Abstract: A system (1000) is disclosed including a resource allocation optimization (RAO) platform (1002) for optimizing the allocation of resources in network (1004) for delivery of assets to user equipment devices (UEDs) (1012). The RAO platform (1002) determines probabilities that certain asset delivery opportunities (ADOs) will occur within a selected time window and uses these probabilities together with information concerning values of asset delivery to determine an optimal use of asset deliveries. In this regard, the RAO platform (1004) received historical data from repository (1014) that facilitates calculation of probabilities that ADOs will occur. Such information may be compiled based on asset delivery records for similar network environments in the recent past or over time.

Abstract: A system (1000) is disclosed including a resource allocation optimization (RAO) platform (1002) for optimizing the allocation of resources in network (1004) for delivery of assets to user equipment devices (UEDs) (1012). The RAO platform (1002) determines probabilities that certain asset delivery opportunities (ADOs) will occur within a selected time window and uses these probabilities together with information concerning values of asset delivery to determine an optimal use of asset deliveries. In this regard, the RAO platform (1004) received historical data from repository (1014) that facilitates calculation of probabilities that ADOs will occur. Such information may be compiled based on asset delivery records for similar network environments in the recent past or over time.

Abstract: A system (1000) is disclosed including a resource allocation optimization (RAO) platform (1002) for optimizing the allocation of resources in network (1004) for delivery of assets to user equipment devices (UEDs) (1012). The RAO platform (1002) determines probabilities that certain asset delivery opportunities (ADOs) will occur within a selected time window and uses these probabilities together with information concerning values of asset delivery to determine an optimal use of asset deliveries. In this regard, the RAO platform (1004) received historical data from repository (1014) that facilitates calculation of probabilities that ADOs will occur. Such information may be compiled based on asset delivery records for similar network environments in the recent past or over time.

Abstract: A system (1000) is disclosed including a resource allocation optimization (RAO) platform (1002) for optimizing the allocation of resources in network (1004) for delivery of assets to user equipment devices (UEDs) (1012). The RAO platform (1002) determines probabilities that certain asset delivery opportunities (ADOs) will occur within a selected time window and uses these probabilities together with information concerning values of asset delivery to determine an optimal use of asset deliveries. In this regard, the RAO platform (1004) received historical data from repository (1014) that facilitates calculation of probabilities that ADOs will occur. Such information may be compiled based on asset delivery records for similar network environments in the recent past or over time.

Abstract: A system (1000) is disclosed including a resource allocation optimization (RAO) platform (1002) for optimizing the allocation of resources in network (1004) for delivery of assets to user equipment devices (UEDs) (1012). The RAO platform (1002) determines probabilities that certain asset delivery opportunities (ADOs) will occur within a selected time window and uses these probabilities together with information concerning values of asset delivery to determine an optimal use of asset deliveries. In this regard, the RAO platform (1004) received historical data from repository (1014) that facilitates calculation of probabilities that ADOs will occur. Such information may be compiled based on asset delivery records for similar network environments in the recent past or over time.

Abstract: A system (1000) is disclosed including a resource allocation optimization (RAO) platform (1002) for optimizing the allocation of resources in network (1004) for delivery of assets to user equipment devices (UEDs) (1012). The RAO platform (1002) determines probabilities that certain asset delivery opportunities (ADOs) will occur within a selected time window and uses these probabilities together with information concerning values of asset delivery to determine an optimal use of asset deliveries. In this regard, the RAO platform (1004) received historical data from repository (1014) that facilitates calculation of probabilities that ADOs will occur. Such information may be compiled based on asset delivery records for similar network environments in the recent past or over time.

Abstract: A system (1000) is disclosed including a resource allocation optimization (RAO) platform (1002) for optimizing the allocation of resources in network (1004) for delivery of assets to user equipment devices (UEDs) (1012). The RAO platform (1002) determines probabilities that certain asset delivery opportunities (ADOs) will occur within a selected time window and uses these probabilities together with information concerning values of asset delivery to determine an optimal use of asset deliveries. In this regard, the RAO platform (1004) received historical data from repository (1014) that facilitates calculation of probabilities that ADOs will occur. Such information may be compiled based on asset delivery records for similar network environments in the recent past or over time.

Abstract: A system (1000) is disclosed including a resource allocation optimization (RAO) platform (1002) for optimizing the allocation of resources in network (1004) for delivery of assets to user equipment devices (UEDs) (1012). The RAO platform (1002) determines probabilities that certain asset delivery opportunities (ADOs) will occur within a selected time window and uses these probabilities together with information concerning values of asset delivery to determine an optimal use of asset deliveries. In this regard, the RAO platform (1004) received historical data from repository (1014) that facilitates calculation of probabilities that ADOs will occur. Such information may be compiled based on asset delivery records for similar network environments in the recent past or over time.

