Media Attribute Detection for Real-Time AR Content Targeting
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Solution Overview
Problem
Current advertising and promotional content targeting methods are inefficient and intrusive, failing to accurately determine user interest in real-time, leading to low efficacy and user frustration, especially in dynamic media environments like online video consumption.
Innovation Solution
A computerized method and system for identifying and characterizing user-cognizable elements in media content using shape and audio analysis, enabling real-time provision of relevant secondary content through augmented reality, without requiring modifications to client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional targeted advertising methods are used based on demographic data and prior user interactions, then advertisers can push secondary content to users, but the accuracy of determining real-time user interest is low and users experience frustration
Solution Approach 1:
The patent uses audio fingerprinting technology to detect and identify specific audio characteristics (temporal, spectral, and cepstral features) in media content, analogous to detecting color changes. This enables precise identification of user-cognizable elements in real-time, allowing advertisers to determine actual user interest accurately without relying on outdated demographic data or prior interactions
Solution Approach 2:
The patent replaces manual analysis of primary content with automated audio processing and machine learning algorithms. The system automatically extracts audio features, compares them against a database of known content, and identifies user-cognizable elements without human intervention, enabling real-time accurate targeting at scale
2Measurement precision
If manual analysis of primary content is performed to target secondary content, then contextually relevant ads can be placed, but the process is highly labor-intensive and does not scale to large numbers of assets
Solution Approach 1:
The patent replaces manual content analysis with automated audio fingerprinting and machine learning systems. The process automatically extracts temporal, spectral, and cepstral features from audio streams, compares them against extensive databases of known content, and identifies user-cognizable elements without human intervention. This enables real-time contextual advertising at scale across millions of media assets
Solution Approach 2:
The patent creates audio fingerprints - compact digital representations - that copy the essential characteristics of audio content. These fingerprints enable rapid comparison and identification without processing the entire original audio stream, allowing the system to analyze vast numbers of media assets efficiently while maintaining high contextual relevance
3Reliability
If pre-roll advertisements are forced as a pre-condition of viewing primary content, then advertisers can ensure visibility, but users become frustrated and may terminate viewing before content begins
Solution Approach 1:
The patent implements dynamic, real-time advertising insertion based on actual user interest detection. Rather than forcing static pre-roll ads, the system continuously monitors audio content, identifies user-cognizable elements, and inserts secondary content adaptively when users express interest through interactions like pausing or seeking. This dynamic approach ensures ad visibility while maintaining user satisfaction
4Measurement precision
If shape-based analysis and audio fingerprinting are used to identify user-cognizable elements in real-time, then contextually relevant secondary content can be provided with high penetration rates, but the processing complexity increases
Solution Approach 1:
The patent segments audio analysis into distinct feature extraction components: temporal features (timing and duration), spectral features (frequency distribution), and cepstral features (perceptual characteristics). Each feature type is processed independently and then combined to create comprehensive audio fingerprints. This segmentation reduces processing complexity by breaking down the overall task into manageable, specialized sub-tasks that can be executed efficiently
Data Source
AI summary
Methods and apparatus for providing attribute-based search of media assets such as video and audio assets, and associated augmented reality functions including dynamic provision of relevant secondary content relating to the identified attribute(s). In one embodiment, media or content assets are ingested into a processing system and processed according to one or more attribute detection, identification, and characterization algorithms. Attributes detected may include for example the presence of tangible items such as clothing or chattels, particular persons such as celebrities, and/or certain contexts such as sporting activities and musical performances, as rendered within the media asset. In one implementation, the characterized assets are provided a unique ID, and stored so as to permit cross-correlation based on, e.g., the identified and characterized attributes. Secondary content (e.g., advertising or promotional content) is correlated to each identified asset, and dynamically served upon a user providing some threshold level of interaction with the attribute.