AI Cue Point Validation for Frame-Accurate Video Ad Breaks
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Solution Overview
Problem
The lack of standardization in video content formatting for online streaming platforms leads to challenges in advertising placement, content regulation, and the need for efficient marker selection, particularly for events like sporting events and award shows, without the constraints of traditional over-the-air broadcasts.
Innovation Solution
A cloud-based system utilizing artificial intelligence for automated cue point detection and validation, enabling frame-accurate marker selection, content moderation, and highlight extraction, with customizable rules and profiles for precise ad-break insertion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated AI-based cue point detection is implemented, then productivity and detection speed are improved, but reliability and precision of ad-break placement may deteriorate due to lack of standardization in video content formatting
Solution Approach 1:
The system implements a two-stage validation process where automated AI detection first identifies potential cue points, then a rule-based validation system verifies these detections against multiple criteria (black frame detection, silence detection, scene change analysis, metadata verification). This feedback loop ensures that automated detections are validated before final placement, resolving the contradiction between speed and reliability.
Solution Approach 2:
The patent introduces an intermediary validation layer between the AI detection system and the final ad-break placement. This intermediary system acts as a mediator that translates unstructured video content into standardized cue points by applying multiple detection algorithms and validation rules, thereby ensuring reliable placement across non-standardized video formats while maintaining automated processing speed.
2Manufacturing precision
If multiple detection algorithms and validation rules are implemented, then manufacturing precision of cue point detection is improved, but device complexity increases
Solution Approach 1:
The system segments the complex detection task into multiple independent algorithms: black frame detection, silence detection, scene change detection, and metadata-based detection. Each algorithm operates independently and contributes to the overall cue point identification. This segmentation allows high precision through multiple validation angles while managing complexity by organizing detection functions into separate, modular components.
Solution Approach 2:
The validation system serves multiple functions simultaneously: it validates AI detections, identifies alternative cue points, ensures ad-break placement compliance, and generates standardized output formats. This multi-functionality reduces overall system complexity by consolidating multiple validation and processing tasks into a single unified validation module rather than requiring separate systems for each function.
3Adaptability or versatility
If frame-accurate marker selection is implemented for various content types, then adaptability is improved, but ease of operation deteriorates due to the need for customizable rules and profiles
Solution Approach 1:
The system manages adaptability through parameter changes by allowing users to configure detection sensitivity, validation thresholds, and priority rules for different content types through adjustable parameters. Different video formats and content types are handled by modifying detection parameters and validation rules rather than requiring fundamentally different processing pipelines, thereby maintaining ease of operation while achieving high adaptability.
Solution Approach 2:
The system performs preliminary action by providing pre-configured detection profiles and validation rule sets for common content types (sports events, award shows, concerts, etc.). Users can select from these pre-configured options for immediate use, while still having the ability to customize parameters when needed. This preliminary configuration reduces operational complexity while maintaining versatility across different content types.
Data Source
AI summary
An automated detection and selection tool to streamline the process of choosing frame accurate points/markers for any type of audio and visual event is disclosed.