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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.
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公开(公告)号：US20180292959A1
公开(公告)日：2018-10-11
申请号：US16003468
申请日：2018-06-08
Applicant: Palantir Technologies Inc.
Inventor： Daniel Erenrich , Anirvan Mukherjee , William Dwyer
IPC: G06F3/0484 , G06Q10/06 , G06F17/22
Abstract: Systems and methods are provided for analyzing entity performance. In accordance with one implementation, a method is provided that includes receiving data associated with a geographic region and transforming the received data into an object model. The method also includes analyzing the object model to associate the received data with a plurality of entities and to associate the received data with a plurality of sub-geographic regions of the geographic region. The method also includes applying a prediction model to the plurality of sub-geographic regions using the object model to determine a predicted performance for at least one entity of the plurality of entities. Further, the method includes determining actual performance for the at least one entity and providing a user interface that includes information associated with the predicted performance, the actual performance, or a combination of the predicted performance and the actual performance.
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公开(公告)号：US10025834B2
公开(公告)日：2018-07-17
申请号：US14306147
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 , Patricio Jones Velez , Holt Wilkins , Diane Wu , Drausin Wulsin , Di Wu , Yu-Hsin Joyce Chen , Baris Kaya
CPC classification number: G06F16/248 , G06F16/26 , G06F16/285 , 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.
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公开(公告)号：US12288143B2
公开(公告)日：2025-04-29
申请号：US17930046
申请日：2022-09-06
Applicant: Palantir Technologies Inc.
Inventor： Daniel Erenrich , Matthew Elkherj
Abstract: Systems and methods are provided for selecting training examples to increase the efficiency of supervised active machine learning processes. Training examples for presentation to a user may be selected according to measure of the model's uncertainty in labeling the examples. A number of training examples may be selected to increase efficiency between the user and the processing system by selecting the number of training examples to minimize user downtime in the machine learning process.
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公开(公告)号：US11861515B2
公开(公告)日：2024-01-02
申请号：US17961822
申请日：2022-10-07
Applicant: Palantir Technologies Inc.
Inventor： Daniel Erenrich , Anirvan Mukherjee
IPC: G06N5/048 , G06Q30/01 , G06F16/2455 , G06Q30/0202 , G06N7/00 , G06Q30/0201 , G06N7/01
CPC classification number: G06N5/048 , G06F16/2455 , G06N7/01 , G06Q30/01 , G06Q30/0202 , G06Q30/0201
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.
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Patent Agency Ranking
公开(公告)号：US20230081135A1
公开(公告)日：2023-03-16
申请号：US18051035
申请日：2022-10-31
Applicant: Palantir Technologies Inc.
Inventor： David Lisuk , Daniel Erenrich , Guodong Xu , Luis Voloch , Rahul Agarwal , Simon Slowik , Aleksandr Zamoshichin , Andre Frederico Cavalheiro Menck , Anirvan Mukherjee , Daniel Chin
IPC: G06F16/18 , G06N7/00 , G06F16/13 , G06F16/188 , G06F30/00
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.
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公开(公告)号：US11521096B2
公开(公告)日：2022-12-06
申请号：US15689757
申请日：2017-08-29
Applicant: Palantir Technologies Inc.
Inventor： Daniel Erenrich , Anirvan Mukherjee
IPC: G06N5/04 , G06N7/00 , G06Q30/00 , G06F16/2455 , G06Q30/02
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.
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公开(公告)号：US11436523B2
公开(公告)日：2022-09-06
申请号：US16027161
申请日：2018-07-03
Applicant: Palantir Technologies Inc.
Inventor： Daniel Erenrich , Matthew Elkherj
Abstract: Systems and methods are provided for selecting training examples to increase the efficiency of supervised active machine learning processes. Training examples for presentation to a user may be selected according to measure of the model's uncertainty in labeling the examples. A number of training examples may be selected to increase efficiency between the user and the processing system by selecting the number of training examples to minimize user downtime in the machine learning process.
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公开(公告)号：US10373078B1
公开(公告)日：2019-08-06
申请号：US15655401
申请日：2017-07-20
Applicant: Palantir Technologies Inc.
Inventor： Rahul Agarwal , Daniel Erenrich
Abstract: In various example embodiments, a vector modeling system is configured to access a set of data distributed across client devices and stored in a structured format. The vector modeling system determines vector parameters and vector templates suitable for the set of data and transforms the set of data from the structured format into a second format including one or more vectors based on one or more transformation strategies. The vector modeling system stores the transformed data and performs machine learning analysis on the vector.
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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.
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