~ Determine a physical
`308
`address for the browser
`session using the location
`data
`
`310
`
`-------------------------· ,t
`
`·-------------------------
`
`Identify subsequent
`return visits to the URL
`from the browser
`
`------------------------------,-----------------------------
`
`312----
`i
`'
`y
`----,
`\
`/
`(
`End
`)
`, ___________________________
`
`/
`
`FIG. 3
`
`Launch Labs EX1003 Page 5
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`

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`

`

`502 _____
`'---./(.. Start
`\.~. ___
`
`;
`/
`
`.
`
`504
`'-,./f
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`Receive data
`►: associated with
`f
`I .... browsersession
`
`508
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`··'-._..,,/
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`//'-
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`.. '-
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`//
`
`·, ..
`'· ..
`'·,
`/
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`Yes
`/ //Unique ID assigned''-,
`. --
`:,-· ----------....,
`►-.
`"-.. ..._ to browser?
`·,., .. ,_
`',,_
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`//
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`506
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`500
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`I
`I
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`e •
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`r:J).
`•
`~
`~
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`~ = ~
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`Assign unique ID
`to browser
`
`510
`/*,
`'·,
`\
`Did
`'"-
`'-·r,
`No
`</// User Opt-In to'--.. .. ">--------.
`",
`Location
`,/
`', Tracking? //
`'··'-.,
`///
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`//
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`Yes l
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`No
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`z 0
`~
`N ....
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`"'
`N
`0
`N
`(,H
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`Install cookie in
`browser
`
`512
`
`Store unique ID in
`local cache of
`browser
`
`514
`
`:
`
`~ ...............................
`
`~' j
`
`Determine latitude'+----516
`and longitude of
`device operating
`browser
`
`+/" 518
`Identify address
`using map and/or 1
`postal data
`
`~1
`
`Associate unique
`ID to identified
`address
`
`, ... /
`t~
`520
`
`FIG. 5
`
`('D
`
`rJJ =(cid:173)
`('D .....
`Ul
`0 ....
`....
`
`O'I
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`522
`(
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`i
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`.. _ .. ____
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`d
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`N w
`'N
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`\0 = N
`
`Launch Labs EX1003 Page 7
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`U.S. Patent
`
`Nov. 21, 2023
`
`Sheet 8 of 16
`
`US 11,823,219 B2
`
`FIG. 8
`
`Launch Labs EX1003 Page 10
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`

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`

`

`Start
`
`1202
`
`1204
`
`Receive campaign level
`data associated with a
`campaign
`
`1214
`
`Identify a proportion of
`campaign impressions
`associated with the time
`of day the session was
`initiated
`
`1216
`
`Determine a probability
`that the session was
`initiated in response to
`
`watching/listening to
`media associated with
`the campaign
`
`1218
`
`End
`
`1200
`
`1208, ..... J ......................................................
`1206
`Identify a session within a
`Determine a date and a
`household associated
`time of day that the
`with a ZIP code covered
`session was initiated
`by the campaign
`
`j
`
`1212
`
`1210
`
`Is the
`Identify a proportion of ---·----------... date within a
`campaign impressions Y
`campaign time
`associated with the date
`es
`period?
`
`No
`
`e •
`
`00
`•
`~
`~
`~
`
`~ = ~
`
`z 0
`~
`N ....
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`"'
`N
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`rJJ =-('D
`('D ....
`....
`0 ....
`....
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`FIG. 12
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`d r.,;_
`"'""'
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`Launch Labs EX1003 Page 14
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`US 11,823,219 B2
`
`1
`LOCATION DETERMINATION USING
`ANONYMOUS BROWSER DATA
`
`CROSS-REFERENCE TO RELATED
`APPLICATIONS
`
`This application is a continuation of U.S. Non-Provisional
`application Ser. No. 17/687,992, filed Mar. 7, 2022, which
`claims priority to U.S. Provisional Patent App. No. 63/208,
`275, filed Jun. 8, 2021, the disclosure of which is hereby
`incorporated by reference herein in its entirety.
`
`BACKGROUND
`
`Various businesses market products using websites. Each
`product may have a dedicated webpage that is accessible
`from the business's website. Consumers access the specific
`webpages for more information regarding the specific prod(cid:173)
`ucts offered by the business entity. In an example, the
`business may be a car dealership (e.g., a car dealer). The car
`dealer may operate specific webpages for specific vehicles.
