Christopher D Patrick

US Patent:

20220374297, Nov 24, 2022

Filed:

Jul 25, 2022

Appl. No.:

17/872277

Inventors:

- SAN FRANCISCO CA, US
Sheeban Raza Zaheer Shaikh - Foster City CA, US
Christopher Patrick - Foster City CA, US
Santanu Bhattacharya - Foster City CA, US

International Classification:

G06F 9/54
G06Q 20/20
G06Q 20/10

Abstract:

A central server receives API calls requesting services. The central server identifies whether the API calls are associated with a merchant. A distribution is constructed based on the API calls. The central server further executes a pre-defined rule to identify a set of the API calls belonging to a maximum percentile in the distribution and a set of the API calls belonging to a minimum percentile in the distribution before estimating a set of the anomalous data points with one or more goodness of fit functions against the maximum percentile and the minimum percentile. A GUI receives a critical value from a user. In response to receiving the critical value, the central server generates probabilities of the set of the anomalous data points before displaying a set of the anomalous data points in response to the probabilities being less than the critical value.

US Patent:

20210096937, Apr 1, 2021

Filed:

Oct 1, 2019

Appl. No.:

16/589990

Inventors:

Nagraj K. Naidu - Foster City CA, US
Sheeban Raza Zaheer Shaikh - Foster City CA, US
Christopher Patrick - Foster City CA, US
Santanu Bhattacharya - Foster City CA, US

International Classification:

G06F 9/54
G06Q 20/20

Abstract:

A central server receives API calls requesting services. The central server identifies whether the API calls are associated with a merchant. A distribution is constructed based on the API calls. The central server further executes a pre-defined rule to identify a set of the API calls belonging to a maximum percentile in the distribution and a set of the API calls belonging to a minimum percentile in the distribution before estimating a set of the anomalous data points with one or more goodness of fit functions against the maximum percentile and the minimum percentile. A GUI receives a critical value from a user. In response to receiving the critical value, the central server generates probabilities of the set of the anomalous data points before displaying a set of the anomalous data points in response to the probabilities being less than the critical value.