DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the application filed on 02/23/2026 for Application No. 17/745,227.
Claims 1 – 3, 5 and 7 – 22 are currently pending and have been examined.
This action is made FINAL.
Continued Examination
In view of the notice of appeal filed on 02/23/2026, PROSECUTION IS HEREBY REOPENED. New grounds of rejection are set forth below.
To avoid abandonment of the application, appellant must exercise one of the following two options:
(1) file a reply under 37 CFR 1.111 (if this Office action is non-final) or a reply under 37 CFR 1.113 (if this Office action is final); or,
(2) initiate a new appeal by filing a notice of appeal under 37 CFR 41.31 followed by an appeal brief under 37 CFR 41.37. The previously paid notice of appeal fee and appeal brief fee can be applied to the new appeal. If, however, the appeal fees set forth in 37 CFR 41.20 have been increased since they were previously paid, then appellant must pay the difference between the increased fees and the amount previously paid.
A Supervisory Patent Examiner (SPE) has approved of reopening prosecution by signing below:
/Erin D Bishop/ Supervisory Patent Examiner, Art Unit 3665
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 3, 5, 7 – 10 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The determination of whether a claim recites patent ineligible subject matter is a 2-step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2)
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
Claim 1 is directed to a system (i.e., a machine). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
Independent claim 1 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection for dependent claims 2, 3, 5 and 7 – 10. Claim 1 recites:
A fleet management system, comprising:
an electrified vehicle including a traction battery pack; and
a control module programmed to create a smart routing control strategy that includes instructions for routing the electrified vehicle along a drive route in a manner that extends an operable life of the traction battery pack, [mental process/step]
wherein the instructions are derived based on a weighted sum cost associated with operating the electrified vehicle along a link of an expected operational area of the electrified vehicle.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “create…” in the context of this claim encompasses a person determining a smart routing control strategy including instructions for a vehicle. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
A fleet management system, comprising:
an electrified vehicle including a traction battery pack; and [machine is merely an object on which the method operates (MPEP 2106.05(b))]
a control module programmed to [applying the abstract idea using generic computing module] create a smart routing control strategy that includes instructions for routing the electrified vehicle along a drive route in a manner that extends an operable life of the traction battery pack, [mental process/step]
wherein the instructions are derived based on a weighted sum cost associated with operating the electrified vehicle along a link of an expected operational area of the electrified vehicle. [pre-solution activity (data gathering)]
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application. Regarding the additional limitation of “an electrified vehicle including a traction battery pack”, the examiner submits that this limitation is merely an object which the operates upon, which does not integrate the exception into a practical application or provide significantly more. The receiving steps of the “weighted sum cost” is merely a pre-solution activity to be used for the data gathering used for the mental process to “create a smart routing strategy”. Furthermore the “control module” is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of ranking information based on network coverage) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception. see MPEP § 2106.05. Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of control module amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. As discussed above, the additional limitation of “an electrified vehicle including a traction battery pack”, the examiner submits that this limitation is merely an object which the operates upon, which does not integrate the exception into a practical application or provide significantly more. The receiving steps of the “weighted sum cost” is merely a pre-solution activity to be used for the data gathering used for the mental process to “create a smart routing strategy”. In addition, these additional limitations (and the combination, thereof) amount to no more than what is well-understood, routine and conventional activity. Hence, the claim is not patent eligible.
Dependent claim(s) 2, 3, 5 and 7 – 10 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Claim 2 states: “comprising a second electrified vehicle including a second traction battery pack.”. The second electrified vehicle including a second traction battery pack is merely an object which the operates upon, which does not integrate the exception into a practical application or provide significantly more. Therefore, dependent claims 2, 3, 5 and 7 – 10 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Please see below for the 35 U.S.C. 101 analysis for claim 20.
101 Analysis – Step 1
Claim 20 is directed to a method (i.e., a method). Therefore, claim 20 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
Independent claim 20 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection. Claim 20 recites:
A route planning method, comprising:
generating a road network that defines an expected operational area an electrified vehicle will travel within during an upcoming trip; [mental process/step]
performing an objective based total cost analysis for determining a lowest cost travel path for completing the upcoming trip, [mental process/step]
wherein the objective based total cost analysis includes analyzing an energy consumption cost, a travel time cost, and a battery life degradation cost associated with operating the electrified vehicle, and
generating a smart routing control strategy for routing the electrified vehicle along the lowest cost travel path during the upcoming trip. [mental process/step]
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “generating…” and “performing…” in the context of this claim encompasses a person able to mentally generate road networks in which an electric vehicle is to travel within (e.g. mentally recalling what roads a vehicle has traveled on and identifying what roads are expected in an area), then able to perform a total cost analysis where the person can determine the lowest cost travel path and finally generating a smart routing strategy based on the determined lowest cost travel path. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
A route planning method, comprising:
generating a road network that defines an expected operational area an electrified vehicle will travel within during an upcoming trip; [mental process/step]
performing an objective based total cost analysis for determining a lowest cost travel path for completing the upcoming trip, [mental process/step]
wherein the objective based total cost analysis includes analyzing an energy consumption cost, a travel time cost, and a battery life degradation cost associated with operating the electrified vehicle, and [pre-solution activity (data gathering)]
generating a smart routing control strategy for routing the electrified vehicle along the lowest cost travel path during the upcoming trip. [mental process/step]
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application. The step of “wherein the objective based total cost analysis includes analyzing an energy consumption cost, a travel time cost, and a battery life degradation cost associated with operating the electrified vehicle” is merely a pre-solution activity to be used for the data gathering used for the mental process to “performing an objective based total cost analysis”. Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception. see MPEP § 2106.05. Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 20 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of control module amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. As discussed above, the step of “wherein the objective based total cost analysis includes analyzing an energy consumption cost, a travel time cost, and a battery life degradation cost associated with operating the electrified vehicle” is merely a pre-solution activity to be used for the data gathering used for the mental process to “performing an objective based total cost analysis”. In addition, these additional limitations (and the combination, thereof) amount to no more than what is well-understood, routine and conventional activity. Hence, the claim is not patent eligible.
