18444360. SYSTEM AND METHODS FOR MACHINE LEARNING TRAINING DATA SELECTION simplified abstract (Google LLC)
Contents
- 1 SYSTEM AND METHODS FOR MACHINE LEARNING TRAINING DATA SELECTION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEM AND METHODS FOR MACHINE LEARNING TRAINING DATA SELECTION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about the Technology
- 1.13 Original Abstract Submitted
SYSTEM AND METHODS FOR MACHINE LEARNING TRAINING DATA SELECTION
Organization Name
Inventor(s)
Chetan Pitambar Bhole of Mountain View CA (US)
Tanmay Khirwadkar of Fremont CA (US)
Sourabh Prakash Bansod of Mountain View CA (US)
Sanjay Mangla of San Jose CA (US)
Deepak Ramamurthi Sivaramapuram Chandrasekaran of San Jose CA (US)
SYSTEM AND METHODS FOR MACHINE LEARNING TRAINING DATA SELECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18444360 titled 'SYSTEM AND METHODS FOR MACHINE LEARNING TRAINING DATA SELECTION
Simplified Explanation
The patent application describes a method where simulation data is used to determine if additional training data is needed to train a machine learning model.
- Simulation data is obtained and evaluated against certain criteria.
- If the simulation data meets the criteria, a second set of training data is acquired.
- The machine learning model is then trained using the second set of training data.
Key Features and Innovation
- Utilizes simulation data to determine the need for additional training data.
- Ensures the machine learning model is trained effectively by acquiring more data if necessary.
Potential Applications
This technology can be applied in various fields such as finance, healthcare, and manufacturing where accurate machine learning models are crucial.
Problems Solved
- Ensures machine learning models are trained effectively.
- Saves time and resources by acquiring additional training data only when needed.
Benefits
- Improved accuracy of machine learning models.
- Efficient use of resources in training machine learning models.
Commercial Applications
- "Optimizing Machine Learning Model Training with Simulation Data" - This technology can be used by companies developing machine learning models to improve their accuracy and efficiency, leading to better decision-making processes.
Prior Art
Readers can explore prior research on the use of simulation data in training machine learning models to understand the evolution of this technology.
Frequently Updated Research
Stay updated on the latest advancements in using simulation data to enhance machine learning model training for cutting-edge applications.
Questions about the Technology
How does this technology improve the training of machine learning models?
This technology ensures that machine learning models are trained effectively by acquiring additional training data only when necessary, leading to improved accuracy.
What are the potential applications of this technology beyond machine learning?
This technology can be applied in various industries such as finance, healthcare, and manufacturing to enhance decision-making processes and improve overall efficiency.
Original Abstract Submitted
Simulation data associated with a simulation test performed with respect to a first set of training data is obtained. Responsive to a determination that the obtained simulation data satisfies one or more criteria, a second set of training data is obtained, where a size of the second set of training data meets or exceeds a size of the first set of training data. A machine learning model is trained using the second set of training data.
