User Confidence Metrics for Third-Party Authentication
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Solution Overview
Problem
Online services systems face challenges in verifying the legitimacy and uniqueness of user accounts, particularly in preventing abuse through machine code interactions and compromised accounts, which existing verification schemes struggle to address effectively.
Innovation Solution
An online services system provides user confidence information to third parties by analyzing social data from user interactions, using a confidence parameter engine to determine validity and quality metrics, and exposing these values via an API, allowing third parties to authenticate users without additional security measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional verification schemes (CAPTCHAs, email verification) are used to verify user accounts, then account legitimacy can be confirmed, but the verification process becomes complex and time-consuming for third parties
Solution Approach 1:
The patent introduces a confidence score as an intermediary metric that mediates between the complex verification processes and the third-party systems. Instead of implementing complex verification schemes directly in third-party systems, the online services system computes confidence scores based on user behavior analysis and provides these scores to third parties via API, simplifying their verification process while maintaining reliability
Solution Approach 2:
The patent performs verification actions in advance by continuously monitoring user behavior and computing confidence scores before third-party verification is needed. The system pre-analyzes user interactions, account creation patterns, and behavioral data to establish confidence levels that are then readily available to third parties, eliminating the need for complex real-time verification
2Reliability
If multiple verification measures are implemented to ensure account uniqueness, then account validity improves, but the time required for verification increases
Solution Approach 1:
The patent implements continuous verification by continuously monitoring user behavior and updating confidence scores in real-time. Instead of performing discrete verification checks that take time, the system continuously analyzes user interactions, account creation patterns, and behavioral data, maintaining up-to-date verification status without requiring additional time for third-party systems
Solution Approach 2:
The system performs verification actions in advance by pre-computing confidence scores based on historical behavior data before third-party verification is needed. This preliminary analysis of user patterns, account creation timing, and interaction history provides immediate verification results, eliminating time delays
3Reliability
If comprehensive user behavior analysis is performed to generate confidence scores, then user trust and account legitimacy improve, but the computational resources and system complexity increase
Solution Approach 1:
The patent segments the complex verification task into distinct modules: one module collects user behavior data, another computes confidence scores based on predefined criteria, and a third module provides API access to third parties. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining comprehensive analysis capabilities
Solution Approach 2:
The confidence score acts as an intermediary that simplifies the interface between complex behavior analysis and third-party systems. The system performs comprehensive analysis internally but presents a simplified confidence metric to third parties via API, reducing system complexity from the perspective of external systems while maintaining thorough verification
Data Source
AI summary
An online services system includes a mechanism for providing user confidence information to an external data consumer, and for determining user contribution quality. Using stored information about user actions and interactions, user confidence is evaluated for one or more parameters associated with the validity of the user's account and/or quality of the user's contributions to the online services system. Confidence values are assigned to each parameter, and the values are exposed to external data consumers. Using stored information, user actions and interactions are correlated with contribution quality to produce a metric indicative of user contribution quality. Users with low quality parameter metrics may have their contributions shown to a smaller audience or have a lower prominence in a news feed.