Video Ad Pacing With Adjustable Granularity
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Solution Overview
Problem
Existing methods for managing the pacing of video ads in online video distribution systems often result in uneven distribution over time, failing to effectively allocate ads based on projected viewership and ad inventory.
Innovation Solution
A method and apparatus for managing video ad delivery that involves serving streaming video with allocated ad breaks, dividing the ad campaign duration into discrete time increments, and allocating ads based on a pacing scheme that takes into account projected viewership numbers, using curve fitting techniques such as cubic splines to adjust ad delivery rates, and subdividing time increments to optimize ad placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If ads are distributed at a constant rate over the ad campaign period, then the system implementation is simple, but the ad distribution becomes uneven with large time gaps between ad deliveries
Solution Approach 1:
The patent applies dynamics by transitioning from a static constant-rate ad distribution model to a dynamic model where the ad delivery rate continuously adjusts based on real-time viewership data. The system calculates instantaneous viewership rates and uses these to dynamically modify ad insertion rates, ensuring ads are delivered at optimal moments when viewers are most engaged, thereby eliminating large time gaps while maintaining system manageability.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring actual viewership data and using this information to adjust ad delivery rates in real-time. The system compares actual viewership against projected viewership patterns and modifies ad insertion accordingly, creating a closed-loop control system that ensures even ad distribution while adapting to changing viewing behaviors.
2Stability of the object's composition
If ads are distributed based on projected viewership using continuous adjustment, then ad distribution uniformity improves, but the system complexity and computational requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the ad campaign period into discrete time intervals (such as hourly or daily segments) and calculating viewership metrics for each segment separately. This allows the system to manage complexity through structured time-based analysis while still achieving uniform ad distribution across the entire campaign period through aggregated optimization.
Solution Approach 2:
The patent utilizes parameter changes by adjusting key variables such as ad insertion rate, time interval granularity, and viewership projection parameters to optimize the balance between distribution uniformity and system complexity. The system can modify these parameters based on campaign stage, content type, and audience demographics to achieve effective ad placement without excessive computational overhead.
3Productivity
If ad delivery is aligned with instantaneous viewership rate, then ad effectiveness improves, but the system must continuously calculate and adjust based on real-time data
Solution Approach 1:
The patent applies preliminary action by pre-calculating projected viewership patterns and preparing ad delivery schedules in advance, rather than reacting to real-time viewership fluctuations alone. This allows the system to proactively position ads at optimal moments while reducing the computational burden of continuous real-time adjustments, as the framework provides a predetermined structure that guides real-time decisions.
Solution Approach 2:
The patent replaces complex real-time mechanical calculation systems with more efficient algorithms that use sampled viewership data and statistical models to predict optimal ad delivery timing. Instead of continuously crunching raw viewership data, the system uses processed metrics and predictive algorithms that achieve similar effectiveness with reduced computational complexity.
Data Source
AI summary
In a streaming video system with included video ad breaks, a pacing component allocates ads to particular ad breaks so as to pace the distribution of ads over a defined ad campaign period according to a pacing protocol using discrete time bins. A curve fitting algorithm may be used to provide increased or adjustable granularity of time bins used in the pacing protocol.