Second-by-Second Video Asset Viewing Measurement
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Solution Overview
Problem
Current methods for measuring television viewership lack the granularity to provide one-second level viewing information across various metrics, such as device, viewer, household, demographic group, geographic group, and service provider system, which is essential for advertisers, content producers, and service providers to accurately assess advertising effectiveness and programming schedules.
Innovation Solution
A computer-implemented method using channel tuning data from video asset viewing devices to measure video asset viewing at a second-by-second level during lead-in periods and correlate it with viewing during target periods, producing longitudinal viewing metrics while maintaining viewer anonymity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional survey techniques are used to measure television viewership, then measurement cost is reduced, but measurement precision deteriorates due to inability to provide one-second level viewing information
Solution Approach 1:
The patent uses set-top box devices as intermediaries between television viewing activity and measurement systems. These set-top boxes automatically capture detailed tuning data including channel changes, program identifiers, and precise timing information at the second level, eliminating the need for traditional survey methods while providing comprehensive viewing metrics without requiring complex survey infrastructure
Solution Approach 2:
The patent replaces manual survey methods with automated electronic data collection through set-top boxes. The system substitutes mechanical/survey-based measurement with electronic logging of channel tuning events, program identification signals, and timestamp data, achieving precise one-second level measurement automation through digital signal processing and automated data capture mechanisms
2Measurement precision
If detailed longitudinal viewing metrics are collected at one-second level, then advertising effectiveness measurement is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the continuous stream of viewing data into discrete one-second intervals, each tagged with channel identifier, program identifier, and timestamp. This segmentation allows advertisers to measure exposure at precise moments when ads or program promotions air, enabling correlation between ad exposure and subsequent viewing behavior while maintaining manageable data structures through time-based partitioning
Solution Approach 2:
The system implements feedback loops where longitudinal viewing data is continuously collected, processed, and correlated to provide real-time or near-real-time metrics on advertising effectiveness. The measured outcomes feed back into the measurement system to refine targeting and evaluation, creating a closed-loop system that improves advertising ROI through data-driven optimization
3Measurement precision
If channel tuning data is collected from multiple devices and platforms, then viewership measurement completeness is improved, but device complexity increases
Solution Approach 1:
The patent employs universal set-top box devices that function across multiple platforms including cable television, satellite television, and internet protocol television systems. These multi-functional devices capture viewing data regardless of the delivery platform, providing comprehensive cross-platform measurement through a single standardized measurement approach that adapts to different service providers and technology infrastructures
Data Source
AI summary
A computer-implemented method of using channel tuning data from a video asset viewing device connected to a network to measure video asset viewing at a second-by-second level during one or more user defined lead-in periods, and then correlating that with video asset viewing during a user defined target period, for the purpose of analyzing how viewing activity during the lead-in period(s) correlates with viewing activity during the target period, thus producing longitudinal viewing metrics; all while maintaining viewer anonymity. Additionally, viewing metrics can be categorized based on user defined demographic, geographic, and histogram groupings representing the percentage of video asset viewing with the result that the analyst is able to gain detailed insight into customer viewing behavior. The lead-in video asset may be any video asset or assets. The target may be any subsequent video asset. The metrics produced are useful to service providers, advertisers, and content producers.