Dynamic User Authentication via Contextual Confidence Scoring
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Solution Overview
Problem
Conventional user authentication methods are static and do not adapt to varying contexts or confidence thresholds, potentially compromising security and user convenience in accessing different types of data or systems.
Innovation Solution
A dynamic user authentication system that employs speech recognition and natural language processing to tailor authentication methods based on contextual factors, such as proximity, noise levels, and user profiles, using a combination of speech, image, and biometric data to adjust authentication techniques and confidence scores in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static authentication methods are used, then system simplicity is maintained, but security and user convenience deteriorate when accessing different types of data
Solution Approach 1:
The patent implements dynamic authentication by adjusting authentication methods and confidence thresholds based on contextual factors such as user proximity, noise levels, and data sensitivity. The system transitions from static to dynamic authentication, where the authentication requirements change in real-time based on environmental and user-specific conditions, thereby improving security without requiring a completely complex new system architecture.
Solution Approach 2:
The system changes authentication parameters dynamically by modifying confidence thresholds and authentication method selections based on contextual parameters. When noise levels are high or proximity is confirmed, the system adjusts the confidence score requirements and may switch between different authentication modalities (speech-only, speech-plus-image, etc.), allowing flexible security adaptation without fixed complexity.
2Reliability
If high confidence thresholds are applied for all data access, then security is improved, but user convenience deteriorates
Solution Approach 1:
The patent applies different confidence thresholds and authentication strictness levels to different data types and access contexts. Sensitive financial data requires higher confidence thresholds and multiple authentication factors, while less sensitive information allows lower thresholds and simpler authentication. This localized quality approach ensures high security where needed without unnecessarily complicating routine access operations.
Solution Approach 2:
The system dynamically adjusts confidence thresholds based on contextual factors such as user proximity verification, environmental noise levels, and data sensitivity classifications. When context indicates high reliability (e.g., user clearly visible in camera, low noise), the system lowers confidence requirements for convenience. When context suggests potential risk, confidence thresholds increase automatically, balancing security and convenience in real-time.
3Reliability
If multiple authentication methods are used, then security is improved, but authentication time and complexity increase
Solution Approach 1:
The patent implements selective multi-factor authentication by applying additional authentication methods only when necessary based on contextual assessment. The system uses speech recognition as the baseline and adds image capture, biometric verification, or other factors only when confidence scores are insufficient or context indicates higher security needs. This partial action approach maintains security through conditional multi-factor authentication while minimizing authentication time for low-risk scenarios.
Solution Approach 2:
The system continuously monitors authentication progress and contextual factors, providing real-time feedback to adjust authentication requirements. If initial speech recognition achieves sufficient confidence in a low-noise environment with confirmed user proximity, the system terminates authentication quickly. If confidence is marginal or context suggests risk, the system feedback-loop adds additional authentication factors, optimizing the balance between security and time investment based on actual authentication needs.
4Ease of operation
If context-based authentication adjustments are made, then user convenience is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal authentication framework that handles multiple authentication methods (speech, image, biometric) and contextual factors (noise, proximity, data sensitivity) through a single integrated system. Rather than creating separate authentication paths for each scenario, the system uses a unified confidence scoring mechanism that automatically adapts to different contexts and data types, improving convenience through consistent behavior while managing complexity through architectural universality.
Data Source
AI summary
Systems, methods, and devices for dynamically authenticating a user are disclosed. A speech-controlled device captures a spoken command, and sends audio data corresponding thereto to a server. The server determines the audio data includes a spoken command to receive content, and therefrom determines a source storing the content. The server also determines threshold user authentication confidence score data associated with the content source. Based at least in part on the threshold user authentication confidence score data, the server determines a user authentication technique, and a device configured to capture user authentication data. The server determines user authentication confidence score data using user authentication data received from the device, and determines weighted user authentication confidence score data therefrom. If the weighted user authentication confidence score data satisfies the threshold user authentication confidence score data, the server receives the requested content from the content source.