Abstract: An addressable asset system (100) includes user equipment devices (UEDs) (102), a cloud decisioning system (CDS) (106) and a business data management system (BDMS) (108). The BDMS (108) collects information that is used by the CDS (106) to manage delivery of assets by the UEDs (102). In an exemplary network architecture, the CDS (106) is independent of the network insertion and delivery equipment. In particular, the broadcast content stream is delivered to the UED (102) by a digital content management (DCM) system (110) and certain assets are separately delivered to the UED (102) by the asset download network (ADN) (112). This allows the CDS (106) to remain independent of the network of the UED (102) such that the CDS (106) can operate across networks.

Abstract: An addressable asset system (100) includes user equipment devices (UEDs) (102), a cloud decisioning system (CDS) (106) and a business data management system (BDMS) (108). The BDMS (108) collects information that is used by the CDS (106) to manage delivery of assets by the UEDs (102). In an exemplary network architecture, the CDS (106) is independent of the network insertion and delivery equipment. In particular, the broadcast content stream is delivered to the UED (102) by a digital content management(DCM) system (110) and certain assets are separately delivered to the UED (102) by the asset download network (AND) (112). This allows the CDS (106) to remain independent of the network of the UED (102) such that the CDS (106) can operate across networks.

Abstract: An addressable asset system (100) includes user equipment devices (UEDs) (102), a cloud decisioning system (CDS) (106) and a business data management system (BDMS) (108). The BDMS (108) collects information that is used by the CDS (106) to manage delivery of assets by the UEDs (102). In an exemplary network architecture, the CDS (106) is independent of the network insertion and delivery equipment. In particular, the broadcast content stream is delivered to the UED (102) by a digital content management (DCM) system (110) and certain assets are separately delivered to the UED (102) by the asset download network (AND) (112). This allows the CDS (106) to remain independent of the network of the UED (102) such that the CDS (106) can operate across networks.

Abstract: An addressable asset system (100) includes user equipment devices (UEDs) (102), a cloud decisioning system (CDS) (106) and a business data management system (BDMS) (108). The BDMS (108) collects information that is used by the CDS (106) to manage delivery of assets by the UEDs (102). In an exemplary network architecture, the CDS (106) is independent of the network insertion and delivery equipment. In particular, the broadcast content stream is delivered to the UED (102) by a digital content management(DCM) system (110) and certain assets are separately delivered to the UED (102) by the asset download network (AND) (112). This allows the CDS (106) to remain independent of the network of the UED (102) such that the CDS (106) can operate across networks.

Abstract: Asset options are multiplexed into a multicast stream. In one implementation, a single stream (300) includes programming (306) and assets (302). A user equipment device delivers one of the assets (302) for delivery in connection with an asset delivery opportunity. The stream (300) also includes metadata (304) to assist the user equipment device in selecting the asset for delivery. Targeted advertising can thus be implemented in shared stream environments while efficiently using available bandwidth.

Abstract: An addressable asset system (100) includes user equipment devices (UEDs) (102), a cloud decisioning system (CDS) (106) and a business data management system (BDMS) (108). The BDMS (108) collects information that is used by the CDS (106) to manage delivery of assets by the UEDs (102). In an exemplary network architecture, the CDS (106) is independent of the network insertion and delivery equipment. In particular, the broadcast content stream is delivered to the UED (102) by a digital content management (DCM) system (110) and certain assets are separately delivered to the UED (102) by the asset download network (ADN) (112). This allows the CDS (106) to remain independent of the network of the UED (102) such that the CDS (106) can operate across networks.

