公开(公告)号：US11928211B2

公开(公告)日：2024-03-12

申请号：US17991119

申请日：2022-11-21

Applicant: Palantir Technologies Inc.

Inventor： Paul Gribelyuk ,  Han Xu ,  Kelvin Lau ,  Pierre Cholet

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

CPC classification number: G06F21/554 ,  G06F16/9024 ,  G06F21/552 ,  G06N20/00

Abstract: 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.

公开(公告)号：US10268735B1

公开(公告)日：2019-04-23

申请号：US15391532

申请日：2016-12-27

Applicant: Palantir Technologies Inc.

Inventor： Nicholas White ,  Han Xu

IPC: G06F17/30

Abstract: In an embodiment, a computer-implemented method comprises calculating a first relational classification score for a first node in a first graph; calculating a second relational classification core for a second node in a second graph; calculating a relational classification matching score for the first node and the second node that is based upon on the first relational classification score and the second relational classification score; calculating a composite score based at least upon the relational classification matching score; generating a canonical tuple that represents a match between the first node and the second node in response to determining that the composite score is equal to or greater than a specified threshold score value.

公开(公告)号：US11507657B2

公开(公告)日：2022-11-22

申请号：US17001472

申请日：2020-08-24

Applicant: Palantir Technologies Inc.

Inventor： Paul Gribelyuk ,  Han Xu ,  Kelvin Lau ,  Pierre Cholet

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

Abstract: 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.

公开(公告)号：US10754946B1

公开(公告)日：2020-08-25

申请号：US16028191

申请日：2018-07-05

Applicant: Palantir Technologies Inc

Inventor： Paul Gribelyuk ,  Han Xu ,  Kelvin Lau ,  Pierre Cholet

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

Abstract: 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.

公开(公告)号：US10970292B1

公开(公告)日：2021-04-06

申请号：US16283646

申请日：2019-02-22

Applicant: Palantir Technologies Inc.

Inventor： Nicholas White ,  Han Xu

IPC: G06F16/2457 ,  G06F16/901 ,  G06F16/28

Abstract: In an embodiment, a computer-implemented method comprises, calculating a first relational classification score for a first node in a first graph; calculating a second relational classification score for a second node in a second graph; calculating a relational classification matching score for the first node and the second node that is based upon on the first relational classification score and the second relational classification score; generating a canonical tuple that represents a match between the first node and the second node based at least upon the relational classification matching score.