Identity Graph Confidence Scoring for Context-Aware User Verification
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Solution Overview
Problem
Existing identity management systems lack flexibility in managing user accounts, often treating them as a single entity once linked, limiting their management and context-dependent usage, especially in retail environments where accurate and generalized user identification is crucial.
Innovation Solution
A user identity management platform utilizing a graph database with identity graphs that allows for flexible management and selection of user account information based on context, using deterministic and probabilistic methods to establish and manage confidence levels between user accounts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If user accounts are aggregated into a single entity once linked, then identity management becomes simpler, but flexibility in managing user accounts across different contexts is reduced
Solution Approach 1:
The patent segments user accounts into distinct entities within a graph database, where each account is represented as a separate node. This allows the system to maintain multiple user accounts as independent entities while still enabling their association through graph relationships, thereby preserving management simplicity while enhancing flexibility across different contexts.
Solution Approach 2:
The patent implements dynamic relationships between user accounts using graph edges that can be created, modified, and deleted based on contextual requirements. This dynamic connection mechanism allows the system to adapt the associations between accounts according to different business contexts, resolving the contradiction between simple aggregation and flexible management.
2Productivity
If user accounts are treated as a single entity, then processing becomes more efficient, but accuracy in identifying specific user contexts is reduced
Solution Approach 1:
By segmenting user accounts into distinct graph nodes, the system can process each account independently for efficiency while simultaneously enabling precise identification of specific user contexts through graph traversals and relationships, thus resolving the contradiction between processing efficiency and identification accuracy.
Solution Approach 2:
The patent introduces graph edges as intermediary relationships between user account nodes, allowing the system to efficiently query and traverse relationships while maintaining accurate context-specific identification. These edges act as mediators that connect accounts without merging them, preserving both efficiency and precision.
3Quantity of substance
If multiple user accounts are linked together, then broader user profile information is available, but confidence in attribute association decreases
Solution Approach 1:
The patent segments user profile attributes into separate graph nodes, allowing the system to access multiple attributes for a comprehensive user profile while maintaining clear associations through graph edges. This segmentation preserves confidence in attribute associations by explicitly defining relationships rather than relying on implicit aggregations.
Solution Approach 2:
The patent employs confidence scores as a parameter to quantify the reliability of attribute associations in the graph database. By storing and managing confidence scores alongside graph edges, the system can provide broader user profile information while explicitly indicating the level of confidence in each association, thus resolving the contradiction between information quantity and association reliability.
Data Source
AI summary
A user identity management platform is provided that manages user identity for an enterprise, such as a retail enterprise. In particular, a specific identity graph structure is provided that allows for flexible management and selection of user account information depending on the context in which that user account information is to be used. Confidence scores may be maintained for nodes and edges and probabilistic techniques associated with account activity may be used to improve confidence of association among nodes within a cluster representing a unique user.