ML Codebook Data Compaction for Faster Blockchain Validation

The patent extracts only the essential information from blockchain data by using machine learning to identify and retain critical features while removing redundant data. This extraction approach maintains data integrity for consensus validation while significantly reducing the volume of data that needs to be transmitted and stored across the network, thereby lowering energy consumption.

The patent transforms blockchain data from its original format into a compacted representation by changing key parameters such as data structure, encoding methods, and storage format. Machine learning models learn optimal parameter transformations that preserve essential information while reducing data size, enabling efficient transmission and storage without compromising consensus reliability.

The patent creates compacted copies of blockchain data using machine learning-generated codebooks that represent the essential information content. These compacted copies are transmitted across the network instead of full data blocks, maintaining validation accuracy while dramatically increasing network transfer rates. Nodes can validate consensus using these compacted representations.

The patent performs preliminary data compaction using machine learning models before data transmission. The codebooks are pre-trained and prepared in advance, allowing rapid compaction of incoming data into compacted representations that can be quickly transmitted and validated, thereby improving network speed without sacrificing validation accuracy.

The patent extracts essential historical information from the complete blockchain data using machine learning codebooks. By identifying and retaining only the critical features and patterns from historical blocks while removing redundant information, the system preserves the essential historical record needed for consensus validation while reducing storage requirements by significant factors.

The patent merges multiple blockchain data representations into a compacted form by combining original data with machine learning-generated codebooks. This merging creates a unified storage structure that maintains historical integrity through the codebook references while physically occupying minimal storage space, as the codebooks serve as compacted proxies for the full historical data.

The patent performs preliminary compaction using pre-trained machine learning codebooks before the mining and validation processes. This preliminary action prepares the data in an optimized format that requires less computational processing during mining operations, thereby maintaining or even improving mining speed while achieving data size reduction. The codebooks are prepared in advance so no additional processing time is required during critical mining operations.

The patent replaces traditional mechanical data processing and compression methods with machine learning-based compaction. The ML models learn optimal compaction strategies that are computationally more efficient than conventional compression algorithms, enabling rapid compaction and decompression operations that do not slow down mining and validation processes. The substitution of ML-based methods for traditional mechanical processing maintains productivity while reducing data volume.