Abstract: A method and apparatus are disclosed for preparing program data for delivery to internet-enabled client devices implementing a targeted advertising scheme. A data combining apparatus (100) and associated data are provided for generating a combined manifest file. The apparatus (100) includes a demultiplexer (104), a data message handler (102), an A/V segmenter (103), and a manifest and metadata combiner (104). A content stream (105) (e.g., MPEG-TS) may be received at demultiplexer (104) and separated into data messages (i.e., metadata) and A/V content. Data message handler (102) may process the data messages into data message files (106) which include metadata. A/V segmenter (103) may segment the A/V content into A/V file chunks and generate manifest files (107) which include PTSs. Manifest and metadata combiner (104) may merge the data message files (106) and manifest files (107) into combined manifest files (108).

Abstract: A system and associated functionality are disclosed for managing and monitoring the delivery of assets to network users to comply with campaign specifications. The system is largely independent of content/asset delivery mechanisms, and thus facilitates management and monitoring of asset delivery with respect to a single device or multiple devices, including, multiple devices of multiple networks or modalities, e.g., a television, a laptop and a tablet.

Abstract: A broadcast targeting system is provided where third party database information is utilized in conjunction with broadcast network client information to identify households within the broadcast network including one or more desired recipients of targeted assets. In instances where a user equipment device of the targeted household includes multiple users (i.e., a multiple-user device), use of third party data in conjunction with an indication of which household member is currently using the multiple user device (e.g., set top box, etc.) allows for more effectively delivering assets targeted to a specific member(s) of the household.

Abstract: A system and associated functionality are disclosed for managing and monitoring the delivery of assets to network users to comply with campaign specifications. The system is largely independent of content/asset delivery mechanisms, and thus facilitates management and monitoring of asset delivery with respect to a single device or multiple devices, including, multiple devices of multiple networks or modalities, e.g., a television, a laptop and a tablet.

Abstract: An addressable asset system (100) includes user equipment devices (UEDs) (102), a cloud decisioning system (CDS) (106) and a business data management system (BDMS) (108). The BDMS (108) collects information that is used by the CDS (106) to manage delivery of assets by the UEDs (102). In an exemplary network architecture, the CDS (106) is independent of the network insertion and delivery equipment. In particular, the broadcast content stream is delivered to the UED (102) by a digital content management (DCM) system (110) and certain assets are separately delivered to the UED (102) by the asset download network (ADN) (112). This allows the CDS (106) to remain independent of the network of the UED (102) such that the CDS (106) can operate across networks.

Abstract: A broadcast targeting system is provided where third party database information is utilized in conjunction with broadcast network client information to identify households within the broadcast network including one or more desired recipients of targeted assets. In instances where a user equipment device of the targeted household includes multiple users (i.e., a multiple-user device), use of third party data in conjunction with an indication of which household member is currently using the multiple user device (e.g., set top box, etc.

Abstract: A broadcast targeting system is provided where third party database information is utilized in conjunction with broadcast network client information to identify households within the broadcast network including one or more desired recipients of targeted assets. In instances where a user equipment device of the targeted household includes multiple users (i.e., a multiple-user device), use of third party data in conjunction with an indication of which household member is currently using the multiple user device (e.g., set top box, etc.) allows for more effectively delivering assets targeted to a specific member(s) of the household.

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 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: A bracket is provided. In an embodiment, the bracket comprises a first seat and a second seat. The first seat is sized to retain a first fluid implement. The second seat is sized to retain a second fluid implement from the bracket. The second seat includes a top opening, a side opening and a front concave portion. The second seat is configured to prevent inadvertent removal of the second fluid implement from the bracket. A fluid delivery apparatus including the bracket, as well as a method for delivering fluid, such as medical fluids including nutritional fluids, and industrial fluids through the delivery apparatus is also provided.

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 bracket is provided. In an embodiment, the bracket comprises a first seat and a second seat. The first seat is sized to retain a first fluid implement. The second seat is sized to retain a second fluid implement from the bracket. The second seat includes a top opening, a side opening and a front concave portion. The second seat is configured to prevent inadvertent removal of the second fluid implement from the bracket. A fluid delivery apparatus including the bracket, as well as a method for delivering fluid, such as medical fluids including nutritional fluids, and industrial fluids through the delivery apparatus is also provided.

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.