IP Library Granted Patent US 12,560,440
Granted Patent B2
US 12,560,440 · App. 17/499,024 · Granted Feb 24, 2026

Systems and methods for mitigating third party contingencies

Inventors: Santo Francisco Brocato (Austin, TX); Kellen Christopher Mollahan (Summit, NJ); Adam Warmoth (Los Angeles, CA); Raphael Max Lurie (Brooklyn, NY)
Assignee: JOBY AERO, INC.
G01C21/3423G01C21/343G01C21/3492G08G5/22G08G5/34
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 12,560,440
App. No.
17/499,024
Granted
Feb 24, 2026
Kind
B2
Abstract

Systems and methods for limiting facilitating a multi-modal transportation itinerary are provided. The system includes a service entity computing system and a vehicle provider computing system that collaborate to create and facilitate an end-to-end multi-modal transportation itinerary. This can include creating a flight schedule during a time window based on multi-modal transportation service data supplied by the service entity. The service entity can generate multi-modal transportation itineraries based on a request and the flight schedule. A rider can select an itinerary and, in response, the service entity can initiate and monitor a first ground leg of the itinerary. Deviations from the first ground leg can be provided to the vehicle provider which can delay or advance a flight based on the deviation. Likewise, deviations from an aerial transportation leg can be provided to the service entity which can delay or advance a second ground leg based on the deviation.

Claims (56)

1 . A computer-implemented method, the method comprising:

accessing, by a computing system comprising one or more computing devices, aircraft data associated with a fleet of aircraft associated with the computing system;

computing, by the computing system, a time window associated with an aerial transportation facility based, at least in part, on the aircraft data, the time window indicative of an availability of one or more aircraft of the fleet of aircraft for providing at least one leg of a multi-modal transportation service, the multi-modal transportation service comprising at least two legs of transportation via at least two different transportation modalities;

accessing, by the computing system from a service entity computing system, data indicative of one or more requests for one or more multi-modal transportation services associated with the aerial transportation facility during the time window, wherein respective requests of the one or more requests indicate an arrive by time to arrive at a destination;

accessing, from the service entity computing system, delay data indicative of a delay associated with a first leg of transportation of the at least two legs of transportation;

in response to accessing the data indicative of the one or more requests and the delay data, computing, by the computing system, a flight schedule and one or more contingency itineraries for each passenger of a plurality of passengers based, at least in part, on the aircraft data, the time window, and the arrive by time, wherein:

the flight schedule comprises one or more flight itineraries for the time window; and

respective flight itineraries of the one or more flight itineraries comprise a floating departure time, the floating departure time indicative of a modifiable departure time based on the arrive by time;

transmitting, by the computing system, the flight schedule and the one or more contingency itineraries to the service entity computing system, wherein the service entity computing system is configured to generate one or more multi-modal transportation itineraries based, at least in part, on the flight schedule, the one or more contingency itineraries, and the floating departure time; and

initiating, by the computing system via communication with the service entity computing system, a match of a service provider to a second leg of transportation of the at least two legs of transportation based on the floating departure time.

2 . The computer-implemented method of claim 1 , wherein the aircraft data comprises location data, component data, and availability data for each aircraft of the fleet of aircraft, and wherein the time window identifies a number of aircraft of the fleet of aircraft that are capable of providing an aerial transportation service from the aerial transportation facility.

3 . The computer-implemented method of claim 2 , wherein the location data identifies a current location of an aircraft, the component data identifies a current range of the aircraft, and the availability data identifies a current assignment for the aircraft.

4 . The computer-implemented method of claim 3 , wherein the time window identifies a current time period at the aerial transportation facility, and wherein the aircraft is capable of providing the aerial transportation service from the aerial transportation facility when the current location identifies the aerial transportation facility, the current range identifies a range above a threshold transportation range, and the current assignment identifies no pending assignments.

5 . The computer-implemented method of claim 2 , wherein the location data identifies a predicted location of an aircraft, the component data identifies a predicted range of the aircraft, and the availability data identifies a predicted assignment for the aircraft.

