Cross-Network Identity Resolution Using Meta-Profiles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing social networking systems are limited to intra-system relationships, requiring users to subscribe to each network to relate their identities and relationships, and fail to effectively correlate identities and relationships across different networks.
Innovation Solution
A system that tracks and indexes Inter-Personal relationships across networks by standardizing identities into spatialized personas, allowing for the correlation of identities across multiple networks and services, using various methods to verify and suggest relationships, and aggregates user data for personalized content suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system uses self-enclosed user accounts to define relationships, then the system can manage relationships within a single network, but it cannot correlate identities across different networks
Solution Approach 1:
The patent introduces a third-party identity resolution system that acts as an intermediary between different social networks. This system receives identity data from multiple networks, resolves identities across networks, and provides standardized relationship information. The intermediary enables cross-network correlation without requiring the networks themselves to integrate their systems, thus achieving versatility while managing complexity through a dedicated resolution layer.
Solution Approach 2:
The system creates a universal identity resolution platform that handles multiple types of identities (user profiles, business entities, organizations) across different network types (social networks, professional networks, business directories). By building a unified approach that works across diverse networks and identity types, the system achieves broad adaptability while using a single standardized processing architecture.
2Ease of operation
If the system requires users to subscribe to each network to relate identities, then each network can maintain its own data, but it increases the burden on users and limits relationship tracking
Solution Approach 1:
The system automatically performs identity resolution and relationship mapping without requiring active user participation in each individual network. Users simply need to connect to the identity resolution service once, and the system autonomously processes their identity data across multiple networks, infers relationships, and builds connection graphs. This eliminates the need for users to manually subscribe to or configure settings in each network while preserving relationship information.
Solution Approach 2:
The system performs preliminary identity resolution and relationship mapping in advance, before users need to access or utilize the relationship data. By pre-processing identity correlations and building relationship graphs automatically, the system prepares the information structure ahead of time, so users don't need to go through complex onboarding procedures when they actually need to view or use relationship data.
3Adaptability or versatility
If the system ties relationships directly to user accounts, then the system can provide personalized service, but it creates a closed ecosystem that cannot access external network data
Solution Approach 1:
The identity resolution system serves as a mediator that safely bridges closed network ecosystems. It receives data from various networks, resolves identities using multiple correlation methods (direct links, mutual connections, attribute matching), and provides standardized relationship information back to requesting systems. This intermediary approach enables data access across networks while maintaining reliability through systematic verification and multiple validation techniques.
Solution Approach 2:
The system transforms different network-specific identity parameters and formats into a standardized universal representation. By converting various network identifiers, relationship types, and data formats into a common parameter set, the system enables consistent processing and reliable correlation across different networks. This parameter standardization maintains the specificity of individual networks while creating a unified view that improves both adaptability and correlation accuracy.
Data Source
AI summary
Profile Information is aggregated from online websites and services to correlate discovered identities to one another via computational analysis of Intra-Personal Relationships, Inter-Personal Relationships, and Profile Data. The information, relationships and content of identities which have been determined to share Intra-Personal relationships are aggregated into Meta-Profiles; the Meta-Profiles are used in place of component Intra-Related identities to optimize computer functions such as social graph operations, content customization, and audience operations (including: analysis, metrics, profiling and targeting). The system processes identities and relationships belonging to individuals registered with the system and those belonging to unregistered users. The system is provided with data from users, via third-party systems, or through automated discovery. These innovations solve problems of online identity disambiguation specific to internet computing, and improve computer operations by optimizing memory footprint and operation calls to minimize the number of nodes and edges traversed.