Recursive Codeword Encoding for Compact Data Synchronization
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Solution Overview
Problem
The rapid growth of data storage demand, exceeding the capacity to store it, and transmission bandwidth limitations pose challenges in efficiently storing, transferring, and synchronizing data, especially with the increasing use of multimedia data and the addition of low-bandwidth devices.
Innovation Solution
A system and method using code word frequency estimation, comprising a reference code library generator, data deconstruction engine, and data reconstruction engine, which processes data into compact formats by generating a reference code library, deconstructing data into chunklets, and reconstructing it for efficient storage and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is used to increase storage capacity, then storage efficiency improves, but compression ratio decreases for multimedia data
Solution Approach 1:
The patent segments data into fixed-size chunklets (e.g., 512-byte blocks) that are processed independently. Each chunklet is hashed and compared against a library of previously seen chunklets, enabling efficient identification of duplicates without processing entire files. This segmentation approach maintains high compression ratios for multimedia data by treating each chunklet as a discrete unit for comparison and storage.
Solution Approach 2:
The patent creates a library of unique chunklets from training data, storing only one copy of each distinct chunklet. When processing new data, the system compares chunklets against this library and stores references to matching entries rather than duplicating data. This copying strategy achieves significant storage capacity increases while maintaining lossless compression for multimedia content.
2Quantity of substance
If physical storage capacity is increased to meet demand, then storage availability improves, but manufacturing capacity is exceeded
Solution Approach 1:
The patent merges multiple data sets by building a shared library of unique chunklets that serves all training data. Instead of creating separate storage structures for each data set, the system consolidates unique chunklets into a single library, significantly reducing total storage requirements. This merging approach enables storage of zettabyte-scale data within petabyte-scale physical capacity by eliminating redundant storage across multiple data sets.
Solution Approach 2:
The patent changes the fundamental parameter of data representation from storing complete data sets to storing compressed references. By transforming the storage model from full-data preservation to reference-based retrieval, the system achieves exponential increases in effective storage capacity without corresponding increases in physical manufacturing capacity.
3Productivity
If data is processed to encode according to frequency patterns, then compression efficiency improves, but computational intensity increases
Solution Approach 1:
The patent performs preliminary processing during a training phase where a library of unique chunklets is built from training data before actual compression begins. This preliminary action pre-computes the frequency patterns and creates the reference library, so that subsequent compression operations only require simple hash comparisons rather than complex frequency analysis. This approach maintains high compression efficiency while dramatically reducing computational intensity during data processing.
Solution Approach 2:
The patent replaces complex mechanical frequency analysis with a hash-based lookup system. Instead of computing frequency patterns in real-time during compression, the system uses hash functions to quickly identify and compare chunklets against the pre-built library. This substitution of computational mechanics maintains compression efficiency while reducing power consumption and computational intensity.
4Measurement precision
If larger data sets are used for frequency estimation, then compression accuracy improves, but memory usage increases
Solution Approach 1:
The patent segments large data sets into fixed-size chunklets (e.g., 512-byte blocks) that can be processed and stored efficiently. By segmenting data into manageable units, the system can build frequency estimates from large data sets without requiring proportional increases in memory, as each chunklet is processed independently and stored as a discrete unit in the library.
Solution Approach 2:
The patent creates a compact library of unique chunklets that serves as a compressed representation of the entire training data set. This library contains only one copy of each distinct chunklet, enabling accurate frequency estimation from large data sets while using minimal memory. The library acts as a efficient index that captures the essential frequency information without storing redundant data.
Data Source
AI summary
A system and method for data storage, transfer, synchronization, and security using recursive encoding, wherein data is deconstructed into chunklets, and is processed through a series of reference code libraries that reduce the data to a sequence of reference codes, and where the output of each reference library is used as the input to the next.