IP Library Granted Patent US 12,743,438
Granted Patent B2
US 12,743,438 · App. 18/891,257 · Granted Sep 22, 2026

Object-centric data analysis system and graphical user interface

Inventors: Alexander Martino (Brooklyn, NY); Charles Perinet (London, GB); Matthieu Beteille (London, GB)
Assignee: Palantir Technologies Inc.
G06F16/248G06F3/0482G06F9/451G06F16/26
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Quick Facts
Patent No.
US 12,743,438
App. No.
18/891,257
Granted
Sep 22, 2026
Kind
B2
Abstract

Systems and methods for analyzing data stored using a data model. The system can receive a user selection of a first object type indicating to perform filtering operations on a first set of data objects, generate a list of object types linked to the first object type based on an ontology, receives a user selection of a second object type, generate a list of properties of the second object type based on an ontology, receive a user selection of a first property from the list of properties, perform a data query determining values associated with the first property, receive a user selection of a first value, and displays information of a subset of data objects being a portion of the first set of data objects that are linked to data objects in the second set of data objects that have a first property value of the first value.

Claims (42)

1 . A system comprising:

one or more non-transitory computer storage mediums configured to store computer-executable instructions; and

one or more hardware processors in communication with the one or more non-transitory computer storage mediums, the one or more hardware processors configured to execute the computer-executable instructions to at least:

receive one or more user inputs indicating a first object type, a second object type linked to the first object type, and a first value of a first property of the second object type;

determine a subset of data objects, of a first set of data objects of the first object type, that are linked to data objects, in a second set of data objects of the second object type, that have a first property value of the indicated first value; and

generate and display a tracker bar depicting a sequence of filter operations applied to determine the subset of data objects, wherein the sequence of filter operations depicted on the displayed tracker bar includes at least: an indication of the first object type, an indication of the second object type, and an indication of the first property.

2 . The system of claim 1 , wherein the one or more hardware processors are further configured to execute the computer-executable instructions to:

in response to receiving the one or more user inputs and determining the subset of data objects:

display one or more visualizations of the subset of data objects on a display.

3 . The system of claim 2 , wherein the one or more visualizations include one or more of: a listogram, a timeline, a numeric distribution, a choropleth map, or a clustering map.

4 . The system of claim 1 , wherein the one or more hardware processors are further configured to execute the computer-executable instructions to:

generate and display the tracker bar configured for receiving the one or more user inputs.

5 . The system of claim 1 , wherein the one or more hardware processors are further configured to execute the computer-executable instructions to:

display groups of object types in a plurality of sets of data objects.

6 . The system of claim 5 , wherein the groups of object types are displayed in groups of related categories.

7 . The system of claim 1 , wherein the one or more hardware processors are further configured to execute the computer-executable instructions to:

display object types in a plurality of sets of data objects.

8 . The system of claim 1 , wherein the one or more hardware processors are further configured to execute the computer-executable instructions to:

save operations performed on the first set of data objects and resulting visualizations, the saved operations and visualizations being restorable for use in a subsequently performed data analysis workflow.

9 . A method comprising:

receiving one or more user inputs indicating a first object type, a second object type linked to the first object type, and a first value of a first property of the second object type;

determining a subset of data objects, of a first set of data objects of the first object type, that are linked to data objects, in a second set of data objects of the second object type, that have a first property value of the indicated first value; and

generating and display a tracker bar depicting a sequence of filter operations applied to determine the subset of data objects, wherein the sequence of filter operations depicted on the displayed tracker bar includes at least: an indication of the first object type, an indication of the second object type, and an indication of the first property.

10 . The method of claim 9 further comprising:

in response to receiving the one or more user inputs and determining the subset of data objects:

displaying one or more visualizations of the subset of data objects on a display.

11 . The method of claim 10 , wherein the one or more visualizations include one or more of: a listogram, a timeline, a numeric distribution, a choropleth map, or a clustering map.

12 . The method of claim 9 , further comprising:

generating and display the tracker bar configured for receiving the one or more user inputs.

13 . The method of claim 9 further comprising:

displaying groups of object types in a plurality of sets of data objects.

14 . The method of claim 13 , wherein the groups of object types are displayed in groups of related categories.

15 . The method of claim 9 further comprising:

displaying object types in a plurality of sets of data objects.

16 . The method of claim 9 further comprising:

saving operations performed on the first set of data objects and resulting visualizations, the saved operations and visualizations being restorable for use in a subsequently performed data analysis workflow.

