Invidi Technologies Corporation

Alden Lloyd Peterson, Ii

Daniel C Wilson

Bruce J Anderson

Patrick Michael Sheehan

Howard Fiderer

Michael G Cristofalo

Earl Cox

Michael Kouritzin

Surrey Kim

Jarett Hailes

Bruce Anderson, Ii

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

Managing addressable asset campaigns across multiple devices

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

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.

Cloud-based decisioning for addressable asset system

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.

Third party data matching for targeted advertising

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.

Method and apparatus to perform real-time audience estimation and commercial selection suitable for targeted advertising

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.

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New Providence, NJ, United States of America

Alden Lloyd Peterson, Ii