AI Virtual Insertion Assessment for Media Streams
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Solution Overview
Problem
The labor-intensive process of assessing media streams for virtual image insertion opportunities in television broadcasting and video content distribution hinders efficient revenue generation through product placement and advertising, as existing technologies fail to automate the identification of suitable locations for virtual insertions effectively.
Innovation Solution
A system and method utilizing artificial intelligence techniques, such as machine learning and deep learning, to automatically decompose digital media streams into candidate-clips, identify viable insertion regions, and categorize them based on attributes like scene context and emotional tone, thereby automating the assessment and valuation of virtual image insertion opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of video content is performed to find suitable locations for virtual insertions, then assessment accuracy can be maintained, but the process becomes enormously labor intensive and time-consuming
Solution Approach 1:
The patent introduces an intermediary system consisting of trained classifiers and machine learning models that act as a bridge between raw video content and human reviewers. These intermediaries pre-assess video segments, identify potential insertion opportunities, and prioritize content for human review, thereby maintaining assessment accuracy while dramatically reducing the labor intensity and time required for manual review of entire video libraries.
2Productivity
If automated systems are used to assess video streams for virtual insertion opportunities, then labor intensity is reduced, but the system complexity increases
Solution Approach 1:
The patent segments the complex assessment task into multiple independent components: video preprocessing, feature extraction, multiple specialized classifiers (scene type, insertion opportunity, emotional tone), and result aggregation. Each component can be developed, trained, and optimized independently, reducing overall system complexity while enabling high-speed automated assessment of video streams for virtual insertion opportunities.
3Measurement precision
If comprehensive attributes are analyzed for each viable clip (scene context, emotional tone, insertion regions), then valuation accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary analysis by pre-processing video content to extract and store key attributes (scene context, emotional tone, viable insertion regions) in structured formats before final valuation is needed. This preliminary action enables rapid retrieval and combination of pre-computed features, achieving comprehensive valuation accuracy without the time penalty of real-time analysis, as attributes are prepared in advance for efficient querying and assessment.
Data Source
AI summary
A system for the augmented assessment of digital media streams for virtual less than insertion opportunities is disclosed. A digital media stream is automatically decomposed, using a programmed digital processor, into one or more candidate-clips have a predetermined minimum length, and no internal shot transitions. These candidate-clips are then examined, and the ones deemed suitable for virtual insertion use, are classified as viable-clips and stored in a digital database. The artificial intelligence techniques of machine learning and deep learning are then used to further classify the viable-clips according to their virtual insertion related attributes that may be attractive to advertisers, such as scene context, emotional tone and contained characters. A value is then assigned to the viable-clips, dependent on their insertion related attributes, and the overlap of those attributes with client requested requirements.