Semantic Audio Ad Matching for Contextual Video Insertion
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Solution Overview
Problem
Existing ad insertion methods in video streams often fail to align advertisements contextually with the storyline, leading to inconsistent and less engaging viewing experiences.
Innovation Solution
A method utilizing speech recognition, semantic analysis, and large generative AI models to identify and insert advertisements with the highest semantic similarity to the primary video content, leveraging SCTE35/SCTE104 markers for precise insertion points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-recorded ads are inserted into video streams based on expected viewership, then ad delivery efficiency is improved, but contextual relevance to the video storyline deteriorates
Solution Approach 1:
The system extracts audio segments from the primary video stream in advance of ad insertion, performs speech recognition transcription, and conducts semantic analysis using LXM models to prepare contextual data before ad selection occurs. This preliminary processing enables context-aware ad matching while maintaining efficient delivery.
Solution Approach 2:
The patent replaces traditional mechanical ad insertion methods (based on simple viewership metrics) with an AI-driven semantic analysis system using LXM models. The system transforms audio segments into vector embeddings and calculates semantic similarity scores to automatically select contextually relevant ads, substituting manual or rule-based selection with intelligent automated analysis.
2Speed
If ads are selected based on expected viewership metrics, then ad insertion speed is improved, but ad effectiveness deteriorates
Solution Approach 1:
The system replaces traditional ad selection metrics (viewership-based) with semantic similarity analysis. LXM models process audio segments, generate vector embeddings, and compute similarity scores to identify contextually relevant ads, maintaining fast insertion speeds while improving effectiveness through intelligent matching.
Solution Approach 2:
The patent changes the selection parameter from viewership metrics to semantic similarity scores. By transforming audio data into vector embeddings and using LXM models to calculate similarity, the system selects ads based on contextual relevance rather than audience size, improving ad effectiveness without sacrificing insertion speed.
3Reliability
If semantic analysis using LXM models is implemented, then contextual relevance of ads is improved, but system complexity increases
Solution Approach 1:
The system segments the complex ad insertion process into distinct modules: audio extraction, speech recognition transcription, semantic analysis using LXM models, vector embedding generation, and ad selection based on similarity scores. This segmentation manages complexity by organizing functions into separate, manageable components.
Solution Approach 2:
The patent introduces intermediate processing steps including speech recognition transcription (converting audio to text) and vector embedding transformation (converting text to numerical representations). These intermediaries bridge the gap between raw audio data and LXM model analysis, simplifying the overall system architecture while maintaining high contextual relevance.
4Measurement precision
If audio segments are extracted and transcribed using speech recognition, then ad matching accuracy is improved, but processing time increases
Solution Approach 1:
The system extracts audio segments and performs speech recognition transcription in advance of the actual ad selection process. By preparing transcribed text and semantic analysis results beforehand, the system reduces real-time processing requirements while maintaining high matching accuracy through thorough preliminary analysis.
Data Source
AI summary
A method for selecting and inserting contextually relevant advertisements into a video stream, executed by a processing system in a network server computing device, encompasses receiving a primary video stream with potential advertisement insertion points indicated by SCTE35/SCTE104 markers, extracting an audio segment from this stream before an advertisement break, obtaining audio from potential advertisements, transcribing both primary and secondary audio segments into textual data, performing semantic analysis and tokenization on this data, creating vector embeddings, and normalizing these embeddings for a feed-forward neural network. The method further involves determining semantic similarity scores between the primary and secondary content through a transformer-based AI model, generating a similarity matrix from these scores, identifying the most contextually aligned advertisement based on these scores, and inserting this advertisement at an indicated break point in the primary video stream.