6 . The computer-implemented method of claim 5 , wherein the time window identifies a future time period, and wherein the aircraft is capable of providing the aerial transportation service from the aerial transportation facility when the predicted location identifies the aerial transportation facility, the predicted range identifies a range above a threshold transportation range, and the predicted assignment identifies no pending assignments.

7 . The computer-implemented method of claim 1 , wherein computing the flight schedule based, at least in part, on the aircraft data, the time window, and the data comprises:

computing, by the computing system, a number of passengers associated with the aerial transportation facility during the time window based, at least in part, on the data indicative of the one or more requests for the one or more multi-modal transportation services;

computing, by the computing system, a number of aircraft for transporting the number of passengers based, at least in part, on the aircraft data; and

computing, by the computing system, a flight itinerary for each aircraft of the number of aircraft during the time window.

8 . The computer-implemented method of claim 7 , wherein the data indicative of the one or more requests for the one or more multi-modal transportation services comprises an estimated time of arrival for the number of passengers at the aerial transportation facility, and wherein the flight itinerary for each aircraft of the number of aircraft is determined based, at least in part, on the estimated time of arrival for the number of passengers.

9 . The computer-implemented method of claim 8 , wherein the flight itinerary for an aircraft is indicative of a departure time from the aerial transport facility.

10 . The computer-implemented method of claim 9 , wherein the departure time from the aerial transport facility is based, at least in part, on the estimated time of arrival for the number of passengers.

11 . One or more tangible, non-transitory computer-readable media storing computer-readable instructions that when executed by one or more processors cause the one or more processors to perform operations, the operations comprising:

accessing a request for a multi-modal transportation service from an origin location to a destination location, wherein the request indicates an arrive by time to arrive at the destination location;

in response to accessing the request, accessing, from a vehicle provider, one or more candidate flight itineraries associated with the multi-modal transportation service;

generating a multi-modal transportation itinerary comprising a plurality of transportation legs that comprise transportation via a plurality of different transportation modalities, wherein the multi-modal transportation itinerary comprises a first ground transportation leg from the origin location to a first aerial transportation facility, an aerial transportation leg from the first aerial transportation facility to a second aerial transportation facility, and a second ground transportation leg from the second aerial transportation facility to the destination location, wherein the aerial transportation leg is indicative of at least one of the one or more candidate flight itineraries obtained from the vehicle provider in response to the request and the arrive by time;

providing, to the vehicle provider, selection data associated with the multi-modal transportation itinerary, wherein the selection data is indicative of the one or more candidate flight itineraries, a plurality of passengers for the one or more candidate flight itineraries, and an estimated time of arrival for each passenger of the plurality of passengers;

initiating the first ground transportation leg of the multi-modal transportation itinerary;

accessing delay data indicative of a delay associated with at least one of the first ground transportation leg, the aerial transportation leg, or the second ground transportation leg;

in response to accessing the delay data, generating one or more contingency itineraries for each passenger of the plurality of passengers associated with the at least one or more candidate flight itineraries, wherein respective flight itineraries of the one or more contingency itineraries comprise a floating departure time, the floating departure time indicative of a modifiable departure time based on the arrive by time;

obtaining, from the vehicle provider, second status data associated with the aerial transportation leg; and

initiating the second ground transportation leg based, at least in part, on the second status data and the floating departure time, wherein initiating the second ground transportation leg of the multi-modal transportation service comprises matching a ground transportation service provider to the second ground transportation leg.

12 . The one or more tangible, non-transitory computer-readable media of claim 11 , wherein initiating the first ground transportation leg of the multi-modal transportation service comprises:

matching a first ground transportation service provider to the first ground transportation leg;

providing aerial transportation data to the first ground transportation service provider, the aerial transportation data indicative of the one or more candidate flight itineraries;

providing first ground transportation data to at least one passenger of the plurality of passengers, the first ground transportation data indicative of a location of the first ground transportation service provider, and an estimated time of arrival of the first ground transportation service provider to the origin location; and

monitoring the location of the first ground transportation service provider.