`Potential customers access the specific webpages for more
`information regarding the specific vehicles. The car dealer
`may be interested in understanding how many times a
`potential customer visits specific vehicle webpages via the
`dealer website. The car dealer may also be interested in
`learning more information about the potential customer.
`Car dealers also usually offer vehicle services, for
`example, such as maintenance services, corrective services, 30
`and collision services. Car owners or operators can choose
`to have their car serviced by the car dealer with whom they
`purchased their car, another local car dealer, a service shop,
`and/or the like. The car owners or operators may access a car
`dealer's service website for more information regarding the 35
`services available.
`
`SUMMARY
`
`Systems, methods, and apparatus are described herein for
`determining a location from anonymous data. For example,
`a computing device may receive anonymous data associated
`with a browser session initialized by a user via a browser on
`a user computing device. The computing device may deter(cid:173)
`mine that the user has not been assigned a unique identifier. 45
`The computing device may determine whether the user
`opted-in to location tracking. If the user opted-out of loca(cid:173)
`tion tracking, the computing device may determine a latitude
`coordinate and a longitude coordinate of the user computing
`device during the browser session. The computing device 50
`may identify a physical address for the user based on the
`latitude coordinate and
`the
`longitude coordinate,
`for
`example, using a map application programming interface
`(API). The computing device may assign the unique iden(cid:173)
`tifier to the user. The computing device may associate the 55
`unique identifier to the physical address. The computing
`device may determine one or more of an address type, a
`name of the user, an age of the user, a gender of the user,
`demographics associated with the user, and/or psychograph-
`ics associated with the user. The computing device may 60
`determine a confidence rating for the user based on a
`frequency of visits to a specific URL, the physical address,
`demographics associated with the user, and/or psychograph-
`ics associated with the user. The confidence rating may be an
`indication of the user's interest in a product. The confidence 65
`rating may be determined using an algorithm (e.g., a learn-
`ing algorithm).
`
`2
`Systems, methods, and apparatus are described herein for
`determining a vehicle health score. A computing device may
`receive information associated with a vehicle. The comput(cid:173)
`ing device may determine a vehicle age and/or a vehicle
`5 mileage based on the received vehicle information. The
`computing device may determine whether the vehicle age is
`greater than a threshold age. The computing device may
`determine whether the vehicle mileage is greater than a
`threshold mileage. The computing device may identify prior
`10 service information for the vehicle. The computing device
`may determine a health score for the vehicle based on the
`vehicle age, the vehicle mileage, and the prior service
`information for the vehicle. The prior service information
`15 may include a last service date and/or a number of miles
`driven since the last service date. The health score may be
`determined using an algorithm (e.g., a learning algorithm).
`The computing device may be configured to send a notifi(cid:173)
`cation to an operator of the vehicle based on the determined
`20 health score. The notification may
`include a service
`reminder or a service coupon when the determined health
`score is less than or equal to a predefined health threshold.
`The notification may include a marketing offer when the
`determined health score is greater than a predefined health
`25 threshold.
`Systems, methods, and apparatus are described herein for
`determining a probability that a browsing session was ini(cid:173)
`tiated in response to watching and/or listening to media
`associated with a campaign. A computing device may
`receive data associated with a campaign. The data associated
`with the campaign may indicate a first set of impression
`proportions on a plurality of dates and a second set of
`impression proportions during a plurality of dayparts. The
`plurality of dates may include the dates within a time period
`of the campaign. The computing device may identify a
`browsing session that visited a specific URL. The browsing
`session may be associated with an address that is within a
`zone covered by the campaign. The computing device may
`determine a date and a time of day that the browsing session
`40 visited the specific URL. The computing device may deter(cid:173)
`mine that the date is within a time period associated with the
`campaign. The computing device may identify a first
`impression proportion of the first set of impression propor-
`tions that is associated with the determined date. The first set
`of impression proportions may include respective impres(cid:173)
`sion proportions of campaign impressions for each of the
`plurality of dates. A campaign impression may include an
`instance of media associated with the campaign being
`watched and/or listened to. The computing device may
`identify a second impression proportion of the second set of
`impression proportions that is associated with the time of
`day. The second set of impression proportions comprises
`respective impression proportions of campaign impressions
`for each of the plurality of dayparts. Each of the plurality of
`dayparts may include a portion of a day. The computing
`device may determine a probability that the browsing ses-
`sion was initiated in response to a user watching or listening
`to media associated with the campaign. The computing
`device may determine whether the user performed a trans(cid:173)
`action associated with the campaign. The computing device
`may determine whether the browsing session was initiated
`by a direct search or an organic search.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`FIG. 1 illustrates a block diagram of an example com(cid:173)
`puting device.
