Google llc (20240232589). Automated, Constraints-Dependent Machine Learning Model Thresholding Mechanisms simplified abstract
Contents
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 20240232589 titled 'Automated, Constraints-Dependent Machine Learning Model Thresholding Mechanisms
Simplified Explanation: The patent application describes computing systems and methods for discrete-valued output classification. It involves determining threshold values for data slices, calculating performance values based on machine-learned models, and making decisions based on safeguard criteria.
- Candidate threshold values are obtained for data slices.
- Performance values are calculated using machine-learned models and threshold values.
- Safeguard criteria are checked and tradeoff logic is applied if criteria are not met.
- Final threshold values are determined for authenticating input data using machine-learned models.
Key Features and Innovation:
- Automated classification of discrete-valued outputs.
- Utilization of machine-learned models for decision-making.
- Adaptive threshold determination based on performance values.
- Safeguard criteria evaluation for data authenticity.
Potential Applications: This technology can be applied in various industries such as finance, healthcare, cybersecurity, and fraud detection for accurate classification and authentication of data.
Problems Solved: The technology addresses the need for efficient and accurate classification of discrete-valued outputs while considering risk tolerance and safeguard criteria.
Benefits:
- Improved accuracy in data classification.
- Automated decision-making based on machine-learned models.
- Enhanced data authenticity verification.
Commercial Applications: Title: Automated Data Classification System for Enhanced Security Measures This technology can be commercialized in industries requiring secure data classification and authentication, such as financial institutions, healthcare organizations, and cybersecurity firms. It can improve operational efficiency and reduce the risk of fraudulent activities.
Prior Art: Prior research in machine learning and data classification methods can provide insights into similar technologies and approaches in the field.
Frequently Updated Research: Stay updated on advancements in machine learning algorithms, data classification techniques, and cybersecurity measures to enhance the capabilities of this technology.
Questions about Data Classification Systems: 1. How does this technology improve data classification processes compared to traditional methods? 2. What are the potential limitations or challenges of implementing this automated data classification system?
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.
