IP Library › Granted Patent US 12,411,664
Granted Patent B2
US 12,411,664 · App. 18/591,590 · Granted Sep 9, 2025

Auto-generating interactive workflow user interfaces for simulated systems

Inventors: Bianca Rahill-Marier (New York, NY); Casey Patton (London, GB); Charles Perinet (London, GB); Julien Levy (London, GB); Robert Fink (Munich, DE)
Assignee: Palantir Technologies Inc.
G06F8/33G06F8/36G06F8/433
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Quick Facts
Patent No.
US 12,411,664
App. No.
18/591,590
Filed
Feb 29, 2024
Granted
Sep 9, 2025
Kind
B2
Art Unit
2193
USPC
717/113
Abstract

Systems, computer program products, and computer-implemented methods for generating interactive graphical user interfaces, software-based workflows, and data integrations using catalogs of workflow applications and auto-generation of aspects of the workflows. A method of the disclosure may include accessing one or more data stores that store: information indicative of one or more data sources, information indicative of one or more data object types, information indicative of one or more applications, and information indicative of compatibilities between the one or more data object types and the one or more applications; receiving a first user input indicating an association between a first data source and a first data object type; and based on the compatibilities and the indicated association, automatically populating each of the one or more applications that is compatible with the first data object type with data from the first data source, wherein populating includes generating interactive graphical user interfaces.

Claims (55)

1. A computer-implemented method for automatically generating interactive graphical user interfaces, the computer-implemented method comprising, by one or more hardware processors executing program instructions:

accessing one or more data stores that store:

information indicative of one or more data sources,

information indicative of one or more data object types,

information indicative of one or more use cases,

information indicative of one or more applications, wherein the one or more applications are associated with the one or more use cases, and

information indicative of compatibilities between the one or more data object types and the one or more applications;

receiving a first user input indicating an association between a first data source and a first data object type;

based on the compatibilities and the indicated association, automatically populating each of the one or more applications that is compatible with the first data object type with data from the first data source; and

generating a first interactive graphical user interface including:

indications of one or more groups of the one or more use cases,

indications of use cases in each of the one or more groups, and

indications of statuses of each of the one or more use cases.

2. The computer-implemented method of claim 1 , further comprising, by the one or more hardware processors executing program instructions:

determining, using the information indicative of the compatibilities and the first data object type, that at least one of the one or more applications are compatible with the first data object type.

3. The computer-implemented method of claim 2 further comprising, by the one or more hardware processors executing program instructions:

generating a second interactive graphical user interface based at least in part on access of the one or more data stores, the second interactive graphical user interface including:

first graphical indications of the one or more data sources,

second graphical indications of the one or more data object types,

third graphical indications of the one or more use cases, and

fourth graphical indications of the compatibilities between the one or more data object types and the one or more applications, wherein the fourth graphical indications link to use cases with which the respective one or more applications are associated; and

in response to receiving the first user input, updating the second interactive graphical user interface to indicate that the first data object type has been associated with the first data source.

4. The computer-implemented method of claim 3 , wherein:

the one or more data stores further store:

information indicative of compatibilities between the one or more data sources and the one or more data object types; and

the second interactive graphical user interface further includes:

fifth graphical indications of the compatibilities between the one or more data sources and the one or more data object types.

5. The computer-implemented method of claim 3 further comprising, by the one or more hardware processors executing program instructions:

in response to a second user input selecting a first use case of the one or more use cases, generating a third interactive graphical user interface including:

indications of data object types associated with any applications associated with the first use case, and

user input elements configured to receive associations of data sources with the data object types associated with any applications associated with the first use case.

6. The computer-implemented method of claim 5 further comprising, by the one or more hardware processors executing program instructions:

in response to a third user input, received via at least one of the user input elements of the third interactive graphical user interface, indicating an association between a second data object type and a second data source, updating at least the third interactive graphical user interface to indicate that the second data object type has been associated with the second data source.

7. The computer-implemented method of claim 5 , wherein the third interactive graphical user interface further includes:

user input elements configured receive functions or actions associated with one or more applications associated with the first use case.

8. The computer-implemented method of claim 2 , wherein at least some of the one or more applications comprise templates of graphical visualizations based on reusable components of software products.

