公开(公告)号：US11726982B1

公开(公告)日：2023-08-15

申请号：US17491302

申请日：2021-09-30

Applicant: Amazon Technologies, Inc.

Inventor： Mohammed Talal Yassar Azam ,  Ahmed Gamal Hamed ,  Mohammad Adnan

IPC: G06F16/23

CPC classification number: G06F16/2365

Abstract: Systems and methods are described for using a recurring event based scheduler to continuously monitor data to detect anomalies within the data. In some aspects, an anomaly detection schedule may be determined for monitoring time series data to detect anomalies based on a data ingestion interval. A plurality of anomaly detection events may be sequentially generated and stored in an event queue at times specified by the anomaly detection schedule. The anomaly detection events may then be processed sequentially from the event queue to trigger execution of a plurality of anomaly detection workflow tasks at the times specified by the anomaly detection schedule. In some cases, execution of individual anomaly detection workflow tasks causes individual portions of time series data to be obtained from a customer data source and processed by an anomaly detection model to detect anomalies in the time series data.

公开(公告)号：US12099515B1

公开(公告)日：2024-09-24

申请号：US17488771

申请日：2021-09-29

Applicant: Amazon Technologies, Inc.

Inventor： Mohammed Talal Yassar Azam ,  Mohammad Adnan ,  Ahmed Gamal Hamed ,  Fnu Syed Furqhan Ulla ,  Shekhar Agrawal ,  Ketan Vijayvargiya

IPC: G06F16/25 ,  G06F16/2458

CPC classification number: G06F16/258 ,  G06F16/2477

Abstract: Systems and methods are described for to detecting anomalies in various forms of data, including non-time series data. In one example, a data ingestion interval for customer data may be determined, where the data ingestion interval specifies a frequency at which data is analyzed to detect analogies in portions of the data corresponding to time windows. A portion of the customer data may then be obtained and aggregated from a data source according to the data ingestion interval. The portion of data may be converted into time series data by appending a time stamp corresponding to the time window of the portion of the data. The anomaly detection service may then process the time series data, using a time series data anomaly model, to detect one or more anomalies in the time series data.

公开(公告)号：US11940983B1

公开(公告)日：2024-03-26

申请号：US17491486

申请日：2021-09-30

Applicant: Amazon Technologies, Inc.

Inventor： Mohammad Adnan ,  Mohammed Talal Yassar Azam ,  Aditya Bahuguna ,  Fnu Syed Furqhan Ulla ,  Devesh Ratho ,  Ankita Verma ,  Ankur Mehrotra

IPC: G06F16/23 ,  G06N20/20

CPC classification number: G06F16/2365 ,  G06N20/20

Abstract: A service to provide anomaly detection receives a request to back-test the service. The request includes information for accessing a dataset of historical data. The service executes workflows to ingest the data, train a plurality of machine learning models to perform anomaly detection, and detect anomalies in the dataset. A representation of the detect anomalies is generated and presented to a user. The service receives an indication to activate the service to provide ongoing anomaly detection services.

公开(公告)号：US10887427B1

公开(公告)日：2021-01-05

申请号：US16190934

申请日：2018-11-14

Applicant: AMAZON TECHNOLOGIES, INC.

Inventor： Mohammad Adnan ,  Manoj Tharwani

IPC: H04L29/08 ,  H04L29/06

Abstract: Techniques for automatically activating a count of device-independent functions to process a received request and other predicted traffic include determining a current count of active functions, a count of functions currently processing requests, a duration for execution of a function, an average time used by a function to process a request, and a current count of client devices accessing the system. A probabilistic value indicating the likelihood of additional traffic may also be determined based on characteristics of the request. These values are used to determine the available capacity for processing requests using the currently active functions and the predicted capacity to be used to process predicted additional traffic. A number of additional functions may be activated based on the difference between the available capacity and the predicted capacity.

公开(公告)号：US11037081B1

公开(公告)日：2021-06-15

申请号：US16190472

申请日：2018-11-14

Applicant: Amazon Technologies, Inc.

Inventor： Mohammad Adnan ,  Manoj Tharwani ,  Biswajit Mishra

IPC: G06Q10/06 ,  G06Q30/02

Abstract: Techniques for dynamically allocating storage space at fulfillment centers for sellers offering items are disclosed herein. In embodiments, information about available bins of a fulfillment center for storing inventory may be obtained. Sales performance factor information for a seller offering items may be received for items that are being stored at a fulfillment center. A capacity of bin numbers and bin sizes may be determined for the seller based on the sales performance factor information and the number of available bins. A threshold space assignment for the seller may be determined based on an inventory group associated with the offered items associated with the seller. Particular bin sizes and bin numbers may be identified at the fulfillment center for the particular seller to store the offered items based on the capacity and threshold space assignment.