Amazon.com

About: Amazon.com is a company organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Service (business) & Service provider. The organization has 13363 authors who have published 17317 publications receiving 266589 citations.

Inventory system with mobile drive unit and inventory holder

Abstract: A system for transporting inventory includes an inventory holder and a mobile drive unit. The inventory holder includes a frame capable of storing inventory items and a docking plate capable of receiving a docking head from underneath. The mobile drive unit, includes a docking head capable of coupling to the docking plate and a drive module capable of propelling the mobile drive unit. The mobile drive unit is further capable to move the inventory holder when the docking head is coupled to the inventory holder.

Failover and recovery for replicated data instances

Abstract: Replicated instances in a database environment provide for automatic failover and recovery. A monitoring component can periodically communicate with a primary and a secondary replica for an instance, with each capable of residing in a separate data zone or geographic location to provide a level of reliability and availability. A database running on the primary instance can have information synchronously replicated to the secondary replica at a block level, such that the primary and secondary replicas are in sync. In the event that the monitoring component is not able to communicate with one of the replicas, the monitoring component can attempt to determine whether those replicas can communicate with each other, as well as whether the replicas have the same data generation version. Depending on the state information, the monitoring component can automatically perform a recovery operation, such as to failover to the secondary replica or perform secondary replica recovery.

Dialog State Tracking: A Neural Reading Comprehension Approach.

Abstract: Dialog state tracking is used to estimate the current belief state of a dialog given all the preceding conversation. Machine reading comprehension, on the other hand, focuses on building systems that read passages of text and answer questions that require some understanding of passages. We formulate dialog state tracking as a reading comprehension task to answer the question what is the state of the current dialog? after reading conversational context. In contrast to traditional state tracking methods where the dialog state is often predicted as a distribution over a closed set of all the possible slot values within an ontology, our method uses a simple attention-based neural network to point to the slot values within the conversation. Experiments on MultiWOZ-2.0 cross-domain dialog dataset show that our simple system can obtain similar accuracies compared to the previous more complex methods. By exploiting recent advances in contextual word embeddings, adding a model that explicitly tracks whether a slot value should be carried over to the next turn, and combining our method with a traditional joint state tracking method that relies on closed set vocabulary, we can obtain a joint-goal accuracy of 47.33% on the standard test split, exceeding current state-of-the-art by 11.75%**.

Multi-tracker object tracking

Abstract: Systems and approaches are provided for tracking an object using multiple tracking processes. By combining multiple lightweight tracking processes, object tracking can be robust, use a limited amount of power, and enable a computing device to respond to input corresponding to the motion of the object in real time. The multiple tracking processes can be run in parallel to determine the position of the object by selecting the results of the best performing tracker under certain heuristics or combining the results of multiple tracking processes in various ways. Further, other sensor data of a computing device can be used to improve the results provided by one or more of the tracking processes.

Cloud-based device interaction

Abstract: A logically centralized system or service, such as a cloud-based content management service, enables intelligent communication between disparate devices. Such communication enables content such as applications and data to be synchronized between various devices, where the synchronization takes into account the capabilities of each device to provide versions and/or formats of that content that are appropriate for each device. A user purchasing an additional device, for example, can have various applications and other content automatically installed or copied over to the additional device, but with versions or formats that take advantage of, and are appropriate for, the capabilities of that additional device. Further, the intelligent communication via a logically centralized service can enable such devices to interact even though those devices might utilize different protocols and/or formats, and might otherwise not be able to communicate or have ever have been tested together.