Abstract: A system and method is provided to track users across applications and devices, and to distinguish similar or different users among a number of commonly-used devices. Accordingly, systems and methods are provided for analyzing user device information to determine different users among a number of devices. In particular, user's behavior when using the devices is observed, and responsive to that observed behavior, is the determined whether a particular users are distinguishable from other users. For instance, it may be useful to distinguish between users that operate the same devices or exist within a similar network (e.g., a home network, a workplace network, etc.) for various purposes. For instance, it may be useful to distinguish between users for displaying advertising to these users, performing some other operation with respect to a subset of the users.

Abstract: Device identifiers for devices, such as computing devices, software, and applications, etc., are analyzed to determine whether the device identifiers are associated with each other (e.g., are connected to each other) and how strongly the device identifiers are associated with each other. A stronger connection between two device identifiers may indicate that the device identifiers are connected to the same user, same computing device, or same household. Connections between device identifiers are identified and/or weighted if the device identifiers are associated with the same network or if the device identifiers use the same login information to access content. The identifiers and their connections may form a device graph. Content is targeted to the clients based on the device graph. The device graph may also be used to perform targeting and orchestration of advertising, attribution reporting, analytics, and content optimization across devices in the device graph.

Abstract: A system and method is provided to track users across applications and devices, and to distinguish similar or different users among a number of commonly-used devices. Accordingly, systems and methods are provided for analyzing user device information to determine different users among a number of devices. In particular, user's behavior when using the devices is observed, and responsive to that observed behavior, is the determined whether a particular users are distinguishable from other users. For instance, it may be useful to distinguish between users that operate the same devices or exist within a similar network (e.g., a home network, a workplace network, etc.) for various purposes. For instance, it may be useful to distinguish between users for displaying advertising to these users, performing some other operation with respect to a subset of the users.

Abstract: A system and method is provided to track users across applications and devices, and to distinguish similar or different users among a number of commonly-used devices. Accordingly, systems and methods are provided for analyzing user device information to determine different users among a number of devices. In particular, user's behavior when using the devices is observed, and responsive to that observed behavior, is the determined whether a particular users are distinguishable from other users. For instance, it may be useful to distinguish between users that operate the same devices or exist within a similar network (e.g., a home network, a workplace network, etc.) for various purposes. For instance, it may be useful to distinguish between users for displaying advertising to these users, performing some other operation with respect to a subset of the users.

Abstract: A system and method is provided to track users across applications and devices, and to distinguish similar or different users among a number of commonly-used devices. Accordingly, systems and methods are provided for analyzing user device information to determine different users among a number of devices. In particular, user's behavior when using the devices is observed, and responsive to that observed behavior, is the determined whether a particular users are distinguishable from other users. For instance, it may be useful to distinguish between users that operate the same devices or exist within a similar network (e.g., a home network, a workplace network, etc.) for various purposes. For instance, it may be useful to distinguish between users for displaying advertising to these users, performing some other operation with respect to a subset of the users.

Abstract: Device identifiers for devices, such as computing devices, software, and applications, etc., are analyzed to determine whether the device identifiers are associated with each other (e.g., are connected to each other) and how strongly the device identifiers are associated with each other. A stronger connection between two device identifiers may indicate that the device identifiers are connected to the same user, same computing device, or same household. Connections between device identifiers are identified and/or weighted if the device identifiers are associated with the same network or if the device identifiers use the same login information to access content. The identifiers and their connections may form a device graph. Content is targeted to the clients based on the device graph. The device graph may also be used to perform targeting and orchestration of advertising, attribution reporting, analytics, and content optimization across devices in the device graph.

Abstract: Device identifiers for devices, such as computing devices, software, and applications, etc., are analyzed to determine whether the device identifiers are associated with each other (e.g., are connected to each other) and how strongly the device identifiers are associated with each other. A stronger connection between two device identifiers may indicate that the device identifiers are connected to the same user, same computing device, or same household. Connections between device identifiers are identified and/or weighted if the device identifiers are associated with the same network or if the device identifiers use the same login information to access content. The identifiers and their connections may form a device graph. Content is targeted to the clients based on the device graph. The device graph may also be used to perform targeting and orchestration of advertising, attribution reporting, analytics, and content optimization across devices in the device graph.

Abstract: Device identifiers for devices, such as computing devices, software, and applications, etc., are analyzed to determine whether the device identifiers are associated with each other (e.g., are connected to each other) and how strongly the device identifiers are associated with each other. A stronger connection between two device identifiers may indicate that the device identifiers are connected to the same user, same computing device, or same household. Connections between device identifiers are identified and/or weighted if the device identifiers are associated with the same network or if the device identifiers use the same login information to access content. The identifiers and their connections may form a device graph. Content is targeted to the clients based on the device graph. The device graph may also be used to perform targeting and orchestration of advertising, attribution reporting, analytics, and content optimization across devices in the device graph.