Distributed system for efficient entity recognition

    公开(公告)号:US12039770B1

    公开(公告)日:2024-07-16

    申请号:US17219715

    申请日:2021-03-31

    CPC classification number: G06V10/75 G06F18/22 H04L67/10

    Abstract: A first encoding representing a set of detected signals is obtained at a sensor-proximity resource of an object recognition application which also includes resources of an analytics service of a provider network. In response to a determination that a cache at the sensor-proximity resource does not include a second encoding which satisfies a similarity criterion with respect to the first encoding, at least a portion of a partition of a spatial index is obtained from another resource selected using an index partition map. A recognition-based action is initiated based on determining that the partition includes an encoding which satisfies the similarity criterion.

    DISTRIBUTED SYSTEM FOR EFFICIENT ENTITY RECOGNITION

    公开(公告)号:US20240320951A1

    公开(公告)日:2024-09-26

    申请号:US18732243

    申请日:2024-06-03

    CPC classification number: G06V10/75 G06F18/22 H04L67/10

    Abstract: A first encoding representing a set of detected signals is obtained at a sensor-proximity resource of an object recognition application which also includes resources of an analytics service of a provider network. In response to a determination that a cache at the sensor-proximity resource does not include a second encoding which satisfies a similarity criterion with respect to the first encoding, at least a portion of a partition of a spatial index is obtained from another resource selected using an index partition map. A recognition-based action is initiated based on determining that the partition includes an encoding which satisfies the similarity criterion.

    Transactional and batch-updated data store search

    公开(公告)号:US11188501B1

    公开(公告)日:2021-11-30

    申请号:US15677750

    申请日:2017-08-15

    Abstract: A search management system and method to perform a search of first set of records maintained in a transactional data store (e.g., a commit log of a relational data store) and a second set of records maintained in a batch-updated data store. The search results corresponding to the transactional data store and the batch-updated data store are merged to generate a search result dataset to provide in response to a search query. The transactional layer or transaction data store is a commit log configured to manage and store records in accordance with recently executed operations (e.g., add and delete record operations) based on communications with one or more customer systems relating to customer data. Records maintained in the commit log are subsequently stored in the batch-updated data store as a result of a batching process.

    Container telemetry
    9.
    发明授权

    公开(公告)号:US10782990B1

    公开(公告)日:2020-09-22

    申请号:US14951334

    申请日:2015-11-24

    Abstract: At least one instance of an application is launched in a set of software containers that are distributed among a set of virtual machine instances. A set of measurements corresponding to resource utilization by a software container of the set of software containers is obtained and a timestamp is generated for the set of measurements. The set of measurements is aggregated, with other sets of measurements corresponding to the set of software containers for the application, into a set of aggregated measurements grouped in a time window group, based at least in part on the timestamp, and, as a result of fulfillment of a condition, the time window group is outputted.

    Image compression and decompression using embeddings

    公开(公告)号:US10652565B1

    公开(公告)日:2020-05-12

    申请号:US15782725

    申请日:2017-10-12

    Abstract: A processing device receives a representation of an image, wherein the image has a first size and the representation has a second size that is smaller than the first size, the representation having been generated from the image by a first portion of a first trained machine learning model. The processing device processes the representation of the image using a second portion of the trained machine learning model to generate a reconstruction of the image and then outputs the reconstruction of the image.

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