Semantic Video Analysis for Contextual Content Delivery
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Solution Overview
Problem
Existing methods for delivering secondary video content often disrupt the immersive experience of main video content by presenting unrelated content, such as advertisements, which can contrast with the preceding scenes and detract from the viewer's engagement.
Innovation Solution
A method involving active video semantic analysis on a segment of the main video content to identify key elements, matching these elements with secondary video content that shares similar elements or categories, and presenting the secondary content immediately after the segment, ensuring coherence and minimizing disruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If secondary video content is selected based on user profile, browsing history, and popularity, then the content can be targeted at the user, but the immersive viewing experience is disrupted by presenting unrelated content that contrasts with the main video scenes
Solution Approach 1:
The patent introduces semantic analysis as an intermediary mechanism between the user profile-based content selection and the actual content delivery. By analyzing the semantic content (visual elements, audio, text) of both the main video and candidate secondary videos, the system mediates the selection process to ensure contextual relevance. This intermediary layer transforms the selection criteria from purely user-profile-based to a hybrid approach that incorporates content semantics, thereby maintaining user targeting while preserving immersive experience.
Solution Approach 2:
The patent changes the selection parameters for secondary video content from traditional metrics (user profile, browsing history, popularity) to include semantic similarity metrics. By introducing new parameters such as visual element matching, audio content alignment, and text keyword relevance, the system transforms the content selection process. This parameter expansion allows the system to evaluate candidate videos based on both user preferences and contextual coherence with the main video, resolving the contradiction between targeted delivery and immersive experience.
2Productivity
If secondary video content is presented as an interruption or follow-on video, then marketing and engagement opportunities are provided, but the key elements of the secondary video contrast with the immediately preceding scenes from the main video
Solution Approach 1:
The patent applies preliminary action by performing semantic analysis on candidate secondary video content before selection. The system pre-analyzes visual elements, audio content, and text keywords of candidate videos and stores this semantic information. When a main video is being played, this pre-computed semantic data is quickly compared against the current video context to identify coherent matches. This preliminary preparation enables rapid selection of contextually appropriate secondary content while maintaining marketing opportunities.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring the semantic content of the main video and using this information to guide secondary video selection. The system provides feedback loops where the analysis of visual elements, audio, and text from the main video feeds into the selection algorithm, which then selects secondary content that aligns with this feedback. This real-time feedback ensures content coherence while preserving marketing engagement opportunities.
3Device complexity
If traditional content selection methods are used based on user profile and popularity, then content delivery is simple and direct, but the viewing experience is abruptly interrupted with unrelated content
Solution Approach 1:
The patent applies segmentation by breaking down the content analysis into distinct modular components: visual element extraction, audio content analysis, and text keyword processing. Each component operates independently but contributes to the overall semantic understanding. This segmentation allows the complex semantic analysis to be implemented as separate, manageable modules that can be processed efficiently, reducing the perceived system complexity while improving content coherence and reducing viewing experience interruptions.
Data Source
AI summary
Various embodiments describe methods, systems, and devices for delivering secondary video content are disclosed. Exemplary implementations may perform, at a processor of a computing device, an active video semantic analysis on a segment of a first video content presented on a viewing device. The active video semantic analysis may identify an active video first visible element visible within the segment. Also, a secondary video content, that is not directly related to the first video content, may be matched based on a prior semantic analysis on the secondary video content that identified a secondary-video first visible element therein that is associated with the active video first visible element from the segment. In addition, the secondary video content may be presented on the viewing device immediately after the segment.