Statistical and neural network approach for data characterization to reduce storage space requirements
US11609695B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 2, 2020 |
| Grant date | Mar 21, 2023 |
| Priority date | — |
| Expiry date | Jan 6, 2041 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH03M7/6088
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A data model is trained to determine whether data is raw, compressed, and/or encrypted. The data model may also be trained to recognize which compression algorithm was used to compress data and predict compression ratios for the data using different compression algorithms. A storage system uses the data model to independently identify raw data. The raw data is grouped based on similarity of statistical features and group members are compressed with the same compression algorithm and may be encrypted after compression with the same encryption algorithm. The data model may also be used to identify sub-optimally compressed data, which may be uncompressed and grouped for compression using a different compression algorithm.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.