Multi-Platform Media Rating Calculation Using Segmentation
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Solution Overview
Problem
Conventional television rating systems fail to accurately represent modern viewing habits and the full audience, as they do not account for multi-platform viewing methods, leading to an incomplete picture of a program's success and advertising value.
Innovation Solution
A system and method for determining multi-platform media ratings, which includes viewing data, audience information, and a co-viewing factor, using a computing device to calculate platform-specific ratings by considering the average number of viewers per device and demographic composition across various platforms like TV, DVR, VOD, and online streaming, and aggregating these to provide a comprehensive multi-platform rating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional television rating systems are used, then the system is simple and easy to operate, but the measurement precision and accuracy of audience representation deteriorate because they do not account for multi-platform viewing methods
Solution Approach 1:
The patent segments the audience measurement system by platform type (broadcast TV, cable, satellite, DVR, VOD, online streaming, mobile). Each platform has its own rating metric calculated separately based on platform-specific viewing data, then these segmented measurements are aggregated to form a comprehensive multi-platform rating. This segmentation allows accurate measurement of each platform's contribution while maintaining overall system manageability.
Solution Approach 2:
The patent creates a universal rating framework that handles multiple viewing platforms through a common methodology. The system uses a unified approach to calculate platform-specific ratings and aggregate them into a comprehensive multi-platform rating, making the system adaptable to various platforms while maintaining consistent measurement standards across all of them.
2Measurement precision
If multi-platform viewing data is collected and analyzed, then the accuracy of audience representation improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent segments viewing data collection and processing by platform type. Each platform's viewing data is collected, processed, and rated separately using platform-specific parameters and methodologies. This segmentation reduces the complexity of handling all data simultaneously while ensuring accurate measurement of each platform's unique viewing patterns.
Solution Approach 2:
The patent introduces platform-specific rating metrics as intermediary calculations between raw viewing data and the final comprehensive rating. These intermediaries (platform-specific ratings) simplify the aggregation process by providing standardized intermediate values that can be combined to form the overall multi-platform rating, reducing the direct complexity of processing all raw data at once.
3Loss of information
If platform-specific ratings are calculated and aggregated, then the information completeness for programming decisions improves, but the loss of time and computational resources increases
Solution Approach 1:
The patent performs preliminary calculations of platform-specific ratings and audience composition factors for each platform before aggregating them into the comprehensive multi-platform rating. This preliminary action organizes and pre-processes the data in a structured manner, reducing the computational burden and time required for the final aggregation while ensuring all necessary information is captured.
Solution Approach 2:
The patent segments the rating calculation process into independent platform-specific calculations that can be performed in parallel. Each platform's rating is calculated separately using its own viewing data and parameters, then these segmented results are aggregated. This segmentation enables more efficient use of computational resources and reduces overall calculation time compared to processing all platforms sequentially.
Data Source
AI summary
There is provided a system comprising a non-transitory memory storing an executable code and a hardware processor executing the executable code to receive first viewing data for a media content including a first total viewing time of the media content on a first viewing platform, receive a total possible number of viewers and a first audience composition percentage of a first audience viewing the media content on the first viewing platform, determine a first ratio by dividing the first total viewing time of the media content on the first viewing platform by a duration of the media content for the first viewing platform, and calculate a first platform rating for the media content by multiplying the first ratio by the first audience composition percentage and a first co-viewing factor and dividing by the total possible number of viewers.