Selection of the primary user for a multi-user device

Abstract

A system and method for identifying the primary user of a shared device leverages authorization events, such as logins of consumers to various publisher websites or apps, as well as data contained in a hypertext transfer protocol (HTTP) header such as user agent and IP address, to determine the user that is most frequently using a multi-user device and most likely to be using that device in the future when a targeted message is sent, while at the same time maximizing the stability of user assignment to those who actually live together in the same household or at the same address. This information is stored in a holistic online reach identity graph, which links cookies and / or devices to identifiers for users, and assigns a value to each user relative to the likelihood that such user is the primary user or less active user of the corresponding device.

Description

Attorney Docket No. RAMP-00313-WOSELECTION OF TH E PRIMARY USER FOR A MULTI-USER DEVICEREFERENCES TO PRIOR APPLICATIONS

[0001] This application claims priority to US provisional patent application no. 63 / 712,904, filedOctober 28, 2024. Such application is incorporated by reference herein in its entirety.BACKGROUN D OF THE INVENTION

[0002] Personally Identifiable Information (PH) refers to data that can be used to identify, contact, or locate a specific individual or user. This includes direct identifiers like names, addresses, and social security numbers, as well as indirect identifiers that can be combined with other information to identify a person.

[0003] Identity resolution is the process of combining multiple identifiers and data points to create a unified, accurate profile of an individual user. It involves linking these disparate pieces of information from different sources to form a cohesive view of a person's identity across digital and offline interactions.

[0004] The data used for identity resolution may be stored in a data structure known as an identity graph. An identity graph is a database that connects various data to a single user profile, enabling organizations to track and understand user behavior across different platforms and devices.

[0005] Identity graphs are often differentiated as being first-party, second-party, or third-party graphs. A first-party identity graph is one that is created and owned by a single organization using data collected directly from the users with which it interacts. A second-party identity graph is the first-party graph of a different organization; data may be shared from second-party identity graphs through a partnership between two or more organizations sharing their first- party data. Third-party identity graphs contain data compiled by data aggregators using dataAttorney Docket No. RAMP-00313-WO from multiple sources, generally without direct relationships with the users. The owners of these third-party identity graphs may provide services to the owners of first-party graphs, whereby the first-party graphs are improved in various ways using data from the provider's more comprehensive third-party graph.

[0006] Onboarding in digital advertising refers to the process of integrating offline customer data into online digital marketing platforms. This allows advertisers to connect their first-party customer information, such as customer relationship management (CRM) data, loyalty program details, or offline purchase histories, with online identifiers like cookies or email addresses. The onboarding process typically involves working with a data onboarding provider who securely matches and anonymizes the offline data with online profiles. This creates a bridge between physical and digital customer interactions, enabling more personalized and targeted messaging campaigns across various digital channels. Successful onboarding enhances audience segmentation, improves cross-channel marketing efforts, and allows for more accurate measurement of marketing impact by connecting online ad exposures with offline behaviors. Global identifiers may be used for onboarding, such as the RampID identifiers supplied by LiveRamp, Inc. of San Francisco, California.

[0007] It is not uncommon for users to share electronic communications devices, including desktop computers, laptop computers, tablets, mobile phones, and connected televisions (CTVs). Even mobile phones, which are often assumed to be highly personal devices, are shared more often than previously thought. It has been found that more than ten percent of mobile devices are shared among multiple users regularly. Investigations by the inventors hereof have shown that an estimated 83% of all electronic communications devices have multiple users.

[0008] The fact that devices are often shared greatly complicates the task of targeted messaging to a device, since the party sending the message cannot know which user of theAttorney Docket No. RAMP-00313-WO multi-user device will see the message. It is therefore important and valuable to determine the primary user of the device, that is, the user who is most likely to see a message that is sent to the device. Previous attempts to determine the primary user have been quite simplistic, such as simply assuming that the first-seen or last-seen user is the primary user, but this does not generate good results. The first-seen approach generates stability in ID assignment to a device, but is often inaccurate. The last-seen approach of ID assignment to a device gets better accuracy, but is highly unstable as the identity of the supposed primary user changes frequently.SUMMARY OF THE INVENTION

[0009] The invention is directed to a system and method for identifying the primary and less active users (secondary, tertiary, etc.) of a shared device for use in targeting digital messaging and maximizing message effectiveness.

