Federated Customer Identifier Generation Across Platforms

The patent introduces an automated customer identification system that acts as an intermediary between multiple platform-specific customer identifier systems. This system collects data from various platforms, applies correlation rules and machine learning algorithms to match identifiers across platforms, and generates federated customer identifiers. This intermediary automation resolves the contradiction by maintaining high identification accuracy through sophisticated algorithms while eliminating manual analyst intervention, thereby dramatically improving productivity.

The patent replaces the mechanical manual analysis process with an automated computational system. Instead of analysts painstakingly examining customer data manually, the system uses automated data collection, correlation rule engines, and machine learning models to perform identifier matching across platforms. This substitution maintains measurement precision through algorithmic accuracy while eliminating the time-consuming manual process, thus resolving the productivity contradiction.

The patent creates a universal federated customer identifier system that works across multiple platforms while maintaining platform-specific identifier flexibility. The system collects data from various platforms using their existing identifier systems, then applies correlation rules and machine learning to create a unified view. This universal approach allows the same customer to be reliably identified across platforms using different platform-specific identifiers, resolving the contradiction between adaptability and reliability.

The patent transforms customer identification from platform-specific parameter sets to a unified federated identifier. By collecting multiple data parameters from different platforms and applying correlation rules with weighted scoring, the system changes the identification parameters from isolated platform identifiers to a comprehensive multi-parameter assessment. This parameter transformation enables reliable customer identity determination while preserving the flexibility of platform-specific identifiers.

The patent implements preliminary data collection and validation steps before performing customer identifier matching. The system proactively collects customer data from multiple platforms, validates data quality, and pre-processes information to ensure accuracy. By performing these preliminary actions, the automated system maintains high measurement precision even when dealing with incomplete or outdated data, while still achieving high productivity through automation.

The patent incorporates feedback mechanisms where the automated identification system continuously learns from matching results and data quality assessments. Machine learning models are trained on identified customer patterns and feedback from successful matches, improving their ability to accurately identify customers even with imperfect data. This feedback loop enables the automated system to maintain high measurement precision while processing large volumes of data efficiently.