Gesture-Based Demographic Inference for Privacy-Safe Ad Targeting
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Solution Overview
Problem
Existing ad targeting systems struggle to determine user demographics accurately without accessing sensitive personal information, which raises privacy concerns and compliance issues with regulatory schemes.
Innovation Solution
Utilizing user gestures and motion data captured by touchscreen devices to analyze unique demographic characteristics, such as swipe patterns and pressure, without requiring access to personal information, and employing machine learning models to derive anonymized demographic profiles for targeted advertising.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ad targeting systems access sensitive personal information to determine user demographics, then demographic determination accuracy is improved, but user privacy protection deteriorates and regulatory compliance becomes problematic
Solution Approach 1:
The patent extracts and utilizes only the necessary behavioral characteristics (gesture patterns, swipe motions, tap rhythms) from user interactions, separating these anonymized features from sensitive personal information. This allows demographic determination without accessing or storing identifiable personal data, thereby maintaining privacy while achieving accurate targeting.
Solution Approach 2:
The system introduces gesture behavior analysis as an intermediary mechanism between user identification and demographic determination. Instead of directly accessing personal information, the system uses gesture patterns as an intermediate data source that indirectly reveals demographic characteristics without exposing sensitive user data.
2Adaptability or versatility
If ad targeting systems use gesture data for demographic determination, then targeted advertising capability is improved, but system complexity increases due to gesture capture and analysis requirements
Solution Approach 1:
The patent leverages the existing touchscreen gesture recognition infrastructure of mobile devices, which already captures gesture data for standard UI interactions. By repurposing this existing multi-functional gesture system for demographic analysis, the patent avoids adding dedicated complex hardware or software while still achieving sophisticated targeted advertising capabilities.
Solution Approach 2:
The system utilizes the device's own existing gesture capture capabilities and processing power to perform demographic analysis locally. This self-service approach eliminates the need for external complex analysis systems, as the device itself generates and processes the gesture data needed for demographic determination.
Data Source
AI summary
Systems and methods for obtaining anonymous demographics from gestures, where the method includes capturing information on one or more gestures performed by a user on a touchscreen of a device, along with motion data of the device. The gesture information and motion data in the form of deltas or deviations is provided to a machine learning (ML) model trained to analyze gestures and motion data and output predicted demographics. The predicted demographics from the ML model are then provided to an advertising provider, which send the device one or more ads targeted to the user based on the demographics. The device then displays the ads. Other embodiments are discussed herein.