Convolutional Quantum Processing for Parallel Consumer Data Aggregation

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Solution Overview

Problem

Current data aggregation procedures in enterprise organizations are inefficient and time-consuming due to the sequential processing of consumer data, lack of a centralized database, and inability to streamline communication, leading to prolonged wait times for consumer request approvals or denials.

Innovation Solution

Utilizing a convolutional network quantum processor to analyze consumer requests and data in real-time or near real-time, identifying dependencies and patterns, and determining a maximum likelihood of success through quantum processing logic to approve or deny requests efficiently.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If sequential processing of consumer data is used, then data analysis thoroughness is maintained, but processing time and decision-making speed increase significantly

Engineering Contradiction:

Improvedata analysis thoroughnessVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent segments consumer data into multiple parallel processing streams, each handled by separate data processing units. These units simultaneously analyze different aspects of consumer data (financial information, transaction history, credit data) rather than processing sequentially, thereby maintaining thorough analysis while reducing overall processing time through parallel execution

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces quantum processing as a new dimensional approach to data analysis. By using quantum bits (qubits) that can exist in multiple states simultaneously, the system processes consumer data in a quantum state space, enabling exponential parallelism and analyzing all possible data configurations concurrently rather than through traditional sequential or even classical parallel methods

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Reliability

If multiple data requests are sent to consumers, then data completeness is improved, but consumer waiting time and communication complexity increase

Engineering Contradiction:

Improvedata completenessVSAvoidconsumer waiting time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary quantum processing and analysis of consumer data immediately upon receipt, rather than waiting for additional data requests to be fulfilled. By using quantum algorithms to rapidly evaluate available data and determine approval likelihood, the system can make preliminary decisions or pre-approve transactions before all consumer data is fully collected, reducing waiting time while maintaining data completeness through subsequent verification

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements a feedback mechanism where quantum processing results are continuously fed back to adjust data collection strategies. The system analyzes which data elements are most critical for decision-making and selectively requests only those specific data points from consumers, rather than requesting all possible data uniformly. This feedback-driven approach minimizes consumer waiting time by focusing data collection on high-impact information

Inventive Principle:

Principle #23Feedback

3Device complexity

If traditional AI models are used for data analysis, then implementation complexity is reduced, but processing speed and parallel capability are insufficient

Engineering Contradiction:

Improveimplementation complexityVSAvoidprocessing speed

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The patent replaces traditional mechanical/AI processing systems with quantum processing mechanisms. Instead of using conventional neural networks or machine learning models that process data sequentially or in limited parallel threads, the system employs quantum circuits and quantum algorithms that inherently provide exponential parallel processing capability, dramatically increasing processing speed while managing complexity through specialized quantum hardware

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12619901B2Intelligently aggregating data using a convolutional network quantum processor

Publication Date: 2026.05.05 BANK OF AMERICA CORP

AI summary

Aspects of the disclosure relate to intelligently aggregating data using a convolutional network quantum processor. Using an artificial intelligence model, a computing platform may identify dependencies derived from a consumer request and corresponding values. For each configuration of dependency and corresponding value, the computing platform may determine a likelihood of success. The computing platform may alter the configurations and may determine a likelihood of success for each altered configuration. The computing platform may train a convolutional network to analyze the consumer request using the configurations, the altered configurations, and/or corresponding likelihoods of success. The convolutional network may generate a function that describes the dependencies derived from the consumer request. Using a quantum processing model, the computing platform may analyze different states of the function in parallel and may determine whether to approve or deny the consumer request based on the analysis.