DATABASE SYSTEMS AND USER INTERFACES FOR PROCESSING DISCRETE DATA ITEMS WITH STATISTICAL MODELS ASSOCIATED WITH CONTINUOUS PROCESSES

    公开(公告)号:US20230214690A1

    公开(公告)日:2023-07-06

    申请号:US18182240

    申请日:2023-03-10

    IPC分类号: G06N5/04 G06N20/00 G06F30/20

    CPC分类号: G06N5/04 G06N20/00 G06F30/20

    摘要: A computer-implemented method is provided to predict one or more expected quantities using a machine learning model. The method system may comprise steps to receive a set of data items associated with one or more characteristics, generate or train a machine learning model using the set of data items and associated characteristics, receive one or more sets of simulation parameters from a user indicating a hypothetical scenario and a time period, and generate user interface data. The user interface data may comprise a time-based chart illustrating the respective time periods. The computing system may further apply machine learning model to the set of simulation parameters to predict a set of expected quantities based on the simulation parameters, aggregate one or more types of expected quantities from the set of expected quantities to determine one or more combined quantities, and include in the user interface indications of the one or more combined quantities. The computing system may then cause the user interface to be presented. In some implementations of the method as disclosed herein, receiving the data items may comprise retrieving one or more discrete events from a data source, and converting the one or more discrete events into one or more continuous quantities.

    System and method for chaining discrete models

    公开(公告)号:US11816555B2

    公开(公告)日:2023-11-14

    申请号:US17171898

    申请日:2021-02-09

    IPC分类号: G06N3/045 G06N3/084 G06N5/04

    CPC分类号: G06N3/045 G06N3/084 G06N5/04

    摘要: Systems, computer program products, and computer-implemented methods for determining relationships between one or more outputs of a first model and one or more inputs of a second model that collectively represent a real world system, and chaining the models together. For example, the system described herein may determine how to chain a plurality of models by training an artificial intelligence system using the nodes of the models such that the trained artificial intelligence system predicts related output and input node connections. The system may then link related nodes to chain the models together. The systems, computer program products, and computer-implemented methods may thus, according to various embodiments, enable a plurality of discrete models to be optimally chained.

    SYSTEM AND METHOD FOR CHAINING DISCRETE MODELS

    公开(公告)号:US20240037374A1

    公开(公告)日:2024-02-01

    申请号:US18483056

    申请日:2023-10-09

    IPC分类号: G06N3/045 G06N3/084 G06N5/04

    CPC分类号: G06N3/045 G06N3/084 G06N5/04

    摘要: Systems, computer program products, and computer-implemented methods for determining relationships between one or more outputs of a first model and one or more inputs of a second model that collectively represent a real world system, and chaining the models together. For example, the system described herein may determine how to chain a plurality of models by training an artificial intelligence system using the nodes of the models such that the trained artificial intelligence system predicts related output and input node connections. The system may then link related nodes to chain the models together. The systems, computer program products, and computer-implemented methods may thus, according to various embodiments, enable a plurality of discrete models to be optimally chained.

    SYSTEM AND METHOD FOR CHAINING DISCRETE MODELS

    公开(公告)号:US20210248447A1

    公开(公告)日:2021-08-12

    申请号:US17171898

    申请日:2021-02-09

    IPC分类号: G06N3/04 G06N3/08 G06N5/04

    摘要: Systems, computer program products, and computer-implemented methods for determining relationships between one or more outputs of a first model and one or more inputs of a second model that collectively represent a real world system, and chaining the models together. For example, the system described herein may determine how to chain a plurality of models by training an artificial intelligence system using the nodes of the models such that the trained artificial intelligence system predicts related output and input node connections. The system may then link related nodes to chain the models together. The systems, computer program products, and computer-implemented methods may thus, according to various embodiments, enable a plurality of discrete models to be optimally chained.