公开(公告)号：US12175434B2

公开(公告)日：2024-12-24

申请号：US17039649

申请日：2020-09-30

Applicant: Amazon Technologies, Inc.

Inventor： Srikanth Venkata Tenneti ,  Arvindh Krishnaswamy ,  Karim Helwani ,  Mehmet Umut Isik ,  Ritwik Giri ,  Fangzhou Cheng ,  Aparna Pandey

IPC: G06Q10/20 ,  G06F16/21 ,  G06F16/906

Abstract: Systems, methods, and apparatuses for detecting anomalies using clusters are described. In some examples, a method includes receiving a request to perform anomaly detection using a plurality of clusters; receiving a data point; determining when the received data point is a part of one of the plurality of clusters utilizing a distance to centers of the one or more clusters, wherein: when the received data point is determined to belong to a normal cluster, assigning the received data point to the determined cluster, updating the cluster, and updating a history for the cluster, when the received data point is determined to belong to an anomalous cluster, raising an anomaly, updating the cluster, and updating a history for the cluster, and when the received data point is determined to not belong to any cluster, raising an anomaly.

公开(公告)号：US20220101270A1

公开(公告)日：2022-03-31

申请号：US17039649

申请日：2020-09-30

Applicant: Amazon Technologies, Inc.

Inventor： Srikanth Venkata Tenneti ,  Arvindh Krishnaswamy ,  Karim Helwani ,  Mehmet Umut Isik ,  Ritwik Giri ,  Fangzhou Cheng ,  Aparna Pandey

IPC: G06Q10/00 ,  G06F16/906 ,  G06F16/21

Abstract: Systems, methods, and apparatuses for detecting anomalies using clusters are described. In some examples, a method includes receiving a request to perform anomaly detection using a plurality of clusters; receiving a data point; determining when the received data point is a part of one of the plurality of clusters utilizing a distance to centers of the one or more clusters, wherein: when the received data point is determined to belong to a normal cluster, assigning the received data point to the determined cluster, updating the cluster, and updating a history for the cluster, when the received data point is determined to belong to an anomalous cluster, raising an anomaly, updating the cluster, and updating a history for the cluster, and when the received data point is determined to not belong to any cluster, raising an anomaly.