公开(公告)号：US20250029129A1

公开(公告)日：2025-01-23

申请号：US18908673

申请日：2024-10-07

Applicant: Amazon Technologies, Inc.

Inventor： Sunil Mallya Kasaragod ,  Abhinav Goyal ,  Yahor Pushkin ,  Srikanth Doss Kadarundalagi Raghura ,  Rishita Rajal Anubhai ,  Kasturi Bhattacharjee ,  Smaranda Muresan ,  Siddharth Chaitanyakumar Varia ,  Federico Torreti

IPC: G06Q30/0204 ,  G06F40/205 ,  G06F40/284 ,  G06Q10/0631

Abstract: A global segmenting and analysis service of a provider network may receive documents (e.g., posts, product reviews) from different applications. The service may analyze the documents to identify target entities and sentiment. The service may generate different levels of sentiment data and store data into a segmented database. For example, the service may store within-document level sentiment, document-level sentiment, and multi-document level sentiment for a target entity. The service may also update the entity taxonomy automatically or with only a small number of sample documents. The client may query the service for the segmented sentiment data.

公开(公告)号：US20230419113A1

公开(公告)日：2023-12-28

申请号：US18465916

申请日：2023-09-12

Applicant: Amazon Technologies, Inc.

Inventor： Sahika Genc ,  Sravan Babu Bodapati ,  Tao Sun ,  Sunil Mallya Kasaragod

IPC: G06N3/08 ,  G06F17/16 ,  G06F16/904 ,  G05D1/02

CPC classification number: G06N3/08 ,  G06F17/16 ,  G06F16/904 ,  G05D1/0221 ,  G05D2201/0213

Abstract: A data source configured to provide a representation of an environment of one or more agents is identified. Using a data set obtained from the data source, a neural network-based reinforcement learning model with one or more attention layers is trained. Importance indicators generated by the attention layers are used to identify actions to be initiated by an agent. A trained version of the model is stored.

公开(公告)号：US11574243B1

公开(公告)日：2023-02-07

申请号：US16451878

申请日：2019-06-25

Applicant: Amazon Technologies, Inc.

Inventor： Sunil Mallya Kasaragod

IPC: G06N20/00 ,  G06F9/50 ,  G06F30/20

Abstract: Techniques for heterogeneous compute instance auto-scaling with reinforcement learning (RL) are described. A user specifies a reward function that generates rewards for use with an application simulation for determining what different instance types should be added to or removed from the application as part of training a RL model. The RL model can be automatically deployed and used to monitor an application to automatically scale the application fleet using heterogenous compute instances.

公开(公告)号：US20210235543A1

公开(公告)日：2021-07-29

申请号：US17227194

申请日：2021-04-09

Applicant: Amazon Technologies, Inc.

Inventor： Sunil Mallya Kasaragod ,  Aran Khanna ,  Calvin Yue-Ren Kuo

IPC: H04W84/12 ,  H04L29/08 ,  H04W88/02

Abstract: A hub device of a network receives data from edge devices and generates a local result. The hub device also sends the data to a remote provider network and receives a result from the remote provider network, wherein the result is based on the data received from the edge devices. The hub device then generates a response based on the local result or the received result. The hub device may determine to correct the local result based on the result received from the remote provider network, and generate the response based on the corrected result. The hub device may generate an initial response before receiving the result from the provider network. For example, the hub device may determine that the confidence level for the local result is above the threshold level and in response, generate the initial response based on the local result.

公开(公告)号：US11620576B1

公开(公告)日：2023-04-04

申请号：US16908359

申请日：2020-06-22

Applicant: Amazon Technologies, Inc.

Inventor： Yunzhe Tao ,  Sahika Genc ,  Tao Sun ,  Sunil Mallya Kasaragod

IPC: G06N20/00 ,  G06N5/02 ,  G06N5/045 ,  G06N20/20

Abstract: A training system may create and train a machine learning model with knowledge transfer. The knowledge transfer may transfer knowledge that is acquired by another machine learning model that has been previously trained to the machine learning model that is under training. The knowledge transfer may include a combination of representation transfer and instance transfer, the two of which may be performed alternatingly. The instance transfer may further include a filter mechanism to selectively identify instances with a satisfactory performance to implement the knowledge transfer.

