Patent · US Active

Systems and methods for calibrating a machine learning model

US10572832B2 · kind B2 · utility

0Cited by

3References

13Claims

0Family size

Assignee

Sift Science, Inc. · US

Inventors

Fred Sadaghiani · San Francisco, US
Aaron Beppu · San Francisco, US
Jacob Burnim · San Francisco, US
Alex Paino · San Francisco, US

Key dates

Filing date	May 14, 2019
Grant date	Feb 25, 2020
Priority date	—
Expiry date	May 14, 2039

Classification

Technology area (CPC G)Physics
CPC primaryG06N20/10
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

Systems and methods include: collecting digital threat scores of an incumbent digital threat machine learning model; identifying incumbent and successor digital threat score distributions; identifying quantiles data of the incumbent digital threat score distribution; collecting digital threat scores of a successor digital threat machine learning model; calibrating the digital threat scores of the successor digital threat score distribution based on the quantiles data of the incumbent digital threat score distribution and the incumbent digital threat score distribution; and in response to remapping the digital threat scores of the successor digital threat score distribution, publishing the successor digital scores in lieu of the incumbent digital threat scores based on requests for digital threat scores.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.