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:
Systems and methods are disclosed that allow for providing targeted asset/advertisements for broadcast-wide programming feeds. The systems and methods allow network platforms to select among asset options provided with a content stream and/or replace assets in the content stream. In one arrangement, after selecting an asset, the asset is inserted into the content stream and the content stream is disseminated to subsequent network platforms (e.g., local platforms). At this time, the local platforms may insert local assets into the content stream in predetermined local asset insertion spots.
Abstract:
A targeted advertising system selects an asset (e.g., ad) for a current user of a user equipment device (e.g., a digital set top box in a cable network). The system can first operate in a learning mode to receive user inputs and develop evidence that can characterize multiple users of the user equipment device audience. In a working mode, the system can process current user inputs to match a current user to one of the identified users of that user equipment device audience. Fuzzy logic and/or stochastic filtering may be used to improve development of the user characterizations, as well as matching of the current user to those developed characterizations. In this manner, targeting of assets can be implemented not only based on characteristics of a household but based on a current user within that household.
Abstract:
A targeted advertising system uses a machine learning tool to select an asset for a current user of a user equipment device, for example, to select an ad for delivery to a current user of a digital set top box in a cable network. The machine learning tool first operates in a learning mode to receive user inputs and develop evidence that can characterize multiple users of the user equipment device audience. In a working mode, the machine learning tool processes current user inputs to match a current user to one of the identified users of that user equipment device audience. Fuzzy logic may be used to improve development of the user characterizations, as well as matching of the current user to those developed characterizations. In this manner, targeting of assets can be implemented not only based on characteristics of a household but based on a current user within that household.
Abstract:
Systems and methods are disclosed that allow for providing targeted asset/advertisements for broadcast-wide programming feeds. The systems and methods allow network platforms to select among asset options provided with a content stream and/or replace assets in the content stream. In one arrangement, after selecting an asset, the asset is inserted into the content stream and the content stream is disseminated to subsequent network platforms (e.g., local platforms). At this time, the local platforms may insert local assets into the content stream in predetermined local asset insertion spots.
Abstract:
A system and associated functionality are described for enabling replacement of in-content advertising (ICA) content in programming segments without creating and distributing full program versions. One or more ICA opportunities (702) are provided in a programming stream (700). To enable replacement of less than the whole program, a replaceable program segment (704) is defined that encompasses the one or more ICA opportunities (702). Multiple versions (706) of the programming segment (704) can then be generated each including different ICA content for the ICA opportunity (702). Individual viewers or viewers belonging to different demographic groups or geographic zones, among other possibilities, may then receive an appropriate one of the versions (706) of the programming segment (704). A trigger (708) may be provided at a time prior to the beginning of the programming segment (704) to facilitate or enable insertion of the appropriate version (706).
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:
Systems and methods presented herein generally provide for the compensation of asset providers and/or communications network providers for the non-consumption of assets provided with programming. In this regard, when users elect not to consume the assets associated with the programming, the asset provider loses the opportunity to present their assets to such non-consuming users and is thereby injured in relation to the price they have paid for the delivery of their assets. In one arrangement, a reporting module at a customer device is operative to identify when a user skips an asset and report that skip event to the network. This information may then be utilized to adjust the bill of the asset provider and/or to bill the user associated with the skip event.
Abstract:
A request for information (RFI) system is provided for use in communications networks including broadcast networks and the internet. In one implementation, a viewer of a cable television network enters an RFI input (1) to a digital set top box using a user remote. Based on this RFI input, the digital set top box transmits a data request (2) to an RFI data center. The RFI data center also receives asset data (3) from an asset database so as to associate the RFI input (1) with a particular asset. An RFI request (4) can then be transmitted to the appropriate asset provider. The asset provider can then provide a report such package of assets or follow-on information (5) back to the RFI data center. The RFI data center may then, in turn, provide the package of assets or follow-on information to a user data terminal, for example, of the cable television network viewer, via access through a web-portal or e-mail (6). The digital set top box may also record inputs from the user to verify consumption of assets and track data requests. Using this information, the RFI data center or an RFI platform may credit value to a rewards account established for the network user based on the user's verified consumption of assets and/or data requests. Further, the RFI data center or RFI platform may be used to collect consumer behavior information, including purchasing decisions made by the user after consumption of assets, and correlate the consumer behavior information with the user's verified asset consumption.
Abstract:
Systems and methods are disclosed that allow for providing targeted asset/advertisements for broadcast-wide programming feeds. The systems and methods allow network platforms to select among asset options provided with a content stream and/or replace assets in the content stream. In one arrangement, after selecting an asset, the asset is inserted into the content stream and the content stream is disseminated to subsequent network platforms (e.g., local platforms). At this time, the local platforms may insert local assets into the content stream in predetermined local asset insertion spots.