公开(公告)号：US10127289B2

公开(公告)日：2018-11-13

申请号：US15233149

申请日：2016-08-10

Applicant: Palantir Technologies Inc.

Inventor： Lawrence Manning ,  Rahul Mehta ,  Daniel Erenrich ,  Guillem Palou Visa ,  Roger Hu ,  Xavier Falco ,  Rowan Gilmore ,  Eli Bingham ,  Jason Prestinario ,  Yifei Huang ,  Daniel Fernandez ,  Jeremy Elser ,  Clayton Sader ,  Rahul Agarwal ,  Matthew Elkherj ,  Nicholas Latourette ,  Aleksandr Zamoshchin

IPC: G06F17/30

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

公开(公告)号：US11704325B2

公开(公告)日：2023-07-18

申请号：US17812984

申请日：2022-07-15

Applicant: Palantir Technologies Inc.

Inventor： Lawrence Manning ,  Rahul Mehta ,  Daniel Erenrich ,  Guillem Palou Visa ,  Roger Hu ,  Xavier Falco ,  Rowan Gilmore ,  Eli Bingham ,  Jason Prestinario ,  Yifei Huang ,  Daniel Fernandez ,  Jeremy Elser ,  Clayton Sader ,  Rahul Agarwal ,  Matthew Elkherj ,  Nicholas Latourette ,  Aleksandr Zamoshchin

IPC: G06F16/00 ,  G06F16/2457 ,  G06F16/35 ,  G06F16/9535 ,  G06F16/28 ,  G06F18/23

CPC classification number: G06F16/24578 ,  G06F16/285 ,  G06F16/35 ,  G06F16/9535 ,  G06F18/23

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

公开(公告)号：US20240320227A1

公开(公告)日：2024-09-26

申请号：US18731699

申请日：2024-06-03

Applicant: Palantir Technologies Inc.

Inventor： Lawrence Manning ,  Rahul Mehta ,  Daniel Erenrich ,  Guillem Palou Visa ,  Roger Hu ,  Xavier Falco ,  Rowan Gilmore ,  Eli Bingham ,  Jason Prestinario ,  Yifei Huang ,  Daniel Fernandez ,  Jeremy Elser ,  Clayton Sader ,  Rahul Agarwal ,  Matthew Elkherj ,  Nicholas Latourette ,  Aleksandr Zamoshchin

IPC: G06F16/2457 ,  G06F16/28 ,  G06F16/35 ,  G06F16/9535 ,  G06F18/23

CPC classification number: G06F16/24578 ,  G06F16/285 ,  G06F16/35 ,  G06F16/9535 ,  G06F18/23

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

公开(公告)号：US12038933B2

公开(公告)日：2024-07-16

申请号：US18325616

申请日：2023-05-30

Applicant: Palantir Technologies Inc.

Inventor： Lawrence Manning ,  Rahul Mehta ,  Daniel Erenrich ,  Guillem Palou Visa ,  Roger Hu ,  Xavier Falco ,  Rowan Gilmore ,  Eli Bingham ,  Jason Prestinario ,  Yifei Huang ,  Daniel Fernandez ,  Jeremy Elser ,  Clayton Sader ,  Rahul Agarwal ,  Matthew Elkherj ,  Nicholas Latourette ,  Aleksandr Zamoshchin

IPC: G06F16/00 ,  G06F16/2457 ,  G06F16/28 ,  G06F16/35 ,  G06F16/9535 ,  G06F18/23

CPC classification number: G06F16/24578 ,  G06F16/285 ,  G06F16/35 ,  G06F16/9535 ,  G06F18/23

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

公开(公告)号：US20230297582A1

公开(公告)日：2023-09-21

申请号：US18325616

申请日：2023-05-30

Applicant: Palantir Technologies Inc.

