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 .
This Office Action is in response to Applicant Amendment and Arguments filed on 5/19/2026.
This Action is made NON-FINAL.
Claim(s) 1-4 and 6-9 are pending for examination.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/19/2026 has been entered.
Response to Arguments
Claim(s) 1-4 and 6-9 were previously rejected under 35 U.S.C. 112(a). In response to Applicant's argument, the 35 U.S.C. 112(a) rejection(s) of claim(s) 1-4 and 6-9 have been withdrawn.
Applicant's arguments with respect to the previous rejection of claims 1-4 and 6-9 under 35 U.S.C. 103 have been considered but are not persuasive.
First applicant argues “The Office asserts that the coefficient ao disclosed by Gillespey teaches the "value Pvo,i for power loss at no load" recited in claim 1. In addition, the Office asserts that the coefficient a2 disclosed by Gillespey teaches the "factor Kphi,i for a current operating point" recited in claim 1. Nothing has been found, or pointed to, in Gillespey which teaches or suggests that the coefficient ao and/or the coefficient a2 is determined in real time, much less that they are determined at runtime in real time without use of characteristic maps or characteristic curves or other static data as now explicitly recited in the claimed embodiment.” Examiner does not rely merely on Gillespey to determine parameters in Realtime as previously discussed. Regarding “without use of characteristic maps or characteristic curves or other static data” See examiner’s updated 103 rejection below which presents the reasoning as to why Gillespey teaches this limitation.
Next applicant argues: “At the bottom of page 4 in the Examiner's Answer, the Examiner asserts that the motivation to combine the teachings in Gillespey and Daum comes from "knowledge generally available to one of ordinary skill in the art," which the Examiner asserts is knowledge of distributed computing. In that regard, the Examiner asserts that "There is no reason to believe that one of ordinary skill in the art would not be able to have the calculations and data storage that is performed on one generic computing device performed on two generic computing devices with data transmission between them." Applicant respectfully disagrees. Distributed computing does not necessarily involve determining values in real time. The Examiner has failed to point to anything which teaches or suggests that the knowledge generally available to one of ordinary skill in the art includes knowledge that all values can be determined in real time, much less knowledge that all values can be determined in one computing device of a distributed system and then transmitted to another computing device of the distributed system in real time. In addition, nothing has been found, or pointed to, Daum which teaches or suggest that all values can be determined at runtime in real time without use of characteristic maps or characteristic curves or other static data.”
The data that would be transferred are mere coefficients and they are being calculated according to Gillespey either by lookup tables or algebraic equations. With modern computers transfer speeds over a network are typically in the Mb/s or even Gb/s with latencies in the millisecond range. Simple coefficient values which amount to small amounts of data would be transferred near instantaneously on such a network. Additionally typical processors run in the Mhz and even Ghz ranges. Retrieving values or solving simply polynomials is again near instantaneous on typical processors at these clock speeds. In fact applicant has not actually disclosed how the coefficients Pvo,I and Kphi,i are calculated. Rather they have only disclosed how they are NOT calculated. Thus, simple calculations using lookup tables or solving simple equations has been assumed.
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 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.
Claim(s) 1-4 and 6-9 are rejected under 35 U.S.C. 103 as being unpatentable over Gillespey et al. (US 20170174097 A1, hereinafter known as Gillespey) in view of Daum et al. (US 20150191183 A1, hereinafter known as Daum) and Fatemi et al. (US 10940771 B1, hereinafter known as Fatemi)
Gillespey and Daum were cited in a previous office action.
Regarding Claim 1, Gillespey teaches A method for optimizing torque distribution in a drive system of a battery electric vehicle that has a plurality of drive axles and a number n of electric machines (e-machines) driving the drive axle, the method comprising:
{Abstract “An electric vehicle includes a plurality of electric drive units coupled to one or more axles of the electric vehicle. The drive units have an associated power loss map that is approximated by a plurality of second-order polynomials. Torque is apportioned between the electric drive units to minimize total power losses based on coefficients of the second-order polynomials for each of the electric drive units and a driver torque demand.”