`
`Launch Labs EX1003 Page 19
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`

`

`3
`FIG. 2 illustrates a block diagram of an example com(cid:173)
`puting network system.
`FIG. 3 is a flow diagram illustrating an example method
`that may be implemented to identify a physical address of a
`user based on anonymous data.
`FIG. 4 is a flow diagram illustrating an example method
`for associating a unique identifier with location data from a
`browser session.
`FIG. 5 is a flow diagram illustrating an example method
`for associating a unique identifier with a physical address of 10
`a user.
`FIG. 6 is a flow diagram illustrating an example method
`for determining a user's interest in a product.
`FIG. 7 is an example graphical user interface for a vehicle
`health assessment.
`FIG. 8 is a flow diagram illustrating an example method
`for determining a health score associated with a vehicle.
`FIG. 9 is a flow diagram illustrating an example method
`for updating a health score associated with a vehicle.
`FIG. 10 is a flow diagram illustrating an example method
`for determining a probability that a browsing session was
`initiated in response to watching and/or listening to media
`associated with a campaign.
`FIG. 11 is a flow diagram illustrating an example method
`for determining whether a household performed a transac(cid:173)
`tion associated with a campaign.
`FIG. 12 is a flow diagram illustrating another example
`method for determining a probability that a browsing session
`was initiated in response to watching and/or listening to 30
`media associated with a campaign.
`FIG. 13 is a flow diagram illustrating another example
`method for determining a probability that a browsing session
`was initiated in response to watching and/or listening to
`media associated with a campaign.
`FIG. 14 is a flow diagram illustrating another example
`method for determining a probability that a browsing session
`was initiated in response to watching and/or listening to
`media associated with a campaign.
`FIG. 15 is a flow diagram illustrating an example method 40
`for associating a unique identifier with a physical address
`associated with a user and then determining a probability
`that that user initiated a browser session in response to
`watching and/or listening media associated with a campaign.
`FIG. 16 is a flow diagram illustrating an example method 45
`for associating a unique identifier with a physical address
`associated with a user and then determining when to send
`notifications to the user.
`
`DETAILED DESCRIPTION
`
`FIG. 1 illustrates a block diagram of an example com(cid:173)
`puting device 100. The computing device 100 may include
`a personal computer, such as a laptop or desktop computer,
`a tablet device, a cellular phone or smartphone, a server, or
`another type of computing device. The computing device
`100 may include a processor 102, a communication interface
`104, a memory 106, a display 108, input devices 110, output
`devices 112, and/or a GPS circuit 114. The computing device
`100 may include additional, different, or fewer components. 60
`The processor 102 may include one or more general
`purpose processors, special purpose processors, conven(cid:173)
`tional processors, digital signal processors (DSPs ), micro(cid:173)
`processors, integrated circuits, a progranimable logic device
`(PLD), application specific integrated circuits (ASICs), or 65
`the like. The processor 102 may perform signal coding, data
`processing, image processing, power control, input/output
`
`US 11,823,219 B2
`
`4
`processing, and/or any other functionality that enables the
`computing device 100 to perform as described herein.
`The processor 102 may store information
`in and/or
`retrieve information from the memory 106. The memory 106
`5 may include a non-removable memory and/or a removable
`memory. The non-removable memory may include random(cid:173)
`access memory (RAM), read-only memory (ROM), a hard
`disk, or any other type of non-removable memory storage.
`The removable memory may include a subscriber identity
`module (SIM) card, a memory stick, a memory card, or any
`other type of removable memory. The memory may be local
`memory or remote memory external to the computing device
`100. The memory 106 may store instructions which are
`executable by the processor 102. Different information may
`15 be stored in different locations in the memory 106.
`The memory 106 may comprise a computer-readable
`storage media or machine-readable storage media that stores
`computer-executable
`instructions
`for performing
`as
`described herein. The computer-executable instructions may
`20 comprise one or more portions of the procedures 300, 400,
`500, 600, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500,
`and/or 1600 for performing as described herein. The pro(cid:173)
`cessor 102 may access the instructions from memory 106 for
`being executed to cause the processor 102 to operate as
`25 described herein, or to operate one or more devices as
`described herein.
