Multimodal Scene Analysis for Privacy-Safe Contextual Advertising
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Solution Overview
Problem
The connected television and streaming media landscape faces challenges in delivering contextually relevant advertisements that align with content context and user preferences while addressing privacy concerns and brand safety, as traditional methods rely on broad demographic targeting and personal data collection, which are less precise and may violate user privacy.
Innovation Solution
A system that performs multimodal content analysis of video, audio, and textual elements to extract contextual characteristics, classifies them according to advertising taxonomies, and generates contextual embeddings for semantic similarity matching, ensuring precise advertisement placement without relying on personal data, while supporting immersive media formats like VR and AR.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional demographic targeting and personal data collection are used for advertisement delivery, then advertisement reach and targeting capability are improved, but user privacy is compromised and brand safety is reduced
Solution Approach 1:
The patent introduces contextual information about media content (genre, mood, themes, scene descriptions) as an intermediary between advertisers and users. Instead of directly using personal user data for targeting, the system uses content context as a mediator to match advertisements with audiences who are naturally interested in the content being viewed, thereby achieving precise targeting without compromising user privacy
Solution Approach 2:
The patent replaces the mechanical system of personal data collection and tracking with a content-based contextual analysis system. Instead of mechanically collecting and processing personal user information, the system analyzes media content attributes (genre, mood, themes, scenes) and uses these contextual features to determine advertisement relevance, substituting data-driven targeting with content-driven targeting
2Ease of operation
If broad demographic targeting is used for advertisement placement, then advertisement delivery simplicity is improved, but advertisement relevance to content context deteriorates
Solution Approach 1:
The patent changes the parameters used for advertisement targeting from broad demographic categories to specific content context parameters including genre, mood, themes, and scene-level descriptions. This parameter transformation enables much finer-grained matching between advertisement content and media context, moving from coarse demographic segmentation to precise contextual alignment without significantly complicating the advertisement placement process
3Adaptability or versatility
If personal user data and behavioral tracking are collected for targeted advertising, then advertisement personalization is improved, but regulatory compliance and user trust deteriorate
Solution Approach 1:
The patent uses media content context as an intermediary to achieve advertisement personalization without directly utilizing sensitive personal user data. The system analyzes content attributes (genre, mood, themes, scenes) and matches advertisements based on contextual relevance rather than personal user behavior, thereby maintaining advertisement personalization capability while ensuring regulatory compliance and building user trust through privacy-preserving methods
4Measurement precision
If scene-level contextual analysis is performed for precise advertisement matching, then advertisement relevance is improved, but system complexity and processing time increase
Solution Approach 1:
The patent segments the media content into discrete scenes and analyzes contextual features at the scene level rather than processing entire media programs as single units. This segmentation enables precise advertisement matching by capturing fine-grained contextual variations within different scenes, while also making the analysis computationally manageable by breaking down complex media content into smaller, more tractable segments that can be processed efficiently
Data Source
AI summary
A system and method for contextual advertising that analyzes video content through multimodal examination of visual, audio, and textual elements to create detailed contextual understanding of individual scenes. The system segments video content into discrete scenes and simultaneously processes each scene to extract contextual characteristics including objects, settings, dialogue, music, and emotional tone. These characteristics are classified according to advertising industry taxonomies and converted into numerical embeddings that enable semantic similarity matching. During video playback, when advertisement opportunities occur, the system identifies the current scene context, analyzes available advertisements using similar techniques, computes similarity scores between scene and advertisement characteristics, and selects contextually appropriate advertisements for seamless integration. This approach enables privacy-compliant advertising that matches advertisement content with scene context rather than relying solely on user behavioral data, improving advertisement relevance and viewer experience.