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    SYSTEMS AND METHODS OF OPTIMIZING RESOURCE ALLOCATION USING MACHINE LEARNING AND PREDICTIVE CONTROL
    1.
    发明公开
    SYSTEMS AND METHODS OF OPTIMIZING RESOURCE ALLOCATION USING MACHINE LEARNING AND PREDICTIVE CONTROL 审中-公开

    公开(公告)号：US20240168808A1

    公开(公告)日：2024-05-23

    申请号：US18426679

    申请日：2024-01-30

    申请人： Nasdaq, Inc.

    发明人： Shihui CHEN ,  Keon Shik KIM ,  Douglas HAMILTON

    IPC分类号： G06F9/50 ,  G06N20/00

    CPC分类号： G06F9/5027 ,  G06F9/50 ,  G06N20/00

    摘要： A computer system includes a transceiver that receives over a data communications network different types of input data from multiple source nodes and a processing system that defines for each of multiple data categories, a set of groups of data objects for the data category based on the different types of input data. Predictive machine learning model(s) predict a selection score for each group of data objects in the set of groups of data objects for the data category for a predetermined time period. Control machine learning model(s) determine how many data objects are permitted for each group of data objects based on the selection score. Decision-making machine learning model(s) prioritize the permitted data objects based on one or more predetermined priority criteria. Subsequent activities of the computer system are monitored to calculate performance metrics for each group of data objects and for data objects actually selected during the predetermined time period. Predictive machine learning model(s) and decision-making machine learning model(s) are adjusted based on the performance metrics to improve respective performance(s).

    SYSTEMS AND METHODS TO GENERATE DATA MESSAGES INDICATING A PROBABILITY OF EXECUTION FOR DATA TRANSACTION OBJECTS USING MACHINE LEARNING
    4.
    发明申请
    SYSTEMS AND METHODS TO GENERATE DATA MESSAGES INDICATING A PROBABILITY OF EXECUTION FOR DATA TRANSACTION OBJECTS USING MACHINE LEARNING 有权

    公开(公告)号：US20230095016A1

    公开(公告)日：2023-03-30

    申请号：US17955640

    申请日：2022-09-29

    申请人： Nasdaq, Inc.

    发明人： Keon Shik KIM ,  Josep PUIG RUIZ ,  Douglas HAMILTON

    IPC分类号： G06N20/20 ,  H04L41/16

    摘要： A computer system includes a transceiver that receives over a data communications network different types of input data and multiple data transaction objects from multiple source nodes. A pre-processor processes the different types of input data and the data transaction objects to generate an input data structure. Based on the input data structure, one or more predictive machine learning models is trained and used to predict a probability of execution of each of the data transaction objects at a future execution time. Output data messages are then generated for transmission by the transceiver over the data communications network indicating the probability of execution for at least one of the data transaction objects at the future execution time.

    DYNAMICALLY SELECTING AMONG LEARNED AND NON-LEARNED INDEXES FOR DATA ACCESS
    7.
    发明申请
    DYNAMICALLY SELECTING AMONG LEARNED AND NON-LEARNED INDEXES FOR DATA ACCESS 有权

    公开(公告)号：US20220245116A1

    公开(公告)日：2022-08-04

    申请号：US17727886

    申请日：2022-04-25

    申请人： Nasdaq, Inc.

    发明人： Jonathan RIVERS ,  Douglas HAMILTON ,  Leonid ROSENFELD

    IPC分类号： G06F16/22 ,  G06N3/08 ,  G06F16/248 ,  G06F16/28

    摘要： The described technology relates to systems and techniques for accessing a database by dynamically choosing an index from a plurality of indexes that includes at least one learned index and at least one non-learned index. The availability of learned and non-learned indexes for accessing the same database provides for flexibility in accessing the database, and the dynamic selection between learned indexes and non-learned indexes provide for choosing the index based on the underlying data in the database and the characteristics of the query. Certain example embodiments provide a learned model that accepts a set of features associated with the query as input, and outputs a set of evaluated weights for respective features, which are then processed according to a set of rules to predict the most efficient index to be used.

    SYSTEMS AND METHODS OF PROCESSING DIVERSE DATA SETS WITH A NEURAL NETWORK TO GENERATE SYNTHESIZED DATA SETS FOR PREDICTING A TARGET METRIC
    8.
    发明申请
    SYSTEMS AND METHODS OF PROCESSING DIVERSE DATA SETS WITH A NEURAL NETWORK TO GENERATE SYNTHESIZED DATA SETS FOR PREDICTING A TARGET METRIC 审中-公开

    公开(公告)号：US20200226468A1

    公开(公告)日：2020-07-16

    申请号：US16744236

    申请日：2020-01-16

    申请人： Nasdaq, Inc.

    发明人： Douglas HAMILTON

    IPC分类号： G06N3/08 ,  G06N3/04 ,  G06N3/063

    摘要： A computer system is provided that is programmed to receive data sets, a target metric, and a parameter that indicates a desired number of synthesized data sets. A memory stores instructions and data including the input data sets, the target metric, the parameter that indicates a desired number of synthesized data sets, and a neural network. The neural network includes a summing node and multiple processing nodes. At least one hardware processor is configured to perform operations where each processing node of a neural network weights input data set values, determines gating operations to select processing operations, and generates a node output by applying the gating operations to weighted input data set values. Weighted node outputs from the processing nodes produce a value for the target parameter. The neural network is iteratively trained by modifying the gating operations, the input weight values, and the node output weight value until the neural network converges. Then, one or more nodes is selected having a larger magnitude node output weight value. For each selected node, a subset of the input data sets and a subset of the gating operations are selected. The selected input data set values are processed with the selected processing nodes using the selected subset of gating operations to produce synthesized data sets. Human-understandable names are generated for each of the synthesized data sets based on names of the selected input data sets and the selected subset of gating operations.