NFT Contract Ranking for Real-Time Batch Configuration Selection

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Solution Overview

Problem

Batch processing configurations are often immutable and lead to performance delays, inefficiencies, and unnecessary resource consumption due to repeated configuration adjustments upon error identification.

Innovation Solution

A quantum computing platform utilizing a non-fungible token contract (NFTC) model to optimize batch processing by training on historical workload information, generating container configurations, and selecting the optimal batch configuration based on optimization criteria, with validation through smart contracts and dynamic feedback loops.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If batch processing configurations are made immutable for stability, then system reliability is improved, but processing speed and adaptability deteriorate due to inability to adjust configurations in real-time

Engineering Contradiction:

Improveconfiguration stabilityVSAvoidbatch processing speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent implements dynamic batch configuration by training a machine learning model on historical workload data to predict optimal configurations in real-time. The model continuously adapts to changing workload patterns without requiring manual reconfiguration, allowing the system to maintain stability through automated decision-making while improving processing speed through real-time optimization.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system performs preliminary training of the machine learning model using historical workload information before actual batch processing occurs. This pre-computation of optimization strategies enables the model to quickly select optimal configurations during real-time processing, avoiding the need for repeated configuration adjustments and improving overall processing efficiency.

Inventive Principle:

Principle #10Preliminary action

2Productivity

If batch configurations are adjusted frequently to improve performance, then processing efficiency is improved, but resource consumption increases due to repeated selection and deployment processes

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidcomputing resource consumption

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The machine learning model is trained in advance on historical workload data to learn optimal configuration patterns. This preliminary learning phase enables the model to make rapid configuration selections during actual processing without requiring repeated full-scale optimization computations, thereby improving efficiency while minimizing additional resource consumption.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses historical workload information and processing outcomes as feedback to continuously refine the machine learning model. This feedback mechanism allows the model to improve its configuration selection accuracy over time, reducing the need for frequent re-optimization and thereby lowering computing resource consumption while maintaining high processing efficiency.

Inventive Principle:

Principle #23Feedback

3Manufacturing precision

If complete re-deployment is performed upon error identification to ensure correctness, then data processing accuracy is improved, but processing time increases due to repeating the entire configuration process

Engineering Contradiction:

Improvedata processing accuracyVSAvoidreconfiguration time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The machine learning model incorporates feedback from historical processing outcomes and error patterns to learn from past mistakes. When errors occur, the model can quickly adjust configurations based on learned patterns rather than requiring complete re-deployment, thereby maintaining data processing accuracy while significantly reducing the time and resources needed for correction.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The model is pre-trained on historical data including error cases and successful configurations. This preliminary learning enables the system to anticipate and prevent errors, or quickly recover from them using pre-learned optimal configurations, avoiding the need to repeat the entire configuration selection and deployment process.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12554550B2Real time optimization apparatus using quantum non-fungible token contract ranking for dynamic code evolution

Publication Date: 2026.02.17 BANK OF AMERICA CORP

AI summary

A quantum computing platform may train, using historical workload information, a non-fungible token contract (NFTC) model. The computing platform may receive, from a workload processing system, a data feed indicating current workload information. The computing platform may generate container configuration outputs comprising configurations for performing batch processing of the data feed. The computing platform may input the container configuration outputs into the NFTC model to select an optimal batch configuration for the batch processing of the data feed by: 1) ranking, based on optimization criteria, the container configuration outputs, and 2) selecting a highest ranked container configuration output of the container configuration outputs. The computing platform may send, to the workload processing system, the optimal batch configuration and commands directing the workload processing system to process the data feed using the optimal batch configuration, which may cause the workload processing system to process the data feed accordingly.