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    Using sensing functions and actuating functions to safely implement actions for IoT devices
    292.
    发明授权
    Using sensing functions and actuating functions to safely implement actions for IoT devices 有权

    公开(公告)号：US12045032B1

    公开(公告)日：2024-07-23

    申请号：US17039897

    申请日：2020-09-30

    Applicant: Amazon Technologies, Inc.

    Inventor： Thibaut Pierre Grandmougin ,  Bart Schouwenaars-Harms

    IPC: G05B19/4155 ,  G06F3/06

    CPC classification number: G05B19/4155 ,  G06F3/0604 ,  G06F3/0655 ,  G06F3/067 ,  G05B2219/34038

    Abstract: An IoT gateway at a client site uses sensing functions and actuating functions to safely implement actions for equipment. The client provides, to the IoT service, specifications for a sensing function and one or more actuating functions that are to communicate with the sensing function. The functions are deployed to the IoT gateway at the client site. The sensing function may be assigned read only access to the equipment and the actuating function may be assigned write only access to the equipment. The sensing function may collect data from the equipment and determine that a condition has been met (e.g., temperature is above an upper limit). In response, the sensing function provides an indication of an action to the actuating function (e.g., via a unique topic). If the action is a supported action, then the actuating function causes the equipment to perform the action; otherwise, it ignores the indicated action.

    Threat sensor deployment and management
    295.
    发明授权
    Threat sensor deployment and management 有权

    公开(公告)号：US12041094B2

    公开(公告)日：2024-07-16

    申请号：US16864959

    申请日：2020-05-01

    Applicant: Amazon Technologies, Inc.

    Inventor： Nima Sharifi Mehr

    IPC: H04L9/40 ,  H04L67/12 ,  G16Y10/75 ,  G16Y40/50

    CPC classification number: H04L63/205 ,  H04L67/12 ,  G16Y10/75 ,  G16Y40/50

    Abstract: Various embodiments of apparatuses and methods for threat sensor deployment and management in a malware threat intelligence system are described. In some embodiments, the system comprises a plurality of threat sensors, deployed at different network addresses and physically located in different geographic regions in a provider network, which detect interactions from sources. In some embodiments, a threat sensor deployment and management service determines a deployment plan for the plurality of threat sensors, including each threat sensor's associated threat data collectors. The threat data collectors can be of different types such as utilizing different communication protocols or ports, or providing different kinds of responses to inbound communications. The different threat sensors can have different lifetimes. The service deploys the threat sensors based on the plan, collects data from the deployed threat sensors, adjusts the deployment plan based on the collected data and the threat sensor lifetimes, and then performs the adjustments.

    Self-supervised federated learning
    296.
    发明授权
    Self-supervised federated learning 有权

    公开(公告)号：US12039998B1

    公开(公告)日：2024-07-16

    申请号：US17665129

    申请日：2022-02-04

    Applicant: Amazon Technologies, Inc.

    Inventor： Chieh-Chi Kao ,  Qingming Tang ,  Ming Sun ,  Viktor Rozgic ,  Spyridon Matsoukas ,  Chao Wang

    IPC: G10L25/78 ,  G06N3/045 ,  G06N3/08 ,  G10L25/21

    CPC classification number: G10L25/78 ,  G06N3/045 ,  G06N3/08 ,  G10L25/21

    Abstract: An acoustic event detection system may employ self-supervised federated learning to update encoder and/or classifier machine learning models. In an example operation, an encoder may be pre-trained to extract audio feature data from an audio signal. A decoder may be pre-trained to predict a subsequent portion of audio data (e.g., a subsequent frame of audio data represented by log filterbank energies). The encoder and decoder may be trained using self-supervised learning to improve the decoder's predictions and, by extension, the quality of the audio feature data generated by the encoder. The system may apply federated learning to share encoder updates across user devices. The system may fine-tune the classifier to improve inferences based on the improved audio feature data. The system may distribute classifier updates to the user device(s) to update the on-device classifier.

