Audience Deduplication via Tree Graph and Jacobian Matrix
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Solution Overview
Problem
Traditional methods for estimating deduplicated audience sizes in media presentation environments face challenges due to inconsistencies in associating return path data with specific viewers, leading to inaccuracies in audience demographics and size calculations.
Innovation Solution
The use of a tree graph association and tree structure to tag nodes as descendants or ancestors based on panel data, combined with a Jacobian matrix approach to solve for Lagrange multipliers, enables efficient estimation of deduplicated audience sizes by modeling relationships between margins and unions of media exposure data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to associate return path data with specific viewers, then the process is simpler, but the accuracy of audience demographics and size calculations deteriorates
Solution Approach 1:
The patent segments the audience measurement problem into distinct components: panelist data collection, return path data processing, and deduplication estimation. By creating separate processing streams for different data types and using a tree graph structure to organize margin and union calculations, the system achieves higher measurement precision while managing complexity through modular organization of computational tasks.
Solution Approach 2:
The patent introduces an intermediary deduplication estimation system that acts as a mediator between raw return path data and final audience metrics. This intermediary layer uses probabilistic modeling and tree graph associations to resolve the conflict between data association simplicity and measurement accuracy, providing a bridge that transforms incomplete return path data into reliable audience demographics.
2Measurement precision
If complex tree graph associations and Jacobian matrix methods are used, then the accuracy of deduplicated audience size estimation improves, but the processing power requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing tree graph associations, margin totals, and union totals before performing deduplication estimation. The system prepares probability distributions and Jacobian matrix structures in advance, allowing the actual deduplication calculation to use these pre-prepared components rather than computing everything from scratch, thereby reducing real-time processing power requirements while maintaining high accuracy.
Solution Approach 2:
The patent implements partial action by focusing computational resources on the most critical aspects of deduplication estimation. Rather than exhaustively processing all possible data combinations, the system uses selective probabilistic sampling and approximation methods that capture the essential deduplication effects with sufficient accuracy while consuming fewer computational resources than complete exhaustive methods would require.
Data Source
AI summary
An example apparatus includes an association controller to generate a tree structure association for a total audience size that accessed a plurality of media items, the tree structure association including a first node representative of a first media item accessed by first audience members of the total audience size and a second node representative of a second media item accessed by second audience members of the total audience size, a matrix generator to generate a matrix by selecting a sum of probabilities value corresponding to the tree structure association, the sum of probabilities value representative of a probability of the first audience members accessing the first media item and storing the sum of probabilities value in an element of the matrix, and a commercial solver to estimate a deduplicated audience size of the total audience size using the matrix.