Linear Non-Linear Media Discrimination via Log Comparison
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing methods for monetizing television content fail to accurately differentiate between linear and non-linear television advertising, leading to misleading audience measurements for advertisers, as commercials in non-linear models are often out of order, fewer, or completely different from linear broadcasts.
Innovation Solution
The proposed solution involves generating and comparing media presentation logs to distinguish between linear and non-linear media presentations using audio fingerprints, watermarks, cue tones, and logo detection, allowing for precise identification of program and non-program media, and processing these logs to determine the source and type of media exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to monetize television content, then revenue generation is possible, but audience measurements are misleading because they cannot differentiate between linear and non-linear advertising
Solution Approach 1:
The patent segments media presentations into distinct categories (linear vs. non-linear) by analyzing the structure and timing of content delivery. The system divides advertising measurements into separate measurable entities based on the presentation model, allowing accurate differentiation and measurement of each type independently.
Solution Approach 2:
The system performs preliminary analysis of media presentation logs to establish the advertising model type before conducting audience measurements. By pre-classifying the presentation format (linear broadcast vs. on-demand streaming), the system ensures that subsequent measurements use the appropriate metrics and avoid conflation of different advertising models.
2Adaptability or versatility
If non-linear media presentation is used, then consumer flexibility is improved, but advertising differentiation from linear models becomes impossible with existing methods
Solution Approach 1:
The patent implements a dynamic measurement system that automatically adapts its measurement approach based on the detected media presentation type. The system continuously monitors presentation logs and adjusts its measurement methodology in real-time, switching between linear broadcast metrics and on-demand streaming metrics as appropriate.
Solution Approach 2:
The system changes measurement parameters based on the advertising model being used. For linear presentations, it uses time-based metrics aligned with broadcast schedules, while for non-linear presentations, it uses content-identification metrics that track actual viewing regardless of timing or sequence.
3Measurement precision
If media presentation logs are generated and compared to differentiate linear and non-linear presentations, then measurement accuracy is improved, but system complexity increases
Solution Approach 1:
The patent creates simplified reference profiles representing typical linear and non-linear presentation patterns. Instead of complex real-time analysis of every log entry, the system compares actual presentation logs against these pre-established reference patterns, significantly reducing computational complexity while maintaining high differentiation accuracy.
Solution Approach 2:
The system introduces an intermediary classification layer that sits between raw media logs and final measurements. This intermediary component automatically categorizes presentations as linear or non-linear based on log analysis, then routes them to appropriate measurement processes, simplifying the overall system architecture.
Data Source
AI summary
Methods and apparatus to discriminate between linear and non-linear media are disclosed An example method to determine whether a media presentation is a linear or a non-linear media presentation comprises generating a reference log comprising a first media identifier of first media and a time at which the first media was presented, accessing a media presentation log comprising a second media identifier of second media and a time at which the second media was presented, and determining whether the second media correspond to a linear media presentation or a non-linear media presentation by comparing the media presentation log to the reference log.