17 . One or more non-transitory, computer-readable storage medium storing computer-executable instructions, which if performed by one or more processors, cause the one or more processors to at least:

receive one or more user inputs indicating a first object type, a second object type linked to the first object type, and a first value of a first property of the second object type;

determine a subset of data objects, of a first set of data objects of the first object type, that are linked to data objects, in a second set of data objects of the second object type, that have a first property value of the indicated first value; and

generate and display a tracker bar depicting a sequence of filter operations applied to determine the subset of data objects, wherein the sequence of filter operations depicted on the displayed tracker bar includes at least: an indication of the first object type, an indication of the second object type, and an indication of the first property.

18 . The one or more non-transitory, computer-readable storage medium of claim 17 , wherein the computer-executable instructions further cause the one or more processors to at least:

generate and display the tracker bar configured for receiving the one or more user inputs.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 20, 2024
From: MARTINO, ALEXANDER; PERINET, CHARLES; BETEILLE, MATTHIEU
To: PALANTIR TECHNOLOGIES INC.
Reel/Frame 068652/0845 →
Continuity (4)
Continuation 17588084 · Jan 28, 2022
Continuation 16716317 · Dec 16, 2019
Provisional Application 62936178 · Nov 15, 2019
Related Publication 20250013656A1 · Jan 9, 2025
References Cited (37)
US 8560494B1 · Downing · 2013 [cited by examiner]
US 9646396B2 · Sharma et al. · 2017 [cited by applicant]
US 9922108B1 · Meiklejohn et al. · 2018 [cited by applicant]
US 10444940B2 · Cervelli et al. · 2019 [cited by applicant]
US 10877984B1 · Martino et al. · 2020 [cited by applicant]
US 11269907B1 · Martino et al. · 2022 [cited by applicant]
US 11580164B1 · Falco et al. · 2023 [cited by applicant]
US 20020124115A1 · McLean et al. · 2002 [cited by applicant]
US 20080172628A1 · Mehrotra et al. · 2008 [cited by applicant]
US 20110040776A1 · Najm · 2011 [cited by examiner]
US 20110175932A1 · Yu et al. · 2011 [cited by applicant]
US 20140155124A1 · Lee et al. · 2014 [cited by applicant]
US 20150012509A1 · Kirn · 2015 [cited by examiner]
US 20150205848A1 · Kumar et al. · 2015 [cited by applicant]
US 20160321064A1 · Sankaransrasimhan et al. · 2016 [cited by applicant]
US 20160350381A1 · Cao et al. · 2016 [cited by applicant]
US 20170115865A1 · Goldenberg et al. · 2017 [cited by applicant]
US 20170185674A1 · Tonkin et al. · 2017 [cited by applicant]
US 20170277738A1 · Danka et al. · 2017 [cited by applicant]
US 20180096417A1 · Cook et al. · 2018 [cited by applicant]
US 20180210725A1 · Vaindiner et al. · 2018 [cited by applicant]
US 20190026260A1 · Schubert et al. · 2019 [cited by applicant]
US 20190392032A1 · Yasui et al. · 2019 [cited by applicant]
US 20200019550A1 · Keenan et al. · 2020 [cited by applicant]
US 20210117051A1 · McRaven et al. · 2021 [cited by applicant]
US 20210141521A1 · Apostolatos et al. · 2021 [cited by applicant]
US 20220147519A1 · Martino et al. · 2022 [cited by applicant]
US 20220374140A1 · Ma et al. · 2022 [cited by applicant]
US 20230075655A1 · Piecko et al. · 2023 [cited by applicant]
EP 3038002 · 2016 [cited by applicant]
EP 3822810 · 2021 [cited by applicant]
U.S. Appl. No. 11/269,907, Object-Centric Data Analysis System and Graphical User Interface, Mar. 8, 2022. [cited by applicant]
U.S. Appl. No. 17/588,084, Object-Centric Data Analysis System and Graphical User Interface, Jan. 28, 2022. [cited by applicant]
Card et al., “Degree-of-Interest Trees: A Component of an Attention-Reactive User Interface”, AVI2002, Advanced Visual Interface, Jan. 2002, pp. 231-245. [cited by applicant]
Van Harmelen et al., “Ontology-Based Information Visualisation”, Oct. 16, 2001, 9 pages. [cited by applicant]
Official Communication for European Patent Application No. 20207467.0 dated Apr. 6, 2021, 10 pages. [cited by applicant]
Official Communication for European Patent Application No. 20207467.0 dated May 29, 2024, 7 pages. [cited by applicant]