Abstract: A pacing platform and functionality allows for controlling the pace of delivery of addressable assets in an addressable asset delivery system (100). The illustrated system (100) generally includes an asset delivery order system (102), a decisioning system (104), UEDs (106) and delivery platforms (108). The system (100) allows for delivery of targeted assets to users of UEDs (106) in connection with asset delivery opportunities of programming provided by one or more program delivery networks (122). The system (100) allows for more even pacing of assets delivered by individual UEDs while still collectively fulfilling the campaigns entered via the order system (102). In addition, the invention allows for operation of the order system (102) so as to avoid accepting campaign requests that likely cannot be fulfilled.

Abstract: A system (1000) is disclosed including a resource allocation optimization (RAO) platform (1002) for optimizing the allocation of resources in network (1004) for delivery of assets to user equipment devices (UEDs) (1012). The RAO platform (1002) determines probabilities that certain asset delivery opportunities (ADOs) will occur within a selected time window and uses these probabilities together with information concerning values of asset delivery to determine an optimal use of asset deliveries. In this regard, the RAO platform (1004) received historical data from repository (1014) that facilitates calculation of probabilities that ADOs will occur. Such information may be compiled based on asset delivery records for similar network environments in the recent past or over time.

Abstract: Embodiments of the invention provide systems and methods for constructing a schedule well before the time of an asset delivery opportunity that associates a collection of one or more assets, potentially from multiple advertisers or asset providers, that are planned to play in each asset delivery opportunity. Specific rules for each device also determine which asset each will play, thereby ensuring that campaigns of total asset delivery and asset delivery pacing are approximately fulfilled. This scheduling can be accomplished using marketing data associated with each user device and can be prepared in a practicable period of time using reasonable processing resources.

Abstract: A system (1000) is disclosed including a resource allocation optimization (RAO) platform (1002) for optimizing the allocation of resources in network (1004) for delivery of assets to user equipment devices (UEDs) (1012). The RAO platform (1002) determines probabilities that certain asset delivery opportunities (ADOs) will occur within a selected time window and uses these probabilities together with information concerning values of asset delivery to determine an optimal use of asset deliveries. In this regard, the RAO platform (1004) received historical data from repository (1014) that facilitates calculation of probabilities that ADOs will occur. Such information may be compiled based on asset delivery records for similar network environments in the recent past or over time.

Abstract: Embodiments of the invention provide systems and methods for constructing a schedule well before the time of an asset delivery opportunity that associates a collection of one or more assets, potentially from multiple advertisers or asset providers, that are planned to play in each asset delivery opportunity. Specific rules for each device also determine which asset each will play, thereby ensuring that campaigns of total asset delivery and asset delivery pacing are approximately fulfilled. This scheduling can be accomplished using marketing data associated with each user device and can be prepared in a practicable period of time using reasonable processing resources.

Abstract: Embodiments of the invention provide systems and methods for constructing a schedule well before the time of an asset delivery opportunity that associates a collection of one or more assets, potentially from multiple advertisers or asset providers, that are planned to play in each asset delivery opportunity. Specific rules for each device also determine which asset each will play, thereby ensuring that campaigns of total asset delivery and asset delivery pacing are approximately fulfilled. This scheduling can be accomplished using marketing data associated with each user device and can be prepared in a practicable period of time using reasonable processing resources.

Abstract: Embodiments of the invention provide systems and methods for constructing a schedule well before the time of an asset delivery opportunity that associates a collection of one or more assets, potentially from multiple advertisers or asset providers, that are planned to play in each asset delivery opportunity. Specific rules for each device also determine which asset each will play, thereby ensuring that campaigns of total asset delivery and asset delivery pacing are approximately fulfilled. This scheduling can be accomplished using marketing data associated with each user device and can be prepared in a practicable period of time using reasonable processing resources.

Abstract: A diamond reference standard contains a diamond composite of 60-90% diamond crystals and 10-40% silicon carbide binding agent doped with a plurality of elements. The standard is prepared by doping a diamond composite containing a silicon carbide binder with a plurality of elements, the presence of which can readily be detected using a variety of instruments, e.g. a mass spectrometer.

Abstract: Embodiments of the invention provide systems and methods for constructing a schedule well before the time of an asset delivery opportunity that associates a collection of one or more assets, potentially from multiple advertisers or asset providers, that are planned to play in each asset delivery opportunity. Specific rules for each device also determine which asset each will play, thereby ensuring that campaigns of total asset delivery and asset delivery pacing are approximately fulfilled. This scheduling can be accomplished using marketing data associated with each user device and can be prepared in a practicable period of time using reasonable processing resources.

Abstract: A diamond reference standard contains a diamond composite of 60-90% diamond crystals and 10-40% silicon carbide binding agent doped with a plurality of elements. The standard is prepared by doping a diamond composite containing a silicon carbide binder with a plurality of elements, the presence of which can readily be detected using a variety of instruments, e.g. a mass spectrometer.