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：US20210064645A1

公开(公告)日：2021-03-04

申请号：US17010187

申请日：2020-09-02

Applicant: Palantir Technologies Inc.

Inventor： Francisco Ferreira ,  Ryan Norris ,  Viktor Nordling ,  Kelvin Lau

IPC: G06F16/36 ,  G06F16/182 ,  G06F16/176

Abstract: A method, performed by one or more processors, is disclosed, comprising providing, to a plurality of parties permitted to communicate data via a shared database, an ontology application associated with a common core ontology, the core ontology defining constraints required to be met for producing, from one or more received datasets, one or more data objects for storing in the shared database. The ontology application may be configured to receive one or more datasets from one or more parties and to use the core database ontology to determine if the received one or more datasets conform to the constraints of the core ontology, and store the received one or more datasets as data objects in the shared database, conditional on the constraints being met.

公开(公告)号：US20220198032A1

公开(公告)日：2022-06-23

申请号：US17226014

申请日：2021-04-08

Applicant: Palantir Technologies Inc.

Inventor： Anton Apostolatos ,  Adam Lieskovský ,  Florian Diegruber ,  Francisco Ferreira ,  Joseph Kane ,  Joanna Peller ,  Kelvin Lau ,  Maciej Laska ,  Mikael Ibrahim Mofarrej ,  Max-Philipp Schrader ,  Philipp Hoefer ,  Spencer McCollester ,  Viktor Nordling

IPC: G06F21/60 ,  G06F16/25

Abstract: A computer-implemented method enforces data security constraints in a data pipeline. The data pipeline takes one or more source datasets as input and performs one or more data transformations on them. The method includes using data defining one or more data security constraints to configure the data pipeline to perform a data transformation on a restricted subset of entries of the source datasets. The restriction is defined by the data defining one or more data security constraints. The method further includes performing the data transformation according to the configuration to produce one or more transformed datasets. The method further includes using the data defining one or more data security constraints to perform a verification on one or more of the transformed datasets to ensure that entries in the one or more of the transformed datasets are restricted as defined by the one or more data security constraints.

公开(公告)号：US12079352B2

公开(公告)日：2024-09-03

申请号：US17226014

申请日：2021-04-08

Applicant: Palantir Technologies Inc.

Inventor： Anton Apostolatos ,  Adam Lieskovský ,  Florian Diegruber ,  Francisco Ferreira ,  Joseph Kane ,  Joanna Peller ,  Kelvin Lau ,  Maciej Laska ,  Mikael Ibrahim Mofarrej ,  Max-Philipp Schrader ,  Philipp Hoefer ,  Spencer McCollester ,  Viktor Nordling

IPC: G06F21/60 ,  G06F16/25

CPC classification number: G06F21/604 ,  G06F16/258

Abstract: A computer-implemented method enforces data security constraints in a data pipeline. The data pipeline takes one or more source datasets as input and performs one or more data transformations on them. The method includes using data defining one or more data security constraints to configure the data pipeline to perform a data transformation on a restricted subset of entries of the source datasets. The restriction is defined by the data defining one or more data security constraints. The method further includes performing the data transformation according to the configuration to produce one or more transformed datasets. The method further includes using the data defining one or more data security constraints to perform a verification on one or more of the transformed datasets to ensure that entries in the one or more of the transformed datasets are restricted as defined by the one or more data security constraints.

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.