`The processor 102 that may communicate with other
`devices via the communication device 104. The communi(cid:173)
`cation device 104 may transmit and/or receive information
`over the network 116, which may include one or more other
`computing devices. The communication device 104 may
`perform wireless and/or wired communications. The com(cid:173)
`munication device 104 may include a receiver, transmitter,
`transceiver, or other device capable of performing wireless
`35 communications via an antenna. The communication device
`104 may be capable of communicating via one or more
`protocols, such as a cellular communication protocol, a
`Wi-Fi communication protocol, Bluetooth®, a near field
`communication
`(NFC) protocol, an
`internet protocol,
`another proprietary protocol, or any other radio frequency
`(RF) or communications protocol. The computing device
`100 may include one or more communication devices 104.
`The processor 102 may be in communication with a
`display 108 for providing information to a user. The infor(cid:173)
`mation may be provided via a user interface on the display
`108. The information may be provided as an image gener-
`ated on the display 108. The display 108 and the processor
`102 may be in two-way communication, as the display 108
`may include a touch-screen device capable of receiving
`50 information from a user and providing such information to
`the processor 102.
`The processor 102 may be in communication with a GPS
`circuit 114 for receiving geospatial information. The pro(cid:173)
`cessor 102 may be capable of determining the GPS coordi-
`55 nates of the wireless communication device 100 based on
`the geospatial information received from the GPS circuit
`114. The geospatial information may be communicated to
`one or more other communication devices to identify the
`location of the computing device 100.
`The processor 102 may be in communication with input
`devices 110 and/or output devices 112. The input devices
`110 may include a camera, a microphone, a keyboard or
`other buttons or keys, and/or other types of input devices for
`sending information to the processor 102. The display 108
`may be a type of input device, as the display 108 may
`include touch-screen sensor capable of sending information
`to the processor 102. The output devices 112 may include
`
`Launch Labs EX1003 Page 20
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`US 11,823,219 B2
`
`5
`speakers, indicator lights, or other output devices capable of
`receiving signals from the processor 102 and providing
`output from the computing device 100. The display 108 may
`be a type of output device, as the display 108 may provide
`images or other visual display of information received from
`the processor 102.
`FIG. 2 illustrates a block diagram of an example com(cid:173)
`puting network system 200. The computing network system
`200 may include one or more computing devices 230a-230n
`that may be capable of communicating digital messages with
`one another, either directly or via the network 220. The
`computing devices 230-230n may be user devices capable of
`logging into a session (e.g., a browsing session) of an
`interactive computing environment and providing real-time
`interactive data via the network 220. The network 220 may
`include a wired and/or wireless network. For example, the
`network 220 may include a Wi-Fi communication network,
`a Wi-MAX communication network, a cellular communi(cid:173)
`cation network (e.g., CDMA, HSPA+, LTE, etc.), and/or a
`television white space (TVWS) communication network.
`The network 220 may include one or more communication
`networks.
`The one or more computing devices 230a-230n may be
`capable of communicating digital messages to and/or receiv(cid:173)
`ing digital messages from the computing device 210 via the
`network 220. The computing device 210 may be a server,
`such as a web server, for providing a user interface to the
`computing devices 230a-230n. The computing device 210
`may be in communication with an application executing
`locally on the computing devices 230a-230n for providing a
`user interface at the computing devices. The display of
`information may be generated locally at the computing
`devices 230a-230n or at the computing device 210 and
`provided via an application (e.g., a web browser) at the
`computing devices 230a-230n.
`One or more of the computing devices 230a-230n may be
`operated by an administrative user capable of configuring
`sessions of an interactive computing environment that may
`be stored at the computing device 210. The computing
`device operated by the administrative user may submit
`credentials to the computing device 210 to allow the session
`to be configured. The session may be accessed by the
`computing devices 230a-230n via the network 220.
