利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

13 条记录 (耗时 0.018 秒)

    Systems and methods for implementing a machine learning approach to modeling entity behavior
    1.
    发明授权
    Systems and methods for implementing a machine learning approach to modeling entity behavior 有权

    公开(公告)号：US11928211B2

    公开(公告)日：2024-03-12

    申请号：US17991119

    申请日：2022-11-21

    申请人： Palantir Technologies Inc.

    发明人： Paul Gribelyuk ,  Han Xu ,  Kelvin Lau ,  Pierre Cholet

    IPC分类号： G06F21/55 ,  G06F16/901 ,  G06N20/00

    CPC分类号： G06F21/554 ,  G06F16/9024 ,  G06F21/552 ,  G06N20/00

    摘要： Systems and methods are provided for implementing a machine learning approach to modeling entity behavior. Fixed information and periodically updated information may be utilized to predict the behavior of an entity. By incorporating periodically updated information, the system is able to maintain an up-to-date prediction of each entity's behavior, while also accounting for entity action with respect to ongoing obligations. The system may generate behavior scores for the set of entities. In some embodiments, the behavior scores that are generated may indicate the transactional risk associated with each entity. Using the behavior scores generated, a user may be able to assess the credit riskiness of individual entities and instruct one or more individuals assigned to the entities to take one or more actions based on the credit riskiness of the individual entities.

    Systems and methods for implementing a machine learning approach to modeling entity behavior
    6.
    发明授权
    Systems and methods for implementing a machine learning approach to modeling entity behavior 有权

    公开(公告)号：US11507657B2

    公开(公告)日：2022-11-22

    申请号：US17001472

    申请日：2020-08-24

    申请人： Palantir Technologies Inc.

    发明人： Paul Gribelyuk ,  Han Xu ,  Kelvin Lau ,  Pierre Cholet

    IPC分类号： G06F21/55 ,  G06N20/00 ,  G06F16/901

    摘要： Systems and methods are provided for implementing a machine learning approach to modeling entity behavior. Fixed information and periodically updated information may be utilized to predict the behavior of an entity. By incorporating periodically updated information, the system is able to maintain an up-to-date prediction of each entity's behavior, while also accounting for entity action with respect to ongoing obligations. The system may generate behavior scores for the set of entities. In some embodiments, the behavior scores that are generated may indicate the transactional risk associated with each entity. Using the behavior scores generated, a user may be able to assess the credit riskiness of individual entities and instruct one or more individuals assigned to the entities to take one or more actions based on the credit riskiness of the individual entities.

    Systems and methods for implementing a machine learning approach to modeling entity behavior
    9.
    发明授权
    Systems and methods for implementing a machine learning approach to modeling entity behavior 有权

    公开(公告)号：US10754946B1

    公开(公告)日：2020-08-25

    申请号：US16028191

    申请日：2018-07-05

    申请人： Palantir Technologies Inc

    发明人： Paul Gribelyuk ,  Han Xu ,  Kelvin Lau ,  Pierre Cholet

    IPC分类号： G06F21/55 ,  G06N20/00 ,  G06F16/901

    摘要： Systems and methods are provided for implementing a machine learning approach to modeling entity behavior. Fixed information and periodically updated information may be utilized to predict the behavior of an entity. By incorporating periodically updated information, the system is able to maintain an up-to-date prediction of each entity's behavior, while also accounting for entity action with respect to ongoing obligations. The system may generate behavior scores for the set of entities. In some embodiments, the behavior scores that are generated may indicate the transactional risk associated with each entity. Using the behavior scores generated, a user may be able to assess the credit riskiness of individual entities and instruct one or more individuals assigned to the entities to take one or more actions based on the credit riskiness of the individual entities.