Content Switch Data Analysis for Commercial Break Detection
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Solution Overview
Problem
Users often switch between content assets to avoid watching commercials, leading to missed parts of shows and a poor viewing experience. Existing technologies lack effective methods to identify commercial break start and end times based on user interactions.
Innovation Solution
A system that analyzes content switch data from users to determine commercial break start and end times by applying best fit curves to the data and predicting these times using previous content switch information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If users switch between content assets to avoid commercials, then users can avoid watching commercials, but users may miss parts of the show they were previously watching
Solution Approach 1:
The system performs preliminary actions by detecting commercial break start times and end times before the user needs to switch content. The server analyzes content switch data to identify commercial break boundaries and provides advance information to users, allowing them to prepare for content transitions without missing show content.
Solution Approach 2:
The system implements feedback mechanisms where the server continuously monitors content switch data from users and provides real-time information about commercial break status. This feedback loop allows the system to detect when commercials are occurring and communicate this information back to users, enabling informed content switching decisions.
2Measurement precision
If the system analyzes content switch data to identify commercial breaks, then the system can accurately depict commercial break boundaries, but the system complexity increases
Solution Approach 1:
The system uses self-service principles by leveraging the content switch data that users naturally generate during their viewing behavior. Instead of requiring complex external monitoring systems, the patent utilizes the inherent data from user content switching actions to automatically detect commercial breaks, reducing the need for additional complex infrastructure.
Solution Approach 2:
The server acts as an intermediary between user content switch data and commercial break detection. The server collects, processes, and analyzes the content switch data to identify commercial break boundaries, serving as a mediating layer that transforms raw user behavior data into meaningful commercial break information without requiring direct complex analysis at the user device.
3Loss of time
If the system predicts commercial break times using previous content switch information, then the system can provide advance alerts to users, but the accuracy may be affected by variations in viewing patterns
Solution Approach 1:
The system performs preliminary analysis of content switch data to establish patterns and predict commercial break times before they actually occur. By analyzing previous viewing behavior and identifying temporal patterns, the system can advance predict commercial break start and end times, giving users head's up for upcoming content transitions.
Solution Approach 2:
The system uses feedback from actual commercial break occurrences to refine and improve prediction accuracy. By comparing predicted break times with actual break times and analyzing user responses, the system continuously adjusts its prediction algorithms to account for variations in viewing patterns and content types, thereby improving reliability over time.
Data Source
AI summary
Systems, apparatuses, and methods are described for determining boundaries within a content asset. A server may collect user interaction data for one or more content assets. Using the user interaction data, the server may determine commercial breaks or scene changes for the content asset. The server may apply best fit curves to the user interaction data, and determine commercial breaks or scene changes based on the best fit curves.