13 . The one or more tangible, non-transitory computer-readable media of claim 12 , wherein initiating the second ground transportation leg of the multi-modal transportation service comprises:

matching a second ground transportation service provider to the second ground transportation leg, the second ground transportation service provider different from the first ground transportation service provider;

providing aerial transportation data to the second ground transportation service provider, the aerial transportation data indicative of the one or more candidate flight itineraries; and

monitoring a location of the second ground transportation service provider.

14 . The one or more tangible, non-transitory computer-readable media of claim 11 , wherein initiating the second ground transportation leg of the multi-modal transportation service comprises:

matching another ground transportation service provider to the second ground transportation leg based.

15 . A computer-implemented method, comprising:

accessing, by a computing system comprising one or more computing devices from a service entity computing system through one or more communication interfaces communicatively connected to the service entity computing system, data indicative of one or more requests for one or more multi-modal transportation services, wherein respective requests of the one or more requests indicate an arrive by time to arrive at a destination location;

in response to accessing the data indicative of the one or more requests, computing, by the computing system, a flight schedule based, at least in part, on the data indicative of the one or more requests for the one or more multi-modal transportation services and the arrive by time, the flight schedule comprising one or more flight itineraries for facilitating the one or more multi-modal transportation services;

accessing, by the computing system from the service entity computing system, selection data associated with a multi-modal transportation itinerary, the multi-modal transportation itinerary comprising a plurality of transportation legs that include transportation via a plurality of different transportation modalities, wherein the multi-modal transportation itinerary comprises a first ground transportation leg from an origin location to a first aerial facility, an aerial transportation leg from the first aerial facility to a second aerial facility, and a second ground transportation leg from the second aerial facility to the destination location, wherein the aerial transportation leg is indicative of a flight itinerary of the one or more flight itineraries;

accessing, by the computing system, delay data indicative of a delay associated with at least one of the first ground transportation leg, the aerial transportation leg, or the second ground transportation leg;

in response to accessing the delay data, generating one or more contingency itineraries for each passenger of a plurality of passengers associated with the flight schedule, wherein respective flight itineraries of the one or more contingency itineraries comprise a floating departure time, the floating departure time indicative of a modifiable departure time based on the arrive by time;

accessing, by the computing system from the service entity computing system, progress data associated with the first ground transportation leg, the progress data based, at least in part, on a progress of the first ground transportation leg and the delay data; and

initiating, by the computing system via communication with the service entity computing system, a match of a service provider to the second ground transportation leg of the plurality of transportation legs based on the floating departure time.

16 . The computer-implemented method of claim 15 , wherein the first ground transportation leg and the second ground transportation leg are coordinated by the service entity computing system.

17 . The computer-implemented method of claim 15 , wherein the aerial transportation leg is coordinated by the service entity computing system.

18 . The computer-implemented method of claim 15 , wherein the flight itineraries are associated with one or more attributes comprising at least one of a departure time, a departure location, a destination time, a destination location, a vehicle capacity, or a vehicle operator associated with the aerial transportation leg.

19 . The computer-implemented method of claim 18 , wherein the selection data is indicative of an initial estimated time of arrival of the first ground transportation leg, and wherein the progress data associated with the first ground transportation leg is indicative of an updated estimated time of arrival later than the initial estimated time of arrival.

20 . The computer-implemented method of claim 15 wherein the aerial transportation leg is provided by a VTOL aircraft.

