Multi-Device Authentication Confidence via Accelerometer Behavioral Modeling
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Solution Overview
Problem
Existing access control systems face challenges in determining user authenticity, especially on mobile devices, due to the limitations of biometric technologies and the risk of unauthorized access, as they often require cumbersome repeated authentication methods that users may avoid, and are not feasible for smaller devices like wearables.
Innovation Solution
A system that determines user authentication confidence by collecting data from multiple devices, using a confidence module to assess device confidence levels based on data quality, type, frequency, accuracy, and consistency, and combining these levels to establish a total confidence level for operational mode settings, including access control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If biometric authentication methods (blood vessel mapping, retina mapping, EKG matching) are used to improve authentication accuracy, then user identity authentication accuracy is improved, but device complexity and hardware requirements increase making them infeasible for smaller mobile devices
Solution Approach 1:
The patent uses accelerometer data to create a behavioral copy or model of the user's authentication pattern. Instead of requiring complex biometric hardware, the system captures motion characteristics through the accelerometer and uses these behavioral patterns for authentication, achieving security without complex hardware
Solution Approach 2:
The patent replaces complex biometric hardware systems (retina mapping, blood vessel mapping) with a simpler mechanical sensing approach using the accelerometer. The mechanical motion detection capability of the accelerometer is substituted for complex optical or physiological sensing systems
2Reliability
If repeated biometric authentication is required to ensure device possession, then security is improved, but user convenience deteriorates making users tend to avoid using these measures
Solution Approach 1:
The patent implements continuous authentication by continuously monitoring accelerometer data during device usage. Instead of requiring repeated discrete authentication actions, the system continuously verifies user identity through ongoing motion pattern analysis, maintaining security without interrupting user workflow
Solution Approach 2:
The system performs self-authentication by automatically analyzing accelerometer data without requiring active user participation. The device monitors its own usage patterns and autonomously determines authentication status, eliminating the need for users to repeatedly initiate biometric verification
3Adaptability or versatility
If simple authentication methods are used for smaller mobile devices, then device compatibility is improved, but authentication security deteriorates
Solution Approach 1:
The patent changes the authentication parameter from static biometric data (fingerprint, retina) to dynamic behavioral parameters captured by the accelerometer. By analyzing motion characteristics, timing patterns, and interaction dynamics, the system achieves enhanced security on simple devices through parameter transformation rather than hardware complexity
Data Source
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AI summary
The present application is directed to user authentication confidence based on multiple devices. A user may possess at least one device. The device may determine a device confidence level that the identity of the user is authentic based on at least data collected by a data collection module in the device. For example, a confidence module in the device may receive the data from the data collection module, determine a quality corresponding to the data and determine the device confidence level based on the quality. If the user possesses two or more devices, at least one of the devices may collect device confidence levels from other devices to determine a total confidence level. For example, a device may authenticate the other devices and then receive device confidence levels for use in determining the total confidence level, which may be used to set an operational mode in a device or system.