Digital Content Targeting via Eye Tracking and Semantic Analysis
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Solution Overview
Problem
Existing methods for targeting advertising and content delivery in digital media struggle to accurately determine user interest, often relying on incomplete or inaccurate metrics such as keyword counting and mouse movement tracking, which fail to account for user engagement and context.
Innovation Solution
The system determines user interest by tracking eye movement, mouse position, scroll bar activity, and semantic analysis of text and images to identify key phrases and words, then uses these metrics to deliver targeted advertising and content by subdividing digital media into regions and comparing normalized word counts to predict user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword counting and mouse movement tracking are used to determine user interest, then the system can provide targeted advertising and content, but the accuracy of determining user interest is insufficient
Solution Approach 1:
The patent combines multiple tracking methods (eye movement tracking, mouse movement tracking, scroll bar tracking) and content analysis methods (keyword counting, semantic analysis) into a unified system. This integration allows the system to cross-validate signals and achieve more accurate user interest determination than any single method could provide alone, while managing complexity through coordinated operation of these components.
Solution Approach 2:
The system employs a multi-functional approach where a single tracking infrastructure serves multiple purposes: eye tracking identifies areas of visual attention, mouse tracking captures interaction patterns, scroll tracking reveals content exploration behavior, and semantic analysis provides context understanding. This multi-functionality allows accurate user interest determination across diverse digital media types without requiring separate specialized systems for each function.
2Measurement precision
If multiple tracking methods (eye movement, mouse position, scroll bar activity) are combined to improve user interest determination, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent merges multiple tracking subsystems (eye tracker, mouse position tracker, scroll bar monitor, semantic analyzer) into an integrated user interest determination system. Each subsystem contributes unique data that complements the others, creating a more robust and accurate measurement of user engagement while sharing common processing infrastructure to manage complexity.
Solution Approach 2:
The system segments the user interest determination process into distinct functional modules: eye movement tracking module, mouse position tracking module, scroll bar activity tracking module, and semantic analysis module. Each module independently processes specific data types and feeds results to a central integration layer, allowing independent optimization and maintenance of each component while achieving comprehensive user interest analysis through their combined output.
3Measurement precision
If the system subdivides digital media into regions and performs semantic analysis to identify key phrases, then content targeting accuracy improves, but processing time and complexity increase
Solution Approach 1:
The patent divides digital media content into distinct regional segments (e.g., header, body, sidebar, footer regions) and performs semantic analysis on each segment independently. This segmentation allows the system to identify key phrases and topics in each region efficiently, then aggregate results to determine overall user interest. The segmented approach reduces processing complexity compared to analyzing entire documents at once while maintaining or improving targeting accuracy through region-specific relevance assessment.
Solution Approach 2:
The system extracts and prioritizes key phrases and semantically significant terms from subdivided content regions, separating these essential elements from the full text corpus. By focusing computational resources on analyzing only the extracted key phrases rather than processing every word in the digital media, the system achieves accurate content targeting with reduced processing time and computational complexity.
Data Source
AI summary
Systems and methods are described to provide additional relevant content to a viewer of digital media such as a webpage. A webpage being viewed is divided into regions and in each region, statistics are compiled on pertinent words and phrases. Statistically significant words and phrases are compared with semantically similar words and phrases in the additional content. Where there is a significant match between the viewed content and available additional content, the additional content is provided to the user.