Network Identity Disclosure Feedback for Accurate User Verification
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Solution Overview
Problem
In networks where users maintain multiple identities, the network value is overstated due to the difficulty in precisely quantifying true users, leading to less value contribution from each user.
Innovation Solution
Implementing techniques for mutual self-disclosed identity verification, incentivizing users to maintain one real identity through biometric and transaction-based authentication, and using a knowledge graph to enhance network interactions and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If users are allowed to maintain multiple identities on the network, then the number of users increases, but the accuracy of user identification and network value calculation deteriorates
Solution Approach 1:
The system implements feedback mechanisms where users receive notifications when their identity behavior is suspicious or when multiple identities are detected. The network provides feedback through reputation scores, transaction validations, and identity verification requests, enabling users to adjust their behavior and maintain single-identity compliance while still allowing high user participation
Solution Approach 2:
The patent introduces intermediary mechanisms such as identity verification services, reputation intermediaries, and transaction mediators that bridge between users and the network core. These intermediaries help verify identities, detect multiple identity attempts, and maintain accurate user counts without preventing legitimate user growth
2Productivity
If users maintain multiple identities, then the network appears more vibrant, but the reliability of network value calculation deteriorates
Solution Approach 1:
The system uses feedback loops to monitor network activity and detect patterns indicative of multiple identity usage. When suspicious patterns are detected, the system adjusts network value calculations by applying correction factors or excluding suspect transactions, maintaining reliable valuation while preserving legitimate high activity levels
Solution Approach 2:
The patent replaces traditional mechanical counting methods with advanced detection algorithms, machine learning models, and behavioral analysis systems that can distinguish between legitimate multiple interactions by the same user versus fraudulent multiple identities, enabling accurate network valuation despite high activity
3Measurement precision
If the system implements strict identity verification, then user identification accuracy improves, but the ease of operation deteriorates
Solution Approach 1:
The system performs preliminary identity verification actions during user registration and onboarding processes. Identity verification, biometric enrollment, and initial reputation establishment are completed in advance, allowing users to operate with high accuracy verification already in place without facing repeated verification hurdles during normal operations
Solution Approach 2:
The patent implements self-service identity verification mechanisms where users autonomously complete verification tasks using their own devices, biometrics, and personal information. Users self-enroll, self-verify, and self-maintain their identity profiles, achieving high identification accuracy while maintaining operational convenience through automated processes
4Reliability
If the system monitors and tracks user identity behavior, then fraud detection capability improves, but the device complexity increases
Solution Approach 1:
The system segments fraud detection functionality into separate, specialized modules including identity verification modules, behavioral analysis modules, transaction monitoring modules, and reputation management modules. Each module handles specific aspects of fraud detection independently, improving detection capability while managing complexity through functional separation and modular architecture
Data Source
AI summary
Techniques described herein include techniques for communicating information about the social behavior of users and transactions performed by users on a computer network. In one example, this disclosure describes a method that includes receiving information about identity disclosure activities performed by each of a plurality of users on the network; determining that the information about identity disclosure activities includes information consistent with a prior identity disclosure activity performed by a first user having a first user status on the network; increasing the first user status; determining that the information about identity disclosure activities includes information that is not consistent with a prior identity disclosure activity performed by a second user having a second user status on the network; and decreasing the second user status.