IP Library › Granted Patent US 12,571,638
Granted Patent B2
US 12,571,638 · App. 17/731,489 · Granted Mar 10, 2026

Information processing device and information processing method

Inventors: Yu Nagata (Chofu, JP); Toshihiro Nakamura (Shizuoka-ken, JP)
Assignee: TOYOTA JIDOSHA KABUSHIKI KAISHA
G01C21/3469G06Q10/047G06Q10/06315G06Q50/40
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 12,571,638
App. No.
17/731,489
Granted
Mar 10, 2026
Kind
B2
Abstract

When a control unit assigns, to each of multiple routes, any of multiple vehicles that is able to travel with a drive source of at least one of an electric motor and an internal combustion engine, categories of the vehicles being different from each other, the control unit determines, based on a cost or an environmental load when the vehicle is caused to travel on the routes with the electric motor and a cost or an environmental load when the vehicle is caused to travel on the routes with the internal combustion engine, combinations of the vehicles and the routes.

Claims (40)

1 . An information processing device comprising at least one memory containing program code; and

at least one processor configured to operate as instructed by the program code, the program code comprising:

assigning code configured to cause at least one of the at least one processor to assign, to each of multiple routes, any of multiple vehicles that is able to travel with a drive source of at least one of an electric motor and an internal combustion engine, categories of the vehicles being different from each other;

calculating code configured to cause at least one of the at least one processor to:

calculate, for each of the multiple routes, a required cost of each of the multiple vehicles and each region of a set of regions through which the route passes;

calculate, for each of the multiple routes, a CO 2 emission amount when each of the multiple vehicles travels a corresponding route of the multiple routes, wherein based on a vehicle, of the multiple vehicles, having both an electric motor and an internal combustion engine, the vehicle is configured to select the drive source based on a CO 2 emission factor associated with a power source from which a battery of the vehicle is charged, or with a fuel consumed by the internal combustion engine;

obtain a relationship between a CO 2 emission amount and an incentive given to a business operator; and

calculate a subsidy received as an incentive based on the CO 2 emission amount;

determining code configured to cause at least one of the at least one processor to determine combinations of the vehicles and the routes and identify a combination having a minimum value, from among a plurality of values corresponding to the combinations of the vehicles and the routes, wherein one of the plurality of values is calculated by subtracting an amount of a subsidy associated with a corresponding combination from a total amount of a required cost associated with the corresponding combination; and

command code configured to cause at least one of the at least one processor to:

generate at least one travel command based on the determined combinations; and

command at least a motor of at least one of the vehicles to travel according to the at least one travel command.

2 . The information processing device according to claim 1 , wherein the determining code is further configured to cause at least one of the at least one processor to determine the drive source of each of the vehicles such that a sum of the required cost or the CO 2 emission amount is the lowest.

3 . The information processing device according to claim 1 , wherein the at least one memory stores information on a cost corresponding to fuel amount for the internal combustion engine and information on a cost corresponding to electric energy when a battery that supplies electric power to the electric motor is charged.

4 . The information processing device according to claim 3 , further comprising receiving code configured to cause at least one of the at least one processor to receive, from a server that provides the information on the cost corresponding to the fuel amount for the internal combustion engine or the information on the cost corresponding to the electric energy when the battery that supplies the electric power to the electric motor is charged, the information on the cost corresponding to the fuel amount for the internal combustion engine or the information on the cost corresponding to the electric energy when the battery that supplies the electric power to the electric motor is charged.

5 . The information processing device according to claim 1 , wherein the calculating code is further configured to cause at least one of the at least one processor to calculate the CO 2 emission amount on a well-to-wheel basis.

6 . The information processing device according to claim 1 , wherein the at least one memory stores information on a CO 2 emission amount when the drive source is the internal combustion engine and information on a CO 2 emission amount when the drive source is the electric motor.

7 . The information processing device according to claim 1 , wherein the at least one memory stores information on the drive source for each of the vehicles.

8 . An information processing method that executes operations comprising:

when a computer assigns, to each of multiple routes, any of multiple vehicles that is able to travel with a drive source of at least one of an electric motor and an internal combustion engine, categories of the vehicles being different from each other,

calculating, for each of the multiple routes, a required cost of each of the multiple vehicles and each region of a set of regions through which the route passes;

calculating, for each of the multiple routes, a CO 2 emission amount when each of the multiple vehicles travels a corresponding route of the multiple routes, wherein based on a vehicle, of the multiple vehicles, having both an electric motor and an internal combustion engine, the vehicle is configured to select the drive source based on a CO 2 emission factor associated with a power source from which a battery of the vehicle is charged, or with a fuel consumed by the internal combustion engine;

obtaining a relationship between a CO 2 emission amount and an incentive given to a business operator; and

calculating a subsidy received as an incentive based on the CO 2 emission amount;

determining combinations of the vehicles and the routes and identifying a combination having a minimum value, from among a plurality of values corresponding to the combinations of the vehicles and the routes, wherein one of the plurality of values is calculated by subtracting an amount of a subsidy associated with a corresponding combination from a total amount of a required cost associated with the corresponding combination;

generating at least one travel command based on the determined combinations; and

commanding at least a motor of at least one of the vehicles to travel according to the at least one travel command.