Therefore, claim(s) 1 – 3, 5, 7 – 10 and 20 are rejected under 35 USC §101 as being directed toward ineligible subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 – 3, 5, 7, 8, 11 – 13 and 16 - 22 are rejected under 35 U.S.C. 103 as being anticipated by Gusikhin et al. (US 9587954 B2) further in view of Macneille et al. (US 20080275644 A1).
Regarding claim 1, Gusikhin teaches a fleet management system, comprising: (Gusikhin: Col. 1, lines 13 – 24: “U.S. Pat. No. 8,290,701 generally discloses vehicle management systems and associated processes considering energy consumption when selecting routes for fleet vehicles. Vehicle management systems and associated processes are described that, in certain embodiments, evaluate vehicle energy usage based on factors such as terrain or elevation, vehicle characteristics, driver characteristics, road conditions, traffic, speed limits, stop time, turn information, traffic information, and weather information, and the like. The features described herein may also be implemented for non-fleet vehicles, such as in personal vehicle navigation systems.”)
an electrified vehicle including a traction battery pack; and (Gusikhin: Col. 3, lines 8 – 17: “FIG. 1a is a simplified, exemplary schematic representation of a vehicle 10. As seen therein, the vehicle 10 may be a battery electric vehicle (BEV), which is an all-electric vehicle propelled by one or more electric machines without assistance from an internal combustion engine. The one or more electric machines of the vehicle 10 may include a traction motor 12. The motor 12 may output torque to a shaft 14, which may be connected to a first set of vehicle drive wheels, or primary drive wheels 16, through a gearbox 18.”)
a control module programmed to create a smart routing control strategy that includes instructions for routing the electrified vehicle along a drive route (Gusikhin: Abstract: “A vehicle system enabling a processor to calculate the most efficient route to a destination based on estimated energy usage, vehicle data, and other inputs until the final destination is reached. The vehicle system may receive location related data that may include the current vehicle location and one or more destination points. The vehicle system may receive one or more energy usage affecting parameters from a vehicle related system. The system may calculate a most efficient route based on the location related data and the one or more energy usage affecting parameters and present the most efficient route to a device. The vehicle system may repeat the steps of receiving, calculating, presenting until a destination is reached.”).
In sum, Gusikhin teaches a fleet management system, comprising: an electrified vehicle including a traction battery pack; and a control module programmed to create a smart routing control strategy that includes instructions for routing the electrified vehicle along a drive route. Gusikhin however does not teach a manner that extends an operable life of the traction battery pack wherein the instructions are derived based on a weighted sum cost associated with operating the electrified vehicle along a link of an expected operational area of the electrified vehicle.
Macneille teaches in a manner that extends an operable life of the traction battery pack, (Macneille: Paragraph 0011: “Such operating characteristics may include, for example, one or more of a predicted travel time to reach the predetermined destination, a predicted fuel economy for the vehicle, and a final state of charge for the battery--i.e., the SOC of the battery when the vehicle reaches the predetermined destination.”; Paragraph 0013: “Such operating characteristics may include, for example, one or more of a predicted travel time to reach the predetermined destination, a predicted fuel economy for the vehicle, and a final state of charge for the battery--i.e., the SOC of the battery when the vehicle reaches the predetermined destination.”)
wherein the instructions are derived based on a weighted sum cost associated with operating the electrified vehicle along a link of an expected operational area of the electrified vehicle (Macneille: Paragraph 0019: “The control system is further configured to execute an algorithm for at least some of the routes available to reach the predetermined destination. The algorithm is configured to use operating characteristics for the vehicle for each of the at least some routes, apply a respective weighting factor to at least one of the operating characteristics, and rank each of the at least some routes based at least in part on the weighted operating characteristics.”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention disclosed by Gusikhin with the teachings of Macneille with a reasonable expectation of success. Gusikhin teaches a system able to calculate an optimal route based on a cost function based on many parameters regarding the vehicle, environment, traffic, and battery of the vehicle. Macneille teaches a similar system being able to determine a route based on, but not limited to, vehicle characteristics such as SOC of the battery. This method utilized a weight sum cost method in which weighing factors are applied to the various different parameters. Macneille teaches the driver or operator of the vehicle to be able to select a final SOC which may or may not reduce the battery’s life in situation where reducing travel time is more important (Macneille: Paragraph 0013). Therefore to one of ordinary skill in the art, the combination of Macneille’s system with the system of Gusikhin would be obvious to try. For example, Gusikhin can utilize Macneillie’s systems calculations to also take into account the battery’s SOC while for determining the optimal route. This method would allow the ability to take into consideration the battery life of the vehicle, thus increasing the operable life of the vehicle.
Regarding claim 2, Gusikhin, as modified, teaches comprising a second electrified vehicle including a second traction battery pack (Gusikhin: Col. 1, lines 25 – 35: “U.S. Patent Application 2013/0046526 generally discloses an apparatus and method for optimizing fuel consumption. A physical dynamics model may be used to simulate a vehicle being driven by a driver along a virtual route, possibly under specified weather conditions. A score for the vehicle may be calculated from estimations, based on the simulation, of fuel efficiency, vehicle drivability, and/or time for completing the route. Simulated (“virtual”) vehicles may be configured from components through a user interface. Scores for the vehicles may be compared to select an optimum vehicle.”; Col. 2, lines 1 – 8: “In a second illustrative embodiment, a machine readable storage medium storing instructions that, when executed, cause a processor to perform a method of communicating with a network system to calculate estimated energy based on a current vehicle location and one or more selected destinations. The method may establish communication with a network system on which a plurality of simulated vehicles may be run.”; Col. 3, lines 8 – 17: “FIG. 1a is a simplified, exemplary schematic representation of a vehicle 10. As seen therein, the vehicle 10 may be a battery electric vehicle (BEV), which is an all-electric vehicle propelled by one or more electric machines without assistance from an internal combustion engine. The one or more electric machines of the vehicle 10 may include a traction motor 12. The motor 12 may output torque to a shaft 14, which may be connected to a first set of vehicle drive wheels, or primary drive wheels 16, through a gearbox 18.”,
Supplemental Note: the system is able to work with multiple electric vehicles).