[0010] In certain embodiments, the invention leverages authorization events, such as logins of consumers to various publisher websites or apps as well as data contained in the HTTP header such as user agent and IP address. These are used to determine the user that is most frequently using a multi-user device and most likely to be using that device in the future when a targeted message is sent, while at the same time maximizing the stability of user assignment to those who actually live together in the same household or at the same address. This information is stored in a holistic online reach identity graph, which links cookies and / or devices to identifiers for users, and assigns a value to each user relative to the likelihood that such user is the primary user or less active user of the corresponding device.

[0011] Tests by the inventors have shown a roughly doubling of accuracy of user message targeting over the prior art methods described above, while simultaneously maximizing stability by using this approach, which prioritizes relationships (and device sharing) that occur in a household.Attorney Docket No. RAMP-00313-WO

[0012] These and other features, objects and advantages of the present invention will become better understood from a consideration of the following detailed description of the preferred embodiments and appended claims in conjunction with the drawings as described following:BRIEF DESCRIPTION OF DRAWINGS

[0013] Fig. 1 is a diagram showing an overall architecture according to an embodiment of the present invention.

[0014] Fig. 2 is a schematic for a computing component of a computing cluster for implementing an embodiment of the present invention.DETAILED DESCRIPTION OF THE INVENTION

[0015] Before the present invention is described in further detail, it should be understood that the invention is not limited to the particular embodiments described, and that the terms used in describing the particular embodiments are for the purpose of describing those particular embodiments only, and are not intended to be limiting, since the scope of the present invention will be limited only by the claims.

[0016] An embodiment of the invention may be described in more detail now with reference toFig. 1. In this example, client device 10 receives a series of login events. Specifically, the system sees ten authorization events associated with a first email 12 and thirty authorization events associated with a second email 14 through a particular publisher website or app 16. This fires a tracking pixel 18 at the website or app 16, which sends a signal to the provider concerning the authorization event. The email and HTTP header 20 is thus sent to the provider at the initialization of each event.

[0017] Once this information is received in the provider system 22, it is stored in an appropriate database forming a portion of provider system 22. This information is used to build a uniqueAttorney Docket No. RAMP-00313-WO data structure known herein as a truly holistic online reach (THOR) graph 24. This graph has nodes corresponding to cookies or mobile ad IDs (MAIDS) from particular user devices, and links those nodes to nodes that correspond to particular identifiers within provider system 22. These person identifiers are unique for each user within a particular geography, such that they may be used to disambiguate individual users. In this way, the THOR graph 24 associates devices (through cookies or MAIDs) with persons through person identifiers.

[0018] Data science platform 30, using either an algorithm approach or a machine-learning(ML) approach, depending upon the particular embodiment of the invention, is then applied to THOR graph 24 at data science calculation block 26 to determine the likelihood that each user identifier corresponds to the primary user for a particular device. A number may be assigned to each user identifier for each device, such as with a value between 0 and 1, where the value is proportional to the likelihood that the user is the primary user. From this, a primary user is calculated for the device or device proxy such as a cookie or a MAID.

[0019] In a particular embodiment when an algorithm is used rather than a machine-learning approach, then the processing within data science platform 30 may proceed in this way. In this example, the device IDs are MAIDs, which are examined from a defined lookback period, such as 34 weeks. Opt-outs are excluded at the beginning of the process, in order to honor opt-out requests as an inherent part of the system.

[0020] The system then uses hashed email addresses as the initial identifiers for persons, and counts the number of distinct timestamps for each MAID and hashed email. The most recent device type is calculated and assigned to the MAID. For example, MAIDs may include IDFAs (used on Apple devices) and AAlDs (used on Google / Android devices), and data science platform 30 ensures that there is no conflicting device type. Simultaneously, the number of distinct timestamps are counted while the first and last timestamp for each device and email address isAttorney Docket No. RAMP-00313-WO identified. At this point, the hashed email address is associated with the corresponding unique person identifier, which in a particular implementation could be the RampID® system from LiveRamp, Inc. The online identity graph 32 may be used to create this association, and may be enhanced by using a client's own onboarding data file 34. The result is a correspondence table between person identifiers and device IDs.

[0021] Continuing with the processing at data science platform 30, and using the table just created, the first and last timestamp for each device is identified along with the number of times that device was seen. The number of times the device was seen may be broken out by MAID type, such as by differentiating between AAID and IDFA. This is repeated for the correspondence between device and personal identifier. The result of this processing is, for each device, the device's first seen timestamp; last seen timestamp; number of times seen; most-seen email address; the first timestamp of the most-seen email address; the last timestamp of the most-seen email address; and the last device type of the most-seen email address. This is repeated for personal identifiers in place of the hashed email addresses.

