Ad Segment Detection via nDVR User Behavior Fusion
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Solution Overview
Problem
Multiple service operators (MSOs) face challenges in determining the location of advertisement segments in video assets, as they often do not receive this information from content sources, making it difficult to replace advertisements in subsequent airings without knowing the ad boundaries.
Innovation Solution
A classifier system is integrated with an nDVR system to detect ad segments by analyzing video content, user behavior, and content similarity, using multiple detectors to fuse outputs and validate ad boundaries, allowing for accurate identification of ad segments for potential replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the MSO delivers video programs without ad segment information from content sources, then the device complexity is reduced, but the measurement precision of ad segment location deteriorates
Solution Approach 1:
The system performs self-service by automatically detecting ad segments using user behavior analysis from nDVR data. Instead of relying on external content sources to provide ad segment information, the MSO's own system analyzes user interactions (fast-forward, rewind, pause behaviors) to identify and mark ad boundaries, making the system self-sufficient for ad segment detection
Solution Approach 2:
User behavior data from nDVR serves as an intermediary to bridge the gap between video content and ad segment identification. The system uses aggregated user interaction patterns as a mediator to infer ad segment locations, transforming user behavioral signals into meaningful ad boundary detections without requiring direct information from content sources
2Measurement precision
If the MSO uses multiple detectors to fuse outputs for ad segment detection, then the measurement precision of ad segments is improved, but the device complexity increases
Solution Approach 1:
The detection system is segmented into multiple specialized detectors, each focusing on specific user behavior patterns (fast-forward detection, rewind detection, pause detection). This segmentation allows each detector to specialize in识别ing particular ad segment characteristics, improving overall detection precision while maintaining manageable complexity through modular design
Solution Approach 2:
Multiple detector outputs are merged through a fusion process in the classifier system. The system combines results from different behavioral detectors and integrates them with content analysis to produce a unified, high-confidence ad segment identification, leveraging the complementary strengths of each detector to achieve superior accuracy
3Reliability
If the MSO analyzes user behavior to detect ad segments, then the reliability of ad segment identification is improved, but the loss of time for processing increases
Solution Approach 1:
The system performs preliminary action by continuously collecting and pre-processing user behavior data from nDVR in the background. User interaction patterns are aggregated and analyzed in advance, so that when ad segment detection is needed, the system can quickly query pre-processed behavioral data rather than analyzing raw data from scratch, reducing processing time while maintaining reliability
Data Source
AI summary
Particular embodiments provide a system to determine ad segments in a video asset to enable subsequent ad replacement in video programs. The system is included in a multiple service operator (MSO) system that broadcasts video programs via a broadcast schedule. The MSO may not know the location of the ad segments in the video asset. To determine the ad segments, the MSO uses a classifier to classify video program segments and advertisements in the video asset. The classifier may be integrated with an nDVR system. By integrating with the nDVR system, particular embodiments may determine user behavior information, such as trick play commands, from the nDVR system. The classifier may use the user behavior information to detect ad segments in the video asset. In one embodiment, the classifier may fuse outputs from different detectors to detect and validate ad segments in the video program.