Amazon Technologies, Inc.

Oracle International Corporation

Yu Liu

William Evan Welbourne

Aniruddha Basak

Sara Parker Hillenmeyer

George Strajan

Sachin Birendra Singh

Hersh Sridhar Iyer

Martin C Eitreim

Ronald G Siemens

Kyle Robert Stanley

Shyam S Sundaramurthy

James McQueen

Jiahu Deng

Ying Gao

Jeffrey R Doering

Xiao Liu

Hailun Yang

Roger Henry Castillo

Rui Song

Doug Hains

Runzhe Wan

Tridiv Sardesai

Thomas Richardson

Will Poff

Raghuram Kudaravalli

Devices and techniques are generally described for sample size prediction for online activity. In various examples, first data related to a first sample of users interacting with an online service during a first time period may be received. In some cases, first key performance indicator (KPI) data related to the first sample of users' interaction with the online service may be received. A predicted sample size of users that will interact with the online service for a second time period following the first time period may be predicted. A predicted statistical power may be determined using the predicted sample size. In some examples, a minimum amount of time to route traffic to the online service may be determined based at least in part on the predicted statistical power.

Sample size and duration prediction for online activity

Systems and methods for usage-based device naming and grouping are disclosed. For example, trigger events that indicate when a device should be renamed, added to a device group, and/or added to a routine may be determined. Usage data representing usage of the device may be received and utilized to determine if a trigger event occurs. When a trigger event occurs, a recommendation for renaming, grouping, etc. may be determined and sent to a user device. Upon acceptance of the recommendation, the device may be renamed, grouped, and/or added to a routine.

Usage-based device naming and grouping

The present disclosure generally relates to systems and methods for generating termination notification of an experiment to be presented through a user interface. In some implementation examples, a termination notification system generates a probability value that is valid as of a time period of an experiment at a predetermined point in time based on at least data sample obtained during the time period, predicted sample sizes of each time period of the experiment, or probability values generated prior to the time period. Responsive to determining that the probability value that is valid as of the time period satisfies a threshold value, the termination notification system causes the user interface to present the termination notification that, when selected, causes the experiment to terminate prior to completion of a predicted duration of the experiment.

Online testing efficiency through early termination

Embodiments of a system and method for evaluating correction submissions with supporting evidence may include, subsequent to a submitter being provided with item description information for an item offered for sale, receiving a correction submission specified by the submitter. The correction submission may specify a proposed modification to an attribute(s) in the item description information. Embodiments may include generating a measure of risk associated with performing the proposed modification to the item description information. Embodiments may also include, based on an evaluation of the measure of risk and one or more risk criteria, generating an indication that specifies whether the proposed modification to the item description information is permitted to be performed. Embodiments may also include receiving information for confirming the validity of the correction submission, which may include an image (e.g., a product image), or a network address (e.g., a URL to manufacturers website), or other information described herein.

System and method for evaluating correction submissions with supporting evidence

In certain embodiments, a Service Deployment Infrastructure (SDI) request engine is disclosed. The SDI request engine performs the tracking, management and provisioning of services subscribed to by customers of the cloud infrastructure system. The SDI request engine is deployed to process large volumes of provisioning requests and deliver time critical applications for customers. The SDI request engine translates each request into a list of tasks of various sizes based on the requirement and configuration of the request. In some embodiments, the SDI request engine imposes control and management on both request and task levels in order to execute, rollback, retry or fail a task automatically and accurately.

Service deployment infrastructure request provisioning

Growing community of inventors

Sammamish, WA, United States of America

Yu Liu