Assignments (4)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 9, 2022
From: BROCATO, SANTO FRANCISCO; WARMOTH, ADAM; MOLLAHAN, KELLEN CHRISTOPHER; LURIE, RAPHAEL MAX
To: UBER TECHNOLOGIES, INC.
Reel/Frame 058934/0846 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 9, 2022
From: UBER TECHNOLOGIES, INC.
To: UBER ELEVATE, INC.
Reel/Frame 058934/0970 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 9, 2022
From: JOBY ELEVATE, INC.
To: JOBY AERO, INC.
Reel/Frame 058937/0870 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 9, 2022
From: UBER ELEVATE, INC.
To: JOBY ELEVATE, INC.
Reel/Frame 058980/0454 →
Continuity (2)
Provisional Application 63090502 · Oct 12, 2020
Related Publication 20220113147A1 · Apr 14, 2022
References Cited (101)
US 3035789A · Young · 1962 [cited by applicant]
US 4022405A · Peterson · 1977 [cited by applicant]
US 5823468A · Bothe · 1998 [cited by applicant]
US 5839691A · Lariviere · 1998 [cited by applicant]
US 5842667A · Jones · 1998 [cited by applicant]
US 6343127B1 · Billoud · 2002 [cited by applicant]
US 6591263B1 · Becker · 2003 [cited by examiner]
US 6892980B2 · Kawai · 2005 [cited by applicant]
US 8016226B1 · Wood · 2011 [cited by applicant]
US 8020804B2 · Yoeli · 2011 [cited by applicant]
US 8311686B2 · Herkes et al. · 2012 [cited by applicant]
US 8733690B2 · Bevirt et al. · 2014 [cited by applicant]
US 8737634B2 · Brown et al. · 2014 [cited by applicant]
US 8849479B2 · Walter · 2014 [cited by applicant]
US 9205930B2 · Yanagawa · 2015 [cited by applicant]
US 9387928B1 · Gentry et al. · 2016 [cited by applicant]
US 9415870B1 · Beckman et al. · 2016 [cited by applicant]
US 9422055B1 · Beckman et al. · 2016 [cited by applicant]
US 9435661B2 · Brenner et al. · 2016 [cited by applicant]
US 9442496B1 · Beckman et al. · 2016 [cited by applicant]
US 9550561B1 · Beckman et al. · 2017 [cited by applicant]
US 9663237B2 · Senkel et al. · 2017 [cited by applicant]
US 9694911B2 · Bevirt et al. · 2017 [cited by applicant]
US 9771157B2 · Gagne et al. · 2017 [cited by applicant]
US 9786961B2 · Dyer et al. · 2017 [cited by applicant]
US 9802702B1 · Beckman et al. · 2017 [cited by applicant]
US 9816529B2 · Grissom et al. · 2017 [cited by applicant]
US 9838436B2 · Michaels · 2017 [cited by applicant]
US 10140873B2 · Adler et al. · 2018 [cited by applicant]
US 10152894B2 · Adler et al. · 2018 [cited by applicant]
US 10216190B2 · Bostick et al. · 2019 [cited by applicant]
US 10249200B1 · Grenier et al. · 2019 [cited by applicant]
US 10304344B2 · Moravek et al. · 2019 [cited by applicant]
US 10330482B2 · Chen et al. · 2019 [cited by applicant]
US 10593215B2 · Villa · 2020 [cited by applicant]
US 10593217B2 · Shannon · 2020 [cited by applicant]
US 10746555B1 · Spielman · 2020 [cited by examiner]
US 10752365B2 · Galzin · 2020 [cited by applicant]
US 10759537B2 · Moore et al. · 2020 [cited by applicant]
US 10768201B2 · Luo et al. · 2020 [cited by applicant]
US 10832581B2 · Westervelt et al. · 2020 [cited by applicant]
US 10836470B2 · Liu et al. · 2020 [cited by applicant]
US 10913528B1 · Moore et al. · 2021 [cited by applicant]
US 10948910B2 · Taveira et al. · 2021 [cited by applicant]
US 10960785B2 · Villanueva et al. · 2021 [cited by applicant]
US 11130566B2 · Mikic et al. · 2021 [cited by applicant]
US 11145211B2 · Goel et al. · 2021 [cited by applicant]
US 11238745B2 · Villa et al. · 2022 [cited by applicant]
US 11295622B2 · Goel et al. · 2022 [cited by applicant]
