Digital Spectrum File Grouping with Stopping Functions
Solution Overview
Problem
Current data compression techniques fail to effectively recognize and utilize similarities in data files, leading to inefficient storage and transmission, particularly in unstructured data environments, and lack the ability to organize and identify highly similar data sets.
Innovation Solution
The method involves creating a digital spectrum to map files in multi-dimensional space, allowing for self-organizing patterns to be identified without keyword or metadata processing, and using stopping functions to optimize file grouping and differentiation, enabling the recognition of similar files and reducing redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional compression techniques are used, then storage capacity is preserved and bandwidth is reduced, but similarity recognition between data files is failed
Solution Approach 1:
The patent transforms file comparison from traditional keyword/metadata analysis into a multi-dimensional spectral space where files are represented as vectors. By mapping files into this higher-dimensional space using digital signal processing techniques (FFT, wavelet transforms), the system can recognize similarities based on content patterns rather than explicit metadata, resolving the contradiction between compression efficiency and similarity recognition.
Solution Approach 2:
The invention replaces traditional mechanical/text-based file analysis methods with signal processing and spectral analysis techniques. Instead of searching for keywords or comparing metadata, the system applies mathematical transforms to convert file contents into spectral representations, enabling automatic similarity detection without relying on structured data formats.
2Quantity of substance
If block level operations are used, then storage efficiency is improved through de-duplication, but meaningful interpretation of stored data is limited
Solution Approach 1:
The patent segments files into overlapping blocks for spectral analysis, allowing both efficient storage through de-duplication and meaningful interpretation through content-based similarity detection. By analyzing spectral characteristics of these segments, the system can identify semantically similar content even when exact byte-level matches are absent, maintaining both storage efficiency and interpretability.
3Ease of operation
If search engines are used to organize data, then hierarchical presentation is provided, but duplicate elimination and migration options are limited
Solution Approach 1:
The system uses spectral similarity measurements to provide feedback-driven duplicate detection and management. By continuously measuring spectral distances between files and comparing them against threshold values, the system can automatically identify duplicates, suggest consolidations, and manage data migration opportunities, enhancing both organization capability and adaptability.
4Quantity of substance
If compression algorithms focus on compression ratios, then storage capacity is optimized, but recognition of data similarities is neglected
Solution Approach 1:
The patent applies spectral analysis and similarity detection before the compression process. By pre-identifying similar files through their spectral characteristics and grouping them accordingly, the system enables compression algorithms to work more effectively on consolidated data sets, achieving both storage optimization and similarity recognition simultaneously.
Data Source
AI summary
Methods and apparatus teach a digital spectrum of a data file. The digital spectrum is used to map a file's position in multi-dimensional space. This position relative to another file's position reveals closest neighbors. Certain of the closest neighbors are grouped together, while others are differentiated. Grouping ceases upon application of a stopping function so that rightly sized, optimum numbers of file groups are obtained. Embodiments of stopping functions relate to curve types in a mapping of numbers of groups per sequential rounds of grouping, recognizing whether groups have overlapping file members or not, and/or determining whether groups meet predetermined numbers of members, to name a few. Properly grouped files can then be further acted upon.