Method for complex events detection using hidden markov models
US10929767B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 25, 2016 |
| Grant date | Feb 23, 2021 |
| Priority date | — |
| Expiry date | Mar 29, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments of the present invention may provide the capability to detect complex events while providing improved detection and performance. In an embodiment of the present invention, a method for detecting an event may comprise receiving data representing measurement or detection of physical parameters, conditions, or actions, quantizing the received data and selecting a number of samples from the quantized data, generating a hidden Markov model representing events to be detected using initial model values based on ideal conditions, wherein a desired output is defined as a sequence of states, and wherein a number of states of the hidden Markov model is less than or equal to the number of samples of the quantized data, adjusting the quantized data and the initial model values to improve accuracy of the model, determining a state sequence of the hidden Markov model, and outputting an indication of a detected event.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.