IP Library Granted Patent US 12,454,294
Granted Patent B2
US 12,454,294 · App. 17/154,793 · Granted Oct 28, 2025

Systems and methods for verifying railcar location

Inventors: Erik L. Gotlund (Green Oaks, IL); David P. Cannon (Chicago, IL); Michael E. Antonakakis (Colorado Springs, CO); Thomas M. Kingsley (Chicago, IL)
Assignee: TTX Company
B61L25/025B61L15/0081H04L67/12H04L67/52H04L67/53B61L2205/02
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 12,454,294
App. No.
17/154,793
Granted
Oct 28, 2025
Kind
B2
Abstract

Systems and methods for remote monitoring and verifying railcar locations in accordance with embodiments of the invention are disclosed. In one embodiment, a computing device for verifying a rail car location includes a processor and a memory storing instructions that, when read by the processor cause the computing device to determine a car load state change for a rail car, identify an event corresponding to the car load state change, wherein the event includes an event location, determine a car location of the rail car, establish a geofence based on the car location, determine location information based on the geofence, and verify the car location based on the location information and the event location.

Claims (58)

1 . A computing device for verifying a rail car location, comprising:

a processor;

a spring sensor; and

a memory storing instructions that, when read by the processor, cause the computing device to:

establish, based on determining a car location of the rail car, a geofence based on the car location;

adjust, based on the geofence, a car load state sensor interval for the spring sensor;

determine, according to the car load state sensor interval, and based on calculating spring usage of a spring associated with the spring sensor, an expected spring height of the spring;

measure, using the spring sensor, an actual spring height of the spring;

determine, based on a comparison of the expected spring height and the actual spring height satisfying a threshold, a car load state change for a rail car;

determine, based on the car load state change, an indicated car location;

identify an event corresponding to the car load state change, wherein the event comprises an event location corresponding to the indicated car location;

override a low-power operating mode associated with the processor in response to determining that the car load state change is a high priority event, the override including controlling a short-range communication device to transmit data about the car load state change; and

verify the car location based on the event location corresponding to the indicated car location and location information.

2 . The computing device of claim 1 , wherein determining the car location comprises determining the car location based on feedback from a global positioning system receiver installed on the rail car.

3 . The computing device of claim 1 , wherein the geofence comprises a circular region centered on the car location.

4 . The computing device of claim 1 , wherein the geofence comprises a polygonal area established around the car location.

5 . The computing device of claim 1 , wherein the location information comprises at least one location name and corresponding location address obtained from a third-party location service.

6 . The computing device of claim 5 , wherein:

the event location further comprises an event location name; and

verifying the car location further comprises determining the event location name matches the location name.

7 . The computing device of claim 1 , wherein the car load state change comprises a change from a loaded state of the rail car to an unloaded state of the rail car.

8 . A computer-implemented method for verifying a rail car location, comprising:

establishing, based on determining a car location of the rail car, a geofence based on the car location;

adjusting, based on the geofence, a car load state sensor interval for a spring sensor;

determining, according to the car load state sensor interval, based on calculating spring usage of a spring associated with the spring sensor, an expected spring height of the spring;

measuring, using the spring sensor, an actual spring height of the spring;

determining, based on a comparison of the expected spring height and the actual spring

height satisfying a threshold, a car load state change for a rail car;

determining, based on the car load state change, an indicated car location;

identifying an event corresponding to the car load state change, wherein the event comprises an event location, corresponding to the indicated car location;

overriding a low-power operating mode in response to determining that the car load state change is a high priority event, the override including controlling a short-range communication device to transmit data about the car load state change; and

verifying the car location based on the event location corresponding to the indicated car location and location information.

9 . The computer-implemented method of claim 8 , wherein determining the car location comprises determining the car location based on feedback from a global positioning system receiver installed on the rail car.

10 . The computer-implemented method of claim 8 , wherein the geofence comprises a circular region centered on the car location.

11 . The computer-implemented method of claim 8 , wherein the geofence comprises a polygonal area established around the car location.

12 . The computer-implemented method of claim 8 , wherein the location information comprises at least one location name and corresponding location address obtained from a third-party location service.

13 . The computer-implemented method of claim 12 , wherein:

the event location further comprises an event location name; and

verifying the car location further comprises determining the event location name matches the location name.

