Scoring Module for Linear Content Consumption Measurement
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Solution Overview
Problem
Generating and processing analytics data for linear content, such as live television streams, is challenging due to the lack of explicit start or end points and fewer user interactions compared to web browsing, making it difficult to determine consumption likelihood.
Innovation Solution
A system that includes a client device receiving interactions and providing information to a server to determine the likelihood of consumption, using a scoring module to process data on user interactions, tune-in/tune-out events, and interstitial hits to generate analytics reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sampling methods or panel-based solutions are used to measure linear content consumption, then implementation complexity is reduced, but measurement precision deteriorates due to inability to capture actual consumption behavior
Solution Approach 1:
The client device automatically generates consumption information without requiring user participation in surveys or panels. The system self-monitors interactions between the user and client device, capturing tune-in/tune-out events, interstitial hits, and interaction data to determine consumption likelihood automatically
Solution Approach 2:
The patent introduces an intermediary scoring module that processes raw interaction data from the client device and transforms it into consumption likelihood scores. This intermediary layer bridges the gap between simple interaction tracking and accurate consumption measurement, maintaining precision while managing complexity
2Measurement precision
If detailed user interaction data is collected and processed, then measurement precision improves, but processing time increases due to near real-time requirements
Solution Approach 1:
The client device continuously monitors and pre-processes user interactions, maintaining a ready state to capture tune-in/tune-out events and interstitial hits as they occur. This preliminary action ensures that when consumption determination is needed, the data is already prepared and can be processed quickly
Solution Approach 2:
The system uses periodic interstitial hits (regular intervals during content playback) to gather interaction data without requiring continuous processing. This periodic sampling approach maintains measurement precision while reducing overall processing time compared to continuous analysis
3Measurement precision
If user interactions are tracked to determine consumption likelihood, then measurement precision improves, but device complexity worsens due to need to monitor multiple interaction types
Solution Approach 1:
The patent segments the consumption determination process into distinct components: tune-in/tune-out event detection, interstitial hit tracking, and interaction type monitoring. Each component handles a specific aspect of user behavior, making the overall system more manageable despite tracking multiple interaction types
Solution Approach 2:
The client device and scoring module are designed to handle multiple types of user interactions through a unified framework. The same infrastructure processes tune-in events, tune-out events, interstitial hits, and various interaction types, reducing complexity compared to having separate systems for each interaction type
Data Source
AI summary
The likelihood of consumption of a linear content stream may be determined. Information that includes data indicative of user interaction with a client device may be received. The information may correspond to a period during which the client device received linear content. The likelihood of consumption may be determined for that period based on the received information.