Semantic Audio Ad Insertion for Contextual Video Streams
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Solution Overview
Problem
Existing video ad insertion technologies often fail to align advertisements with the storyline or context of the video stream, leading to inconsistent and less engaging ad placements.
Innovation Solution
A method utilizing speech recognition, semantic analysis, and large generative artificial intelligence models to identify and insert advertisements with the highest semantic similarity to the primary video content, based on SCTE35/SCTE104 markers, by transcribing audio segments into textual data, performing semantic analysis, and generating vector embeddings for similarity scoring.
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 consistency with the video storyline deteriorates
Solution Approach 1:
The system dynamically adapts ad selection from static pre-recorded choices to real-time contextual matching. By continuously analyzing video stream content through speech recognition and semantic analysis, the system dynamically selects ads that match the current storyline context, resolving the contradiction between efficient pre-scheduled delivery and contextual consistency.
Solution Approach 2:
The system implements feedback loops where ad selections are continuously evaluated against actual video content context. Speech recognition transcribes video dialogue, semantic analysis evaluates contextual relevance, and this feedback informs real-time ad selection decisions, ensuring both efficient delivery and contextual appropriateness.
2Reliability
If speech recognition and semantic analysis are used to select ads, then contextual relevance is improved, but system complexity increases
Solution Approach 1:
The complex ad selection system is segmented into distinct functional modules: speech recognition for transcription, semantic analysis for context evaluation, and ad selection for final decision-making. This modular segmentation manages system complexity by making each component independent and specialized, while collectively achieving high contextual relevance.
Solution Approach 2:
Semantic analysis acts as an intermediary layer between speech recognition transcription and ad selection. This intermediary translates raw transcript data into contextual meaning and relevance scores, simplifying the interface between data processing and decision-making components while enhancing contextual relevance.
3Reliability
If ads are selected based on storyline context, then viewer engagement is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and transcribing video dialogue content before ad selection is needed. Speech recognition converts audio to text in advance, and semantic analysis prepares contextual representations, so that when ad selection is required, the processing time is minimized while maintaining high viewer engagement through contextual relevance.
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 insertion 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 Al 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.