}
using a controller to calculate a power loss of each e-machine i for the torque distribution and determine a minimum of a total power loss Pv,ges of the drive system in real time; using the controller to control each e-machines i so that the e-machines jointly provide a torque requested MAnf, wherein each e-machine i generates a torque Mi determined for the minimum of the total power loss Pv,ges and assigned to the e-machine i;
{Para [0050] “The processes, methods, or algorithms disclosed herein can be deliverable to/implemented by a processing device, controller, or computer, which can include any existing programmable electronic control unit or dedicated electronic control unit.”
Para [0049] “A benefit of the described system is that torque may be apportioned between the electric machines and drive units to minimize power losses. In addition, the system enables fast runtime optimization of the torque distribution without performing time-consuming iterative optimization algorithms. Further, the system described may be used on any configuration utilizing multiple electric machines and is not necessarily limited to the AWD example presented.”
Para [0036-0037] “A total electric drive power loss may be expressed as:
P.sub.loss,total=ΣP.sub.loss,fr+ΣP.sub.loss,rr (3)
[0037] The summation provides for multiple electric machines on each of the axles. That is, the power loss for each electric-drive unit coupled to the axle is summed together. For the discussion to follow, one electric machine per axle is assumed. In general, the total electric drive power loss may be expressed as the sum of the power loss associated with each of the electric machines.”
Para [0039-0040] “The driver demand torque may be equal to the sum of all of the torques provided by the drive units as follows:
T.sub.o=T.sub.fr+T.sub.rr (4)
The optimization goal is to find the torque distribution between the electric motors that satisfies the driver demand torque and minimizes the drive unit power losses.”
The power loss is being calculated in real-time as discussed in Para [0033-0040] specifically para [0040] “The above expressions may be computed in real-time knowing the driver demand torque and the drive unit power loss coefficients.”
}
wherein, when the requested torque MAnf, is requested, a value Pvo,g for current drag losses and a factor KPhi,i for a current operating point used by the controller from each e-machines i, and the controller calculates the power loss of each e-machine i according to:
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wherein the value PVo,i and the factor Kphi,i are determined at runtime
{Para [0030] “During operation, the drive units 180, 182 may experience power losses due to friction and operating characteristics of the components that make up the drive unit. The drive units 180, 182 may be characterized by a power loss function. The power loss function may be configured to estimate the amount of power lost in the drive unit 180, 182 during vehicle operation. The power loss function may be a function of the electric machine efficiency, the power electronics module efficiency, and friction within the electric machines and associated gearboxes and the related gear train. The power loss function estimates an amount of power lost at the output of the drive unit 180, 182 from the power that is input to the drive unit 180, 182. Each of the drive units 180, 182 may have a different power loss function. Each of the drive units 180, 182 may be optimized for a different operating range. For example, one may be optimized to reduce losses under steady-state high-speed conditions. The other drive unit may be optimized to reduce losses under low-speed conditions. As such, an operating strategy may be implemented to determine which of the drive units 180, 182 should be operating under the present and anticipated operating conditions. Additionally, when a drive unit includes multiple electric machines, an operating strategy may be implemented to determine how much power should be provided by each of the electric machines within the drive unit.”
Para [0033] “The drive unit power loss map may be approximated with one or more mathematical expressions. A second-order polynomial of the form ax.sup.2+bx+c may be selected. The coefficients (a, b, c) may be derived from the drive unit power loss map. The approximation may include a plurality of sets of coefficients for different operating conditions. For example, a set of coefficients may correspond to a particular drive unit speed and/or a particular voltage input. For example, for an AWD vehicle having a front axle motor and a rear axle motor, the power loss approximations may be expressed as:”
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Where the a2 term can be considered the factor KPhi,I and the a0 term can be considered the drag losses Pvo,g. T can be considered the torque Mi
As speed and voltage are variable during driving it can be said that the coefficients are be determined using dynamic data rather than “other static data”.
