Neural Network Gaze Analysis for Content Engagement
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Solution Overview
Problem
Current content management systems fail to effectively engage users with presented content, leading to significant financial losses due to ignored and incorrectly targeted content, such as unread brochures and skipped ads.
Innovation Solution
A content management system that determines user engagement through a neural network analyzing gaze and auditory assessments, adjusting content presentation based on sensor data, and offering rewards for active engagement, ensuring that content is delivered to interested users and optimized for their preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If content is delivered to users through traditional media channels, then content distribution reach is improved, but user engagement and effectiveness deteriorate
Solution Approach 1:
The system continuously monitors user engagement metrics (clicks, views, interactions) and uses this feedback to dynamically adjust content delivery strategies, targeting parameters, and optimization algorithms to improve engagement effectiveness
Solution Approach 2:
The system dynamically changes delivery parameters such as timing, frequency, format, and targeting criteria based on real-time engagement data and user behavior patterns to optimize content effectiveness
2Quantity of substance
If traditional content delivery methods are used, then distribution coverage is improved, but financial loss due to ignored content increases
Solution Approach 1:
The system performs preliminary analysis of user profiles, preferences, and behavior patterns before content delivery to pre-identify high-value targets and optimize content selection, reducing waste on unlikely converters
Solution Approach 2:
The system dynamically adjusts targeting parameters, content format, and delivery timing based on real-time performance data to maximize ROI and minimize financial loss from ignored content
3Quantity of substance
If content targeting is broadened to increase reach, then distribution coverage is improved, but targeting precision deteriorates
Solution Approach 1:
The system segments the user base into distinct groups based on behavior patterns, preferences, and engagement levels, allowing tailored content delivery to each segment while maintaining overall broad coverage
Solution Approach 2:
The system applies different content strategies, formats, and targeting parameters to different user segments and contexts, optimizing for local precision while maintaining global reach
Data Source
AI summary
Disclosed are systems, methods, and non-transitory computer-readable media for determining user engagement with a content item. A computing device accesses at least one image of eyes of a user that is captured while a client device is presenting a first content item on a display of the client device. The computing device determines, based on using the at least one image as input in a neural network, a gaze of the user. The gaze including coordinates at which the user is looking in relation to the client device. The neural network was trained based on machine generated images of a modeled human user looking at various coordinates. The computing device determines, based on the gaze of the user, an engagement score for the user. The engagement score indicates a level of engagement of the user with the first content item.