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    Demand forecasting via direct quantile loss optimization
    1.
    发明授权
    Demand forecasting via direct quantile loss optimization 有权

    公开(公告)号：US10783442B1

    公开(公告)日：2020-09-22

    申请号：US15384007

    申请日：2016-12-19

    Applicant: Amazon Technologies, Inc.

    Inventor： Kari E. J. Torkkola ,  Ru He ,  Wen-Yu Hua ,  Alexander Matthew Lamb ,  Balakrishnan Narayanaswamy ,  Zhihao Cen

    IPC: G06N3/04 ,  G06N7/00 ,  G06N3/08

    Abstract: Techniques described herein include a method and system for item demand forecasting that utilizes machine learning techniques to generate a set of quantiles. In some embodiments, several item features may be identified as being relevant to an item forecast and may be provided as inputs to a regression module, which may calculate a set of quantiles for each item. A set of quantiles may comprise a number of confidence levels or probabilities associated with calculated demand values for an item. In some embodiments, costs associated with the item may be used to select an appropriate quantile associated (e.g., based on a corresponding confidence level). In some embodiments, an item demand forecast may be generated based on the calculated demand value associated with the selected quantile. In some embodiments, one or more of the item may be automatically ordered based on that item demand forecast.

    Goal-oriented dialog systems and methods
    8.
    发明授权
    Goal-oriented dialog systems and methods 有权

    公开(公告)号：US10963819B1

    公开(公告)日：2021-03-30

    申请号：US15716987

    申请日：2017-09-27

    Applicant: Amazon Technologies, Inc.

    Inventor： Rashmi Gangadharaiah ,  Charles Elkan ,  Balakrishnan Narayanaswamy

    IPC: G06Q10/02 ,  G06N3/04 ,  G06F40/44 ,  G06F40/56 ,  H04L12/58

    Abstract: A goal-oriented dialog system interacts with a user over one or more turns of dialog to determine a goal expressed by the user; the dialog system may then act to fulfill the goal by, for example, calling an application-programming interface. The user may supply dialog via text, speech, or other communication. The dialog system includes a first trained model, such as a translation model, to encode the dialog from the user into a context vector; a second trained model, such as another translation model, determines a plurality of candidate probabilities of items in a vocabulary. A language model determines responses to the user based on the input from the user, the context vector, and the plurality of candidate probabilities.

    Query performance prediction using multiple experts
    9.
    发明授权
    Query performance prediction using multiple experts 有权

    公开(公告)号：US12248473B1

    公开(公告)日：2025-03-11

    申请号：US18540496

    申请日：2023-12-14

    Applicant: Amazon Technologies, Inc.

    Inventor： Zhengchun Liu ,  Gaurav Saxena ,  Balakrishnan Narayanaswamy ,  Kaihui Zheng ,  Mohammad Rezaur Rahman ,  Tim Kraska

    IPC: G06F16/2453

    Abstract: A future workload may be predicted for a database system using analysis of queries submitted for execution. A feature vector for a query may be determined according to a query plan for the query. If the feature vector has not been previously seen, or has not been sufficiently seen, by the database system, a machine learning inference may be used to predict performance characteristics of the query, the machine learning system trained using previous feature vectors and performance characteristics of executed queries. If the feature vector has been sufficiently seen previously by the database system, a history of performance characteristics of previous queries with similar or the same feature vector may be used to predict the performance characteristics. The predictions may then be used to configure or reconfigure processing cluster(s) of the database system.

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