9. The computer-implemented method of claim 1 , wherein the one or more use cases include at least one of: supply chain management, predictive maintenance, predictive customer care, plant operation monitoring, aftersales parts alerts and allocation, inspection planning, maintenance planning, asset or plant overview, fleet monitoring, issue identification, root cause analysis, recall scoping and part traceability, or sales lead generation.

10. The computer-implemented method of claim 1 , wherein the indications of statuses include indications of numbers of data object types associated with the respective use cases that have been associated with data sources.

11. The computer-implemented method of claim 1 , wherein the indications of statuses include indications of deployment statuses of the respective use cases or respective groups.

12. The computer-implemented method of claim 11 further comprising, by the one or more hardware processors executing program instructions:

in response to a fourth user input, received via the first interactive graphical user interface, selecting a second use case, at least one of:

cause display of an application associated with the selected use case, or

cause display of a graphical user interface to configure an application associated with the selected use case.

13. The computer-implemented method of claim 12 further comprising, by the one or more hardware processors executing program instructions, at least one of:

determining the compatibilities between the one or more data object types and the one or more applications; or

determining the compatibilities between the one or more data sources and the one or more data object types.

14. The computer-implemented method of claim 13 , wherein the compatibilities are determined based at least in part on data application programming interfaces (“APIs”) associated with the one or more data sources, the one or more data object types, and/or the one or more applications.

15. The computer-implemented method of claim 13 , wherein the compatibilities are determined based at least in part on properties associated with the one or more data object types as defined by an ontology.

16. The computer-implemented method of claim 15 , wherein the compatibilities are determined further based on at least one of: user inputs, fuzzy matching, stemming, internationalization and localization, or completeness scores.

17. The computer-implemented method of claim 16 further comprising, by the one or more hardware processors executing program instructions, at least one of:

based at least in part on the determined compatibilities, determine a compatibility of the first application with the one or more data sources and/or the one or more data object types.

18. The computer-implemented method of claim 13 further comprising, by the one or more hardware processors executing program instructions, at least one of:

based at least in part on the determined compatibilities, determine suggestions for applications of the one or more applications that are compatible with the one or more data sources and/or the one or more data object types.

19. A system comprising: a computer readable storage medium having program instructions embodied therewith; and one or more processors configured to execute the program instructions to cause the system to perform the computer-implemented method of claim 1 .

20. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by one or more processors to cause the one or more processors to perform the computer-implemented method of claim 1 .

