Accelerated model training from disparate and heterogeneous sources using a meta-database
US12406183B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 12, 2022 |
| Grant date | Sep 2, 2025 |
| Priority date | — |
| Expiry date | Jul 4, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system for training a model from a subset of data representing decentrally stored source databases. A key variable repository module operably couples the databases and includes an AI program with a scanner algorithm and a profiler algorithm. The scanner algorithm receives the training data from a source interface, compresses the training data, and synchronizes the training data with the meta-data using a meta-database interface. The profiler algorithm receives the meta-data from the meta-database interface, generates granular data types for the meta-data, determines training variables indicative of the meta-data, generates variable probability distributions, produces training variable associations, and modifies the meta-database to include the probability distributions and associations using the meta-data interface. The key interface allows for searching the meta-database for training variables, variable probability distributions, and/or variable associations. A model of the system may be trained in less time with a subset of data associated with the training variable.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.