`FIG. 3 is a flow diagram of an example method 300 that
`may be implemented by one or more computing devices
`(e.g., such as the computing devices 230a-230n shown in
`FIG. 2) to identify a physical address of a user based on
`anonymous data. The method 300, or portions thereof, may
`be performed to enable engagement with the user based on
`one or more return visits to a uniform resource locator
`(URL). Engagement with the user may include sending
`notification(s) to the user, determining one or more adver(cid:173)
`tisements for the user, track service needs for a vehicle
`operated by the user or someone in the user's household,
`and/or the like. For example, the user may be an owner/
`operator of the vehicle. The notification(s) may include
`emails, text messages, mobile phone notifications, phone
`calls, advertisements, and/or the like. The method 300, or
`portions thereof, may be performed at a single computing
`device or may be distributed across multiple computing
`devices (e.g., multiple servers and/or a user device). The
`method 300, or portions thereof, may be performed to enable
`users, such as administrative users, to determine that the user
`has a specific interest in one or more products. The method
`300, or portions thereof, may be performed to enable the
`administrative users to quantify the user's interest in the
`product(s). The method 300, or portions thereof, may be
`
`6
`performed to enable adaptive generation of notifications to
`the user based on the specific interest in the product and/or
`the physical address associated with the user. The method
`300 may comprise
`instructions
`that may be stored in
`5 memory as computer-readable or machine-readable storage
`media that may be executed by the one or more computing
`devices for executing the method 300. The method 300, or
`portions thereof, may reduce the amount of processing
`resources used by the computing device during a predeter-
`lO mined period (e.g., day). The method 300, or portions
`thereof, may improve the functionality of a computer net(cid:173)
`work system (e.g., such as the computing network system
`200 shown in FIG. 2) associated with engagement of the
`15 user. In addition, the method 300, or portions thereof, may
`implement a distributed network architecture, as shown in
`FIG. 2, which may reduce the amount of signaling between
`a user computing device and one or more administrative
`computing devices (e.g., such as the computing devices
`20 230a, 230b, 230n shown in FIG. 2) and may reduce the
`amount of processing resources consumed by the adminis(cid:173)
`trative computing device(s).
`The method 300 may start, at 302, when a user computing
`device ( e.g., such as computing device 210 shown in FIG. 2)
`25 accesses a URL. For example, a user may initialize a
`browser application on the user computing device and may
`navigate to a website (e.g., the URL) within the browser
`application using a browser session. The user may visit
`various URLs associated with a brand ( e.g., manufacturer,
`30 company, etc.). Each of the URLs may correspond to a
`product sold by the brand.
`As illustrated in FIG. 3, a computing device (e.g., such as
`computing device 230a, 230b, or 230n shown in FIG. 2)
`35 may receive, at 304, data from the browser session on the
`user computing device. The computing device may be
`associated with the URL. For example, the computing
`device may be a server that administers and/or manages a
`resource associated with the URL. The computing device
`40 may initialize a script (e.g., javascript, PHP, Python, Ruby,
`Groovy, Perl, and/or the like) when the data is received via
`the browser session. The computing device may retrieve the
`data using the script. The data may be associated with the
`user, the user computing device, and/or the browser appli-
`45 cation associated with the browser session. The data may be
`anonymous data ( e.g., data that includes no personally
`identifiable
`information associated with the user). The
`anonymous data may include a time, a date, one or more
`website URLs, a referring URL, a browser type, a language,
`50 an IP address, and/or location data. The computing device
`may determine a device type based on the browser type. For
`example, the computing device may determine whether the
`user computing device is a mobile device based on the
`browser type. The location data may include a latitude
`55 coordinate, a longitude coordinate, and/or a device accuracy
`indication. The device accuracy indication may indicate the
`accuracy associated with the user computing device's mea(cid:173)
`surements of the latitude coordinate and the longitude coor(cid:173)
`dinate. For example, the device accuracy indication may
`60 indicate the accuracy of the user computing device's GPS
`(e.g., such as the GPS 114 shown in FIG. 1). The device
`accuracy may depend on the device type. For example, a
`mobile device may indicate a device accuracy of approxi(cid:173)
`mately 2 meters or less and a computer that accesses the
`65 URL via a router may indicate a device accuracy of approxi(cid:173)
`mately 70 meters. The anonymous data may be included in
`a header (e.g., a HTTP request header) received from the
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`Launch Labs EX1003 Page 21
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`US 11,823,219 B2
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`7
`user computing device, included in the IP address of the user
`computing device, included in scripts at the application(cid:173)
`level, etc.
`The computing device may determine, at 306, location
`data associated with the browser session. For example, the
`data received from the browser session may include the
`location data, as described herein. Additionally or alterna(cid:173)
`tively, the computing device may determine the location data
`using a tracking cookie installed in the browser application
`on the user computing device. Using IP address and/or router
`location information may not provide accurate enough loca(cid:173)
`tion data to identify a precise physical address for the
`user/user computing device. For example, an accuracy of
`approximately 70 meters could include a plurality of physi(cid:173)
`cal addresses within that radius. Using latitude and longitude
`location data of a mobile device may provide more accurate
`location data to enable identification of the precise physical
`address for the user/user computing device.
