Microsoft technology licensing, llc (20240119615). TRACKING THREE-DIMENSIONAL GEOMETRIC SHAPES simplified abstract
Contents
- 1 TRACKING THREE-DIMENSIONAL GEOMETRIC SHAPES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TRACKING THREE-DIMENSIONAL GEOMETRIC SHAPES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
TRACKING THREE-DIMENSIONAL GEOMETRIC SHAPES
Organization Name
microsoft technology licensing, llc
Inventor(s)
Lingzhi L. Allen of Redmond WA (US)
Wolfgang M. Pauli of Seattle WA (US)
TRACKING THREE-DIMENSIONAL GEOMETRIC SHAPES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240119615 titled 'TRACKING THREE-DIMENSIONAL GEOMETRIC SHAPES
Simplified Explanation
The abstract describes a patent application for a machine learning model that uses a set of geometric shapes to estimate spatial parameters for objects identified in image data. The model updates its learning rate in response to external events and integrates updates to the spatial parameters over time.
- Machine learning model uses geometric shapes to estimate spatial parameters for objects in image data
- Learning rate of the model is updated based on external events
- Spatial parameters are estimated by fitting objects to geometric shapes
- Updates to spatial parameters are integrated over time
- Spatial estimate of objects in image data is generated
Potential Applications
This technology could be applied in various fields such as:
- Object recognition in computer vision
- Autonomous driving for identifying and tracking objects on the road
- Robotics for object manipulation and navigation
Problems Solved
The technology addresses the following issues:
- Accurate estimation of spatial parameters for objects in image data
- Real-time updates to spatial parameters based on external events
- Integration of temporal information for improved object tracking
Benefits
The benefits of this technology include:
- Enhanced object recognition and tracking capabilities
- Improved accuracy in estimating spatial parameters
- Adaptive learning rate for better model performance
Potential Commercial Applications
A potential commercial application for this technology could be:
- Advanced surveillance systems for monitoring and tracking objects in real-time
Possible Prior Art
One possible prior art for this technology could be:
- Existing machine learning models for object recognition and tracking in image data
Unanswered Questions
How does this technology compare to traditional object recognition methods?
This technology offers a more adaptive and accurate approach to estimating spatial parameters for objects in image data compared to traditional methods. By using geometric shapes and updating the learning rate based on external events, the model can provide more precise spatial estimates.
What are the limitations of this technology in terms of scalability and complexity of objects?
The technology may face challenges in scaling to complex and highly detailed objects, as the geometric shapes used for estimation may not always accurately represent intricate structures. Additionally, the integration of temporal information for updates may introduce complexity in handling a large number of objects simultaneously.
Original Abstract Submitted
a set of geometric shapes to be applied by a machine learning model to objects identified in image data is defined. a learning rate of the machine learning model is updated in response to external events. the machine learning model is used to estimate spatial parameters for each of the objects identified in the image data. the spatial parameters are estimated by fitting the objects to the set of geometric shapes. updates to the spatial parameters are temporally integrated. a spatial estimate of the objects identified in the image data is generated.