Regarding claim 3, Gusikhin, as modified, does not teach wherein the smart routing control strategy includes additional instructions for routing the second electrified vehicle in a manner that extends an operable life of the second traction battery pack.
Macneille teaches wherein the smart routing control strategy includes additional instructions for routing the second electrified vehicle in a manner that extends an operable life of the second traction battery pack (Macneille: Paragraph 0011: “Such operating characteristics may include, for example, one or more of a predicted travel time to reach the predetermined destination, a predicted fuel economy for the vehicle, and a final state of charge for the battery--i.e., the SOC of the battery when the vehicle reaches the predetermined destination.”; Paragraph 0013: “Such operating characteristics may include, for example, one or more of a predicted travel time to reach the predetermined destination, a predicted fuel economy for the vehicle, and a final state of charge for the battery--i.e., the SOC of the battery when the vehicle reaches the predetermined destination.”)
and further wherein the additional instructions are derived based on a second total weight sum cost associated with operating the second electrified vehicle along a link of a second expected operational area of the second electrified vehicle (Macneille: Paragraph 0019: “The control system is further configured to execute an algorithm for at least some of the routes available to reach the predetermined destination. The algorithm is configured to use operating characteristics for the vehicle for each of the at least some routes, apply a respective weighting factor to at least one of the operating characteristics, and rank each of the at least some routes based at least in part on the weighted operating characteristics.”; Paragraph 0033: “Although the driver may have complete control over the priorities assigned to the operating preferences, embodiments of the present invention may include limits on the driver's choices. For example, in the case of a commercial fleet vehicle, or lease vehicle, the vehicle owner may choose to have limits programmed into the control system to promote the owner's priorities. As described above, operating the vehicle with certain operating preferences may decrease battery life--in some cases this decrease may be significant. A vehicle owner may choose to limit a driver's choices to help ensure that the battery life is not too severely compromised. Such limits can be preprogrammed into the system in such a way that they cannot be modified by the driver.”,
Supplemental Note: the owner’s priorities set the weights of the different parameters thus, multiple weighted sums for the various vehicles).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention disclosed by Gusikhin with the teachings of Macneille with a reasonable expectation of success. As stated in claim 1, Gusikhin teaches a system able to calculate an optimal route based on a cost function based on many parameters regarding the vehicle, environment, traffic, and battery of the vehicle. Macneille teaches a similar system being able to determine a route based on, but not limited to, vehicle characteristics such as SOC of the battery. This method utilized a weight sum cost method in which weighing factors are applied to the various different parameters. Macneille teaches the driver or operator of the vehicle to be able to select a final SOC which may or may not reduce the battery’s life in situation where reducing travel time is more important (Macneille: Paragraph 0013). Therefore to one of ordinary skill in the art, the combination of Macneille’s system with the system of Gusikhin would be obvious to try. For example, Gusikhin can utilize Macneillie’s systems calculations to also take into account the battery’s SOC while for determining the optimal route. This method would allow the ability to take into consideration the battery life of the vehicle, thus increasing the operable life of the vehicle. This can further be applied to multiple vehicles within the fleet of Gusikhin.
Regarding claim 5, Gusikhin, as modified, teaches wherein the control module is a component of a cloud-based server system, (Gusikhin: Col. 8, lines 41 – 47: “In all solutions, it is contemplated that at least the vehicle computing system (VCS) located within the vehicle itself is capable of performing the exemplary processes. A VCS communicating with a nomadic device using BLUETOOTH may communicate with a cloud based computing system to receive trip data related to a drive route.”: Col. 11, line 49 – Col. 12, line 10: “Based on the vehicle parameters, the remote server and/or VCS may determine the available energy as well as potential losses, as represented by block 230. Available energy includes the stored energy in the battery and/or the fuel in the tank. Potential losses may include frictional losses associated with tire pressure, or energy losses from running accessories such as air conditioning. After the least energy route has been calculated by block 226 using vehicle routing problem solutions with the energy cost function based on topography/terrain, weather, traffic, vehicle type and driver characteristics, the system may determine a most efficient and/or optimal route for the driver in block 227 by also taking into consideration the calculated times required to travel the one or more routes calculated in block 225. The determined optimal route may be used by the system to calculate the DTE function, as represented by block 234. The DTE calculation uses all the data and variables collected by the remote server and/or VCS to determine if the route completion based on the driver's itinerary and/or destination inputs is likely or not. For example, the DTE function uses destination and arrival-time information automatically extracted from the user's calendar and/or from the drivers input. The DTE function may also acquire necessary vehicle status information and all drive information along with driver profile information to obtain the low energy route while considering travel time. The DTE program can also process the battery model and/or fuel model based on retrieved parameters.”,
Supplemental Note: the remote server which can be a cloud based computing system, is utilized for route calculation)
and further wherein the cloud-based server system is operably connected to a map data server, a traffic data server, a weather data server, and a charging station server, and (Gusikhin: Col. 10, line 62 – Col. 11, line 9: “As represented by block 224, the VCS may also take into account environmental factors such as weather, traffic, or topography/terrain which indicates changes in elevation, for example. The VCS may wirelessly receive environmental factors from one or more remote servers including, but not limited to, a weather website, traffic website, and/or map website. The VCS uses forecasts of the weather and traffic, as well as knowledge of the topography to estimate how far the remaining charge and/or fuel level will take a vehicle along any specific route to the intended destination. Estimates of the accuracy of these forecasts can also be made using mathematical models of forecast accuracy. The mathematical models may be generated and calculated off-board on a cloud that may be in wireless communication with the VCS.”; Col. 8, line 64 – Col. 9, line 3: “If the BEV knows the destination and required arrival time of a given calendar event, or series of events, the BEV may plan a trip to optimize energy consumption as well as minimize travel time to each calendar event while ensuring the driver is able to make it a final location to recharge the main battery 26 within the vehicle's range.”)
