Specified-Time TV Viewing Prediction with Reach-Calibrated Probabilities
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Solution Overview
Problem
Conventional audience measurement systems struggle to accurately predict future TV viewing behavior and ratings by properly accounting for past viewing patterns of individual panelists on both coarse and fine time scales, and in relation to specific programming, as well as to identify demographics that may be candidates for particular programming before transmission.
Innovation Solution
A system and method that models past viewing behavior using probability distributions and recursively calculates future viewing behavior, utilizing respondent-level data to predict ratings and performance metrics for future TV programs and transmission schedules, incorporating machine learning models to adjust probabilities based on demographic categories and network reach descriptors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional audience measurement systems use traditional methods to measure TV viewing behavior, then the system complexity remains low, but the prediction accuracy of future viewing behavior and ratings deteriorates
Solution Approach 1:
The patent segments the viewing audience into multiple demographic categories (e.g., age groups, gender, household income) and analyzes viewing behavior patterns separately for each segment. This allows the system to capture complex viewing behaviors and predict future ratings more accurately by considering subgroup-specific patterns rather than treating the audience as a homogeneous group.
Solution Approach 2:
The system performs preliminary analysis of historical viewing data to identify and model viewing behavior patterns before predicting future ratings. By pre-processing and analyzing past viewing activities across multiple time scales, the system builds probabilistic models that can accurately forecast future viewing behavior without requiring complex real-time computations during prediction.
2Measurement precision
If the system analyzes past viewing patterns at high resolution across multiple time scales, then the prediction accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system pre-processes historical viewing data to extract and store viewing behavior patterns at multiple time scales (e.g., daily, weekly, seasonal) before prediction is needed. By performing this analysis in advance and caching the results, the system can quickly retrieve pre-computed patterns during prediction without reprocessing the entire historical dataset, thus reducing real-time computational burden.
Solution Approach 2:
The patent divides the time-scale analysis into discrete segments (different time granularities) and processes them independently. By segmenting the temporal dimension and analyzing each time scale separately, the system can optimize processing for each granularity level and combine results efficiently, reducing overall processing time compared to a monolithic approach.
3Measurement precision
If the system incorporates demographic data and network reach descriptors to adjust probabilities, then the prediction accuracy for specific programming candidates improves, but the data processing complexity increases
Solution Approach 1:
The patent segments the prediction process into distinct stages: first analyzing viewing patterns by demographic segments, then adjusting probabilities using network reach descriptors and programming characteristics. By segmenting the data processing into these modular stages, the system can handle complex multi-dimensional data systematically and reduce overall processing complexity through structured approach.
Solution Approach 2:
The system adjusts probability parameters based on network reach descriptors and programming characteristics by modifying the predicted probabilities through calculated adjustments. This parameter-based approach allows the system to incorporate additional data dimensions (demographics, network reach, programming type) without fundamentally changing the processing architecture, thus improving prediction accuracy while managing data processing complexity.
Data Source
AI summary
Methods and systems for determining if end-users are expected to be receiving transmissions from a multimedia network at a particular time. Data including end-user type, a multimedia network, a particular time slot of the repeating cycles, and a network reach descriptor may be received. End-users may be identified by end-user type. For each end-user, a probability of receiving transmissions from the multimedia network during time slots prior to the particular time slot may be determined, based on previous viewing activities. Each probability may be adjusted by an offset such that an average of the adjusted probabilities corresponds to the network reach descriptor. A determination may be made of whether or not each end-user is expected to have been receiving transmissions from the multimedia network at the particular time slot, based on the adjusted respective probability.