IP Library Granted Patent US 12,081,594
Granted Patent B2
US 12,081,594 · App. 18/497,243 · Granted Sep 3, 2024

Highly scalable four-dimensional geospatial data system for simulated worlds

Inventors: Jason Crabtree (Vienna, VA); Andrew Sellers (Monument, CO)
Assignee: QOMPLX LLC
H04L63/20G06F16/2477G06F16/951H04L63/1425H04L63/1441
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,081,594
App. No.
18/497,243
Granted
Sep 3, 2024
Kind
B2
Abstract

A system and method for providing time-series geospatial data and a world-scale simulation platform used to generate simulated-world environments by rendering data-dense geographical regions corresponding to heterogenous sourced data and formats for highly scalable parallel simulations, and comprised of a multi-dimensional time-series database used for enabling query support across multiple simulations via individual simulation and entity swimlanes for cyber, physical and cyber-physical entities and regions.

Claims (45)

1. A system for data-dense geographical regions using heterogenous source data and formats for enabling highly scalable parallel world simulations, comprising:

a computer system comprising a memory and a processor;

an indexed geospatial tile subsystem, comprising a first plurality of programming instructions stored in the memory and operating on the processor, wherein the first plurality of programming instructions, when operating on the processor, cause the computer system to:

retrieve a plurality of geospatial image tiles corresponding to a geographic region;

retrieve a plurality of geotagged data corresponding to the geographic region;

calculate a geohash for each piece of retrieved geotagged data, wherein a geohash is an encoded geographic location comprising a short string of letters and digits; and

overlay the geohash on the corresponding geospatial image tile containing the geographic coordinates of the geohash;

a geospatial action outcome simulation subsystem, comprising a second plurality of programming instructions stored in the memory and operating on the processor, wherein the second plurality of programming instructions, when operating on the processor, cause the computer system to:

execute geospatial simulations for each of a plurality of unique process swimlanes; and

a multidimensional time-series database, comprising a third plurality of programming instructions that, when operating on the processor, cause the computer system to assign each geographic region and the corresponding retrieved data to a unique process swimlane; and

an application interface subsystem, comprising a fourth plurality of programming instructions stored in the memory and operating on the processor, wherein the third plurality of programming instructions, when operating on the processor, cause the computer system to:

render at least one of the geospatial simulations to a user interface; perform at least one query on at least one of the geospatial simulations; and retrieve map overlay data for at least one of the geospatial simulations.

2. The system of claim 1 , wherein the geohash comprises three keywords that identify a specific location in a local geospatial region.

3. The system of claim 1 , wherein geospatial image tiles are vector tiles.

4. The system of claim 1 , wherein the geospatial image tiles are raster tiles.

5. The system of claim 1 , wherein at least of portion of the geospatial tiles correspond to the time progression of the predictive analyses carried out by the operating system.

6. The system of claim 1 , wherein the geohashes are hyperlinks to datasets.

7. The system of claim 1 , wherein the plurality of geotagged data comprises data from sensors.

8. The system of claim 1 , further comprising filters that refine overlay map data, data sets, and data sources.

9. The system of claim 8 , further comprising filters for time-series data.

10. A method for data-dense geographical regions that using heterogenous source data and formats for enabling highly scalable parallel world simulations, comprising the steps of:

using an indexed geospatial tile subsystem of a computing system comprising a processor and a memory:

retrieving a plurality of geospatial image tiles corresponding to a geographic region from a plurality of sources;

retrieving a plurality of geotagged data corresponding to the geographic region;

calculating a geohash for each piece of retrieved geotagged data, wherein a geohash is an encoded geographic location comprising a short string of letters and digits; and

overlaying the geohash on the corresponding geospatial image tile containing the geographic coordinates of the geohash;

using an indexed geospatial tile subsystem:

executing geospatial simulations for each unique process swimlane in the multidimensional time-series database; and

rendering at least one of the geospatial simulations on a web interface;

using geospatial action outcome simulation subsystem:

performing at least one query on at least one of the geospatial simulations;

using a multidimensional time-series database subsystem:

assigning each geographic region and the corresponding retrieved data to a unique process swimlane; and

using an application interface subsystem:

rendering at least one of the geospatial simulations to a user interface;

performing at least one query on at least one of the geospatial simulations; and

retrieve map overlay data for at least one of the geospatial simulations.