[0022] Using all of the information previously derived, processing at data science platform 30 continues by counting the number of personal identifiers seen per device, which will be referred to herein as "device count." Each MAID-to-personal identifier pairing is then ranked based on the previously calculated counts, and each personal identifier is ranked by the device count, referred to herein simply as "rank." Records where the rank is less than a particular value, for example a value of 30, may be discarded in certain implementations. For the remaining records, for each device ID, data science platform 30 selects the single "best" personal identifier using the personal identifier count. It then creates a table that associates each device ID with the best personal identifier. This process is repeated except that the top three personal identifiers are identified by count, and a table is created that associates the "top" personal identifiers withAttorney Docket No. RAMP-00313-WO each device ID. These two tables may be used to determine the primary (dominant) user 28, in different manners depending upon the embodiment of the invention, such as by assigning a score as previously described.

[0023] Data science platform 30 then applies this information using the provider's online identity graph 32. This is a comprehensive identity graph containing information concerning all known users in the relevant geography. The data science platform 30 can then use this information to push 36 the appropriate targeted message to the corresponding primary user 28 for a particular device, using the additional information about the user from the online identity graph 32 for purposes of reaching such primary user 28.

[0024] The methods described herein may in various embodiments be implemented by any combination of hardware and software. For example, in one embodiment, the methods may be implemented by a computer system (e.g., a computer system as in Fig. 2) or a collection of computer systems, each of which includes one or more hardware processors executing program instructions stored on a computer-readable physical storage medium coupled to the hardware processors. The program instructions may implement the functionality described herein (e.g., the functionality of various hardware servers and other components that implement the network-based cloud and non-cloud computing resources described herein). The various methods as illustrated in the figures and described herein represent example implementations. The order of any method may be changed, and various elements may be added, modified, or omitted.

[0025] Fig. 2 is a block diagram illustrating an example computer hardware system, according to various embodiments. Computer system 140 may implement a hardware portion of a cloud computing system as forming parts of the various implementations of the present invention. Computer system 140 may be any of various types of hardware devices, including, but notAttorney Docket No. RAMP-00313-WO limited to, a commodity server, personal computer system, desktop computer, laptop or notebook computer, mainframe computer system, handheld computer, workstation, network computer, a consumer device, application server, physical storage device, telephone, mobile telephone, or in general any type of computing node, compute node, compute device, and / or hardware computing device.

[0026] Computer system 140 includes one or more hardware processors 140a, 141b... Mln (any of which may include multiple processing cores, which may be single or multi-threaded) coupled to a physical system memory 142 via an input / output (I / O) interface 144. Computer system 140 further may include a network interface 146 coupled to I / O interface 144. In various embodiments, computer system 140 may be a single processor system including one hardware processor 141a, or a multiprocessor system including multiple hardware processors 141a, 141b... Mln as illustrated in Fig. 2.

[0027] Processors 141a, etc. may be any suitable processors capable of executing computing instructions. For example, in various embodiments, processors 141a, etc. may be general- purpose or embedded processors implementing any of a variety of instruction set architectures. In multiprocessor systems, each of processors 141a, etc. may commonly, but not necessarily, implement the same instruction set. The computer system 140 also includes one or more hardware network communication devices (e.g., network interface 146) for communicating with other systems and / or components over a communications network, such as a local area network, wide area network, or the Internet. For example, a client application executing on system 140 may use network interface 146 to communicate with a server application executing on a single hardware server or on a cluster of hardware servers that implement one or more of the components of the systems described herein in a cloud computing environment as implemented in various sub-systems. In another example, an instance of a server applicationAttorney Docket No. RAMP-00313-WO executing on computer system 140 may use network interface 146 to communicate with other instances of an application that may be implemented on other computer systems.

[0028] In the illustrated embodiment, computer system 140 also includes one or more physical persistent storage devices 148 and / or one or more I / O devices 150. In various embodiments, persistent storage devices 148 may correspond to disk drives, tape drives, solid-state memory or drives, other mass storage devices, or any other persistent storage devices. Computer system 140 (or a distributed application or operating system operating thereon) may store instructions and / or data in persistent storage devices 148, as desired, and may retrieve the stored instructions and / or data as needed. For example, in some embodiments, computer system 140 may implement one or more nodes of a control plane or control system, and persistent storage 148 may include the solid-state drives (SSDs) attached to that server node. Multiple computer systems 140 may share the same persistent storage devices 148 or may share a pool of persistent storage devices, with the devices in the pool representing the same or different storage technologies, including such technologies as described above.

