Active Metadata and Reinforcement Learning for Dynamic Block Sizing

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Solution Overview

Problem

Cryptocurrency transaction processing is inefficient due to slow transaction speeds, network congestion, high resource consumption, inequitable transaction prioritization, scalability limitations, environmental concerns, and the trade-off between security and efficiency in Proof of Work (PoW) and Proof of Minting (PoM) systems.

Innovation Solution

The integration of active metadata and reinforcement learning to optimize transaction processing by queuing transactions with detailed information, dynamically adjusting block sizes based on transaction sizes, and incentivizing efficient processing through TPS rewards.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If Proof of Work (PoW) is used to ensure security, then network security is improved, but transaction processing speed deteriorates

Engineering Contradiction:

Improvenetwork securityVSAvoidtransaction processing speed

Core Design Contradiction:

ReliabilityVSSpeed

Solution Approach 1:

The patent segments the transaction processing workflow into distinct phases: transaction pooling, validation, block creation, and confirmation. By dividing the monolithic PoW process into manageable segments with parallel processing capabilities, the system maintains security while improving throughput and reducing individual transaction wait times.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements preliminary actions by pre-validating transactions before they enter the mempool, pre-assembling candidate blocks with transactions in advance, and performing preliminary difficulty adjustments. These preliminary actions reduce the computational burden during critical processing phases, speeding up transaction confirmation while maintaining PoW security.

Inventive Principle:

Principle #10Preliminary action

2Productivity

If block size is increased to process more transactions, then transaction throughput is improved, but network congestion worsens

Engineering Contradiction:

Improvetransaction throughputVSAvoidnetwork congestion

Core Design Contradiction:

ProductivityVSQuantity of substance

Solution Approach 1:

The patent implements dynamic block size adjustment mechanisms that adapt block capacity based on network conditions, transaction priorities, and congestion levels. Block sizes are not fixed but dynamically modified to optimize throughput while preventing excessive congestion, allowing the system to respond flexibly to varying network demands.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent changes key parameters such as block size limits, transaction fee requirements, and confirmation thresholds based on network conditions. By dynamically adjusting these parameters, the system can increase throughput during low-congestion periods while preventing overload during high-demand periods, balancing productivity and congestion management.

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If smaller transactions are prioritized to improve equity, then transaction fairness is improved, but overall processing efficiency deteriorates

Engineering Contradiction:

Improvetransaction fairnessVSAvoidprocessing efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The patent applies local quality by implementing different prioritization rules for different transaction types, sizes, and fee levels. Instead of a uniform approach, small transactions receive enhanced priority in certain contexts (improving fairness) while large high-fee transactions are processed efficiently in others (maintaining productivity). This localized differentiation resolves the contradiction between equity and efficiency.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent incorporates feedback mechanisms that monitor transaction processing patterns, network congestion, and fairness metrics. Based on this feedback, the system dynamically adjusts prioritization algorithms to balance fairness and efficiency - for example, increasing small transaction priority when fairness metrics indicate disparity, while maintaining overall system productivity through adaptive parameter adjustment.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250252436A1Active Metadata and Reinforcement Learning to Improve TPS Processing

Publication Date: 2025.08.07 BANK OF AMERICA CORP

AI summary

Innovative systems and methods address the challenges in blockchain transaction processing, particularly in Proof of Work (POW) and Proof of Minting (POM) consensus mechanisms. These challenges encompass slow transaction processing, network congestion, high resource consumption, transaction prioritization issues, scalability limitations, environmental concerns, and the trade-off between security and efficiency. To mitigate these issues, innovative solutions combine active metadata and reinforcement learning to optimize cryptocurrency transaction processing, enhancing the Transactions Per Second (TPS) rate in Crypto Mining and Minting. Users initiate transactions, which are queued with detailed information. Active metadata efficiently processes and assigns transactions, while reinforcement learning dynamically adjusts block sizes based on transaction sizes. This streamlines transaction processing, incentivizes efficient block creation, and improves overall blockchain network performance. Key features include advanced queue management, dynamic block sizing, and a TPS-based reward system. The inventions revolutionize cryptocurrency transaction processing, promoting efficiency, fairness, and sustainability in blockchain networks.