Media Asset Qualification Using Streaming-Period Filtering
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Solution Overview
Problem
Existing media monitoring systems face challenges in efficiently crediting media assets, particularly for non-linear media, due to the lack of consistent media identifiers and unpredictable tuning periods, leading to increased computational burden and difficulty in qualifying reference media assets.
Innovation Solution
A central facility maintains a media reference database for non-linear media, filters candidate assets based on streaming periods, and reduces computational burden by crediting media assets with identifiers, comparing them to reference assets, and ignoring those that do not overlap with streaming periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If media monitoring systems process all candidate media assets for qualification, then measurement precision is improved, but computational burden increases
Solution Approach 1:
The system performs preliminary crediting of media assets with identifiers before the qualification process. By pre-processing and tagging media assets with identification information, the system reduces the computational burden during the actual qualification phase while maintaining accurate identification and measurement capabilities.
Solution Approach 2:
The system extracts and processes only the specific subset of media assets that are candidates for qualification, rather than processing all media assets. This selective extraction approach reduces computational burden while maintaining measurement precision for the relevant assets.
2Adaptability or versatility
If the system processes all media assets including non-linear media without identifiers, then adaptability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary crediting and identification of media assets before the qualification process. By pre-tagging media assets with identifiers and determining their crediting status, the system enables faster processing during qualification while maintaining the ability to handle diverse media types including non-linear media.
Solution Approach 2:
The system segments the media asset processing into distinct phases: crediting phase (where media assets are tagged with identifiers and classified as linear or non-linear) and qualification phase (where only uncredited assets are processed). This segmentation allows the system to handle diverse media types efficiently by preparing them in advance.
3Measurement precision
If the system qualifies and stores all media assets in the reference database, then measurement precision is improved, but bandwidth consumption increases
Solution Approach 1:
The system extracts and processes only the specific subset of media assets that are candidates for qualification and meet the qualification criteria. By selectively storing only qualified media assets in the reference database, the system maintains measurement precision for relevant assets while reducing bandwidth consumption associated with transferring and storing all media assets.
Solution Approach 2:
The system performs preliminary filtering and qualification determination before storing media assets in the reference database. By pre-assessing which media assets qualify for storage based on their crediting status and identification, the system reduces unnecessary data transfer and storage operations, thereby reducing bandwidth consumption while maintaining database completeness for qualified assets.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed to identify candidates for media asset qualification. Example apparatus disclosed herein include a media creditor to determine whether to credit a first media asset to linear media or non-linear media, the non-linear media including subscription video on demand (SVOD). Disclosed example apparatus also include a media asset candidate controller to: classify the first media asset as a non-candidate for media asset qualification in response to the first media asset being credited to the linear media or the non-linear media. In some examples, the media asset candidate controller is to determine whether to classify the first media asset as a candidate for media asset qualification based on whether the first media asset remains uncredited by the media creditor and the first media asset overlaps a streaming period.


