Capacitance User Identification Through Unprompted Input Patterns
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Solution Overview
Problem
Traditional user identification methods on computing devices, such as login screens and biometric scanners, are cumbersome and time-consuming, detracting from the user experience and efficiency.
Innovation Solution
A capacitance module with electrodes, a processor, and memory that determines user identity by comparing input attributes like speed, movement, gesture endpoints, pressure, and typing patterns through machine learning, creating a user profile and authenticating the user identity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional login screens or biometric scanners are used for user identification, then authentication security is maintained, but user experience and interaction efficiency deteriorate due to cumbersome and time-consuming processes
Solution Approach 1:
The system performs user identification automatically by analyzing input characteristics (typing speed, gesture patterns, pressure) without requiring explicit user authentication actions. The capacitance module continuously monitors and compares these characteristics against stored user profiles, enabling seamless, background authentication that improves efficiency while maintaining security
Solution Approach 2:
User profiles containing authentication characteristics are pre-stored in the memory during a calibration phase. This preliminary action enables rapid comparison and identification during actual use, eliminating the need for time-consuming login processes while maintaining accurate user verification
2Measurement precision
If explicit authentication methods like passwords or biometric scanners are implemented, then user identification accuracy is ensured, but time consumption increases
Solution Approach 1:
The capacitance module continuously monitors user input characteristics (typing speed, gesture endpoints, pressure patterns) throughout normal device interaction. This continuous data collection and comparison against stored profiles enables accurate user identification to occur in the background during natural usage, eliminating separate authentication steps and reducing time loss while maintaining high identification accuracy
3Measurement precision
If multiple user profiles with detailed attributes are stored for accurate identification, then user identification precision improves, but memory requirements and data processing complexity increase
Solution Approach 1:
The system extracts and stores only the most discriminative input characteristics (typing speed, gesture endpoint patterns, pressure attributes, movement patterns) that are essential for user identification. By selectively extracting these key features rather than storing all possible input data, the system achieves high identification precision while minimizing memory requirements and data processing complexity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables quick and accurate user identification without explicit authentication, enhancing user experience and efficiency by leveraging unique user interaction patterns.
Implementation Method 1
a capacitance module may include a set of electrodes, a processor in communication with the set of electrodes... detecting a response to the human-verification prompt using a user input sensor in the capacitance module
Data Source
AI summary
A capacitance module may include a set of electrodes, a processor in communication with the set of electrodes, and memory in communication with the processor. The memory may include programmed instructions that cause the capacitance module, when executed, to determine a user identity by comparing input attributes of an unprompted input with at least one user attribute stored in the memory.