… using information from each of the map data server, the traffic data server, the weather data server, and the charging station server (Gusikhin: Col. 11, line 56 – Col. 12, line 9: “After the least energy route has been calculated by block 226 using vehicle routing problem solutions with the energy cost function based on topography/terrain, weather, traffic, vehicle type and driver characteristics, the system may determine a most efficient and/or optimal route for the driver in block 227 by also taking into consideration the calculated times required to travel the one or more routes calculated in block 225. The determined optimal route may be used by the system to calculate the DTE function, as represented by block 234. The DTE calculation uses all the data and variables collected by the remote server and/or VCS to determine if the route completion based on the driver's itinerary and/or destination inputs is likely or not. For example, the DTE function uses destination and arrival-time information automatically extracted from the user's calendar and/or from the drivers input. The DTE function may also acquire necessary vehicle status information and all drive information along with driver profile information to obtain the low energy route while considering travel time. The DTE program can also process the battery model and/or fuel model based on retrieved parameters”; Col. 9, lines 4 – 21: “Referring to FIG. 2 is a flowchart illustration of a high-level strategy for trip evaluation using trip data obtained from a user interface. BEVs may have a limited range or distance that can be traveled before the main battery 26 is depleted. Drivers need to know whether the range of the BEV is sufficient based on the battery capacity. BEV's need to know the driver's itinerary or calendar schedule prior to starting the trip. If the BEV knows the destination and required arrival time of a given calendar event, or series of events, the BEV may plan a trip to optimize energy consumption as well as minimize travel time to each calendar event while ensuring the driver is able to make it to a final location to recharge the main battery 26 within the vehicle's range. The BEV may also change a route calculation based on a variety of factors that are not currently being considered, allowing constant updating so routes change dynamically based on changing conditions including weather, environmental, and/or traffic.”; Col. 13, lines 29 – 35: “At step 310, once the battery and propulsion simulation is complete, the process may estimate energy consumption based on the vehicle route. The process may also estimate one or more time variables including, but not limited to, the time it may take to arrive to the one or more destinations, time remaining before charging the battery, and/or time it may take to return home or to another charging station.”; Col. 13, line 64 – Col. 14, line 6: “The energy consumption and time prediction model may be running on the one or more control modules located in a vehicle or at a remote location in communication with the VCS. The remote locations may include a server and/or in a cloud computing environment. In one embodiment, the one or more control modules running the energy consumption and time prediction model may receive additional vehicle data including, but not limited to, specific environment conditions at the vehicle, the vehicle coefficient of drag, tire pressure, air density, and/or elevation data.”,
Supplemental Note: the vehicle system is able to utilize cloud servers to gather the specified data and perform the energy-cost formula).
In sum, Gusikhin teaches wherein the control module is a component of a cloud-based server system, and further wherein the cloud-based server system is operably connected to a map data server, a traffic data server, a weather data server, and a charging station server, using information from each of the map data server, the traffic data server, the weather data server, and the charging station server. Gusikhin however does not teach wherein the weighted sum cost is derived.
Macneille teaches further wherein the weighted sum cost is derived (Macneille: Paragraph 0019: “The control system is further configured to execute an algorithm for at least some of the routes available to reach the predetermined destination. The algorithm is configured to use operating characteristics for the vehicle for each of the at least some routes, apply a respective weighting factor to at least one of the operating characteristics, and rank each of the at least some routes based at least in part on the weighted operating characteristics.”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention disclosed by Gusikhin with the teachings of Macneille with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses.
Regarding claim 7, Gusikhin, as modified, teaches wherein the instructions include a charging/parking strategy for resting after the electrified vehicle completes the drive route (Gusikhin: Col. 9, lines 4 – 21: “Referring to FIG. 2 is a flowchart illustration of a high-level strategy for trip evaluation using trip data obtained from a user interface. BEVs may have a limited range or distance that can be traveled before the main battery 26 is depleted. Drivers need to know whether the range of the BEV is sufficient based on the battery capacity. BEV's need to know the driver's itinerary or calendar schedule prior to starting the trip. If the BEV knows the destination and required arrival time of a given calendar event, or series of events, the BEV may plan a trip to optimize energy consumption as well as minimize travel time to each calendar event while ensuring the driver is able to make it to a final location to recharge the main battery 26 within the vehicle's range. The BEV may also change a route calculation based on a variety of factors that are not currently being considered, allowing constant updating so routes change dynamically based on changing conditions including weather, environmental, and/or traffic.”).
Regarding claim 8, Gusikhin, as modified, does not teach wherein the weighted sum cost is a weighted sum of an energy consumption cost, a travel time cost, and a battery life degradation cost associated with operating the electrified vehicle over the link.
Macneille teaches wherein the weighted sum cost is a weighted sum of an energy consumption cost, a travel time cost, and a battery life degradation cost associated with operating the electrified vehicle over the link (Macneille: Paragraph 0011: “Such operating characteristics may include, for example, one or more of a predicted travel time to reach the predetermined destination, a predicted fuel economy for the vehicle, and a final state of charge for the battery--i.e., the SOC of the battery when the vehicle reaches the predetermined destination.”; Paragraph 0013: “Such operating characteristics may include, for example, one or more of a predicted travel time to reach the predetermined destination, a predicted fuel economy for the vehicle, and a final state of charge for the battery--i.e., the SOC of the battery when the vehicle reaches the predetermined destination.”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention disclosed by Gusikhin with the teachings of Macneille with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses.