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 6, 2024
From: RAHILL-MARIER, BIANCA; PATTON, CASEY; PERINET, CHARLES; LEVY, JULIEN; FINK, ROBERT
To: PALANTIR TECHNOLOGIES INC.
Reel/Frame 066671/0606 →
Continuity (4)
Continuation 17583058 · Jan 24, 2022
Provisional Application 63201110 · Apr 13, 2021
Provisional Application 63141408 · Jan 25, 2021
Related Publication 20240201956A1 · Jun 20, 2024
References Cited (86)
US 6957186B1 · Guheen · 2005 [cited by examiner]
US 7051036B2 · Rosnow et al. · 2006 [cited by applicant]
US 7685140B2 · Jackson · 2010 [cited by applicant]
US 7788631B2 · Sieron et al. · 2010 [cited by applicant]
US 8121874B1 · Guheen · 2012 [cited by examiner]
US 8498985B2 · Spence et al. · 2013 [cited by applicant]
US 9032312B2 · Barnes et al. · 2015 [cited by applicant]
US 9275482B1 · Dannelongue · 2016 [cited by applicant]
US 10113408B2 · Pobedinski et al. · 2018 [cited by applicant]
US 10740292B2 · Haddad · 2020 [cited by applicant]
US 11567639B2 · Rickard et al. · 2023 [cited by applicant]
US 11650728B2 · Floren et al. · 2023 [cited by applicant]
US 11816555B2 · Rickard et al. · 2023 [cited by applicant]
US 11947934B2 · Rahill-Marier et al. · 2024 [cited by applicant]
US 12061780B2 · Rickard et al. · 2024 [cited by applicant]
US 20040107085A1 · Moosburger et al. · 2004 [cited by applicant]
US 20050010901A1 · Udler · 2005 [cited by applicant]
US 20100050152A1 · Gilboa · 2010 [cited by applicant]
US 20130289774A1 · Day · 2013 [cited by applicant]
US 20150220311A1 · Salter · 2015 [cited by applicant]
US 20150348310A1 · Watanabe et al. · 2015 [cited by applicant]
US 20160097270A1 · Pobedinski et al. · 2016 [cited by applicant]
US 20160110369A1 · Cervelli et al. · 2016 [cited by applicant]
US 20160180466A1 · Caldwell · 2016 [cited by applicant]
US 20160210270A1 · Kelly et al. · 2016 [cited by applicant]
US 20180024731A1 · Sanches et al. · 2018 [cited by applicant]
US 20180181093A1 · Schulz et al. · 2018 [cited by applicant]
US 20180218042A1 · Krishnan · 2018 [cited by examiner]
US 20180322396A1 · Ahuja-Cogny et al. · 2018 [cited by applicant]
US 20180337940A1 · Seiver · 2018 [cited by applicant]
US 20200124753A1 · Halsey et al. · 2020 [cited by applicant]
US 20200162340A1 · Rossi et al. · 2020 [cited by applicant]
US 20200311199A1 · Yan et al. · 2020 [cited by applicant]
US 20210119576A1 · Padullaparthi et al. · 2021 [cited by applicant]
US 20210248447A1 · Rickard et al. · 2021 [cited by applicant]
US 20210255748A1 · Rickard et al. · 2021 [cited by applicant]
US 20220075515A1 · Floren et al. · 2022 [cited by applicant]
US 20220236965A1 · Rahill-Marier et al. · 2022 [cited by applicant]
US 20230125026A1 · Rickard et al. · 2023 [cited by applicant]
US 20230259263A1 · Floren et al. · 2023 [cited by applicant]
US 20240037374A1 · Rickard et al. · 2024 [cited by applicant]
EP 3107014 · 2016 [cited by applicant]
EP 3866024 · 2021 [cited by applicant]
EP 3869414 · 2021 [cited by applicant]
WO WO2022056529 · 2022 [cited by applicant]
WO WO2022159890 · 2022 [cited by applicant]
Vodislav, Dan. “HandMove: a system for creating animated user interface components by direct manipulation.” IFIP International Conference on Engineering for Human-Computer Interaction. Boston, MA: Springer US, 1995. pp.… [cited by examiner]
Myers, Brad, Scott E. Hudson, and Randy Pausch. “Past, present, and future of user interface software tools.” ACM Transactions on Computer-Human Interaction (TOCHI) 7.1 (2000): pp. 3-28. (Year: 2000). [cited by examiner]
Clerckx, Tim, Kris Luyten, and Karin Coninx. “DynaMo-AID: A design process and a runtime architecture for dynamic model-based user interface development.” IFIP International Conference on Engineering for Human-Computer … [cited by examiner]
U.S. Appl. No. 18/067,505, Interactive Graphical User Interfaces for Simulated Systems, filed Dec. 16, 2022. [cited by applicant]
U.S. Appl. No. 18/483,056, System and Method for Chaining Discrete Models, filed Oct. 9, 2023. [cited by applicant]
U.S. Appl. No. 18/297,490, Interactive Graphical User Interfaces for Simulated Systems, filed Apr. 7, 2023. [cited by applicant]
U.S. Appl. No. 17/583,058, Auto-Generating Interactive Workflow User Interfaces for Simulated Systems, filed Jan. 24, 2022. [cited by applicant]
Ahmed et al., “BranchConnect: Image Categorization with Learned Branch Connections”, Mar. 12, 2018, IEEE Winter Conference on Applications of Computer Vision, pp. 1244-1253. [cited by applicant]
Ahmed et al., “MaskConnect: Connectivity Learning by Gradient Descent”, Jul. 28, 2018, pp. 1-25. [cited by applicant]
Ba et al., “Blending Diverse Physical Priors with Neural Networks”, Oct. 1, 2019, pp. 1-15. [cited by applicant]
