公开(公告)号：US11748610B1

公开(公告)日：2023-09-05

申请号：US15934712

申请日：2018-03-23

Applicant: Amazon Technologies, Inc.

Inventor： Orchid Majumder ,  Vineet Khare ,  Leo Parker Dirac ,  Saurabh Gupta

IPC: G06N3/08 ,  G06N3/044 ,  G06F9/54 ,  G06F9/455

CPC classification number: G06N3/08 ,  G06F9/45558 ,  G06F9/547 ,  G06N3/044 ,  G06F2009/45575 ,  G06F2009/45595

Abstract: Techniques for sequence to sequence (S2S) model building and/or optimization are described. For example, a method of receiving a request to build a sequence to sequence (S2S) model for a use case, wherein the request includes at least a training data set, generating parts of a S2S algorithm based on the at least one use case, determined parameters, and determined hyperparameters, and training a S2S algorithm built from the parts of the S2S algorithm using the training data set to generate the S2S model is detailed.

公开(公告)号：US20200167687A1

公开(公告)日：2020-05-28

申请号：US16201864

申请日：2018-11-27

Applicant: Amazon Technologies, Inc.

Inventor： Sahika Genc ,  Sunil Mallya Kasaragod ,  Leo Parker Dirac ,  Bharathan Balaji ,  Saurabh Gupta

IPC: G06N20/00 ,  G06F17/50 ,  B25J9/16

Abstract: A simulation application container executes a simulation of a system in a simulation environment, through which an agent representing the system uses a reinforcement learning model to operate within the simulation environment. The simulation application container obtains data indicating how the agent performed in the simulation environment and transmits this data to a robot application container. The robot application container uses the data to update the reinforcement learning model and provides the updated reinforcement learning model to perform another iteration of the simulation and continue training the reinforcement learning model.

公开(公告)号：US12248815B2

公开(公告)日：2025-03-11

申请号：US18642668

申请日：2024-04-22

Applicant: Amazon Technologies, Inc.

Inventor： Ramyanshu Datta ,  Ishaaq Chandy ,  Arvind Sowmyan ,  Wei You ,  Kunal Mehrotra ,  Kohen Berith Chia ,  Andrea Olgiati ,  Lakshmi Naarayanan Ramakrishnan ,  Saurabh Gupta

IPC: G06F9/46 ,  G06F9/50

Abstract: A post-task-completion retention period for which a computing resource is to be retained, without de-activating the resource, on behalf of a set of requesters of machine learning tasks is determined at a machine learning service. A first task, identified at the service prior to expiration of the retention period at a first computing resource at which a second task has completed, is initiated at the first computing resource. In response to obtaining an indication of a third task and determining that a threshold criterion associated with the retention period satisfies a criterion, the third task is initiated at an additional computing resource. The additional computing resource is de-activated after the third task completes, without waiting for the retention period to expire.

公开(公告)号：US12229600B1

公开(公告)日：2025-02-18

申请号：US17482272

申请日：2021-09-22

Applicant: Amazon Technologies, Inc.

Inventor： Ramyanshu Datta ,  Zhihan Li ,  Arun Babu Nagarajan ,  Arvind Sowmyan ,  Kohen Berith Chia ,  Wei You ,  Ishaaq Chandy ,  Kunal Mehrotra ,  Andrea Olgiati ,  Lakshmi Naarayanan Ramakrishnan ,  Saurabh Gupta

IPC: G06F9/50

Abstract: Parameters of a pool of computing resources to be utilized for machine learning tasks from a set of entities are stored, including a category of the computing resources, and a post-task-completion retention period during which, after completion of a task, at least a portion of data stored at the resource is not to be deleted. A compute instance of the pool is assigned to a task requested from the set of entities after determining that one or more configuration settings of the instance satisfy a preference indicated in the request for the task, and that the retention period of the instance relative to a completion of an earlier task on the instance has not expired. A result of the task is stored.

公开(公告)号：US12118456B1

公开(公告)日：2024-10-15

申请号：US16198730

申请日：2018-11-21

Applicant: Amazon Technologies, Inc.

Inventor： Sahika Genc ,  Bharathan Balaji ,  Urvashi Chowdhary ,  Leo Parker Dirac ,  Saurabh Gupta ,  Vineet Khare ,  Sunil Mallya Kasaragod

IPC: G06N3/08 ,  G06F30/20

CPC classification number: G06N3/08 ,  G06F30/20

Abstract: A machine learning environment utilizing training data generated by customer networks. A reinforcement learning machine learning environment receives and processes training data generated by simulated hosted, or integrated, customer networks. The reinforcement learning machine learning environment corresponds to machine learning clusters that receive and process training data sets provided by the integrated customer networks. The customer networks include an agent process that collects training data and forwards the training data to the machine learning clusters. The machine learning clusters can be configured in a manner to automatically process the training data without requiring additional user inputs or controls to configure the application of the reinforcement learning machine learning processes.

公开(公告)号：US11861490B1

公开(公告)日：2024-01-02

申请号：US16198726

申请日：2018-11-21

Applicant: Amazon Technologies, Inc.