[0029] Computer system 140 includes one or more physical system memories 142 that may store code / instructions 143 and data 145 accessible by processor(s) 141a, etc. The system memories 142 may include multiple levels of memory and memory caches in a system designed to swap information in memories based on access speed, for example. The interleaving and swapping may extend to persistent storage devices 148 in a virtual memory implementation, where memory space is mapped onto the persistent storage devices 148. The technologies used to implement the system memories 142 may include, by way of example, static randomaccess memory (RAM), dynamic RAM, read-only memory (ROM), non-volatile memory, solid- state memory, or flash-type memory.Attorney Docket No. RAMP-00313-WO

[0030] As with persistent storage devices 148, multiple computer systems 140 may share the same system memory systems 142 or may share a pool of system memories 142. System memory or memory systems 142 may contain program instructions 143 that are executable by processor(s) 141a, etc. to implement the routines described herein.

[0031] In various embodiments, program instructions 143 may be encoded in binary, Assembly language, any interpreted language such as Java, compiled languages such as C / C++, or in any combination thereof; the particular languages given here are only examples. In some embodiments, program instructions 143 may implement multiple separate clients, server nodes, and / or other components.

[0032] In some implementations, program instructions 143 may include instructions executable to implement an operating system (not shown), which may be any of various operating systems, such as UNIX, LINUX, Solaris™, MacOS™, or Microsoft Windows™. Any or all of program instructions 143 may be provided as a computer program product, or software, that may include a non-transitory computer-readable storage medium having stored thereon instructions, which may be used to program a computer system (or other electronic devices) to perform a process according to various implementations. A non-transitory computer-readable storage medium may include any mechanism for storing information in a form (e.g., software or processing application) readable by a machine (e.g., a physical computer).

[0033] Generally speaking, a non-transitory computer-accessible medium may include computer-readable storage media or memory media such as magnetic or optical media, e.g., disk or DVD / CD-ROM, coupled to or in communication with computer system 140 via I / O interface 144. A non-transitory computer-readable storage medium may also include any volatile or non-volatile media such as RAM or ROM that may be included in some embodiments of computer system 140 as system memory 142 or another type of memory. In otherAttorney Docket No. RAMP-00313-WO implementations, program instructions may be communicated using optical, acoustical or other form of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.) conveyed via a communication medium such as a network and / or a wired or wireless link, such as may be implemented via network interface 606. Network interface 146 may be used to interface with other devices 142, which may include other computer systems or any type of external electronic device.

[0034] In some embodiments, system memory 142 may include data store 145, as described herein. In general, system memory 142 and persistent storage 148 may be accessible on other devices 142 through a network and may store data blocks, replicas of data blocks, metadata associated with data blocks, and / or their state, database configuration information, and / or any other information usable in implementing the routines described herein.

[0035] In one embodiment, I / O interface 144 may coordinate I / O traffic between processors141a, etc., system memory 142, and any peripheral devices in the system, including through network interface 146 or other peripheral interfaces. In some embodiments, I / O interface 144 may perform any necessary protocol, timing or other data transformations to convert data signals from one component (e.g., system memory 142) into a format suitable for use by another component (e.g., processors 141a, etc.).

[0036] In some embodiments, I / O interface 144 may include support for devices attached through various types of peripheral buses, such as a variant of the Peripheral Component Interconnect (PCI) bus standard or the Universal Serial Bus (USB) standard, as examples. Also, in some embodiments, some or all of the functionality of I / O interface 144, such as an interface to system memory 142, may be incorporated directly into processor(s) 141a, etc.

[0037] Network interface 146 may allow data to be exchanged between computer system 140 and other devices attached to a network, such as other computer systems (which mayAttorney Docket No. RAMP-00313-WO implement one or more storage system server nodes, primary nodes, read-only node nodes, and / or clients of the database systems described herein), for example. In addition, I / O interface 144 may allow communication between computer system 140 and various I / O devices 150 and / or remote storage 148. Input / output devices 150 may, in some embodiments, include one or more display terminals, keyboards, keypads, touchpads, scanning devices, voice or optical recognition devices, or any other devices suitable for entering or retrieving data by one or more computer systems 140. These may connect directly to a particular computer system 140 or generally connect to multiple computer systems 140 in a cloud computing environment, grid computing environment, or other system involving multiple computer systems 140.

