Google llc (20240193475). SYSTEM AND METHODS FOR MACHINE LEARNING TRAINING DATA SELECTION simplified abstract

SYSTEM AND METHODS FOR MACHINE LEARNING TRAINING DATA SELECTION

Organization Name

google llc

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 20240193475 titled 'SYSTEM AND METHODS FOR MACHINE LEARNING TRAINING DATA SELECTION

Simplified Explanation

The patent application describes a process where simulation data is used to determine if certain criteria are met. If so, a larger set of training data is obtained to train a machine learning model.

• The process involves obtaining simulation data from a simulation test.
• If the simulation data meets certain criteria, a second set of training data is acquired.
• The size of the second set of training data is equal to or greater than the size of the first set.
• A machine learning model is then trained using the second set of training data.

Key Features and Innovation

• Utilization of simulation data to determine the need for additional training data.
• Adaptive approach to acquiring training data based on simulation results.
• Efficient training of machine learning models by expanding the training dataset as needed.

Potential Applications

The technology can be applied in various fields such as:

• Predictive analytics
• Autonomous systems
• Financial forecasting

Problems Solved

• Efficient utilization of simulation data in training machine learning models.
• Adaptive approach to acquiring training data based on simulation results.
• Improved accuracy and performance of machine learning models.

Benefits

• Enhanced accuracy and performance of machine learning models.
• Cost-effective training process by acquiring additional data only when necessary.
• Increased efficiency in training complex models.

Commercial Applications

• Predictive maintenance in manufacturing industries.
• Fraud detection in financial services.
• Autonomous vehicle navigation systems.

Prior Art

Readers can explore prior art related to this technology in the fields of machine learning, simulation testing, and data acquisition methodologies.

Frequently Updated Research

Stay updated on advancements in machine learning training techniques, simulation data analysis, and adaptive learning algorithms relevant to this technology.

Questions about the Technology

How does this technology improve the efficiency of machine learning model training?

This technology improves efficiency by dynamically acquiring additional training data based on simulation results, optimizing the training process.

What are the potential applications of this technology beyond machine learning models?

The technology can be applied in various fields such as predictive analytics, autonomous systems, and financial forecasting, expanding its utility beyond machine learning.

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.