Inventor： Saurabh Gupta ,  Bharathan Balaji ,  Leo Parker Dirac ,  Sahika Genc ,  Vineet Khare ,  Ragav Venkatesan ,  Gurumurthy Swaminathan

IPC: G06N3/08 ,  G06N3/04 ,  G06F18/214 ,  G06F18/21

CPC classification number: G06N3/08 ,  G06F18/214 ,  G06F18/2178 ,  G06N3/04

Abstract: A machine learning environment utilizing training data generated by customer environments. A reinforced learning machine learning environment receives and processes training data generated by independently hosted, or decoupled, customer environments. The reinforced learning machine learning environment corresponds to machine learning clusters that receive and process training data sets provided by the decoupled customer environments. The customer environments include an agent process that collects training data and forwards the training data to the machine learning clusters without exposing the customer environment. The machine learning clusters can be configured in a manner to automatically process the training data without requiring additional user inputs or controls to configured the application of the reinforced learning machine learning processes.

公开(公告)号：US11704299B1

公开(公告)日：2023-07-18

申请号：US17205373

申请日：2021-03-18

Applicant: Amazon Technologies, Inc.

Inventor： Tanya Bansal ,  Vidhi Kastuar ,  Saurabh Gupta ,  Alex Tang ,  Lakshmi Naarayanan Ramakrishnan ,  Stefano Stefani ,  Xingyuan Wang ,  Mukesh Karki

IPC: G06F7/00 ,  G06F16/22 ,  G06F21/60 ,  G06F16/21 ,  G06F16/25 ,  G06N20/00 ,  G06F18/214

CPC classification number: G06F16/2291 ,  G06F16/219 ,  G06F16/252 ,  G06F18/214 ,  G06F21/602 ,  G06N20/00

Abstract: Techniques and technologies for providing a fully managed datastore for clients to securely store, discover, retrieve, remove, and share curated data, or features, to develop machine learning (ML) models in an efficient manner. The feature store service may provide clients with the ability to create and store feature groups that include features and associated metadata providing clients with a quick understanding of features so that they may determine which features are suitable for training ML models and/or use with ML models. The feature store service may provide first a data store configured to store the most recent values associated with a feature group, such that client can access the features and utilize ML models to make real-time predictions with low latency and high throughput, and a second datastore configured to store historical values associated with a feature group, such that a client can utilize the features to train ML models.

公开(公告)号：US11567504B1

公开(公告)日：2023-01-31

申请号：US16128785

申请日：2018-09-12

Applicant: AMAZON TECHNOLOGIES, INC.

Inventor： Shi Bai ,  Amin Hani Atrash ,  Saurabh Gupta

IPC: G05D1/02 ,  H04W4/40 ,  G01C21/20 ,  G06V20/10 ,  H04W4/02

Abstract: A robot that is able to move about an environment determines a wait location in the environment to wait at when not otherwise in use. The wait location may be selected based on various factors including position of objects, next scheduled use, previous usage of the robot, availability of wireless connectivity, user traffic patterns, user presence, visibility of the surrounding environment, and so forth. The robot moves to that location and maintains a pose at that location, such as orienting itself to allow onboard sensors a greatest possible view of the environment. If a wait location is occupied, the robot may move to another wait location.

公开(公告)号：US11501794B1

公开(公告)日：2022-11-15

申请号：US16875425

申请日：2020-05-15

Applicant: Amazon Technologies, Inc.

Inventor： Yelin Kim ,  Yang Liu ,  Dilek Hakkani-tur ,  Thomas Nelson ,  Anna Chen Santos ,  Joshua Levy ,  Saurabh Gupta

IPC: G10L25/63 ,  G10L15/26 ,  G10L15/18 ,  H04N5/247 ,  H04N5/232 ,  G05D1/00 ,  G05D1/02 ,  G06T7/70 ,  G06V20/10 ,  G06V40/10 ,  G06V40/16

Abstract: Described herein is a system for improving sentiment detection and/or recognition using multiple inputs. For example, an autonomously motile device is configured to generate audio data and/or image data and perform sentiment detection processing. The device may process the audio data and the image data using a multimodal temporal attention model to generate sentiment data that estimates a sentiment score and/or a sentiment category. In some examples, the device may also process language data (e.g., lexical information) using the multimodal temporal attention model. The device can adjust its operations based on the sentiment data. For example, the device may improve an interaction with the user by estimating the user's current emotional state, or can change a position of the device and/or sensor(s) of the device relative to the user to improve an accuracy of the sentiment data.

公开(公告)号：US11995476B1

公开(公告)日：2024-05-28

申请号：US17482276

申请日：2021-09-22

Applicant: Amazon Technologies, Inc.

Inventor： Ramyanshu Datta ,  Ishaaq Chandy ,  Arvind Sowmyan ,  Wei You ,  Kunal Mehrotra ,  Kohen Berith Chia ,  Andrea Olgiati ,  Lakshmi Naarayanan Ramakrishnan ,  Saurabh Gupta

IPC: G06F9/46 ,  G06F9/50

CPC classification number: G06F9/5038 ,  G06F9/5022 ,  G06F9/5055

Abstract: A post-task-completion retention period for which a computing resource is to be retained, without de-activating the resource, on behalf of a set of requesters of machine learning tasks is determined at a machine learning service. A first task, identified at the service prior to expiration of the retention period at a first computing resource at which a second task has completed, is initiated at the first computing resource. In response to obtaining an indication of a third task and determining that a threshold criterion associated with the retention period satisfies a criterion, the third task is initiated at an additional computing resource. The additional computing resource is de-activated after the third task completes, without waiting for the retention period to expire.