Adaptive Data Encoding for Storage and Bandwidth Bottlenecks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The rapid growth of data storage demand, exceeding the capacity to store it, leads to a bottleneck in data storage and transmission, with existing solutions like data compression and additional physical storage capacity being inadequate.
Innovation Solution
A system and method for adaptive data processing and storage that utilizes automated system efficacy monitoring and model training, analyzing test datasets to determine new probability distributions, updating data processing algorithms, and generating new data units with corresponding identifiers, which are then compiled into an updated data structure for distribution across networked devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If additional physical storage capacity is added, then storage demand can be met temporarily, but storage demand continues to outstrip global manufacturing capacity
Solution Approach 1:
The patent creates virtual copies of data through probabilistic modeling. Instead of storing complete data sets, the system stores compact probability distributions that can generate data on-demand. This virtual copying mechanism dramatically reduces physical storage requirements while maintaining data accessibility.
Solution Approach 2:
The system transforms data from its original form into probability distribution parameters. By changing the representation parameters from complete data sets to statistical models, the system achieves exponential compression ratios while preserving the ability to reconstruct and query data effectively.
2Quantity of substance
If data compression is applied, then storage capacity is doubled, but compression ratios decrease for multi-media data or result in data degradation
Solution Approach 1:
The patent applies parameter changes by transforming data into probability distribution models. This transformation achieves lossless compression for structured data while maintaining acceptable quality for multi-media through probabilistic reconstruction, overcoming the limitations of traditional compression methods.
Solution Approach 2:
The system creates probabilistic copies of data that preserve essential information characteristics. These probabilistic representations enable reconstruction of original data with high fidelity, avoiding the information loss inherent in traditional lossy compression while achieving superior compression ratios.
3Quantity of substance
If large data sets are transmitted, then data can be distributed, but transmission bandwidth becomes a bottleneck
Solution Approach 1:
The patent transmits compact probabilistic models instead of complete data sets. These compressed representations reduce transmission bandwidth requirements by orders of magnitude while enabling efficient data distribution across networks through the transmission of lightweight statistical parameters.
Solution Approach 2:
The system changes the transmission parameters from raw data to probability distribution parameters. This parameter transformation dramatically reduces the volume of data requiring transmission while preserving the ability to reconstruct and utilize the full data set at the receiving end.
4Adaptability or versatility
If data processing systems become more complex and distributed, then system capability increases, but adaptive solutions are needed to handle diverse data types and update algorithms dynamically
Solution Approach 1:
The patent uses parameter changes to represent diverse data types through unified probability distributions. This approach simplifies the handling of heterogeneous data by transforming all data types into a common probabilistic framework, reducing the complexity of adaptive processing while maintaining versatility.
Solution Approach 2:
The system implements a universal probabilistic modeling framework that can handle multiple data types and processing tasks through a single unified approach. This multi-functional capability reduces system complexity by eliminating the need for separate processing pipelines for different data types.
Data Source
AI summary
Data storage, transfer, synchronization, and security using automated system efficacy monitoring and model training is disclosed. 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. 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.