Chao H Li

US Patent:

20220237670, Jul 28, 2022

Filed:

Apr 15, 2022

Appl. No.:

17/721594

Inventors:

- Bentonville AR, US
Elham SHAABANI - Redwood City CA, US
Chao LI - San Carlos CA, US
Matyas A. SUSTIK - San Francisco CA, US

International Classification:

G06Q 30/02
G06K 9/62
G06Q 20/20
G06N 20/00
G06Q 10/06

Abstract:

This application relates to apparatus and methods for identifying anomalies within data, such as pricing data. In some examples, a computing device receives data updates and selects a machine learning model to apply to the data update. The computing device may train the machine learning model with features generated based on historical purchase order data. An anomaly score is generated based on application of the machine learning model. Based on the anomaly score, the data update is either allowed, or denied. In some examples, the computing device re-trains the machine learning model with detected anomalies. In some embodiments, the computing device prioritizes detected anomalies for further investigation. In some embodiments, the computing device identifies the cause of the anomalies by identifying at least one feature that is causing the anomaly.

US Patent:

20200380570, Dec 3, 2020

Filed:

May 30, 2019

Appl. No.:

16/427238

Inventors:

- Bentonville AR, US
Elham SHAABANI - Redwood City CA, US
Chao LI - San Carlos CA, US
Matyas A. SUSTIK - San Francisco CA, US

International Classification:

G06Q 30/02
G06K 9/62
G06Q 10/06
G06N 20/00
G06Q 20/20

Abstract:

This application relates to apparatus and methods for identifying anomalies within data, such as pricing data. In some examples, a computing device receives data updates and selects a machine learning model to apply to the data update. The computing device may train the machine learning model with features generated based on historical purchase order data. An anomaly score is generated based on application of the machine learning model. Based on the anomaly score, the data update is either allowed, or denied. In some examples, the computing device re-trains the machine learning model with detected anomalies. In some embodiments, the computing device prioritizes detected anomalies for further investigation. In some embodiments, the computing device identifies the cause of the anomalies by identifying at least one feature that is causing the anomaly.

US Patent:

20200380571, Dec 3, 2020

Filed:

May 30, 2019

Appl. No.:

16/427241

Inventors:

- Bentonville AR, US
Elham SHAABANI - Redwood City CA, US
Chao LI - San Carlos CA, US
Matyas A. SUSTIK - San Francisco CA, US

International Classification:

G06Q 30/02
G06K 9/00
G06F 17/11
G06Q 10/06
G06N 20/00
G06Q 20/20

Abstract:

This application relates to apparatus and methods for identifying anomalies within data, such as pricing data. In some examples, a computing device receives data updates and selects a machine learning model to apply to the data update. The computing device may train the machine learning model with features generated based on historical purchase order data. An anomaly score is generated based on application of the machine learning model. Based on the anomaly score, the data update is either allowed, or denied. In some examples, the computing device re-trains the machine learning model with detected anomalies. In some embodiments, the computing device prioritizes detected anomalies for further investigation. In some embodiments, the computing device identifies the cause of the anomalies by identifying at least one feature that is causing the anomaly.