Qualcomm incorporated (20240320540). MACHINE LEARNING (ML) BASED SOFTWARE KERNEL SELECTION simplified abstract
Contents
MACHINE LEARNING (ML) BASED SOFTWARE KERNEL SELECTION
Organization Name
Inventor(s)
Abhilash Sudhir Maradwar of Hyderabad (IN)
Deepthi Sasidhara Menon of Hyderabad (IN)
Sumit Kumar Bhuin of Ranchi (IN)
MACHINE LEARNING (ML) BASED SOFTWARE KERNEL SELECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240320540 titled 'MACHINE LEARNING (ML) BASED SOFTWARE KERNEL SELECTION
Simplified Explanation: The patent application focuses on selecting software kernels for machine learning models.
Key Features and Innovation:
- Inputting multiple valid software kernels into a trained ML model engine.
- Configuring the engine to create a trained ML model using the kernels.
- Generating a software kernel selected by the ML model.
Potential Applications: This technology can be applied in various industries such as healthcare, finance, and autonomous vehicles for optimizing machine learning models.
Problems Solved: This technology addresses the challenge of efficiently selecting software kernels for machine learning models to improve performance and accuracy.
Benefits:
- Enhanced performance of machine learning models.
- Increased accuracy in predictions.
- Streamlined process for selecting software kernels.
Commercial Applications: The technology can be utilized in industries such as healthcare for medical diagnosis systems, finance for fraud detection algorithms, and autonomous vehicles for object recognition systems.
Prior Art: Researchers can explore prior art related to software kernel selection in machine learning models to understand the existing technologies and advancements in this field.
Frequently Updated Research: Stay updated on research related to software kernel selection in machine learning models to leverage the latest advancements and techniques in the industry.
Questions about Software Kernel Selection in Machine Learning Models: 1. What are the key challenges in selecting software kernels for machine learning models? 2. How does the trained ML model engine improve the selection process of software kernels?
Original Abstract Submitted
aspects of the disclosure are directed to kernel selection. in accordance with one aspect, disclosed is an apparatus and method for inputting a plurality of valid software kernels to a trained machine learning (ml) model engine; configuring the trained ml model engine to generate a first trained machine learning (ml) model based on the plurality of valid software kernels; and using the first trained ml model to generate a machine learning (ml)-selected software kernel based on the plurality of valid software kernels.
