Multilevel constraint-based randomization adapting case-based learning to fuse sensor data for autonomous predictive analysis
US9396441B1 · kind B1 · utility
16Cited by
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Key dates
| Filing date | Sep 30, 2013 |
| Grant date | Jul 19, 2016 |
| Priority date | — |
| Expiry date | Jun 26, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The invention is a method and system updating the automated responses of an autonomous system using sensor data from heterogeneous sources. An array of cases representing known situations are stored as data structures in a non-transitory memory. Each case in the array of cases is associated with an action to create a database of identifiable situation-action pairs. The system determines an acceptable range of correctness of partial matches of sensed data for new cases to the data properties of known cases and creates and overwrites now situation-action pairs in a process of autonomous learning of new responses.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.