Gaming Biometric Access With Continuous User Continuity Checks
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Solution Overview
Problem
Existing biometric access systems lack robust methods to verify user identity and maintain user continuity, particularly in gaming environments, leading to potential security breaches and unauthorized access.
Innovation Solution
Implementing a biometric system that combines physical and behavioral biometric data verification, using sensors to monitor user identity and detect changes in user behavior, ensuring continuous authentication through fingerprint readers, iris scanners, and monitoring heart rate, body temperature, and keystroke patterns to maintain user continuity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional biometric access systems are used, then device complexity is reduced, but reliability of user identity verification deteriorates
Solution Approach 1:
The patent combines multiple biometric verification methods (fingerprint, iris, facial recognition) into a unified access control system. This integration allows the system to leverage the strengths of each individual biometric method while compensating for their weaknesses, thereby improving overall reliability of user identity verification without requiring separate systems for each method.
Solution Approach 2:
The system implements a universal biometric verification platform that can handle multiple types of biometric data (physical, behavioral, physiological) through a single integrated architecture. This multi-functional approach allows the system to adapt to different verification needs and scenarios while maintaining a consistent user experience and management interface.
2Reliability
If continuous biometric monitoring is implemented, then user continuity verification is improved, but use of energy increases
Solution Approach 1:
The system implements periodic biometric monitoring at strategically determined intervals rather than continuous monitoring. The verification frequency is adjusted based on risk assessment, user behavior patterns, and security requirements, allowing the system to maintain reliable user continuity verification while significantly reducing energy consumption compared to constant monitoring.
Solution Approach 2:
The monitoring intensity and frequency are dynamically adjusted based on contextual factors such as location, time of day, user behavior anomalies, and security risk levels. This dynamic approach allows the system to increase verification rigor when risks are detected while reducing monitoring during low-risk periods, optimizing the balance between reliability and energy consumption.
3Measurement precision
If multiple biometric verification methods are combined, then measurement precision of user identity is improved, but device complexity increases
Solution Approach 1:
The verification system is segmented into distinct functional modules, each responsible for a specific biometric modality (fingerprint recognition module, iris recognition module, facial recognition module). This segmentation allows for independent optimization, maintenance, and configuration of each verification method while maintaining a unified system architecture that manages the complexity through modular design.
Data Source
AI summary
The present application provides methods and corresponding systems for accessing services on a gaming device which include receiving at least one item of identity verification data from a user of a gaming device; enabling at least one service, such as a wager-type game, on the gaming device based on a match between the at least one item of identity verification data received and at least one item of identity verification data obtained previously; displaying an interface screen comprising graphic objects associated with the wager-type game and at least one selectable element for the user to submit a gaming command and a wagering command during game play; obtaining at least one item of user change data from a user during game play; and prompting the user for identity verification data when a user change is suspected based on the at least one item of user change data.