9 . The information processing method according to claim 8 , wherein the computer determines the drive source of each of the vehicles such that the sum of the required cost or the CO 2 emission amount is the lowest.

10 . The information processing method according to claim 8 , wherein the computer causes at least one memory to store information on a cost corresponding to fuel amount for the internal combustion engine and information on a cost corresponding to electric energy when a battery that supplies electric power to the electric motor is charged.

11 . The information processing method according to claim 10 , wherein the computer receives, from a server that provides the information on the cost corresponding to the fuel amount for the internal combustion engine or the information on the cost corresponding to the electric energy when the battery that supplies the electric power to the electric motor is charged, the information on the cost corresponding to the fuel amount for the internal combustion engine or the information on the cost corresponding to the electric energy when the battery that supplies the electric power to the electric motor is charged.

12 . The information processing method according to claim 8 , wherein the computer calculates the CO 2 emission amount on a well-to-wheel basis.

13 . The information processing method according to claim 8 , wherein the computer causes at least one memory that stores information on CO 2 emission amount when the drive source is the internal combustion engine and information on CO 2 emission amount when the drive source is the electric motor.

14 . The information processing device according to claim 1 , wherein the relationship between a CO 2 emission amount and the incentive is obtained from an external server.

15 . The information processing method according to claim 8 , wherein the relationship between a CO 2 emission amount and the incentive is obtained from an external server.

16 . The information processing device according to claim 1 , wherein the calculating code is further configured to:

calculate an amount obtained by subtracting the subsidy from the total cost required for all combinations in which each of the multiple vehicles travels each of the multiple routes.

17 . The information processing method according to claim 8 , further comprising:

calculating an amount obtained by subtracting the subsidy from the total cost required for all combinations in which each of the multiple vehicles travels each of the multiple routes.

18 . The information processing device according to claim 1 , wherein the CO 2 emission factor is calculated using a CO 2 emission amount information database mapping regions with corresponding CO 2 emission amounts during fuel consumption and CO 2 emission amounts during electric power consumption.

