Audience Measurement Entropy Probability Estimation
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Solution Overview
Problem
Current audience measurement methods rely on panelist households to estimate media audience characteristics, which can be resource-intensive and inefficient, particularly when dealing with large datasets and partial aggregate data.
Innovation Solution
The use of partial panelist data to estimate portions of a population matching activity and characteristic combinations based on entropy probabilities, constructing a constraint matrix and combination total set to calculate these probabilities, reducing the computational burden and improving data processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional audience measurement methods using panelist households are employed, then audience characteristics can be estimated, but processor utilization and network bandwidth consumption increase significantly
Solution Approach 1:
The patent applies partial action by using only a subset of available panelist data (partial aggregate data) rather than processing complete datasets from all panelist households. This selective approach estimates audience characteristics while reducing computational burden and network bandwidth consumption, directly resolving the contradiction between measurement precision and resource utilization
Solution Approach 2:
The patent extracts only the necessary portions of audience measurement data required for characteristic estimation, separating essential information from redundant data. By taking out only the critical aggregate metrics needed for entropy probability calculations, the system maintains estimation accuracy while minimizing processor utilization and network bandwidth consumption
2Measurement precision
If complete panelist data is processed to ensure accurate audience metrics, then measurement precision improves, but data processing time and computational resources increase
Solution Approach 1:
The patent processes only partial aggregate data from panelist households rather than complete datasets, achieving sufficient measurement precision for audience characteristics while significantly reducing data processing time. This partial action approach extracts the essential information needed for entropy probability calculations without the computational overhead of processing all available data
Solution Approach 2:
The patent performs preliminary aggregation of panelist data into summary statistics before conducting entropy probability calculations. By pre-processing data into aggregate forms that capture essential audience characteristics, the system reduces subsequent computational complexity and processing time while maintaining measurement accuracy
Data Source
AI summary
Methods and apparatus to determine characteristics of media audiences are disclosed. An example method includes creating a constraint matrix based on a first activity associated with a first characteristic of a population, the first activity associated with a second characteristic of the population, and a first combination associated with at least one of the first activity, the first characteristic, and the second characteristic. The example method includes creating a combination total set based on a first measurement for the first activity associated with the first characteristic and a second measurement for the first activity associated with the second characteristic. The example method includes computing a first entropy probability based on the constraint matrix and the combination total set. The example method includes estimating a first portion of the population that matches the first combination based on the first entropy probability.