Regarding claim 11, Gusikhin teaches an electrified vehicle, comprising:
a traction battery pack; and (Gusikhin: Col. 3, lines 8 – 17)
a control module programmed to receive a smart routing control strategy that includes instructions for routing the electrified vehicle along a drive route (Gusikhin: Abstract).
In sum, Gusikhin teaches an electrified vehicle, comprising: a traction battery pack; and a control module programmed to receive a smart routing control strategy that includes instructions for routing the electrified vehicle along a drive route. Gusikhin teaches in a manner that extends an operable life of the traction battery pack, wherein the smart routing control strategy is derived based on a weighted sum cost associated with operating the electrified vehicle along a link of an expected operational area of the electrified vehicle.
Macneille teaches in a manner that extends an operable life of the traction battery pack, (Macneille: Paragraph 0011; Paragraph 0013)
wherein the smart routing control strategy is derived based on a weighted sum cost associated with operating the electrified vehicle along a link of an expected operational area of the electrified vehicle (Macneille: Paragraph 0019).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention disclosed by Gusikhin with the teachings of Macneille with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses.
Regarding claim 12, Gusikhin, as modified, teaches wherein the instructions include a charging/parking strategy for resting after the electrified vehicle completes the drive route (Gusikhin: Col. 9, lines 4 – 21: “Referring to FIG. 2 is a flowchart illustration of a high-level strategy for trip evaluation using trip data obtained from a user interface. BEVs may have a limited range or distance that can be traveled before the main battery 26 is depleted. Drivers need to know whether the range of the BEV is sufficient based on the battery capacity. BEV's need to know the driver's itinerary or calendar schedule prior to starting the trip. If the BEV knows the destination and required arrival time of a given calendar event, or series of events, the BEV may plan a trip to optimize energy consumption as well as minimize travel time to each calendar event while ensuring the driver is able to make it to a final location to recharge the main battery 26 within the vehicle's range. The BEV may also change a route calculation based on a variety of factors that are not currently being considered, allowing constant updating so routes change dynamically based on changing conditions including weather, environmental, and/or traffic.”).
Regarding claim 13, Gusikhin, as modified, does not teach wherein the weighted sum cost is a weighted sum of an energy consumption cost, a travel time cost, and a battery life degradation cost associated with operating the electrified vehicle over the link.
Macneille teaches wherein the weighted sum cost is a weighted sum of an energy consumption cost, a travel time cost, and a battery life degradation cost associated with operating the electrified vehicle over the link (Macneille: Paragraph 0011: “Such operating characteristics may include, for example, one or more of a predicted travel time to reach the predetermined destination, a predicted fuel economy for the vehicle, and a final state of charge for the battery--i.e., the SOC of the battery when the vehicle reaches the predetermined destination.”; Paragraph 0013: “Such operating characteristics may include, for example, one or more of a predicted travel time to reach the predetermined destination, a predicted fuel economy for the vehicle, and a final state of charge for the battery--i.e., the SOC of the battery when the vehicle reaches the predetermined destination.”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention disclosed by Gusikhin with the teachings of Macneille with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses.
Regarding claim 16, Gusikhin, as modified, teaches wherein the smart routing control strategy is received from a cloud-based server system (Gusikhin: Col. 8, lines 41 – 47: “In all solutions, it is contemplated that at least the vehicle computing system (VCS) located within the vehicle itself is capable of performing the exemplary processes. A VCS communicating with a nomadic device using BLUETOOTH may communicate with a cloud based computing system to receive trip data related to a drive route.”: Col. 11, line 49 – Col. 12, line 10: “Based on the vehicle parameters, the remote server and/or VCS may determine the available energy as well as potential losses, as represented by block 230. Available energy includes the stored energy in the battery and/or the fuel in the tank. Potential losses may include frictional losses associated with tire pressure, or energy losses from running accessories such as air conditioning. After the least energy route has been calculated by block 226 using vehicle routing problem solutions with the energy cost function based on topography/terrain, weather, traffic, vehicle type and driver characteristics, the system may determine a most efficient and/or optimal route for the driver in block 227 by also taking into consideration the calculated times required to travel the one or more routes calculated in block 225. The determined optimal route may be used by the system to calculate the DTE function, as represented by block 234. The DTE calculation uses all the data and variables collected by the remote server and/or VCS to determine if the route completion based on the driver's itinerary and/or destination inputs is likely or not. For example, the DTE function uses destination and arrival-time information automatically extracted from the user's calendar and/or from the drivers input. The DTE function may also acquire necessary vehicle status information and all drive information along with driver profile information to obtain the low energy route while considering travel time. The DTE program can also process the battery model and/or fuel model based on retrieved parameters.”,
Supplemental Note: the remote server which can be a cloud based computing system, can be utilized for route calculation).
Regarding claim 17, Gusikhin, as modified, teaches wherein the electrified vehicle is part of a vehicle fleet (Gusikhin: Col. 1, lines 13 – 24: “U.S. Pat. No. 8,290,701 generally discloses vehicle management systems and associated processes considering energy consumption when selecting routes for fleet vehicles. Vehicle management systems and associated processes are described that, in certain embodiments, evaluate vehicle energy usage based on factors such as terrain or elevation, vehicle characteristics, driver characteristics, road conditions, traffic, speed limits, stop time, turn information, traffic information, and weather information, and the like. The features described herein may also be implemented for non-fleet vehicles, such as in personal vehicle navigation systems.”).