    Evaluating biometric authorization systems with synthesized images
    297.
    发明授权
    Evaluating biometric authorization systems with synthesized images 有权

    公开(公告)号：US12039027B1

    公开(公告)日：2024-07-16

    申请号：US17709262

    申请日：2022-03-30

    Applicant: Amazon Technologies, Inc.

    Inventor： Xiang Xu ,  Hao Zhou ,  Jonathan Wu ,  Joseph P Tighe

    IPC: G06F21/32

    CPC classification number: G06F21/32

    Abstract: A system for evaluating a biometric authorization system is described. The biometric authorization system is configured to apply a facial recognition model to image data to make an authorization determination based on detection of synthesized image data and based on matching a reference image to the image data. The system is also configured to execute one or more synthetic image data attack protocols to evaluate the biometric authorization system. The system also generates, according to one or more synthetic image data generation techniques, an evaluation set of image data comprising synthesized representations of a target and sends one or more authorization requests using the evaluation set of image data to the biometric authorization system. The system generates an evaluation of the biometric authorization system for synthetic image data attack analysis based on respective responses to the one or more authorization requests received from the biometric authorization system.

    Minimizing connection loss when changing database query engine versions
    298.
    发明授权
    Minimizing connection loss when changing database query engine versions 有权

    公开(公告)号：US12038946B1

    公开(公告)日：2024-07-16

    申请号：US18194579

    申请日：2023-03-31

    Applicant: Amazon Technologies, Inc.

    Inventor： Ramesh Shankar ,  Raman Mittal

    IPC: G06F16/23 ,  G06F16/27

    CPC classification number: G06F16/27 ,  G06F16/2329

    Abstract: Connection loss may be minimized for performing database query engine changes. A distributed database system may include different instances of the query engine that provide access to a database. When an event to change the version of the query engine is detected, a copy of the database may be created and a new instance of the query engine created. Read-only access to the database may be maintained using the different instances of the query engine while the new instance may be upgraded to the different version of the query engine. Upon successful installation of the different version of the query engine at the new instance, the new instance may be given read-write access to the database using the copy of the database and other database instances may be upgraded to the different version of the query engine.

    COMPUTER-IMPLEMENTED MULTI-SCALE MACHINE LEARNING MODEL FOR THE ENHANCEMENT OF COMPRESSED VIDEO
    300.
    发明公开
    COMPUTER-IMPLEMENTED MULTI-SCALE MACHINE LEARNING MODEL FOR THE ENHANCEMENT OF COMPRESSED VIDEO 审中-公开

    公开(公告)号：US20240236345A1

    公开(公告)日：2024-07-11

    申请号：US18186084

    申请日：2023-03-17

    Applicant: Amazon Technologies, Inc.

    Inventor： Kiran Mukesh Misra ,  Christopher Andrew Segall ,  Byeongdoo Choi

    IPC: H04N19/42 ,  H04N19/117 ,  H04N19/12 ,  H04N19/136 ,  H04N19/172 ,  H04N19/176 ,  H04N19/60 ,  H04N19/82

    CPC classification number: H04N19/42 ,  H04N19/117 ,  H04N19/12 ,  H04N19/136 ,  H04N19/172 ,  H04N19/176 ,  H04N19/60 ,  H04N19/82

    Abstract: The present disclosure relates to methods, apparatus, systems, and non-transitory computer-readable storage media for training and using a multi-scale machine learning model for the enhancement of compressed video. According to some examples, a computer-implemented method includes receiving a video at a content delivery service; performing an encode on a frame of the video by the content delivery service that coverts the frame from a pixel domain to a transform domain and back to the pixel domain to generate first pixel values and a first residual for a block of the frame at a first resolution; generating a first set of features, by a machine learning model of the content delivery service, for an input, at a first resolution, of the first pixel values and the first residual of the block; generating a second set of features, by the machine learning model of the content delivery service, for an input, at a second lower resolution, of second pixel values and a second residual of the block; upsampling the second set of features to the first resolution to generate an upsampled second set of features; generating a modified version of the frame based on the first set of features and the upsampled second set of features; and transmitting the modified version of the frame to a frame buffer or from the content delivery service to a viewer device.

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