`At 308, the computing device may determine a physical
`address ( e.g., a postal address) for the browser session using
`the location data. The physical address may indicate a postal
`address at which the user accessed the URL in the browser
`session. For example, the computing device may generate, at
`308, the physical address using a map API (e.g., such as
`Bing Maps API, Mapbox API, OpenStreetMap API, Leaflet
`API, OpenLayers API, Google Maps API, and/or another
`map API). For example, the computing device may translate,
`at 308, the received latitude and longitude coordinates into
`the physical address using the map APL The computing
`device may determine an address type (e.g., residential,
`apartment/condo building, single family home, commercial,
`and/or the like) based on the physical address. For example,
`the computing device may determine the address type using
`a postal service APL The computing device may use the
`address type to determine a type and/or frequency of noti(cid:173)
`fications sent to the user. The user may access the URL at
`multiple locations/addresses (e.g., home, work, store, res(cid:173)
`taurant, friend's home, etc.). The user computing device
`may identify which location/address is a primary address
`(e.g., home) and which location( s )/ address( es) are secondary
`addresses, for example, based on the frequency of accessing
`the URL at each location/address. The user computing
`device may associate the secondary address(es) to the pri(cid:173)
`mary address.
`The computing device may determine other user infor(cid:173)
`mation based on the physical address such as a name
`associated with the user, an age of the user, a gender of the
`user, demographics associated with the user, and/or psycho(cid:173)
`graphics associated with the user. The demographics asso(cid:173)
`ciated with the user may include race, marital status, house(cid:173)
`hold size, occupation, income, education, and/or living
`status. The psychographics associated with the user may
`include personality
`traits, lifestyles,
`interests, opinions,
`beliefs, values, etc. The computing device may determine
`the type and/or the frequency of notifications sent to the user
`based on the other information. The computing device may
`determine the content of the notifications based on the other
`information.
`The computing device may generate a profile for the user,
`for example, based on the physical address and/or the other
`user information. The user profile may be used to track a
`plurality of factors associated with the user and the user's
`activity. For example, the user profile may track which
`URLs the user accesses, the frequency with which the user
`accesses the URL( s ), demographics associated with the user,
`the address( es) associated with the user, the devices asso(cid:173)
`ciated with the user etc. For example, the computing device
`
`5
`
`8
`may associate multiple unique identifiers having the same
`location data with the same user profile. The user profile may
`be used to generate a targeted marketing campaign.
`At 310, the computing device may be configured to
`identify subsequent return visits to the URL ( e.g., and related
`URLs) from the browser, the user computing device, and/or
`the physical address. For example, the computing device
`may store a unique ID in the browser application ( e.g., using
`a cookie or using the local browser cache) to recognize that
`10 another browser session accessing the URL is from the user
`computing device at the same physical address. When
`another user computing device at the same physical address
`accesses the URL, the computing device may determine that
`15 that other user computing device is the same user and/or user
`household. The computing device may identify, at 310, that
`the user computing device is accessing the URL at a
`secondary address ( e.g., associated with the user and/or user
`profile). The computing device may store a timestamp and
`20 the URL of each subsequent return visit from the browser,
`the user computing device, and/or the physical address. That
`is, the computing device may create a log ofURLs accessed
`by a user profile associated with the browser, the user
`computing device, and/or the physical address. The log may
`25 track the timestamp and URL of each website accessed via
`the browser. The method 300 may end, at 312.
`FIG. 4 is a flow diagram of an example method 400 that
`may be implemented by one or more computing devices
`(e.g., such as the computing devices 230a-230n shown in
`3° FIG. 2) to associate a unique identifier with location data
`from a browser session. The method 400, or portions
`thereof, may be performed to enable engagement with the
`user based on one or more visits to a URL. Engagement with
`35 the user may include sending notification(s) to the user,
`determining one or more advertisements for the user, track
`service needs for a vehicle owned and/or operated by the
`user or someone in the user's household, and/or the like. The
`notification(s) may include emails, text messages, mobile
`40 phone notifications, phone calls, advertisements, and/or the
`like. The method 400, or portions thereof, may be performed
`at a single computing device or may be distributed across
`multiple computing devices (e.g., multiple servers and/or a
`user device). The method 400, or portions thereof, may be
`45 performed to enable users, such as administrative users, to
`determine that the user has a specific interest in one or more
`products. The method 400, or portions thereof, may be
`performed to enable the administrative users to quantify the
`user's interest in the product(s). The method 400, or portions
`50 thereof, may be performed