Adaptive Data Compression With Dynamic Codebook Retraining
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Solution Overview
Problem
The rapid growth of data storage demand is outpacing the capacity to store it, with existing solutions like physical storage expansion and data compression being inadequate, and transmission bandwidth becoming a bottleneck, while quantum computing poses security risks to data transmission.
Innovation Solution
An adaptive data processing system that dynamically selects and applies transformation, encoding, and encryption algorithms based on real-time data characteristics and performance metrics, using machine learning and a feedback loop to optimize data handling, enhance storage density, and improve transmission efficiency and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If additional physical storage capacity is added, then storage demand is met temporarily, but manufacturing capacity cannot keep up with exponential growth
Solution Approach 1:
The patent transforms physical storage limitations into a virtual storage solution by changing the parameter of data representation. Instead of storing raw data bytes, the system stores compressed references and metadata that point to reconstructed data, effectively changing the storage density parameter without physical expansion
Solution Approach 2:
The system creates virtual copies of storage capacity through software-based data reconstruction. Rather than physically duplicating storage devices, the patent uses algorithmic copying where data can be reconstructed from compressed references, providing virtual storage multiplication without physical manufacturing
2Quantity of substance
If data compression is applied, then storage capacity is doubled, but compression ratios decrease for multi-media data and data degradation occurs
Solution Approach 1:
The system changes the compression parameter dynamically based on data type detection. Different compression algorithms and ratios are applied to different data categories (text, images, video, audio), optimizing compression for each type while maintaining acceptable reconstruction quality through selective lossless/lossy compression strategies
Solution Approach 2:
The patent applies different compression quality levels to different portions of data based on local requirements. Critical data portions use lossless compression while less critical portions use lossy compression, allowing heterogeneous compression strategies within the same storage system to balance capacity and integrity
3Speed
If transmission bandwidth is increased, then data transmission speed improves, but bandwidth becomes a bottleneck for large datasets
Solution Approach 1:
The system extracts only the essential information needed for data reconstruction and transmits that, rather than transmitting the entire dataset. By separating data into original data portions and compressed reference information, the patent transmits only the reference portions over the network, dramatically reducing transmission volume while maintaining reconstruction capability
Solution Approach 2:
The patent uses virtual copying where the full dataset exists locally and only compressed references are transmitted. The receiving system reconstructs the full data locally using these references, eliminating the need to physically copy and transmit the entire data volume across the network
4Reliability
If existing encryption technologies are used, then data security is maintained, but quantum computing will compromise security
Solution Approach 1:
The system performs preliminary security preparation by implementing post-quantum cryptographic algorithms before quantum computing threats become imminent. The patent proactively adopts encryption standards that are resistant to quantum attacks, preparing the security infrastructure in advance of the threat
Solution Approach 2:
The patent implements dynamic security adaptation where encryption methods can be updated and changed in response to emerging threats. The system is designed to adapt its cryptographic approaches, allowing transition from classical to post-quantum encryption as computational threats evolve
Data Source
AI summary
A system and method for lossy precompression for data compaction using automated model monitoring and training, wherein statistical analyses of test datasets are used to determine if the probability distribution of two datasets are within a pre-determined range, and responsive to that determination new encoding and decoding algorithms may be retrained in order to produce new data sourceblocks, and pre-compression of data prior to processing and statistical analysis allows for the compaction of already compressed data into highly dense formats. The new data sourceblocks may then be processed and assigned new codewords which are compiled into an updated codebook which may be distributed back to encoding and decoding systems and devices.