Visual Salience Mapping via Ocular Response Analysis
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Solution Overview
Problem
Current eye tracking devices are limited in their ability to quantify and map visual salience effectively, particularly in dynamic visual stimuli, and lack methods to compare individual or group ocular responses to visual stimuli, hindering applications in advertising and market research.
Innovation Solution
A system and method utilizing a processor with software to receive and analyze ocular response data from eye trackers, determining distribution of visual resources based on biological factors like retinal cells, and generating displays to represent areas of maximal salience over time, creating an attentional funnel to identify heightened attention areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional eye tracking devices are used to record eye movements, then basic fixation and saccade data can be obtained, but the ability to quantify and map visual salience effectively is limited
Solution Approach 1:
The patent introduces an intermediary computational model that incorporates biological factors (retinal cell distribution, cortical magnification) to transform raw eye tracking data into quantified visual salience maps. This intermediary layer bridges the gap between simple eye movement recording and sophisticated salience analysis without requiring fundamental changes to the eye tracking hardware itself.
Solution Approach 2:
The patent replaces direct mechanical/optical measurement of visual salience with a computational approach that uses eye tracking data combined with biological models. Instead of attempting to directly measure visual processing mechanisms, the system substitutes a computational framework that infers salience from observable eye movements and known biological constraints.
2Adaptability or versatility
If eye tracking data is collected from groups of individuals to determine distribution of visual resources, then comparison capabilities are enabled, but the complexity of data processing and analysis increases
Solution Approach 1:
The patent merges individual eye tracking data across multiple subjects by combining their visual resource distributions into a group-level representation. This consolidation enables comparative analysis while reducing the computational burden of processing each individual dataset separately, as the system processes aggregated spatial and temporal patterns across the group.
Solution Approach 2:
The patent adds temporal and group-level dimensions to the analysis by determining visual resource distributions at multiple time points and aggregating across individuals. This dimensional expansion enables dynamic visualization of attentional patterns over time and comparison across groups, transforming static eye position data into rich spatiotemporal representations.
3Loss of information
If visual salience mapping is performed for dynamic visual stimuli, then attentional patterns over time can be identified, but the complexity of temporal analysis increases
Solution Approach 1:
The patent performs preliminary analysis by determining visual resource distributions at discrete time points throughout the stimulus presentation. This preliminary temporal sampling enables subsequent reconstruction of attentional patterns and identification of salient moments without requiring continuous, computationally intensive processing of every temporal instant.
Solution Approach 2:
The patent implements a dynamic analysis framework that adapts to the temporal structure of visual stimuli. The system processes eye tracking data at multiple time points and generates evolving visual salience maps that reflect changing attentional patterns, enabling identification of when and where attention is heightened during dynamic stimuli presentation.
Data Source
AI summary
A system for quantifying and mapping visual salience to a visual stimulus, including a processor, software for receiving data indicative of a group of individual's ocular responses to a visual stimulus, software for determining a distribution of visual resources at each of at least two times for each of at least a portion of the individuals. The system further including software for determining and quantifying a group distribution of visual resources at each of the at least two times and software for generating a display of the group's distribution of visual resources to the visual stimulus.