公开(公告)号：US20210056083A1

公开(公告)日：2021-02-25

申请号：US17093381

申请日：2020-11-09

Applicant: Palantir Technologies Inc.

Inventor： David Lisuk ,  Daniel Erenrich ,  Guodong Xu ,  Luis Voloch ,  Rahul Agarwal ,  Simon Slowik ,  Aleksandr Zamoshchin ,  Andre Frederico Cavalheiro Menck ,  Anirvan Mukherjee ,  Daniel Chin

IPC: G06F16/18 ,  G06N7/00 ,  G06F16/13 ,  G06F30/00 ,  G06F16/188

Abstract: A model management system provides a centralized repository for storing and accessing models. The model management system receives an input to store a model object in a first model state generated based on a first set of known variables. The model management system generates a first file including a first set of functions defining the first model state and associates the first file with a model key identifying the model object. The model management system receives an input to store the model object in a second model state having been generated based on the first model state and a second set of known variables. The model management system generates a second file including a second set of functions defining the second model state and associates the second file with the model key. The model management system identifies available versions of the model object based on the model key.

公开(公告)号：US20180322175A1

公开(公告)日：2018-11-08

申请号：US16023251

申请日：2018-06-29

Applicant: Palantir Technologies Inc.

Inventor： Feridun Arda Kara ,  Eli Bingham ,  John Garrod ,  Daniel Erenrich ,  Anirvan Mukherjee ,  Ted Mabrey ,  Andrew Ash ,  Zachary Bush ,  Allen Cai ,  Winnie Chai ,  Greg Cohan ,  Chris Dorsey ,  William Dwyer ,  Gilad Gray ,  Sean Kelley ,  Dennis Kwon ,  Chris Lewis ,  Greg Martin ,  Parvathy Menon ,  Brian Ngo ,  Asli Ozyar ,  Mike Reilly ,  Jacob Scott ,  Ankit Shankar ,  Matt Sills ,  Spencer Stamats ,  Geoff Stowe ,  Samir Talwar ,  Engin Ural ,  Patricio Jose Velez ,  Holt Wilkins ,  Diane Wu ,  Drausin Wulsin ,  Di Wu ,  Yu-hsin Joyce Chen ,  Baris Kaya

IPC: G06F17/30 ,  G06Q10/06

CPC classification number: G06F17/30554 ,  G06F17/30572 ,  G06F17/30598 ,  G06Q10/0639

Abstract: Systems and methods are provided for analyzing entity performance. In one implementation, a method is provided that includes recognizing an identifier associated with an entity and accessing a data structure comprising information associated with a plurality of interactions. The method also comprises identifying one or more interactions of the plurality of interactions based on the recognized identifier. The method further comprises processing the information of the identified interactions to analyze a performance of the entity and providing the processed information to display the performance of the entity on a user interface.

公开(公告)号：US20180082305A1

公开(公告)日：2018-03-22

申请号：US15689757

申请日：2017-08-29

Applicant: Palantir Technologies Inc.

Inventor： Daniel Erenrich ,  Anirvan Mukherjee

IPC: G06Q30/00

CPC classification number: G06Q30/01 ,  G06N5/048 ,  G06N7/005

Abstract: Systems and methods are disclosed for determining a propensity of an entity to take a specified action. In accordance with one implementation, a method is provided for determining the propensity. The method includes, for example, accessing one or more data sources, the one or more data sources including information associated with the entity, forming a record associated with the entity by integrating the information from the one or more data sources, generating, based on the record, one or more features associated with the entity, processing the one or more features to determine the propensity of the entity to take the specified action, and outputting the propensity.

公开(公告)号：US20180018564A1

公开(公告)日：2018-01-18

申请号：US15625169

申请日：2017-06-16

Applicant: Palantir Technologies Inc.