[0038] Multiple input / output devices 150 may be present in communication with computer system 140 or may be distributed on various nodes of a distributed system that includes computer system 140. In some embodiments, similar input / output devices may be separate from computer system 140 and may interact with one or more nodes of a distributed system that includes computer system 140 through a wired or wireless connection, such as over network interface 146. Network interface 146 may commonly support one or more wireless networking protocols (e.g., Wi-Fi / I EEE 802.11, or another wireless networking standard).

[0039] Network interface 146 may support communication via any suitable wired or wireless general data networks, such as other types of Ethernet networks, for example. Additionally, network interface 146 may support communication via telecommunications / telephony networks such as analog voice networks or digital fiber communications networks, via storage area networks such as Fibre Channel SANs, or via any other suitable type of network and / or protocol. In various embodiments, computer system 140 may include more, fewer, or different components than those illustrated in Fig. 2 (e.g., displays, video cards, audio cards, peripheral devices, or an Ethernet interface).Attorney Docket No. RAMP-00313-WO

[0040] Any of the distributed system embodiments described herein, or any of their components, may be implemented as one or more network-based services in the cloud computing environment. For example, a read-write node and / or read-only nodes within the database tier of a hardware database system may present database services and / or other types of physical data storage services that employ the distributed storage systems described herein to clients as network-based services.

[0041] In some embodiments, a network-based service may be implemented by a software and / or hardware system designed to support interoperable machine-to-machine interaction over a network. A web service may have an interface described in a machine-processable format. Other systems may interact with the network-based service in a manner prescribed by the description of the network-based service's interface. For example, the network-based service may define various operations that other systems may invoke, and may define a particular application programming interface (API) to which other systems may be expected to conform when requesting the various operations.

[0042] In various embodiments, a network-based service may be requested or invoked through the use of a message that includes parameters and / or data associated with the network-based services request. Such a message may be formatted according to a particular markup language such as Extensible Markup Language (XML), and / or may be encapsulated using a protocol. To perform a network-based services request, a network-based services client may assemble a message including the request and convey the message to an addressable endpoint (e.g., a Uniform Resource Locator (URL)) corresponding to the web service, using an Internet-based application layer transfer protocol such as Hypertext Transfer Protocol (HTTP).Attorney Docket No. RAMP-00313-WO

[0043] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0044] Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention, a limited number of the exemplary methods and materials are described herein. It will be apparent to those skilled in the art that many more modifications are possible without departing from the inventive concepts herein.

[0045] All terms used herein should be interpreted in the broadest possible manner consistent with the context.

[0046] When a grouping is used herein, all individual members of the group and all combinations and sub-combinations possible of the group are intended to be individually included.

[0047] When a range is stated herein, the range is intended to include all sub-ranges within the range, as well as all individual points within the range.

[0048] When "about," "approximately," or like terms are used herein, they are intended to include amounts, measurements, or the like that do not depart significantly from the expressly stated amount, measurement, or the like, such that the stated purpose of the apparatus or process is not lost.

[0049] All references cited herein are hereby incorporated by reference to the extent that there is no inconsistency with the disclosure of this specification.

[0050] The present invention has been described with reference to certain preferred and alternative embodiments that are intended to be exemplary only and not limiting to the full scope of the present invention, as set forth in the appended claims.

Claims

Attorney Docket No. RAMP-00313-WOClaims1. A method for identifying a primary user of a shared device, comprising: receiving a plurality of authorization events associated with a device, each authorization event corresponding to a user identifier; storing the authorization events, device information, and corresponding user identifiers in a database; constructing a holistic online reach graph based on the stored authorization events, and associated device details and observations, wherein the graph comprises nodes corresponding to device identifiers, user behavioral and location data, and user identifiers; applying one of an algorithm or a machine-learning approach to the holistic online reach graph to determine a likelihood that each user identifier corresponds to a primary user of the device; assigning a value to each user identifier for the device, wherein the value is proportional to the likelihood that the user identifier corresponds to the primary user; determining the primary user of the device based on the assigned values; and updating an online identity graph with the determined primary user information.