US 20050033614A1 · Lettovsky · 2005 [cited by examiner]
US 20050216301A1 · Brown · 2005 [cited by examiner]
US 20100079342A1 · Smith et al. · 2010 [cited by applicant]
US 20100305984A1 · Ben-Yitschak · 2010 [cited by examiner]
US 20130261956A1 · Marks · 2013 [cited by examiner]
US 20140179535A1 · Stückl et al. · 2014 [cited by applicant]
US 20150161696A1 · Jones · 2015 [cited by examiner]
US 20150276410A1 · Lamoriniere · 2015 [cited by examiner]
US 20150379437A1 · Reich · 2015 [cited by examiner]
US 20160203422A1 · Demarchi · 2016 [cited by examiner]
US 20160311529A1 · Brotherton-Ratcliffe et al. · 2016 [cited by applicant]
US 20160364823A1 · Cao · 2016 [cited by examiner]
US 20170197710A1 · Ma · 2017 [cited by applicant]
US 20170268891A1 · Dyrnaes · 2017 [cited by examiner]
US 20170357914A1 · Tulabandhula et al. · 2017 [cited by applicant]
US 20180018887A1 · Sharma et al. · 2018 [cited by applicant]
US 20180053425A1 · Adler et al. · 2018 [cited by applicant]
US 20180091605A1 · Nickels · 2018 [cited by examiner]
US 20180216988A1 · Nance · 2018 [cited by applicant]
US 20180308064A1 · Glaser · 2018 [cited by examiner]
US 20180308366A1 · Goel · 2018 [cited by examiner]
US 20180354636A1 · Darnell et al. · 2018 [cited by applicant]
US 20190146508A1 · Dean et al. · 2019 [cited by applicant]
US 20190164439A1 · High · 2019 [cited by examiner]
US 20190197643A1 · Cochran · 2019 [cited by examiner]
US 20190221127A1 · Shannon · 2019 [cited by applicant]
US 20190228351A1 · Simpson · 2019 [cited by examiner]
US 20190316849A1 · Abrego et al. · 2019 [cited by applicant]
US 20190325757A1 · Goel · 2019 [cited by examiner]
US 20190339720A1 · Petersen · 2019 [cited by examiner]
US 20190383622A1 · Aich · 2019 [cited by examiner]
US 20200041291A1 · Dunnette · 2020 [cited by examiner]
US 20200103922A1 · Nonami et al. · 2020 [cited by applicant]
US 20200182637A1 · Kumar · 2020 [cited by examiner]
US 20200300645A1 · Schirano · 2020 [cited by examiner]
US 20200388166A1 · Rostamzadeh et al. · 2020 [cited by applicant]
US 20210089972A1 · Gaines · 2021 [cited by examiner]
US 20220114655A1 · Chen · 2022 [cited by examiner]
EP 0945841 · 1999 [cited by applicant]
EP 2698749 · 2014 [cited by applicant]
EP 3499634A1 · 2019 [cited by applicant]
JP 2010095246A · 2010 [cited by applicant]
JP 2013086795A · 2013 [cited by applicant]
WO WO2018009914A2 · 2018 [cited by examiner]
WO WO2018023556A1 · 2018 [cited by applicant]
WO WO2019089677A1 · 2019 [cited by applicant]
WO WO2020252024A1 · 2020 [cited by applicant]
Bennaceur et al., “Passenger-centric urban air mobility: Fairness trade-offs and operational efficiency”, Transportation Research Part C: Emerging Technologies, 2022, 29 pages. [cited by applicant]
Jong, “Optimizing cost effectiveness and flexibility of air taxis: A case study for optimization of air taxi operations”, University of Twente, Master's thesis, 2007, 62 pages. [cited by applicant]
Miao et al., “Data-driven robust taxi dispatch under demand uncertainties”, IEEE Transactions on Control Systems Technology 27, No. 1, 2017, 16 pages. [cited by applicant]
Miao et al., “Taxi dispatch with real-time sensing data in metropolitan areas: A receding horizon control approach”, In Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems, 2015, 15 page… [cited by applicant]
Uber, “Fast-forwarding to a future of on-demand urban air transportation”, 2016, 99 pages. [cited by applicant]