Determining temporal patterns in sensed data sequences by hierarchical decomposition of hidden Markov models
US7542949B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 12, 2004 |
| Grant date | Jun 2, 2009 |
| Priority date | — |
| Expiry date | Jun 15, 2025 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method determines temporal patterns in data sequences. A hierarchical tree of nodes is constructed. Each node in the tree is associated with a composite hidden Markov model, in which the composite hidden Markov model has one independent path for each child node of a parent node of the hierarchical tree. The composite hidden Markov models are trained using training data sequences. The composite hidden Markov models associated with the nodes of the hierarchical tree are decomposed into a single final composite Markov model. The single final composite hidden Markov model can then be employed for determining temporal patterns in unknown data sequences.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.