Inventor： Daniel Erenrich ,  Anirvan Mukherjee ,  Matthew Elkherj ,  Maxim Kesin ,  Adam Eltoukhy ,  Jason Lee

IPC: G06N3/08 ,  G06N3/00 ,  G06F17/30

CPC classification number: G06N3/08 ,  G06F16/313 ,  G06F16/3334 ,  G06F16/93 ,  G06N3/004

Abstract: Various systems and methods are provided that identify prior art patent references for a subject patent application. For example, the system preprocesses a corpus of patent references to identify keywords that are present in each of the patent references, n-grams present in the corpus, and a weighting associated with the identified n-grams. To identify prior art patent references, the system requests a user to provide a patent application. The system extracts n-grams found in the provided patent application and orders the n-grams based on the assigned n-gram weights. The system compares the top Y-rated n-grams with the identified keywords and retrieves patent references that include a keyword that matches one of the top Y-rated n-grams. The system re-ranks the retrieved patent references using, for example, artificial intelligence. The top Z-ranked retrieved patent references are transmitted to a user device for display in a user interface.

公开(公告)号：US09727622B2

公开(公告)日：2017-08-08

申请号：US14306154

申请日：2014-06-16

Applicant: PALANTIR TECHNOLOGIES INC.

Inventor： Feridun Arda Kara ,  Eli Bingham ,  John Garrod ,  Daniel Erenrich ,  Anirvan Mukherjee ,  Ted Mabrey ,  Andrew Ash ,  Zachary Bush ,  Allen Cai ,  Winnie Chai ,  Greg Cohan ,  Chris Dorsey ,  William Dwyer ,  Gilad Gray ,  Sean Kelley ,  Dennis Kwon ,  Chris Lewis ,  Greg Martin ,  Parvathy Menon ,  Brian Ngo ,  Asli Ozyar ,  Mike Reilly ,  Jacob Scott ,  Ankit Shankar ,  Matt Sills ,  Spencer Stamats ,  Geoff Stowe ,  Samir Talwar ,  Engin Ural ,  PJ Valez ,  Holt Wilkins ,  Diane Wu ,  Drausin Wulsin ,  Di Wu ,  Joyce Yu-Hsin Chen ,  Bar Kaya

IPC: G06F17/30 ,  G06Q10/06

CPC classification number: G06F17/30554 ,  G06F17/30572 ,  G06F17/30598 ,  G06Q10/0639

Abstract: Systems and methods are provided for analyzing entity performance. In one implementation, a method is provided that includes accessing a data structure comprising a plurality of interactions associated with multiple entities. The method also includes evaluating one or more interactions of the plurality of interactions associated with a consuming entity of the multiple entities. The method further includes determining whether the one or more interactions associated with the consuming entity comprise an identified location information of the consuming entity.

公开(公告)号：US20160253672A1

公开(公告)日：2016-09-01

申请号：US14726353

申请日：2015-05-29

Applicant: Palantir Technologies, Inc.

Inventor： Sean Hunter ,  Samuel Rogerson ,  Anirvan Mukherjee

IPC: G06Q20/40 ,  H04L29/08

CPC classification number: G06Q20/4016 ,  G06Q40/06 ,  H04L67/10

Abstract: A computer system implements a risk model for detecting outliers in a large plurality of transaction data, which can encompass millions or billions of transactions in some instances. The computing system comprises a non-transitory computer readable storage medium storing program instructions for execution by a computer processor in order to cause the computing system to receive first features for an entity in the transaction data, receive second features for a benchmark set, the second features corresponding with the first features, determine an outlier value of the entity based on a Mahalanobis distance from the first features to a benchmark value representing an average for the second features. The output of the risk model can be used to prioritize review by a human data analyst. The data analyst's review of the underlying data can be used to improve the model.

Abstract translation: 计算机系统实现用于检测大量多个事务数据中的异常值的风险模型，其在一些情况下可以包含数百万或数十亿次的事务。 该计算系统包括一个非暂时的计算机可读存储介质，其存储用于由计算机处理器执行的程序指令，以便使计算系统接收交易数据中的实体的第一特征，接收用于基准集的第二特征，第二特征 对应于第一特征的特征，基于从第一特征到表示第二特征的平均值的基准值的马氏距离距离来确定实体的离群值。 风险模型的输出可用于将人力资源分析师的审查优先考虑在内。 数据分析师对底层数据的回顾可用于改进模型。