Video Stream Manifest Analysis for DAI Misalignment Detection
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Solution Overview
Problem
Existing methods for detecting dynamic ad insertion (DAI) misalignment in Over The Top (OTT) streaming are inefficient, time-consuming, and expensive, leading to degraded user experiences and financial losses due to inaccurate timing data provided by content providers, which are often corrected manually on a limited scale.
Innovation Solution
An automated backend server analyzes manifest files and video streams to identify misalignments by checking for aligned black frames, bumper sequences, and scene changes, providing statistics for content providers to correct timing data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection methods are used to identify DAI misalignment, then detection accuracy can be maintained, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent replaces manual mechanical review processes with automated electronic detection systems. The system uses electronic manifest files, video stream data, and automated algorithms to detect DAI misalignment, substituting human operators with computational processes that maintain accuracy while dramatically reducing detection time and cost.
Solution Approach 2:
The patent introduces an automated backend server as an intermediary between content providers and quality assurance processes. This server acts as a mediator that automatically receives manifest files, performs detection algorithms, and generates reports, eliminating the need for direct manual intervention while preserving detection precision.
2Reliability
If manual correction of timing data is performed on a limited scale, then quality control can be maintained, but financial losses increase due to degraded user experiences
Solution Approach 1:
The patent enables self-service correction by providing content providers with automated detection reports that include specific timing data errors and corrective recommendations. Content providers can independently review and correct their manifest files based on system-generated feedback, eliminating the need for expensive manual correction services while maintaining quality standards.
Solution Approach 2:
The system implements a feedback loop where detection results are communicated back to content providers with actionable insights. The automated reports provide specific information about misalignment issues, allowing content providers to make informed corrections and improve their DAI timing data, thereby scaling quality control without proportionally increasing costs.
3Productivity
If automated detection systems are implemented, then detection speed and scalability improve, but system complexity increases
Solution Approach 1:
The patent divides the detection system into distinct modular components: manifest file processing, video stream analysis, alignment verification, and report generation. Each module performs a specific function independently, making the overall complex system manageable through clear segmentation of responsibilities and data flows.
Solution Approach 2:
The automated backend server is designed as a universal platform that can handle multiple detection tasks and work with different content formats. The system processes various types of manifest files, supports different video stream formats, and provides consistent detection capabilities across diverse inputs, reducing the need for multiple specialized systems.
Data Source
AI summary
Systems, apparatus, articles of manufacture, and methods are disclosed. An example apparatus to identify advertisement misalignment, the apparatus comprising: interface circuitry; machine-readable instructions; and programmable circuitry to at least one of instantiate or execute the machine-readable instructions to: identify a timestamp using a manifest file of a video stream, the timestamp corresponding to a start of a dynamic advertisement insertion (DAI); form a list of consecutive video segments, the list including at least a content segment that ends at the timestamp and a DAI segment that begins at the timestamp; determine that a transition from the content segment to the DAI segment fails to satisfy a quality threshold; and flag the timestamp as a misalignment.