Affective Valence Thumbnail Selection for Video
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Solution Overview
Problem
Current digital advertising methods lack the ability to effectively utilize the subtle affective valence present in visual information, such as video frames, to influence user behavior, particularly in the context of online video platforms where video length and ad placement are increasing, and where neutral or mildly affective content is often overlooked.
Innovation Solution
A method utilizing a crowd-compute model that integrates affective valence perceptions, big data, computer vision, and machine learning to predict the most visually appealing thumbnail from a video stream by assessing valence through behavioral and neural responses, allowing for the generation of reliable predictions and dynamic user-specific thumbnail selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional digital advertising methods are used, then advertising can be delivered, but the ability to effectively utilize subtle affective valence in visual information is lost
Solution Approach 1:
The system performs preliminary analysis of video frames to identify those with positive affective valence before ad placement decisions are made. By pre-processing and categorizing video content based on emotional content, the system can make more effective advertising decisions without losing the subtle affective information that would otherwise be overlooked.
Solution Approach 2:
The patent introduces an intermediary affective analysis system that bridges traditional advertising delivery and the subtle emotional content of video frames. This intermediary layer analyzes micro-valence in visual information and uses it to inform advertising decisions, allowing the system to capture and utilize affective valence information that traditional methods would ignore.
2Productivity
If video length and ad placement are increased, then more advertising opportunities are created, but user engagement may be negatively impacted by overlooking neutral or mildly affective content
Solution Approach 1:
The system applies different quality standards to different video frames based on their affective content. Rather than treating all frames uniformly, it identifies and prioritizes frames with positive affective valence for ad placement, while potentially skipping or de-prioritizing neutral frames. This local differentiation ensures that advertising opportunities are maximized in emotionally resonant moments without compromising overall user engagement.
3Device complexity
If neutral or mildly affective content is overlooked, then processing complexity is reduced, but advertising effectiveness is diminished
Solution Approach 1:
The system creates a simplified copy or representation of the affective valence information from video frames. Rather than performing complex analysis on every single visual detail, it extracts and stores key affective features that can be used for advertising decisions. This copying approach reduces processing complexity while retaining the essential affective information needed for effective advertising.
Data Source
AI summary
Access is provided to optimal thumbnails that are extracted from a stream of video. Using a processing device configured with a model that incorporates preferences generated by the brain and behavior from the perception of visual images, the optimal thumbnail(s) for a given video is/are selected, stored and/or displayed.