Bellmann, “Interactive Simulations and advanced Visualization with Modelica,” Proceedings of the 48th Scandinavian Conference on Simulation and Modeling (SIMS 2007), vol. 43, Oct. 8, 2009, pp. 541-550. [cited by applicant]
Cal et al., “Link Prediction Approach for Opportunistic Networks Based on Recurrent Neural Network”, 2019 IEEE Access, vol. 7, pp. 2017-2025. [cited by applicant]
Cai et al., “ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware”, Feb. 23, 2019, pp. 1-13. [cited by applicant]
Chen et al., “Exploiting Structural and Temporal Evolution in Dynamic Link Prediction”, Oct. 2018, CIKM'18, pp. 427-436. [cited by applicant]
Das et al., “Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks”, May 1, 2017, 10 pages. [cited by applicant]
Gregor et al., “Learning Fast Approximations of Sparse Coding”, Proceedings of the 27th International Conference on Machine Learning, 2010, 8 pages. [cited by applicant]
Guo et al., “A recurrent neural network based health indicator for remaining useful life prediction of bearings”, 2017 Neurocomputing 240, pp. 98-109. [cited by applicant]
Han et al. “Building an on-line geospatial analysis system with ajax and web services”, 2009 Congress on Services-I, IEEE, 2009, pp. 408-413. [cited by applicant]
Kali et al., “Sliding Mode with Time Delay Control for MIMO Nonlinear Systems With Unknown Dynamics,” 2015, 6 pages. [cited by applicant]
Lei et al., “GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamics Networks”, Jan. 26, 2019, 9 pages. [cited by applicant]
Liu et al., “DARTS: Differentiable Architecture Search”, Apr. 23, 2019, pp. 1-13. [cited by applicant]
Mandal et al., “Integrating existing scientific workflow systems: the Kepler/Pegasus example”, Proceedings of the 2nd workshop on Workflows in support of large-scale science, 2007, pp. 21-28. [cited by applicant]
Marru et al. “Apache airavata: a framework for distributed applications and computational workflows”, Proceedings of the 2011 ACM workshop on Gateway computing environments, 2011, pp. 21-28. [cited by applicant]
Reichstein et al., “Deep learning and process understanding for data-driven Earth system science”, Feb. 14, 2019, Nature vol. 566, pp. 195-204. [cited by applicant]
SCIL Systems & Control Innovation Lab, “The DLR Visualization Library,” retrieved from the Internet: https://web.archive.org/web/20200813110336/https://www.systemcontrolinnovationlab.de/the-dir-visualization/library/, A… [cited by applicant]
Wright et al., “Real-Time Black-Box Modelling with Recurrent Neural Networks”, Proceedings of the 22nd International Conference on Digital Audio Effects (DAFx-19), Sep. 2019, 9 pages. [cited by applicant]
Wu et al., “FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search”, Dec. 14, 2018, pp. 1-10. [cited by applicant]
Zaamout et al., “Improving Neural Networks Classification Through Chaining”, Sep. 11, 2012, Artificial Neural Networks and Machine Learning ICANN, 8 pages. [cited by applicant]
Official Communication for European Patent Application No. 21156704.5 dated Jul. 9, 2021, 7 pages. [cited by applicant]
Official Communication for European Patent Application No. 21156705.2 dated Jul. 23, 2021, 11 pages. [cited by applicant]
International Search Report and Written Opinion for International Application No. PCT/US2021/071395 dated Dec. 14, 2021, 16 pages. [cited by applicant]
International Search Report and Written Opinion for International Application No. PCT/US2022/013705 dated Apr. 21, 2022, 14 pages. [cited by applicant]
Bock et al. “Advantageous usage of textual domain-specific languages for scenario-driven development of automated driving functions.” 2019 IEEE International Systems Conference (SysCon). IEEE, 2019, pp. 1-8. [cited by applicant]
Casati et al. “Using patterns to design rules in workflows.” IEEE Transactions on Software Engineering 26.8 (2000): pp. 760-785. [cited by applicant]
Han, Youngmahn. “Bioworks: a workflow system for automation of bioinformatics analysis processes.”2011 International Conference on Ubiquitous Computing and Multimedia Applications. IEEE, 2011, pp. 76-81. [cited by applicant]
Kuhn et al., “Introspective Black Box Failure Predicition for Autonomous Driving”, 2020 IEEE Intelligent Vehicles Symposium (IV), pp. 7. [cited by applicant]
Rubiolo et al., “Knowledge Discovery Through Ontology Matching: An Approach Based on an Artificial Neural Network Model”, Information Sciences, vol. 194, Jul. 1, 2012, pp. 107-119. [cited by applicant]
Official Communication for European Patent Application No. 21156704.5 dated Oct. 6, 2023, 4 pages. [cited by applicant]
Official Communication for European Patent Application No. 21156705.2 dated Jan. 3, 2024, 9 pages. [cited by applicant]
Official Communication for European Patent Application No. 22709433.1, dated Apr. 2, 2025, 6 pages. [cited by applicant]