The patent's subject area is Method and System for Hardware and Software Based User Identification for Advertisement Fraud Detection. It introduces a smart framework for detecting digital advertisement fraud and online commerce fraud, by combining plurality of hardware and software level data to distinguish genuine human actions from bot-initiated or manipulated activity. Unlike conventional approaches that rely solely on software identifiers, this system also analyses multi-layered data in real-time through various hardware touchpoints like device sensors, biometrics and connectivity signals. The fraud detection engine builds unique device/user behavioural profiles and calculates probabilistic scores by correlating data with pre-defined baselines, cross-device mapping and digital fingerprinting techniques.
The system's real-time design employs supervised & unsupervised machine learning and intelligent algorithms to adapt to the continuously evolving fraud patterns. It also analyses data from third-party connected devices (such as IoT, smart wearables, connected TVs, etc.) which are assigned unique identities and communicate via wireless protocols. By correlating this data with sensor-based indicators such as gyroscope, accelerometer data, touch interactions and other user device signals, the system strengthens cross-device fraud detection and enables blocking of fraudulent publishers/devices in real-time. This significantly reduces cost leakages and optimises campaign efficiencies at scale across a plurality of connected devices.
This marks the 16th patent granted to Affle.
The inventors of this patent include Anuj Khanna Sohum, Charles Yong and Anurag Singh.
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