Regarding claim 18, Gusikhin, as modified, teaches wherein the electrified vehicle is a plug-in type electrified vehicle (Gusikhin: Col. 4, lines 42 – 54: “The vehicle 10, which is shown as a BEV, may further include an alternating current (AC) charger 50 for charging the main battery 26 using an off-vehicle AC source. The AC charger 50 may include power electronics used to convert the off-vehicle AC source from an electrical power grid to the DC voltage required by the main battery 26, thereby charging the main battery 26 to its full state of charge. The AC charger 50 may be able to accommodate one or more conventional voltage sources from an off-vehicle electrical grid (e.g., 110 volt, 220 volt, etc.). The AC charger 50 may be connected to the off-vehicle electrical grid using an adaptor, shown schematically in FIG. 1 as a plug 52.”).
Regarding claim 19, Gusikhin, as modified, teaches based on information from each of a map data server, a traffic data server, a weather data server, and a charging station server (Gusikhin: Col. 11, line 56 – Col. 12, line 9: “After the least energy route has been calculated by block 226 using vehicle routing problem solutions with the energy cost function based on topography/terrain, weather, traffic, vehicle type and driver characteristics, the system may determine a most efficient and/or optimal route for the driver in block 227 by also taking into consideration the calculated times required to travel the one or more routes calculated in block 225. The determined optimal route may be used by the system to calculate the DTE function, as represented by block 234. The DTE calculation uses all the data and variables collected by the remote server and/or VCS to determine if the route completion based on the driver's itinerary and/or destination inputs is likely or not. For example, the DTE function uses destination and arrival-time information automatically extracted from the user's calendar and/or from the drivers input. The DTE function may also acquire necessary vehicle status information and all drive information along with driver profile information to obtain the low energy route while considering travel time. The DTE program can also process the battery model and/or fuel model based on retrieved parameters”; Col. 9, lines 4 – 21: “Referring to FIG. 2 is a flowchart illustration of a high-level strategy for trip evaluation using trip data obtained from a user interface. BEVs may have a limited range or distance that can be traveled before the main battery 26 is depleted. Drivers need to know whether the range of the BEV is sufficient based on the battery capacity. BEV's need to know the driver's itinerary or calendar schedule prior to starting the trip. If the BEV knows the destination and required arrival time of a given calendar event, or series of events, the BEV may plan a trip to optimize energy consumption as well as minimize travel time to each calendar event while ensuring the driver is able to make it to a final location to recharge the main battery 26 within the vehicle's range. The BEV may also change a route calculation based on a variety of factors that are not currently being considered, allowing constant updating so routes change dynamically based on changing conditions including weather, environmental, and/or traffic.”).
In sum, Gusikhin teaches based on information from each of a map data server, a traffic data server, a weather data server, and a charging station server.
Macneille teaches wherein the weighted sum cost is generated (Macneille: Paragraph 0019: “The control system is further configured to execute an algorithm for at least some of the routes available to reach the predetermined destination. The algorithm is configured to use operating characteristics for the vehicle for each of the at least some routes, apply a respective weighting factor to at least one of the operating characteristics, and rank each of the at least some routes based at least in part on the weighted operating characteristics.”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention disclosed by Gusikhin with the teachings of Macneille with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses.
Regarding claim 20, Gusikhin teaches a route planning method, comprising: (Gusikhin: Abstract: “A vehicle system enabling a processor to calculate the most efficient route to a destination based on estimated energy usage, vehicle data, and other inputs until the final destination is reached. The vehicle system may receive location related data that may include the current vehicle location and one or more destination points. The vehicle system may receive one or more energy usage affecting parameters from a vehicle related system. The system may calculate a most efficient route based on the location related data and the one or more energy usage affecting parameters and present the most efficient route to a device. The vehicle system may repeat the steps of receiving, calculating, presenting until a destination is reached.”)
generating a road network that defines an expected operational area an electrified vehicle will travel within during an upcoming trip; (Gusikhin: Col. 3, lines 8 – 17: “FIG. 1a is a simplified, exemplary schematic representation of a vehicle 10. As seen therein, the vehicle 10 may be a battery electric vehicle (BEV), which is an all-electric vehicle propelled by one or more electric machines without assistance from an internal combustion engine. The one or more electric machines of the vehicle 10 may include a traction motor 12. The motor 12 may output torque to a shaft 14, which may be connected to a first set of vehicle drive wheels, or primary drive wheels 16, through a gearbox 18.”; Col. 12, lines 19 – 38: “The system may continuously monitor changes in the trip and determine whether a detoured has taken place, as represented by block 242. If the vehicle has not arrived at its destination, the system may continuously lookup data for map, topography, weather, and/or traffic to ensure an optimal route to the destination is selected. For example, if the driver makes an impulse decision to stop at a local retail store in the area to do some shopping, the VCS may continuously lookup, calculate, and determine an optimal route to the selected destination once the driver enters the vehicle and continues his trip. In another example, an accident may occur in an upcoming route during the trip to a destination causing traffic in an area that was originally determined to be a part of the optimal route to the selected destination. The system may continuously monitor environmental and traffic factors to update, calculate, and determine the optimal route while in transit to the destination. The system may end the evaluation of a trip once the driver has arrived at their destination as represented by block 244.”)
… generating a smart routing control strategy for routing the electrified vehicle along the lowest cost travel path during the upcoming trip (Gusikhin: Col. 11, line 56 – Col. 12, line 9: “After the least energy route has been calculated by block 226 using vehicle routing problem solutions with the energy cost function based on topography/terrain, weather, traffic, vehicle type and driver characteristics, the system may determine a most efficient and/or optimal route for the driver in block 227 by also taking into consideration the calculated times required to travel the one or more routes calculated in block 225. The determined optimal route may be used by the system to calculate the DTE function, as represented by block 234. The DTE calculation uses all the data and variables collected by the remote server and/or VCS to determine if the route completion based on the driver's itinerary and/or destination inputs is likely or not. For example, the DTE function uses destination and arrival-time information automatically extracted from the user's calendar and/or from the drivers input. The DTE function may also acquire necessary vehicle status information and all drive information along with driver profile information to obtain the low energy route while considering travel time. The DTE program can also process the battery model and/or fuel model based on retrieved parameters”).
