Projection Weight Determination via Census Data Integration
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Solution Overview
Problem
Current internet audience measurement methods lack accuracy in projecting usage data from a sample group to a larger user base, as they fail to effectively account for variations in usage patterns across different user groups and resources, leading to incomplete and biased reporting.
Innovation Solution
A system that determines projection weights by analyzing usage data from both a sample group of client systems with monitoring applications and a larger group with beacon-included resources, minimizing errors in usage measurement projections by using quadratic programming to distribute weights that reflect demographic and usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If projection weights are determined using only sample group usage data, then measurement coverage is limited, but projection accuracy to the larger user base deteriorates
Solution Approach 1:
The patent merges two distinct data sources: sample group usage data (from monitoring applications) and larger group usage data (from beacon instructions). By combining these datasets and determining projection weights based on both, the system achieves both accurate projection to the larger user base and comprehensive measurement coverage, resolving the contradiction between precision and coverage.
2Reliability
If monitoring applications are installed on all client systems, then measurement completeness improves, but system complexity and deployment difficulty increase
Solution Approach 1:
The patent introduces beacon instructions as an intermediary mechanism. Instead of requiring monitoring applications on all client systems, the system uses beacon instructions embedded in resources that automatically collect usage data from the larger group. This intermediary approach achieves measurement completeness without the deployment complexity of universal application installation.
3Measurement precision
If projection weights are optimized to minimize measurement error, then projection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent determines projection weights by minimizing the error between usage measurements from the sample group and the larger group. This parameter optimization approach adjusts the weights to achieve the best projection accuracy. While computationally intensive, the method provides a systematic way to optimize accuracy based on the relationship between the two datasets.
Data Source
AI summary
Projection weights may be configured to project usage of a first set of resources by users of a first group of clients systems to usage of the first set of resources by users in a larger group of users. Users of the first group of client systems may be a sample of a larger group of users that access resources on the network using client systems. The projection weights may be determined based on a first set of usage data and a second set of usage data. The first set of usage data may be determined based on information received from monitoring applications installed on a first group of client systems. The second set of usage data may be determined based on information received from a second group of client systems sent as a result of beacon instructions included with a second set of resources accessed by the second group of client systems.