Cross-Channel Identifier Generation for Disparate Data Correlation

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Solution Overview

Problem

Identifying and correlating data across disparate sources and systems with different identifiers is challenging, leading to difficulties in data integration and management.

Innovation Solution

A system that automatically and dynamically generates cross-channel identifiers using a large language model to correlate data points, verifies consistency, and applies shared identifiers based on confidence scores, with a feedback AI engine for real-time adjustments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional data correlation methods are used across disparate sources, then data integration can be achieved, but computing resources are consumed and accuracy is reduced due to manual identifier mapping

Engineering Contradiction:

Improvedata correlation accuracyVSAvoididentifier mapping complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent introduces a large language model as an intermediary that automatically generates cross-channel identifiers and correlates data points across disparate sources. This intermediary system eliminates the need for manual identifier mapping by using AI to understand and connect data from different channels, thereby improving accuracy while reducing the complexity of data integration processes

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces traditional mechanical/manual data correlation methods with an AI-based system. Instead of manually mapping identifiers between different data sources, the system uses a large language model to automatically generate cross-channel identifiers and correlate data points, substituting human effort and traditional computational methods with intelligent automation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If real-time data correlation is implemented across multiple sources, then data integration efficiency is improved, but network congestion and computing resource usage increase

Engineering Contradiction:

Improvedata integration efficiencyVSAvoidcomputing resource consumption

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The patent implements preliminary action by pre-processing and analyzing data points as they are received from different sources. The system proactively generates cross-channel identifiers and establishes correlations in advance, rather than performing heavy computational tasks later when data needs to be integrated. This approach improves real-time efficiency while distributing computing load more evenly

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent employs dynamic identifier generation where the system adapts to different data sources and formats in real-time. The large language model dynamically creates appropriate cross-channel identifiers based on the specific characteristics of each data source, allowing flexible and efficient data correlation without rigid pre-defined mapping rules, thereby improving productivity while managing computing resources

Inventive Principle:

Principle #15Dynamics

3Adaptability or versatility

If manual identifier mapping is performed for data correlation, then data from disparate sources can be connected, but time consumption and human effort increase

Engineering Contradiction:

Improvedata source compatibilityVSAvoiddata correlation time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent implements self-service by enabling the system to automatically generate cross-channel identifiers and correlate data points without human intervention. The large language model autonomously analyzes data from disparate sources, understands their relationships, and creates appropriate identifiers and correlations, eliminating the need for manual identifier mapping and significantly reducing time consumption

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent changes the fundamental parameter of identifier creation from manual assignment to AI-generated cross-channel identifiers. By transforming how identifiers are created and assigned, the system achieves universal compatibility across different data sources without requiring time-consuming manual mapping, as the large language model adapts to various data formats and structures automatically

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20260044620A1Systems and methods for automatically and dynamically generating a cross-channel identifeir for disparate data in an electronic network

Publication Date: 2026.02.12 BANK OF AMERICA CORP

AI summary

Systems, computer program products, and methods are described herein for automatically and dynamically generating a cross-channel identifier for disparate data in an electronic network. The present invention is configured to identify a first data point generated at a first instance and from a first data source; identify a second data point generated at a second instance and from a second data source; correlate, by a large language model, the first data point with the second data point; generate, based on the correlation, a shared identifier for the first data point and the second data point; identify a current data point at a current instance; verify, by the large language model, the current data point is consistent with the first data point and the second data point; and apply, based on the verification of the current data point, the shared identifier to the current data point.