18087357. CLIFFORD NEURAL LAYERS FOR MULTIVECTOR SYSTEM MODELING simplified abstract (MICROSOFT TECHNOLOGY LICENSING, LLC)
Contents
- 1 CLIFFORD NEURAL LAYERS FOR MULTIVECTOR SYSTEM MODELING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CLIFFORD NEURAL LAYERS FOR MULTIVECTOR SYSTEM MODELING - 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
CLIFFORD NEURAL LAYERS FOR MULTIVECTOR SYSTEM MODELING
Organization Name
MICROSOFT TECHNOLOGY LICENSING, LLC
Inventor(s)
Johannes Brandstetter of Amsterdam (NL)
Max Welling of Redmond WA (US)
Jayesh Kumar Gupta of Redmond WA (US)
CLIFFORD NEURAL LAYERS FOR MULTIVECTOR SYSTEM MODELING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18087357 titled 'CLIFFORD NEURAL LAYERS FOR MULTIVECTOR SYSTEM MODELING
Simplified Explanation
The patent application discusses devices, systems, and methods for machine learning modeling of a system that operates on a multivector object. The method involves receiving the multivector object as an input, operating on it using a Clifford layer with neurons implementing a multivector kernel, and generating a multivector output representing the state of the system.
- Machine learning modeling of a system operating on a multivector object
- Method involves using a Clifford layer with neurons implementing a multivector kernel
- Input is a multivector object representing the state of the system
- Output is a multivector output representing the system's state
Potential Applications
This technology could be applied in various fields such as robotics, autonomous vehicles, computer vision, and natural language processing.
Problems Solved
This technology helps in efficiently modeling and analyzing systems that operate on multivector objects, which can be complex and challenging to handle using traditional methods.
Benefits
The use of machine learning and multivector modeling can lead to improved accuracy, efficiency, and performance in analyzing and understanding complex systems.
Potential Commercial Applications
Potential commercial applications include advanced robotics systems, autonomous vehicles, smart surveillance systems, and intelligent automation solutions.
Possible Prior Art
Prior art may include research on machine learning models for complex systems, multivector analysis techniques, and applications of Clifford algebra in data processing.
What are the limitations of this technology in real-world applications?
Real-world implementation of this technology may face challenges such as computational complexity, data scalability, and integration with existing systems.
How does this technology compare to traditional machine learning methods?
This technology offers a unique approach to modeling systems that operate on multivector objects, providing a more comprehensive and accurate representation compared to traditional methods.
Original Abstract Submitted
Generally discussed herein are devices, systems, and methods for machine learning (ML) modeling of a system that operates on a multivector object. A method includes receiving, by an ML model, the multivector object as an input that represents a state of the multivector system. The method includes operating, by the ML model and using a Clifford layer that includes neurons that implement a multivector kernel, on the multivector input to generate a multivector output that represents the state of the multivector system responsive to the multivector input.