14 . The computer-implemented method of claim 8 , wherein the car load state change comprises a change from a loaded state of the rail car to an unloaded state of the rail car.

15 . A non-transitory machine-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform steps comprising:

establishing, based on determining a car location of a rail car, a geofence based on the car location;

adjusting, based on the geofence, a car load state sensor interval for a spring sensor;

determining, based on calculating spring usage of a spring associated with the spring sensor, an expected spring height of the spring;

measuring, using the spring sensor, an actual spring height of the spring;

determining, based on a comparison of the expected spring height and the actual spring height satisfying a threshold, a car load state change for a rail car;

determining, based on the car load state change, an indicated car location;

identifying an event corresponding to the car load state change, wherein the event comprises an event location, corresponding to the indicated car location;

overriding a low-power operating mode associated with the processor in response to determining that the car load state change is a high priority event, the override including controlling a short-range communication device to transmit data about the car load state change; and

verifying the car location based on the event location corresponding to the indicated car location and location information.

16 . The non-transitory machine-readable medium of claim 15 , wherein determining the car location comprises determining the car location based on feedback from a global positioning system receiver installed on the rail car.

17 . The non-transitory machine-readable medium of claim 15 , wherein the geofence comprises a circular region centered on the car location.

18 . The non-transitory machine-readable medium of claim 15 , wherein the geofence comprises a polygonal area established around the car location.

19 . The non-transitory machine-readable medium of claim 15 , wherein:

the location information comprises at least one location name and corresponding location address obtained from a third-party location service;

the event location further comprises an event location name; and

verifying the car location further comprises determining the event location name matches the location name.

