Google llc (20240135152). Automated, Constraints-Dependent Machine Learning Model Thresholding Mechanisms simplified abstract
Automated, Constraints-Dependent Machine Learning Model Thresholding Mechanisms
Organization Name
Inventor(s)
Madhav Datt of Mountain View CA (US)
Surabhi Choudhary of Chennai (IN)
Nikhil Shirish Ketkar of Bengaluru (IN)
Automated, Constraints-Dependent Machine Learning Model Thresholding Mechanisms - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135152 titled 'Automated, Constraints-Dependent Machine Learning Model Thresholding Mechanisms
Simplified Explanation
The patent application describes computing systems and methods for discrete-valued output classification using machine learning models and threshold values. Here is a simplified explanation of the abstract:
- Obtaining a candidate threshold value for a data slice
- Calculating performance value using the model and threshold
- Determining if safeguard criterion is satisfied
- Adjusting threshold value based on tradeoff logic
- Authenticating input data based on final threshold
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- Potential Applications
This technology could be applied in fraud detection systems, security screening processes, and quality control measures in manufacturing.
- Problems Solved
This innovation helps in automating decision-making processes based on machine learning models, improving accuracy and efficiency in classification tasks.
- Benefits
The technology offers a more reliable and consistent method for determining the authenticity of input data, reducing the risk of errors and false positives.
- Potential Commercial Applications
"Enhancing Data Security with Discrete-Valued Output Classification Technology"
- Possible Prior Art
One possible prior art could be traditional threshold-based classification systems that do not incorporate machine learning models for decision-making.
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- Unanswered Questions
- How does this technology handle dynamic data streams or real-time processing?
The patent application does not specify how the system adapts to changing data inputs or operates in real-time scenarios.
- What are the computational requirements for implementing this technology at scale?
The document does not provide information on the computational resources needed to deploy this system in large-scale applications.
Original Abstract Submitted
provided are computing systems, methods, and platforms for a discrete-valued output classification. the operations can include obtaining a candidate threshold value for a first slice in a plurality of data slices. additionally, the operations can include calculating, using a candidate machine-learned model and the candidate threshold value, a first performance value associated with a first risk tolerance value. moreover, the operations can include determining, based on the first performance value, that a safeguard criterion for the first slice has not been satisfied. in response to the determination that the safeguard criterion for the first slice has not been satisfied, the operations can include performing a tradeoff logic operation to determine the final threshold value. subsequently, the operations can include determining, using the candidate machine-learned model, whether input data is authentic based on the final threshold value.
