Data compression based on co-clustering of multiple parameters for AI training
US11714834B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 21, 2022 |
| Grant date | Aug 1, 2023 |
| Priority date | — |
| Expiry date | Feb 18, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/23
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Co-clustering of at least some parameters is employed to reduce data transferred between edge and cloud resources. Single-parameter cluster information, including cluster counts, for each of two or more parameters of interest is accessed. Each parameter may represent a time series of numeric values sent from an IoT unit to an edge device. A co-clustering ratio is determined for each unique parameter pair. The co-clustering ratio indicates whether the number of clusters produced by a co-clustering algorithm applied to a group of parameters is less than the number of clusters required to represent the parameters without co-clustering. Co-cluster groups may be identified based on the cluster ratios. For each co-cluster group, the co-clustering algorithm may be invoked to produce compressed encodings of numeric value tuples. The compressed encoding is then transmitted to a cloud computing resource and decoded into a tuple of surrogate values.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.