20 . The non-transitory machine-readable medium of claim 15 , wherein the car load state change comprises a change from a loaded state of the rail car to an unloaded state of the rail car.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 18, 2021
From: GOTLUND, ERIK L.; CANNON, DAVID P.; ANTONAKAKIS, MICHAEL E.; KINGSLEY, THOMAS M.
To: TTX COMPANY
Reel/Frame 056273/0699 →
Continuity (3)
Provisional Application 63133010 · Dec 31, 2020
Provisional Application 62964726 · Jan 23, 2020
Related Publication 20210291883A1 · Sep 23, 2021
References Cited (74)
US 6311109B1 · Hawthorne et al. · 2001 [cited by applicant]
US 6487488B1 · Peterson, Jr. et al. · 2002 [cited by applicant]
US 6490523B2 · Doner · 2002 [cited by applicant]
US 6622067B1 · Lovelace, II et al. · 2003 [cited by applicant]
US 6668216B2 · Mays · 2003 [cited by applicant]
US 6691064B2 · Vroman · 2004 [cited by applicant]
US 6837550B2 · Dougherty et al. · 2005 [cited by applicant]
US 7685884B2 · Degutis et al. · 2010 [cited by applicant]
US 7688218B2 · LeFebvre et al. · 2010 [cited by applicant]
US 7698962B2 · LeFebvre et al. · 2010 [cited by applicant]
US 7769509B2 · Gaughan et al. · 2010 [cited by applicant]
US 8045962B2 · Schullian et al. · 2011 [cited by applicant]
US 8234917B2 · Burkhart et al. · 2012 [cited by applicant]
US 8781671B2 · Beck et al. · 2014 [cited by applicant]
US 8812175B2 · Baker · 2014 [cited by applicant]
US 8874304B2 · Friesen et al. · 2014 [cited by applicant]
US 8924117B2 · Kull · 2014 [cited by applicant]
US 9020667B2 · Haas et al. · 2015 [cited by applicant]
US 9026281B2 · Murphy et al. · 2015 [cited by applicant]
US 9365223B2 · Martin et al. · 2016 [cited by applicant]
US 9403517B2 · Kernwein et al. · 2016 [cited by applicant]
US 9469198B2 · Cooper et al. · 2016 [cited by applicant]
US 9981673B2 · Martin et al. · 2018 [cited by applicant]
US 10081377B2 · Shubs, Jr. et al. · 2018 [cited by applicant]
US 10137915B2 · LeFebvre et al. · 2018 [cited by applicant]
US 20030060938A1 · Duvall · 2003 [cited by examiner]
US 20060047419A1 · Diendorf · 2006 [cited by examiner]
US 20090299550A1 · Baker · 2009 [cited by applicant]
US 20120046811A1 · Murphy et al. · 2012 [cited by applicant]
US 20130245880A1 · McQuade · 2013 [cited by examiner]
US 20130342362A1 · Martin · 2013 [cited by applicant]
US 20140060979A1 · Martin et al. · 2014 [cited by applicant]
US 20140263895A1 · Dickenson et al. · 2014 [cited by applicant]
US 20150099461A1 · Holden · 2015 [cited by examiner]
US 20150183445A1 · Gotlund et al. · 2015 [cited by applicant]
US 20150219487A1 · Maraini · 2015 [cited by applicant]
US 20150232079A1 · Martin et al. · 2015 [cited by applicant]
US 20160082988A1 · Kurz · 2016 [cited by applicant]
US 20160272228A1 · LeFebvre · 2016 [cited by examiner]
US 20160325767A1 · LeFebvre et al. · 2016 [cited by applicant]
US 20170084094A1 · Worden et al. · 2017 [cited by applicant]
US 20170210401A1 · Mian · 2017 [cited by applicant]
US 20170297595A1 · Ryan · 2017 [cited by applicant]
US 20170343377A1 · Holden et al. · 2017 [cited by applicant]
US 20180222504A1 · Birch · 2018 [cited by examiner]
US 20200103269A1 · Bell · 2020 [cited by applicant]
US 20210291883A1 · Gotlund et al. · 2021 [cited by applicant]
US 20220119021A1 · Gotlund et al. · 2022 [cited by applicant]
US 20230171525A1 · Ramasundaram et al. · 2023 [cited by applicant]
CN 106600951A · 2017 [cited by applicant]
CN 107615312A · 2018 [cited by applicant]
CN 110264580A · 2019 [cited by applicant]
EP 1535418B1 · 2007 [cited by applicant]
EP 4093648A1 · 2022 [cited by applicant]
EP 4271602A1 · 2023 [cited by applicant]
KR 20160000031A · 2016 [cited by applicant]
WO 2021150908A1 · 2021 [cited by applicant]
WO 2022147337A1 · 2022 [cited by applicant]
Mar. 17, 2023—(AU) Examination Report—App. No. 2021209920. [cited by applicant]
Apr. 4, 2022—(WO) International Search Report & Written Opinion—PCT/US21/065801. [cited by applicant]
Sawley K et al: “The effect of hollow-worn wheels on vehicle stability in straight track,” Wear, Elsevier Sequoia, Luasanne, CH, vol. 258, No. 7-8, Mar. 1, 2005 (Mar. 1, 2005), pp. 1100-1108, XP004731843, ISSN: 0043-164… [cited by applicant]
Nov. 23, 2022—(IN) Examination Report—App. No. 202247047658. [cited by applicant]
Aug. 8, 2023—(EP) Rules 161(1) and 162 EPC Communication—App 21841161.9. [cited by applicant]
Sep. 27, 2023—(CA) 1st Office Action—App 3,165,833. [cited by applicant]
Dec. 18, 2023—(CN) 1st Office Action—App 202180023332.X. [cited by applicant]
Dec. 13, 2023—(US) Non-Final Office Action—U.S. Appl. No. 18/203,485. [cited by applicant]
Dec. 18, 2023—(US) Non-Final Office Action—U.S. Appl. No. 18/203,447. [cited by applicant]
Apr. 15, 2024—(CN) Notice of Grant and Registration—App 202180023332.X. [cited by applicant]
Jul. 26, 2024—(US) Non-Final Office Action—U.S. Appl. No. 18/203,447. [cited by applicant]
Jul. 1, 2024—(US) Final Office Action—U.S. Appl. No. 18/203,485. [cited by applicant]
Oct. 25, 2024—(CA) 1st Office Action—App 3,203,906. [cited by applicant]
Nov. 26, 2024—(WO) Int. Search Report & Written Opinion—App. No. PCT/US24/42797. [cited by applicant]
Sep. 4, 2025—(EP) Exam Report—App. No. 21707057.2. [cited by applicant]
Jul. 8, 2025—(CA) Notice of Allowance—App. No. 3,165,833. [cited by applicant]