公开(公告)号：US20220100963A1

公开(公告)日：2022-03-31

申请号：US17039919

申请日：2020-09-30

Applicant: Amazon Technologies, Inc.

Inventor： Rishita Rajal Anubhai ,  Yahor Pushkin ,  Graham Vintcent Horwood ,  Yinxiao Zhang ,  Ravindra Manjunatha ,  Jie Ma ,  Alessandra Brusadin ,  Jonathan Steuck ,  Shuai Wang ,  Sameer Karnik ,  Miguel Ballesteros Martinez ,  Sunil Mallya Kasaragod ,  Yaser Al-Onaizan

IPC: G06F40/30 ,  G06F40/295 ,  G06N20/00

Abstract: Methods, systems, and computer-readable media for event extraction from documents with co-reference are disclosed. An event extraction service identifies one or more trigger groups in a document comprising text. An individual one of the trigger groups comprises one or more textual references to an occurrence of an event. The one or more trigger groups are associated with one or more semantic roles for entities. The event extraction service identifies one or more entity groups in the document. An individual one of the entity groups comprises one or more textual references to a real-world object. The event extraction service assigns one or more of the entity groups to one or more of the semantic roles. The event extraction service generates an output indicating the one or more trigger groups and one or more entity groups assigned to the semantic roles.

公开(公告)号：US11288415B2

公开(公告)日：2022-03-29

申请号：US16201872

申请日：2018-11-27

Applicant: Amazon Technologies, Inc.

Inventor： Sunil Mallya Kasaragod ,  Sahika Gene ,  Leo Parker Dirac ,  Bharathan Balaji ,  Eric Li Sun ,  Marthinus Coenraad De Clercq Wentzel ,  Brian James Townsend ,  Pramod Ravikumar Kumar

IPC: G06F17/10 ,  G06F30/20 ,  G06N20/00

Abstract: A simulation workflow manager obtains a set of parameters for simulation of a system and training of a reinforcement learning model for optimizing an application of the system. In response to obtaining the set of parameters, the simulation workflow manager configures a first compute node that includes a training application for training the reinforcement learning model. The simulation workflow manager also configures a second compute note with a simulation application to perform the simulation of the system in a simulation environment. Data is generated through execution of the simulation in the second compute node that is provided to the first compute node to cause the training application to use the data to train the reinforcement learning model.

公开(公告)号：US11108575B2

公开(公告)日：2021-08-31

申请号：US15660859

申请日：2017-07-26

Applicant: Amazon Technologies, Inc.

Inventor： Sunil Mallya Kasaragod ,  Aran Khanna ,  Calvin Yue-Ren Kuo

IPC: H04L12/12 ,  H04L12/24 ,  H04L29/06 ,  G06N20/00 ,  H04L29/08 ,  H04L29/14 ,  H04W4/029

Abstract: A model training service of a provider network receives data from edge devices of a remote network. The model training service analyzes the received data. The model training service may also analyze global data from other edge devices of other remote networks. The model training service may then generate updates to local data processing models based on the analysis. The updates are configured to update the local data processing models at the edge devices of the remote network. The provider network deploys the updates to the remote network. The updates are then applied to the data processing models of the edge devices.

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：US20190037040A1

公开(公告)日：2019-01-31

申请号：US15660860

申请日：2017-07-26

Applicant: Amazon Technologies, Inc.

Inventor： Sunil Mallya Kasaragod ,  Aran Khanna ,  Calvin Yue-Ren Kuo

IPC: H04L29/08 ,  H04W84/12 ,  H04L12/02

Abstract: Edge devices of a network collect data. An edge device may determine whether to process the data using a local data processing model or to send the data to a tier device. The tier device may receive the data from the edge device and determine whether to process the data using a higher tier data processing model of the tier device. If the tier device determines to process the data, then the tier device processes the data using the higher tier data processing model, generates a result based on the processing, and sends the result to an endpoint (e.g., back to the edge device, to another tier device, or to a control device). If the tier device determines not to process the data, then the tier device may send the data on to another tier device for processing by another higher tier model.