Contextual Media Matching for Targeted Video Content Placement
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Solution Overview
Problem
Determining where and what targeted media content to present within media content to minimize disruption and maximize user interest and engagement is challenging.
Innovation Solution
A system and method that processes media content to extract contextual features and information, using machine learning algorithms to match and integrate targeted media content with media segments based on embeddings generated from visual, audio, and timed text signals, enhancing relevance and user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If targeted media content is inserted within media content, then user interest and engagement can be maximized, but disruption to the media content increases
Solution Approach 1:
The patent applies local quality by analyzing specific contextual features of different media segments (visual, audio, text signals) and matching targeted content to specific locations where it best fits the local context, rather than uniformly inserting content throughout. This ensures targeted content is placed in segments where it naturally aligns with the media flow, minimizing disruption while maximizing engagement.
Solution Approach 2:
The system uses feedback mechanisms by continuously analyzing media content segments, extracting contextual features, and evaluating match quality between targeted content and media segments. This feedback loop enables dynamic adjustment of content placement decisions to optimize engagement while maintaining media integrity.
2Measurement precision
If contextual analysis is performed on media segments, then relevance of targeted content increases, but processing complexity increases
Solution Approach 1:
The patent segments media content into discrete portions and analyzes contextual features (visual, audio, text) of each segment separately. This segmentation approach enables precise contextual understanding of individual segments while managing processing complexity through modular analysis of distinct media components rather than treating the entire media stream as a single unit.
Solution Approach 2:
The system employs multi-functional processing that extracts multiple types of contextual features (visual, audio, text) using the same analytical framework. This universal approach allows the system to handle diverse media types and targeted content formats through a single processing pipeline, reducing overall system complexity while maintaining high measurement precision.
Data Source
AI summary
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for processing, understanding, and defining video content. An example can include determining a first set of contextual features associated with a first portion of a media content item; identifying at least one contextual feature from the first set of contextual features that is associated with one or more targeted media content items; and selecting, based on the at least one contextual feature, a first targeted media content item from the one or more targeted media content items, wherein the first targeted media content item includes content that is related to the first portion of the media content item, and wherein the first targeted media content item is selected for presentation after the first portion of the media content item.