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    Training network with discrete weight values
    11.
    发明授权
    Training network with discrete weight values 有权

    公开(公告)号：US12299555B2

    公开(公告)日：2025-05-13

    申请号：US17894798

    申请日：2022-08-24

    Applicant: Amazon Technologies, Inc.

    Inventor： Steven L. Teig ,  Eric A. Sather

    IPC: G06N3/04 ,  G06F17/12 ,  G06F17/18 ,  G06F18/211 ,  G06N3/08 ,  G06N3/084 ,  G06N20/00

    Abstract: Some embodiments provide an electronic device that includes a set of processing units and a set of machine-readable media. The set of machine-readable media stores sets of instructions for applying a network of computation nodes to an input received by the device. The set of machine-readable media stores at least two sets of machine-trained parameters for configuring the network for different types of inputs. A first of the sets of parameters is used for applying the network to a first type of input and a second of the sets of parameters is used for applying the network to a second type of input.

    Bulk loader scaling
    12.
    发明授权
    Bulk loader scaling 有权

    公开(公告)号：US12299496B1

    公开(公告)日：2025-05-13

    申请号：US17677904

    申请日：2022-02-22

    Applicant: Amazon Technologies, Inc.

    Inventor： Ilie Gabriel Tanase ,  Serkan Turgut ,  Yejin Yoon ,  Deep Saxena ,  Guangqiang Li ,  Utkarsh Raj ,  Karthik Rajan ,  Sainath Chowdary Mallidi ,  Sudhanshu Gupta ,  Ankesh Khandelwal

    IPC: G06F9/50 ,  G06F16/901

    Abstract: Systems and methods are described for allocating computing resources to load data into a database. In some aspects, a database service obtains a request to load a number of data chunks. Responsive to the request, the database service scales a number of worker threads to load the data chunks. The scaling may include selecting a maximum number of worker threads to load (e.g., concurrently) the data chunks based on a number of data chunks, and assigning individual worker threads to load individual data chunks. The scaling may additionally include tracking a number of available worker threads relative to the maximum number of worker threads to reassign a first worker thread to load another data chunk upon the completing loading a prior data chunk, or assign a new worker thread that has become available based on a change in the maximum number of worker threads.

    Knowledge-based taint policy inference
    13.
    发明授权
    Knowledge-based taint policy inference 有权

    公开(公告)号：US12299134B1

    公开(公告)日：2025-05-13

    申请号：US17936993

    申请日：2022-09-30

    Applicant: Amazon Technologies, Inc.

    Inventor： Peixuan Li ,  Yingjun Lyu ,  Qiang Zhou ,  Lee Pike ,  Michael McDougall ,  Thodoris Sotiropoulos

    IPC: G06F21/57 ,  G06N5/022

    Abstract: Security vulnerability analysis may be performed using policy inference. Application code may have operations that are labeled according to the respective functions that they perform. Some operations may be labeled according to a knowledge database of known operations while others may be inferred through similarity to known operations. The knowledge database may be associated with libraries of programmatic interfaces. Once components of the application code are labeled, a vulnerability database may be that identifies potential vulnerabilities based on data sources, data sinks and threat mitigation operations. Using the labeled operations, one or more potential vulnerabilities may be identified based on labeled data sources and data sinks. The application may then be evaluated for potential security threats based on the identified potential vulnerabilities.

    Non-invasive automated beverage dispenser evaluation system
    15.
    发明授权
    Non-invasive automated beverage dispenser evaluation system 有权

    公开(公告)号：US12297094B1

    公开(公告)日：2025-05-13

    申请号：US18141315

    申请日：2023-04-28

    Applicant: Amazon Technologies, Inc.

    Inventor： Romil Shah ,  Theodore Charles Bakanas ,  Cory Jines

    IPC: G06Q10/20 ,  B67D1/00 ,  B67D1/08 ,  G06N20/00

    Abstract: Systems and techniques for non-invasive beverage quality check and automated maintenance scheduling for automated beverage dispensers. The techniques include using a camera to evaluate performance of the machine and evaluate beverage quality using visual techniques. The system provided herein uses machine learned models to determine beverage quality and evaluate machine operation by using an existing or retrofitted camera in proximity to the dispenser. One or more machine learned algorithms may be used for of evaluating the dispenser and dispensed beverages along various metrics to identify any fault in the system over time. If the quality degrades over time or an error is detected, the system may recalibrate the machine or alert a maintenance system to schedule maintenance for the machine.

    Generating and evaluating insertion markers in media
    17.
    发明授权
    Generating and evaluating insertion markers in media 有权

    公开(公告)号：US12294771B1

    公开(公告)日：2025-05-06

    申请号：US17854394

    申请日：2022-06-30

    Applicant: Amazon Technologies, Inc.

    Inventor： Kalpesh Bhagwandas Thavrani ,  Bharanidharan Arul Janakiammal ,  Pranav Kapoor ,  Caileigh Ann Robertson Halbach ,  Abhinaw Dixit ,  Durga Prasad Guntoju

    IPC: H04N21/845 ,  H04N21/234 ,  H04N21/431

    Abstract: An insertion marker mechanism may be used to generate and evaluate insertion markers in media content. The system may place candidate markers at silent pauses and/or anomalies such as changes in pitch/timbre of an audio signal, and/or at points where a video has a blank screen. In response to a request for the media content, the system may place secondary content at a subset of the candidate markers. The system may select different insertion markers in response to different playback requests of the content. Based on feedback such as user engagement with the secondary content and/or drop rate, the system may determine which insertion markers correlate with better engagement and/or retention. Based on the feedback, the system may prioritize content insertion at better performing markers, while continuing to insert a smaller proportion of secondary content randomly at lower performing markers to monitor for changes in user behavior over time.

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