In sum, Gusikhin teaches a route planning method, comprising: generating a road network that defines an expected operational area an electrified vehicle will travel within during an upcoming trip;
generating a smart routing control strategy for routing the electrified vehicle along the lowest cost travel path during the upcoming trip.
Macneille teaches performing an objective based total cost analysis for determining a lowest cost travel path for completing the upcoming trip, wherein the objective based total cost analysis includes analyzing an energy consumption cost, a travel time cost, and a battery life degradation cost associated with operating the electrified vehicle, and (Macneille: Paragraph 0011: “Such operating characteristics may include, for example, one or more of a predicted travel time to reach the predetermined destination, a predicted fuel economy for the vehicle, and a final state of charge for the battery--i.e., the SOC of the battery when the vehicle reaches the predetermined destination.”; Paragraph 0013: “Such operating characteristics may include, for example, one or more of a predicted travel time to reach the predetermined destination, a predicted fuel economy for the vehicle, and a final state of charge for the battery--i.e., the SOC of the battery when the vehicle reaches the predetermined destination.”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention disclosed by Gusikhin with the teachings of Macneille with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses.
Regarding claim 21, Gusikhin, as modified, does not teach the weighted sum cost calculated utilizing battery life degradation cost of the link.
Macneille teaches wherein the weighted sum cost is calculated using an equation expressed as follows:
PNG
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371
521
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Greyscale
(Macneille: Paragraphs 0041 – 0045:
PNG
media_image2.png
349
576
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Greyscale
).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention disclosed by Gusikhin with the teachings of Macneille with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function regarding a weighted sum cost and therefore rejected under the same pretenses.
Regarding claim 22, Gusikhin does not teach wherein the battery life degradation cost includes a cost associated with an amount of battery degradation of the traction battery pack that will be incurred while operating the electrified vehicle over the link.
Macneille teaches wherein the battery life degradation cost includes a cost associated with an amount of battery degradation of the traction battery pack that will be incurred while operating the electrified vehicle over the link (Macneille: Paragraph 0011: “Such operating characteristics may include, for example, one or more of a predicted travel time to reach the predetermined destination, a predicted fuel economy for the vehicle, and a final state of charge for the battery--i.e., the SOC of the battery when the vehicle reaches the predetermined destination.”; Paragraph 0013: “Such operating characteristics may include, for example, one or more of a predicted travel time to reach the predetermined destination, a predicted fuel economy for the vehicle, and a final state of charge for the battery--i.e., the SOC of the battery when the vehicle reaches the predetermined destination.”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention disclosed by Gusikhin with the teachings of Macneille with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function regarding a operable life of the battery and therefore rejected under the same pretenses.
Claims 9 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Gusikhin et al. (US 9587954 B2) and Macneille et al. (US 20080275644 A1) as applied respectively to independent claims 1 and 11 above, further in view of Colonna et al. (US 20230199513 A1).
Regarding claim 9, Gusikhin, as modified, does not teach wherein the control module is further programmed to generate an origin-destination matrix for deriving the weighted sum cost.
Colonna teaches wherein the control module is further programmed to generate an origin-destination matrix for deriving the weighted sum cost (Colonna: Paragraph 0009: “A typical method for collecting empirical data used to compute O-D matrices related to a specific RoI is based on submitting questionnaires to, or performing interviews with, inhabitants of the RoI, and/or to inhabitants of the neighboring areas, about their habits in relation to their movements, and/or by installing vehicle count stations along routes of the RoI for counting the number of vehicles moving along such routes.”; Paragraph 0040: “According to an aspect of the present disclosure, a method, implemented by a data processing system, for computing Origin-Destination matrices indicative of movements, in a geographic area of interest, of physical entities being users of mobile communication terminals configured to be adapted to interact with a mobile communication network comprising a plurality of network cells covering said geographic area of interest, each Origin-Destination matrix being related to a respective time slot of an observation time period and comprising a plurality of entries.”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Gusikhin with the teachings of Colonna with a reasonable expectation of success. Colonna teaches the ability to compute origin-destination matrices of users travelling, which includes users travelling in a vehicle along a route. One of ordinary skill in the art would find the creation of these matrices as a simple substitution with the system of Gusikhin. Gusikhin teaches the ability of routing from a home to multiple destinations (Gusikhin: Col. 12, line 61 – Col. 13, line 3). Therefore the origin-destination matrices as taught by Colonna and the routing to multiple destinations as taught by Gusikhin are merely a simple substitution as both methods can be interchanged and the origin/destination of the vehicle can still be evaluated.
Regarding claim 14, Gusikhin, as modified, does not teaches wherein the smart routing control strategy is further derived based on an origin-destination matrix.
Colonna teaches wherein the smart routing control strategy is further derived based on an origin-destination matrix (Colonna: Paragraph 0009: “A typical method for collecting empirical data used to compute O-D matrices related to a specific RoI is based on submitting questionnaires to, or performing interviews with, inhabitants of the RoI, and/or to inhabitants of the neighboring areas, about their habits in relation to their movements, and/or by installing vehicle count stations along routes of the RoI for counting the number of vehicles moving along such routes.”; Paragraph 0040: “According to an aspect of the present disclosure, a method, implemented by a data processing system, for computing Origin-Destination matrices indicative of movements, in a geographic area of interest, of physical entities being users of mobile communication terminals configured to be adapted to interact with a mobile communication network comprising a plurality of network cells covering said geographic area of interest, each Origin-Destination matrix being related to a respective time slot of an observation time period and comprising a plurality of entries.”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Gusikhin with the teachings of Colonna with a reasonable expectation of success. Please refer to the rejection of claim 9 as both claim the same function and therefore rejected under the same pretenses.
Claims 10 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Gusikhin et al. (US 9587954 B2) and Macneille et al. (US 20080275644 A1) as applied respectively to independent claims 1 and 11 above, further in view of Zhao et al. (US 20210061278 A1) and Wu et al. (CN109934405B).