19 . The information processing device according to claim 1 , wherein the subsidy received as an incentive is based on a CO 2 emission amount that varies by each region and a time of travel of one of the multiple vehicles.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 28, 2022
From: NAGATA, YU; NAKAMURA, TOSHIHIRO
To: TOYOTA JIDOSHA KABUSHIKI KAISHA
Reel/Frame 059767/0567 →
Priority Claims (1)
JP 2021-113630 · Jul 8, 2021 · national
Continuity (1)
Related Publication 20230011007A1 · Jan 12, 2023
References Cited (92)
US 9182764B1 · Kolhouse · 2015 [cited by examiner]
US 9671241B2 · Tang · 2017 [cited by examiner]
US 9792575B2 · Khasis · 2017 [cited by examiner]
US 9922469B1 · Ashton · 2018 [cited by examiner]
US 11551315B2 · Sung · 2023 [cited by examiner]
US 11609571B2 · McKenzie · 2023 [cited by examiner]
US 11655642B2 · Bertram · 2023 [cited by examiner]
US 11774255B2 · Oliver Gomila · 2023 [cited by examiner]
US 11959758B2 · Diamond · 2024 [cited by examiner]
US 11976927B2 · Bruns · 2024 [cited by examiner]
US 20040093264A1 · Shimizu · 2004 [cited by examiner]
US 20080015975A1 · Ivchenko · 2008 [cited by examiner]
US 20080154671A1 · Delk · 2008 [cited by examiner]
US 20090144149A1 · Sakakibara · 2009 [cited by examiner]
US 20090164114A1 · Morikawa · 2009 [cited by examiner]
US 20090210295A1 · Edholm · 2009 [cited by examiner]
US 20100145569A1 · Bourque · 2010 [cited by examiner]
US 20100332132A1 · Okude · 2010 [cited by examiner]
US 20110022527A1 · Onishi · 2011 [cited by examiner]
US 20110029170A1 · Hyde · 2011 [cited by examiner]
US 20110077805A1 · Hyde · 2011 [cited by examiner]
US 20110099100A1 · Onishi · 2011 [cited by examiner]
US 20110137768A1 · Onishi · 2011 [cited by examiner]
US 20110145438A1 · Sakamoto · 2011 [cited by examiner]
US 20110270486A1 · Stevens · 2011 [cited by examiner]
US 20120022904A1 · Mason · 2012 [cited by examiner]
US 20120226624A1 · Song · 2012 [cited by examiner]
US 20120290506A1 · Muramatsu · 2012 [cited by examiner]
US 20130006530A1 · Nishiuma · 2013 [cited by examiner]
US 20130096815A1 · Mason · 2013 [cited by examiner]
US 20130144811A1 · Padmalayam Narayana Kurup · 2013 [cited by examiner]
US 20130218427A1 · Mukhopadhyay · 2013 [cited by examiner]
US 20130297199A1 · Kapp · 2013 [cited by examiner]
US 20140039988A1 · Londergan · 2014 [cited by examiner]
US 20140136025A1 · Cooper · 2014 [cited by examiner]
US 20150248639A1 · Maney · 2015 [cited by examiner]
US 20160076899A1 · Macneille · 2016 [cited by examiner]
US 20160247109A1 · Scicluna · 2016 [cited by examiner]
US 20160334233A1 · Baverstock · 2016 [cited by examiner]
US 20160364679A1 · Cao · 2016 [cited by examiner]
US 20170069041A1 · Akiba · 2017 [cited by examiner]
US 20170088000A1 · Payne · 2017 [cited by examiner]
US 20170154301A1 · Stevenson · 2017 [cited by examiner]
US 20170199045A1 · George · 2017 [cited by examiner]
US 20190107407A1 · Bucsan · 2019 [cited by examiner]
US 20190120640A1 · Ho · 2019 [cited by examiner]
US 20190228375A1 · Laury · 2019 [cited by examiner]
US 20190285425A1 · Ludwick · 2019 [cited by examiner]
US 20200049523A1 · Kato et al. · 2020 [cited by applicant]
US 20200175432A1 · Iwasaki · 2020 [cited by examiner]
US 20200262424A1 · Kong · 2020 [cited by examiner]
US 20200284600A1 · Oliver Gomila · 2020 [cited by examiner]
US 20210012230A1 · Hayes · 2021 [cited by examiner]
US 20210055120A1 · Roth · 2021 [cited by examiner]
US 20210080955A1 · Wilkinson · 2021 [cited by examiner]
US 20210241626A1 · Nishimura · 2021 [cited by examiner]
US 20210285787A1 · Alekseenko · 2021 [cited by examiner]
US 20210291687A1 · Ferguson · 2021 [cited by examiner]
US 20220156693A1 · Singh · 2022 [cited by examiner]
US 20220164722A1 · Sadeghianpourhamami · 2022 [cited by examiner]
US 20220252415A1 · Meroux · 2022 [cited by examiner]
US 20230022823A1 · Hirano · 2023 [cited by examiner]
US 20230023960A1 · Nagata · 2023 [cited by examiner]
US 20230044784A1 · Hauser · 2023 [cited by examiner]
US 20230044920A1 · Watanabe · 2023 [cited by examiner]
US 20230059112A1 · Ito · 2023 [cited by examiner]
US 20230138671A1 · Ashby · 2023 [cited by examiner]
US 20230417564A1 · Salter · 2023 [cited by examiner]
CA 3132053A1 · 2020 [cited by examiner]
CN 110785631A · 2020 [cited by applicant]
DE 102017203015A1 · 2018 [cited by examiner]
JP 2001078304A · 2001 [cited by examiner]
JP 2009134450A · 2009 [cited by examiner]
JP 2010188808A · 2010 [cited by examiner]
JP 2010266204A · 2010 [cited by examiner]
JP 2011000915A · 2011 [cited by examiner]
JP 2011003002A · 2011 [cited by examiner]
JP 2015011576A · 2015 [cited by examiner]
JP 2019144948A · 2019 [cited by examiner]
WO WO2014127849A1 · 2014 [cited by examiner]
WO 2018230646A1 · 2018 [cited by applicant]
JP 2019144948 A machine translation (Year: 2019). [cited by examiner]
JP 2011003002 A machine translation (Year: 2011). [cited by examiner]
JP 2001078304 A machine translation (Year: 2001). [cited by examiner]
JP-2009134450-A machine translation (Year: 2009). [cited by examiner]
JP2010266204A machine translation (Year: 2010). [cited by examiner]
JP2011000915A machine translation (Year: 2011). [cited by examiner]
JP2015011576A machine translation (Year: 2015). [cited by examiner]
WO2014127849A1 machine translation (Year: 2014). [cited by examiner]
JP 201018880 A machine translation (Year: 2010). [cited by examiner]
JP 2010188808 A machine translation (Year: 2010). [cited by examiner]
DE-102017203015-A1 machine translation (Year: 2018). [cited by examiner]