2. The method of claim 1, wherein the authorization events comprise login events to publisher websites or applications.

3. The method of claim 1, wherein the device identifiers comprise one or more of cookies, mobile ad IDs, CTV IDs, and proxies for such identifiers associated with the device.

4. The method of claim 1, wherein the user and household identifiers are unique for each user and household within a particular geography.Attorney Docket No. RAMP-00313-WO5. The method of claim 1, further comprising the step of using the updated online identity graph to target digital messaging to the primary user of the device.

6. The method of claim 1, wherein the value assigned to each user identifier is between 0 and 1.

7. The method of claim 1, further comprising receiving event and device signal information from a tracking pixel, whether in batch or real time, from a publisher website or mobile app provider in response to an authorization event.

8. A system for identifying a primary user of a shared device, comprising: a processor; a memory coupled to the processor and storing instructions that, when executed by the processor, cause the system to: receive a plurality of authorization events associated with a device, each authorization event corresponding to a user identifier; store the authorization events and corresponding user identifiers in a database; construct a holistic online reach graph based on the stored authorization events, wherein the graph comprises nodes corresponding to device identifiers and user identifiers and at least one of user behavioral and pseudonymous location data; apply an algorithm or a machine-learning approach to the holistic online reach graph to determine a likelihood that each user identifier corresponds to a primary user of the device; assign a value to each user identifier for the device, wherein the value is proportional to the likelihood that the user identifier corresponds to the primary user; determine the primary user of the device based on the assigned values; andAttorney Docket No. RAMP-00313-WO update an online identity graph with the determined primary user information.

9. The system of claim 8, wherein the authorization events comprise login events to publisher websites or mobile applications.

10. The system of claim 8, wherein the device identifiers comprise at least one of cookies, mobile Ad IDs, CTV IDs, RampIDs, and proxies associated with the device.

11. The system of claim 8, wherein the user identifiers are unique for each user within a particular geography.

12. The system of claim 8, wherein the instructions further cause the system to use the updated online identity graph to target digital messaging to the primary user of the device.

13. The system of claim 8, wherein the instructions further cause the system to receive device information or user information or both from a publisher tracking pixel signal, or other such mechanism, implemented on a website or mobile application in response to an authorization event.

14. The system of claim 8, wherein constructing the holistic online reach graph comprises linking nodes corresponding to device IDs from user devices to nodes corresponding to user identifiers within a provider's system.

15. A method for targeting digital messaging to a primary user of a shared device, comprising: receiving a plurality of authorization events associated with a device, each authorization event corresponding to a user identifier; storing the authorization events and corresponding user identifiers in a database; constructing a holistic online reach graph based on the stored authorization events, wherein the graph comprises nodes corresponding to device identifiers and user identifiers;Attorney Docket No. RAMP-00313-WO applying one of an algorithm or a machine-learning approach to the holistic online reach graph to determine a likelihood that each user identifier corresponds to a primary user of the device; assigning a value to each user identifier for the device, wherein the value is proportional to the likelihood that the user identifier corresponds to the primary user that is most likely to be the dominant user seen in the future; determining the primary user of the device based on the assigned values; retrieving additional information about the primary user from the online identity graph; and sending a targeted digital message to the shared device based on the additional information about the primary user.

16. The method of claim 15, wherein constructing the holistic online reach graph comprises linking nodes corresponding to device IDs from user devices to nodes corresponding to user identifiers within a provider's system.

17. A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to: receive a plurality of authorization events associated with a device, each authorization event corresponding to a user identifier; store the authorization events and corresponding user identifiers in a database; construct a holistic online reach graph based on the stored authorization events, wherein the graph comprises nodes corresponding to device identifiers and user identifiers;Attorney Docket No. RAMP-00313-WO apply one of an algorithm or a machine-learning approach to the holistic online reach graph to determine a likelihood that each user identifier corresponds to a primary user of the device; assign a value to each user identifier for the device, wherein the value is proportional to the likelihood that the user identifier corresponds to the primary user; determine the primary user of the device based on the assigned values; and update an online identity graph with the determined primary user information.

18. The method of claim 17, wherein the authorization events comprise login events to publisher websites or applications.

19. The method of claim 17, wherein the device identifiers comprise one or more of cookies, mobile Ad IDs, CTV IDs, RampIDs, or proxies associated with the device.

20. The method of claim 17, further comprising the step of using the updated online identity graph to target digital messaging to the primary user of the device.

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