|
|
Found results for
 patents
1.
|
ARTIFICIALLY INTELLIGENT UNCERTAINTY QUANTIFICATION FOR ESTIMATES OF EVOLUTION MODEL PARAMETERS
| Application Number |
US2024046752 |
| Publication Number |
2025/059565 |
| Status |
In Force |
| Filing Date |
2024-09-13 |
| Publication Date |
2025-03-20 |
| Owner |
MACSO TECHNOLOGIES LIMITED (New Zealand)
|
| Inventor |
- Goh, Hwan
- Samiei, Saba
- Russell, Vincent
- Zhang, Hanxiao
- Rivard, Philippe
|
Abstract
In some embodiments of the invention, a method for estimating parameters of an evolution model includes identifying an evolution model; obtaining a set of training data values, where each value in the set is associated with a parameter-of-interest (Pol) associated with the evolution model; obtaining a noise model representing noise affecting the output of the evolution model; obtaining a prior model that represents prior information on characteristics of the parameter-of-interest; constructing a loss function for a neural network, where the loss function incorporates the set of training data values, the evolution model, the noise model, and the prior model; and training the neural network with the loss function to obtain updated weights.
IPC Classes ?
- G06N 3/086 - Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
- G06F 18/2111 - Selection of the most significant subset of features by using evolutionary computational techniques, e.g. genetic algorithms
- G06N 20/20 - Ensemble learning
- G06F 3/045 - Digitisers, e.g. for touch screens or touch pads, characterised by the transducing means using resistive elements, e.g. a single continuous surface or two parallel surfaces put in contact
- G06F 18/10 - Pre-processingData cleansing
|
2.
|
Artificially Intelligent Uncertainty Quantification for Estimates of Evolution Model Parameters
| Application Number |
18885359 |
| Status |
Pending |
| Filing Date |
2024-09-13 |
| First Publication Date |
2025-03-13 |
| Owner |
MACSO Technologies Limited (New Zealand)
|
| Inventor |
- Goh, Hwan
- Samiei, Saba
- Russell, Vincent
- Zhang, Hanxiao
- Rivard, Philippe
|
Abstract
In some embodiments of the invention, a method for estimating parameters of an evolution model includes identifying an evolution model; obtaining a set of training data values, where each value in the set is associated with a parameter-of-interest (PoI) associated with the evolution model; obtaining a noise model representing noise affecting the output of the evolution model; obtaining a prior model that represents prior information on characteristics of the parameter-of-interest; constructing a loss function for a neural network, where the loss function incorporates the set of training data values, the evolution model, the noise model, and the prior model; and training the neural network with the loss function to obtain updated weights.
IPC Classes ?
- G06N 3/086 - Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
|
3.
|
MACHINE LEARNING BASED AUDITORY ACTIVITY MONITORING FOR LIVESTOCK HEALTH AND WELFARE
| Application Number |
US2023081575 |
| Publication Number |
2024/118757 |
| Status |
In Force |
| Filing Date |
2023-11-29 |
| Publication Date |
2024-06-06 |
| Owner |
MACSO TECHNOLOGIES LIMITED (New Zealand)
|
| Inventor |
- Samiei, Saba
- Goh, Hwan
- Zhang, Hanxiao
- Rivard, Philippe
|
Abstract
Health and welfare of animals may be performed by network edge devices using neural networks trained on a server. The network edge devices may include a sensor for sensing conditions of an enclosure or range for animals, and a processor for performing neural network processing to indicate activity of interest detected by the sensor. Reports of activity of interest may be communicated to a server or other compute device for display on a dashboard.
|
|
