Single Truth Model for Multi-Demographic Estimation
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Solution Overview
Problem
Traditional demographic modeling approaches require multiple iterations for each demographic category, leading to increased computational resources and time, especially when estimating household demographics with multiple members, and often rely on unreliable third-party models.
Innovation Solution
A single truth model is applied to viewer data to generate coefficients for classes of demographic combinations, reducing the need for individualized models and allowing for efficient estimation of demographic probabilities without extensive computational resources, while also evaluating and correcting third-party models for accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple individualized models are applied to estimate each demographic category separately, then measurement precision of demographic estimates is improved, but computational resources and time required increase significantly
Solution Approach 1:
The patent combines multiple demographic models into a single integrated model that estimates multiple demographic categories simultaneously. Instead of running separate models for each demographic (e.g., age, gender, income), the system uses one unified model that processes all demographic estimations in parallel, thereby maintaining measurement precision while dramatically improving productivity and reducing computational resources.
Solution Approach 2:
The patent creates a universal demographic model that performs multiple functions by estimating various demographic categories (age, gender, income, education, etc.) within a single modeling framework. This multi-functional approach eliminates the need for multiple specialized models, resolving the contradiction between maintaining accurate estimates and reducing computational overhead.
2Reliability
If multiple iterations are performed for each demographic category, then reliability of demographic estimates is improved, but loss of time increases
Solution Approach 1:
The patent merges multiple iterative modeling processes into a single iterative process that updates all demographic estimates simultaneously. By combining the iterations, the system maintains the reliability improvements that come from iterative refinement while avoiding the time loss that would result from repeating separate iterations for each demographic category.
Solution Approach 2:
The patent implements continuous demographic estimation by maintaining a single active model that continuously refines all demographic categories together. This continuous approach ensures reliable estimates through iterative improvement without the interruptions and time losses associated with stopping and restarting separate models for different demographics.
3Adaptability or versatility
If traditional demographic modeling is applied to households with multiple members, then comprehensive demographic coverage is improved, but device complexity increases
Solution Approach 1:
The patent develops a universal modeling system that handles households of any size and composition through a single unified approach. The model automatically adapts to different household structures (single-person, couples, families with children, multi-generational households) without requiring separate modeling logic, thereby improving versatility while keeping system complexity manageable.
Solution Approach 2:
The patent segments the demographic estimation problem into distinct categories (age, gender, income, education, etc.) that can be processed independently within the unified model. This segmentation allows the system to handle complex multi-member households by breaking down the estimation task into manageable components while maintaining overall system simplicity through the unified framework.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed to improve multi-demographic modeling efficiency. An example apparatus includes a feature set aggregator to segregate training data based on feature sets of interest, and to identify households that participate in at least one of the feature sets of interest, a class enumerator to reduce multi-demographic model iterations by enumerating demographic combinations for the identified households, the enumerated demographic combinations including a single identifier to represent a combination of two or more demographic categories, and a modeling engine to generate training coefficients associated with respective ones of the enumerated demographic combinations.