Pointer Movement Graph Analysis for Human-Automated Classification
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Solution Overview
Problem
Traditional risk detection methods fail to accurately differentiate between human and automated computer interactions, particularly in modeling pointer trajectory patterns, leading to inefficiencies in detecting malicious activities and fraudulent transactions.
Innovation Solution
The technique combines pointer trajectory patterns and biometric data using a graph convolutional neural network and machine learning classifiers, such as XGBoost, to determine whether an entity interacting with a user interface is human or automated, thereby generating accurate authorization decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional risk detection methods are used to analyze user activity, then the system can process transactions, but the accuracy in differentiating between human and automated computer interactions deteriorates
Solution Approach 1:
The patent segments pointer movement analysis into multiple independent components: trajectory patterns, speed profiles, acceleration patterns, and temporal characteristics. Each component is analyzed separately by dedicated machine learning models, allowing complex behavior to be broken down into manageable features that improve differentiation accuracy without overwhelming system complexity
Solution Approach 2:
The patent transitions from traditional 2D pointer coordinate analysis to multi-dimensional analysis by incorporating temporal dimensions (speed, acceleration over time), spatial dimensions (trajectory curvature, area coverage), and behavioral dimensions (movement patterns, pause durations). This dimensional expansion enables more accurate distinction between human and automated interactions
2Reliability
If traditional pointer movement analysis is used, then the system maintains simplicity, but the detection of malicious activities and fraudulent transactions becomes less accurate
Solution Approach 1:
The patent implements feedback mechanisms where the risk assessment system continuously learns from detected pointer movement patterns. Machine learning models are trained on historical data including both legitimate and malicious interactions, with results fed back to refine future detection accuracy. This creates a self-improving system that increases reliability while managing complexity through automated learning
Solution Approach 2:
The patent changes multiple parameters simultaneously to enhance detection reliability: analyzing pointer position coordinates, calculating speed and acceleration parameters, measuring temporal intervals between movements, and evaluating spatial trajectory characteristics. By transforming raw pointer data into multiple derived parameters, the system achieves higher detection accuracy without requiring proportional increases in system complexity
3Measurement precision
If comprehensive pointer trajectory analysis is implemented, then the distinction between human and automated interactions improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary analysis of pointer movement characteristics during the interaction itself, before final authorization decisions are required. Key features such as trajectory patterns, speed profiles, and temporal metrics are extracted and pre-processed in real-time, allowing the final classification decision to be made quickly based on already-computed features rather than calculating everything at the decision moment
Solution Approach 2:
The patent employs dynamic analysis where the level of pointer movement analysis adapts based on risk indicators. High-risk scenarios trigger more comprehensive analysis while low-risk interactions receive streamlined processing. This dynamic approach maintains high accuracy for suspicious activities while reducing processing time for legitimate users, balancing precision with efficiency
Data Source
AI summary
Techniques are disclosed relating to determining whether to authorize a requested action based on whether an entity is an automated computer. In some embodiments, a computer system tracks, at a user interface of a computing device, a sequence of pointer movements. The computer system maps, based on coordinate locations of pointer movements in the sequence, respective movements in the sequence to a plurality of functional areas. Based on the mapping, the computer system generates a movement graph and determines, based on the movement graph, whether an entity associated with the sequence of pointer movements is an automated computer. In response to receiving a request to authorize an action at the computing device, the computer system generates, based on the determining, an authorization decision for the action and transmits the authorization decision to the computing device. Determining whether the entity is an automated computer may advantageously prevent fraudulent activity.