11. The method of claim 10 , wherein the geohash comprises three keywords that identify a specific location in a local geospatial region.

12. The method of claim 10 , wherein geospatial image tiles are vector tiles.

13. The method of claim 10 , wherein the geospatial image tiles are raster tiles.

14. The method of claim 10 , wherein at least a portion of the geospatial tiles correspond to the time progression of the predictive analyses carried out by operating system.

15. The method of claim 10 , wherein the geohashes are hyperlinks to datasets.

16. The method of claim 10 , wherein the plurality of geotagged data comprises data from sensors.

17. The method of claim 10 , further comprising filters that refine overlay map data, data sets, and data sources.

18. The system of claim 17 , further comprising filters for time-series data.

Assignments (4)
CORRECTIVE ASSIGNMENT TO CORRECT THE RECEIVING PARTY DATA COMPANY NAME PREVIOUSLY RECORDED ON REEL 67566 FRAME 797. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT. Recorded Sep 25, 2024
From: QOMPLX, INC.
To: QPX LLC
Reel/Frame 069048/0586 →
CHANGE OF NAME Recorded May 29, 2024
From: QPX LLC
To: QOMPLX LLC
Reel/Frame 067557/0279 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 29, 2024
From: QOMPLX, INC.
To: QOMPLX LLC
Reel/Frame 067566/0797 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 24, 2024
From: CRABTREE, JASON; SELLERS, ANDREW
To: QOMPLX, INC.
Reel/Frame 067524/0966 →
Continuity (46)
Continuation 17106997 · Nov 30, 2020
Continuation In Part 15931534 · May 13, 2020
Continuation In Part 16777270 · Jan 30, 2020
Continuation In Part 16720383 · Dec 19, 2019
Continuation 15823363 · Nov 27, 2017
Continuation In Part 15725274 · Oct 4, 2017
Continuation In Part 15655113 · Jul 20, 2017
Continuation In Part 15616427 · Jun 7, 2017
Continuation In Part 14925974 · Oct 28, 2015
Continuation In Part 15237625 · Aug 15, 2016
Continuation In Part 15206195 · Jul 8, 2016
Continuation In Part 15186453 · Jun 18, 2016
Continuation 15166158 · May 26, 2016
Continuation In Part 15141752 · Apr 28, 2016
Continuation In Part 15091563 · Apr 5, 2016
Continuation In Part 14986536 · Dec 31, 2015
Continuation In Part 14925974 · Oct 28, 2015
Continuation In Part 15683765 · Aug 22, 2017
Continuation In Part 15409510 · Jan 18, 2017
Continuation In Part 15376657 · Dec 13, 2016
Continuation In Part 15237625 · Aug 15, 2016
Continuation In Part 16718906 · Dec 18, 2019
Continuation 15879182 · Jan 24, 2018
Continuation In Part 15850037 · Dec 21, 2017
Continuation In Part 15673368 · Aug 9, 2017
Continuation In Part 15376657 · Dec 13, 2016
Continuation In Part 15489716 · Apr 17, 2017
Continuation In Part 15409510 · Jan 18, 2017
Continuation In Part 15905041 · Feb 26, 2018
Continuation In Part 15237625 · Aug 15, 2016
Continuation In Part 16191054 · Nov 14, 2018
Continuation In Part 15655113 · Jul 20, 2017
Continuation In Part 16654309 · Oct 16, 2019
Continuation In Part 15847443 · Dec 19, 2017
Continuation In Part 15790457 · Oct 23, 2017
Continuation In Part 15790327 · Oct 23, 2017
Continuation In Part 15616427 · Jun 7, 2017
Continuation In Part 15141752 · Apr 28, 2016
Continuation In Part 15616427 · Jun 7, 2017
Continuation In Part 15489716 · Apr 17, 2017
Continuation In Part 16660727 · Oct 22, 2019
Continuation 15229476 · Aug 5, 2016
Continuation In Part 15206195 · Jul 8, 2016
Provisional Application 62568298 · Oct 4, 2017
Provisional Application 62568291 · Oct 4, 2017
Related Publication 20240064179A1 · Feb 22, 2024