Regarding claim 10, Gusikhin, as modified, does not teach wherein the control module is configured to execute a shortest path algorithm.
Zhao teaches wherein the control module is configured to execute a shortest path algorithm (Zhao: Paragraph 0040: “At process block 213, a shortest path first (SPF) algorithm is employed to select an energy-efficient candidate route.”) .
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention disclosed by Gusikhin with the teachings of Zhao with a reasonable expectation of success. Both Gusikhin and Zhao teach the ability to gather vehicle position and destination information to determine the best route to that destination. Gusikhin teaches the ability to determine the most efficient route to a destination, therefore one with knowledge in the art would find it obvious to try to implement the shortest path algorithm as taught by Zhao into the calculation of Gusikhin’s system. Gusikhin’s most efficient route determination teaches the ability to gather traffic, environment and weather data for its calculations, the addition of the shortest path algorithm as taught by Zhao can also allow the system to determine the shortest distance to travel to reach the destination. For example, on a clear day with no traffic, the shortest path may be the fastest and most efficient path to a destination which the system of Gusikhin will now be able to determine. Gusikhin in combination with Zhao however still do not teach a modified simulated annealing algorithm for preparing the smart routing control strategy.
Wu teaches and a modified simulated annealing algorithm for preparing the smart routing control strategy (Wu: Technical Field: “The invention relates to the technical field of path planning, in particular to a time-limited multi-vehicle and multi-vehicle path planning method based on a simulated annealing algorithm.”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention disclosed by Gusikhin with the teachings of Wu with a reasonable expectation of success. Both Gusikhin and Zhao teach the ability to gather vehicle position and destination information to determine the best route to that destination. The simulated annealing algorithm is used by Wu to determine the most effective planned route for multiple vehicle types with multiple time restrictions and multiple paths (Wu: Page 1, lines 42 – 44: “The technical problem to be solved by the present invention is to provide a time-limited multi-vehicle and multi-vehicle path planning method based on the simulated annealing algorithm, which can effectively solve the path planning with multiple vehicle types, multiple shipments of vehicles, and working time restrictions. problem.“). Gusikhin’s most efficient route determinations teaches the ability to gather traffic, environment and weather data for its calculations, the addition of the simulated annealing algorithm as taught by Wu would allow Gusikhin to function with multiple vehicles with different time restrictions and multiple destinations. For example, in a fleet system multiple vehicle types may be utilized, the simulated annealing algorithm as taught by Wu would increase the efficiency to what vehicle types should be making certain trips and what route they should traverse on to make it within the time limits. This can be further refined by the system of Gusikhin which can implement traffic, environment and weather data into the determination process. Both of these methods working together increase the efficiency of the route determination system used by Gusikhin therefore would be obvious to try by one with knowledge in the art.
Regarding claim 15, Gusikhin, as modified, does not teach wherein the smart routing control strategy is further derived via a shortest path algorithm.
Zhao teaches wherein the smart routing control strategy is further derived via a shortest path algorithm (Zhao: Paragraph 0040: “At process block 213, a shortest path first (SPF) algorithm is employed to select an energy-efficient candidate route.”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention disclosed by Gusikhin with the teachings of Zhao with a reasonable expectation of success. Please refer to the rejection of claim 10 as both claims state the same functional language, therefore rejected under the same pretenses. Gusikhin in combination with Zhao however still do not teach a modified simulated annealing algorithm for preparing the smart routing control strategy.
Wu teaches a modified simulated annealing algorithm (Technical Field: “The invention relates to the technical field of path planning, in particular to a time-limited multi-vehicle and multi-vehicle path planning method based on a simulated annealing algorithm.”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention disclosed by Gusikhin with the teachings of Wu with a reasonable expectation of success. Please refer to the rejection of claim 10 as both claims state the same functional language, therefore rejected under the same pretenses.
Response to Arguments
Applicant’s arguments, see section A. The rejection of claims 1 – 3, 5, 7 – 9, 11 – 14 and 16 – 20 as allegedly anticipated is improper of the APPEAL BRIEF, filed 02/23/2026, with respect to the rejection(s) of claim(s) 1 – 3, 5, 7 – 9, 11 – 14 and 16 – 20 under 35 U.S.C. 102(a)(1) have been fully considered and are persuasive. Applicant states that the prior art of Gusikhin does not teach the claim limitations regarding a weighted sum cost, battery degradation costs and an origin-destination matrix. Examiner agrees, therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Macneille (US 20080275644 A1) and Colonna (US 20230199513 A1). Please see section Claim Rejections - 35 USC § 101 for the prior art citations and motivation to combine with the prior art of Gusikhin.
Applicant’s arguments, see section B. The rejection of claims 10 and 15 as allegedly anticipated is improper of the APPEAL BRIEF, filed 02/23/2026, with respect to the rejection(s) of claim(s) 10 and 15 under 35 U.S.C. 103 have been fully considered and are persuasive. Applicant stated that the “weighted sum cost” was not properly taught by Gusikhin for independent claims 1 and 11, therefore it cannot properly be combined with the prior art of Zhao and Wu to teach the claim limitations of 10 and 15. Examiner agrees and is not using the prior art of Macneillie to teach the “weighted sum cost” claim limitation, and therefore the combination with Zhao and Wu is proper in teaching the claim limitations of claims 10 and 15.
Applicant’s arguments, see section C. The rejection of claims 21 and 22 as allegedly anticipated is improper of the APPEAL BRIEF, filed 02/23/2026, with respect to the rejection(s) of claim(s) 21 and 22 under 35 U.S.C. 1023 have been fully considered and are persuasive. However, upon further consideration, a new ground(s) of rejection is made in view of Macneille (US 20080275644 A1).
Conclusion
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/SHIVAM SHARMA/Examiner, Art Unit 3665
/Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665
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