公开(公告)号：US20170060930A1

公开(公告)日：2017-03-02

申请号：US15239585

申请日：2016-08-17

Applicant: PALANTIR TECHNOLOGIES INC.

Inventor： Matthew Elkherj ,  Xavier Falco ,  Pierre Cholet ,  Giulio D'Ali' Aula ,  Andrew Enrich

IPC: G06F17/30

CPC classification number: G06F17/30345 ,  G06F17/30554 ,  G06F17/30557 ,  G06Q30/0255 ,  G06Q30/0271 ,  G06Q30/0282 ,  G06Q50/01

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a feature clustering of users, user correlation database access, and user interface generation system. The system can obtain information stored in different databases located across geographic regions, and determine unique users from the different information. The information can be included in unique records in the databases, with each record describing a particular user, and with each user described with imperfect identifying information. The system can analyze the different information utilizing machine learning models, and can associate each record with a particular unique user. The system can obtain identifications of items associated with each user, and determine the propensity of the user to disassociate with one or more items, or determine likelihoods of future association with different items not presently associated with the user.

Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于用户的特征聚类，用户关联数据库访问和用户界面生成系统。 系统可以获取存储在跨地理区域的不同数据库中的信息，并根据不同信息确定唯一用户。 信息可以包含在数据库中的唯一记录中，每个记录描述一个特定用户，并且每个用户都用不完备的识别信息进行描述。 该系统可以利用机器学习模型分析不同的信息，并且可以将每个记录与特定的唯一用户相关联。 该系统可以获得与每个用户相关联的项目的标识，并且确定用户与一个或多个项目分离的倾向，或者确定将来与当前未与用户相关联的不同项目的关联的可能性。

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：US12261872B2

公开(公告)日：2025-03-25

申请号：US17445172

申请日：2021-08-16

Applicant: Palantir Technologies Inc.

Inventor： Corentin Petit ,  Jacob Albertson ,  Marissa Kimball ,  Paul Baseotto ,  Pierre Cholet ,  Timur Iskhakov ,  Victoria Galano

IPC: H04L9/40 ,  G06F9/451 ,  G06N20/00

Abstract: Systems and methods are provided for enhanced machine learning refinement and alert generation. An example method includes accessing datasets storing customer information reflecting transactions of customers. Individual risk scores are generated for the customers based on the customer information. Generating the risk score includes providing identified occurrences of scenario definitions and customer information as input to one or more machine learning models, the scenario definitions identifying occurrences of specific information reflected in the datasets, with the machine learning models assign respective risk scores to the customers. An interactive user interface is presented. The interactive user presents summary information associated with the risk scores, with the interactive user interfaces enabling an investigation into whether a particular customer is exhibiting risky behavior, responds to user input indicating feedback usable to update the one or more machine learning models or scenario definitions, with the feedback triggering updating of the machine learning models.

公开(公告)号：US11126609B2

公开(公告)日：2021-09-21

申请号：US16198614

申请日：2018-11-21

Applicant: Palantir Technologies Inc.

Inventor： Matthew Elkherj ,  Xavier Falco ,  Pierre Cholet ,  Giulio D'Ali' Aula ,  Andrew Ehrich

IPC: G06Q30/02 ,  G06F16/23 ,  G06F16/25 ,  G06F16/248 ,  G06Q50/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a feature clustering of users, user correlation database access, and user interface generation system. The system can obtain information stored in different databases located across geographic regions, and determine unique users from the different information. The information can be included in unique records in the databases, with each record describing a particular user, and with each user described with imperfect identifying information. The system can analyze the different information utilizing machine learning models, and can associate each record with a particular unique user. The system can obtain identifications of items associated with each user, and determine the propensity of the user to disassociate with one or more items, or determine likelihoods of future association with different items not presently associated with the user.

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：US20220201030A1

公开(公告)日：2022-06-23

申请号：US17445172

申请日：2021-08-16

Applicant: Palantir Technologies Inc.

Inventor： Corentin Petit ,  Jacob Albertson ,  Marissa Kimball ,  Paul Baseotto ,  Pierre Cholet ,  Timur Iskhakov ,  Victoria Galano

IPC: H04L29/06 ,  G06F9/451 ,  G06N20/00

Abstract: Systems and methods are provided for enhanced machine learning refinement and alert generation. An example method includes accessing datasets storing customer information reflecting transactions of customers. Individual risk scores are generated for the customers based on the customer information. Generating the risk score includes providing identified occurrences of scenario definitions and customer information as input to one or more machine learning models, the scenario definitions identifying occurrences of specific information reflected in the datasets, with the machine learning models assign respective risk scores to the customers. An interactive user interface is presented. The interactive user presents summary information associated with the risk scores, with the interactive user interfaces enabling an investigation into whether a particular customer is exhibiting risky behavior, responds to user input indicating feedback usable to update the one or more machine learning models or scenario definitions, with the feedback triggering updating of the machine learning models.

公开(公告)号：US20190108249A1

公开(公告)日：2019-04-11

申请号：US16198614

申请日：2018-11-21

Applicant: Palantir Technologies Inc.

Inventor： Matthew Elkherj ,  Xavier Falco ,  Pierre Cholet ,  Giulio D'Ali' Aula ,  Andrew Ehrich

IPC: G06F17/30 ,  G06Q50/00 ,  G06Q30/02

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a feature clustering of users, user correlation database access, and user interface generation system. The system can obtain information stored in different databases located across geographic regions, and determine unique users from the different information. The information can be included in unique records in the databases, with each record describing a particular user, and with each user described with imperfect identifying information. The system can analyze the different information utilizing machine learning models, and can associate each record with a particular unique user. The system can obtain identifications of items associated with each user, and determine the propensity of the user to disassociate with one or more items, or determine likelihoods of future association with different items not presently associated with the user.

公开(公告)号：US10140327B2

公开(公告)日：2018-11-27

申请号：US15239585

申请日：2016-08-17

Applicant: PALANTIR TECHNOLOGIES INC.

Inventor： Matthew Elkherj ,  Xavier Falco ,  Pierre Cholet ,  Giulio D'Ali' Aula ,  Andrew Ehrich

IPC: G06Q30/02 ,  G06F17/30 ,  G06Q50/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a feature clustering of users, user correlation database access, and user interface generation system. The system can obtain information stored in different databases located across geographic regions, and determine unique users from the different information. The information can be included in unique records in the databases, with each record describing a particular user, and with each user described with imperfect identifying information. The system can analyze the different information utilizing machine learning models, and can associate each record with a particular unique user. The system can obtain identifications of items associated with each user, and determine the propensity of the user to disassociate with one or more items, or determine likelihoods of future association with different items not presently associated with the user.