Inventor： Lawrence Manning ,  Rahul Mehta ,  Daniel Erenrich ,  Guillem Palou Visa ,  Roger Hu ,  Xavier Falco ,  Rowan Gilmore ,  Eli Bingham ,  Jason Prestinario ,  Yifei Huang ,  Daniel Fernandez ,  Jeremy Elser ,  Clayton Sader ,  Rahul Agarwal ,  Matthew Elkherj ,  Nicholas Latourette ,  Aleksandr Zamoshchin

IPC: G06F16/2457 ,  G06F16/35 ,  G06F16/9535 ,  G06F16/28 ,  G06F18/23

CPC classification number: G06F16/24578 ,  G06F16/35 ,  G06F16/9535 ,  G06F16/285 ,  G06F18/23

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

公开(公告)号：US20170052958A1

公开(公告)日：2017-02-23

申请号：US15233149

申请日：2016-08-10

Applicant: Palantir Technologies Inc.

Inventor： Lawrence Manning ,  Rahul Mehta ,  Daniel Erenrich ,  Guillem Palou Visa ,  Roger Hu ,  Xavier Falco ,  Rowan Gilmore ,  Eli Bingham ,  Jason Prestinario ,  Yifei Huang ,  Daniel Fernandez ,  Jeremy Elser ,  Clayton Sader ,  Rahul Agarwal ,  Matthew Elkherj ,  Nicholas Latourette ,  Aleksandr Zamoshchin

IPC: G06F17/30

CPC classification number: G06F17/3053 ,  G06F17/30705 ,  G06F17/30867

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

Abstract translation: 公开了计算机实现的系统和方法，用于自动聚类和规范地识别各种数据结构中的相关数据。 数据结构可以包括多个记录，其中每个记录与相应实体相关联。 根据一些实施例，系统和方法还包括通过将记录分组成对来识别与相应实体相关联的记录簇，分析相应的对以确定该对的两个成员与公共实体相关联的概率，以及识别 一组重叠的对以生成与公共实体相关的记录集合。 可以通过分析记录字段并识别相似性来进一步分析集群以确定相应实体的规范名称或其他属性。

公开(公告)号：US20250094171A1

公开(公告)日：2025-03-20

申请号：US18598333

申请日：2024-03-07

Applicant: Palantir Technologies Inc.

Inventor： Zachary Isaacson ,  Akshay Agrawal ,  Rowan Gilmore ,  Rohith Kasar ,  Leslie Bresnahan

IPC: G06F9/30

Abstract: At least some embodiments of the present disclosure are related to methods and systems for evaluating, generating, and/or prototyping data pipelines. In certain embodiments, a system is configured to perform operations include: receiving an input dataset, the input dataset including a data schema; generating a first prompt based on the input dataset and a first prompt structure having one or more text strings and one or more blanks; providing the first prompt to a language model; receiving a use case generated by the language model for the input dataset, the use case including a description of how to use the input dataset; generating a data pipeline based on the use case; and applying the data pipeline to the input dataset to generate an output dataset.

公开(公告)号：US20220374454A1

公开(公告)日：2022-11-24

申请号：US17812984

申请日：2022-07-15

Applicant: Palantir Technologies Inc.

Inventor： Lawrence Manning ,  Rahul Mehta ,  Daniel Erenrich ,  Guillem Palou Visa ,  Roger Hu ,  Xavier Falco ,  Rowan Gilmore ,  Eli Bingham ,  Jason Prestinario ,  Yifei Huang ,  Daniel Fernandez ,  Jeremy Elser ,  Clayton Sader ,  Rahul Agarwal ,  Matthew Elkherj ,  Nicholas Latourette ,  Aleksandr Zamoshchin

IPC: G06F16/28 ,  G06K9/62

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

公开(公告)号：US20190079937A1

公开(公告)日：2019-03-14

申请号：US16189040

申请日：2018-11-13

Applicant: Palantir Technologies Inc.

Inventor： Lawrence Manning ,  Rahul Mehta ,  Daniel Erenrich ,  Guillem Palou Visa ,  Roger Hu ,  Xavier Falco ,  Rowan Gilmore ,  Eli Bingham ,  Jason Prestinario ,  Yifei Huang ,  Daniel Fernandez ,  Jeremy Elser ,  Clayton Sader ,  Rahul Agarwal ,  Matthew Elkherj ,  Nicholas Latourette ,  Aleksandr Zamoshchin

IPC: G06F17/30

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.