TV Viewership Data Processing for Content Schedule Generation
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Solution Overview
Problem
Current methods lack graphical tools to intuitively and rapidly process large amounts of television viewership data for generating effective electronic content schedules, making it difficult for companies to reach their target audience efficiently.
Innovation Solution
A processor-based method that receives TV viewing data and demographic information, allows users to set target audience criteria and KPIs, calculates spot watching probabilities, generates probabilistic segments, and creates an electronic content schedule by selecting spot packages based on calculated scores, using a graphical user interface for visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large amounts of TV viewership data are processed manually, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent replaces manual mechanical processing of viewership data with automated computational systems. The processor automatically receives TV viewing data files, calculates spot watching probabilities, generates probabilistic segments, and creates content schedules without manual intervention, thereby maintaining measurement precision while dramatically improving productivity.
Solution Approach 2:
The system performs self-service by automatically processing viewership data through integrated algorithms that calculate probabilities, generate segments, and optimize content schedules without external manual processing. The processor autonomously handles the entire workflow from data reception to schedule generation, eliminating the bottleneck of manual analysis.
2Manufacturing precision
If complex statistical calculations are performed for each spot package, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex processing task into distinct modular components: receiving viewing data, calculating spot watching probabilities, generating probabilistic segments, evaluating spot packages against KPIs, and selecting optimal packages. This segmentation maintains manufacturing precision through systematic statistical calculations while reducing device complexity by organizing functions into separate processing stages.
Solution Approach 2:
The system manages complexity by dynamically adjusting parameters such as probability thresholds, KPI weights, and segment selection criteria based on user input and performance requirements. This allows the processor to maintain high manufacturing precision for spot package selection while adapting the level of computational complexity to match specific campaign needs.
3Adaptability or versatility
If probabilistic segments are generated through statistical selection, then adaptability is improved, but loss of information increases
Solution Approach 1:
The patent creates probabilistic copies of audience segments based on statistical patterns in the viewership data. Instead of working with the complete original dataset, the system generates representative probabilistic segments that capture the essential characteristics and viewing behaviors of target audiences. This copying approach maintains adaptability for various targeting scenarios while minimizing information loss through statistically rigorous sampling methods.
Data Source
AI summary
System and method embodiments are described that enable generation of an electronic content schedule. In a method embodiment, one or more data files including TV viewing data and descriptive data of a plurality of individuals are received. Target audience criteria, target TV content, and criteria for key performance indicators (KPIs) are received. KPIs for a target segment are tracked. Spot watching probabilities for each individual in the target segment are calculated. A plurality of spot packages is generated based on the target TV content. For each spot package, a probabilistic segment is generated based on the spot watching probabilities and a plurality of KPIs are calculated. Scores corresponding to the spot packages are generated based on the plurality of KPIs and based on tracked KPIs of the target segment. The content schedule is generated by selecting a spot package selected from the plurality of spot packages based on the scores.