Microsoft technology licensing, llc (20240112053). DETERMINATION OF AN OUTLIER SCORE USING EXTREME VALUE THEORY (EVT) simplified abstract
Contents
- 1 DETERMINATION OF AN OUTLIER SCORE USING EXTREME VALUE THEORY (EVT)
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DETERMINATION OF AN OUTLIER SCORE USING EXTREME VALUE THEORY (EVT) - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
DETERMINATION OF AN OUTLIER SCORE USING EXTREME VALUE THEORY (EVT)
Organization Name
microsoft technology licensing, llc
Inventor(s)
Laurent Boue of Petah Tikva (IL)
DETERMINATION OF AN OUTLIER SCORE USING EXTREME VALUE THEORY (EVT) - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240112053 titled 'DETERMINATION OF AN OUTLIER SCORE USING EXTREME VALUE THEORY (EVT)
Simplified Explanation
The patent application abstract describes a method for identifying anomalous subsets of data by selecting a subset with a specific feature, determining parameters, implementing an extreme value theory algorithm to calculate a probability value for the feature, generating an outlier score based on the probability value, and identifying the subset as anomalous if the outlier score is above a threshold.
- Subset of data with a feature is selected from a dataset
- Parameters are determined from the selected subset
- Extreme value theory algorithm is used to calculate a probability value for the feature
- Outlier score is generated based on the probability value
- Subset is identified as anomalous if the outlier score is above a threshold
Potential Applications
This technology can be applied in various fields such as finance, cybersecurity, fraud detection, and anomaly detection in large datasets.
Problems Solved
This technology helps in efficiently identifying anomalous subsets of data, which can be crucial for detecting fraud, cybersecurity threats, and other irregularities in datasets.
Benefits
The benefits of this technology include improved accuracy in anomaly detection, faster identification of outliers, and enhanced security measures in various industries.
Potential Commercial Applications
The potential commercial applications of this technology include financial institutions, cybersecurity companies, e-commerce platforms, and any organization dealing with large datasets requiring anomaly detection.
Possible Prior Art
One possible prior art for this technology could be existing outlier detection algorithms and methods used in data analysis and anomaly detection.
Unanswered Questions
How does this technology compare to existing outlier detection methods?
This article does not provide a direct comparison with existing outlier detection methods, leaving the reader to wonder about the specific advantages and limitations of this technology in comparison to others.
What are the specific parameters used in the extreme value theory algorithm for calculating the probability value?
The article does not delve into the specific parameters used in the extreme value theory algorithm, leaving the reader curious about the technical details of the calculation process.
Original Abstract Submitted
a subset of data that includes a feature may be selected from a dataset. parameters from the selected subset of data are determined and an extreme value theory (evt) algorithm is implemented to determine a probability value for the feature based at least in part on the determined parameters. based on the determined probability value for the feature, an outlier score is generated for the feature. based on the outlier score being above a threshold, the subset is identified as anomalous.