Additionally, characteristics curves are described according to Wikipedia as “In mathematics, the method of characteristics is a technique for solving particular partial differential equations. Typically, it applies to first-order equations, though in general characteristic curves can also be found for hyperbolic and parabolic partial differential equations. The method is to reduce a partial differential equation (PDE) to a family of ordinary differential equations (ODEs) along which the solution can be integrated from some initial data given on a suitable hypersurface ….. For a first-order PDE, the method of characteristics discovers so called characteristic curves along which the PDE becomes an ODE.[1][2] Once the ODE is found, it can be solved along the characteristic curves and transformed into a solution for the original PDE.”
The use of curves derived from PDEs are not recited in Gillespey
Lastly, a characteristic map, specifically a Frobenius characteristic map, is described as “In mathematics, especially representation theory and combinatorics, a Frobenius characteristic map is an isometric isomorphism between the ring of characters of symmetric groups and the ring of symmetric functions. It builds a bridge between representation theory of the symmetric groups and algebraic combinatorics. This map makes it possible to study representation problems with help of symmetric functions and vice versa.”
No such method of generating a characteristic map recited in Gillespey.
Characteristic curve and characteristic map appear not to have any widely distributed definition except for the ones identified above.
Rather it appears the map of Gillespey is mearly using a table lookup to generate the power loss map.
Para [0046] “Referring again to FIG. 4, at block 406, the coefficients for each of the second-order polynomials may be computed and stored in non-volatile memory of the controller represented by a coefficient data store 412. For each of the drive units, a set of second-order parameters for each of the selected operating speeds and voltages may be stored in controller non-volatile memory. A table or database of second-order parameters may be stored and indexed by drive unit speed and/or input voltage. The drive unit speed may be proportional to the vehicle speed while the vehicle is operating. The coefficients may be stored in non-volatile memory of the controller (e.g., system controller 148).”
}
Wherein the total power loss of the drive system calculated according to:
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{Para [0036-0037] “A total electric drive power loss may be expressed as:
P.sub.loss,total=ΣP.sub.loss,fr+ΣP.sub.loss,rr (3)
[0037] The summation provides for multiple electric machines on each of the axles. That is, the power loss for each electric-drive unit coupled to the axle is summed together. For the discussion to follow, one electric machine per axle is assumed. In general, the total electric drive power loss may be expressed as the sum of the power loss associated with each of the electric machines.”
}
And wherein the minimum of the total power loss is determined by varying the torques Mi of each e-machine i, according to:
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{ Para [0039-0040] “The driver demand torque may be equal to the sum of all of the torques provided by the drive units as follows:
T.sub.o=T.sub.fr+T.sub.rr (4)
The optimization goal is to find the torque distribution between the electric motors that satisfies the driver demand torque and minimizes the drive unit power losses.”
}
Gillespey does not teach, values and factors for the operating curve are determined in real time
and transmitting motor parameters from the motor to the controller.
However, Daum teaches wherein, when the requested torque MAnf, is requested, data related to
{Para [0085] “At 504, motor measurements may be received that represent operating parameters of the traction motors. In some embodiments, the operating parameters may relate directly to a performance or output of the traction motor, such as torque, horsepower, tractive effort, motor current, motor slip, or motor speed. The operating parameters may also relate directly to one or more inputs that cause the directly effect the performance of the traction motor, such as a supply current, excitation frequency, and supply voltage. Other operating parameters may be monitored as well, such as wheel slip, axle and/or wheel diameters, thermal rise, impedance, reactance, etc.”
Fig. 9 which shows the controller receives the measurements in 504 after control inputs, e.g. a torque request.
Para [0051] “Also shown in FIG. 2, the rail vehicle system 200 may include a number of detection devices 291-295. The detection devices 291-295 of the control system 206 may be located at various points in the propulsion-generating system 200. For example, the detection devices 293, 294 are coupled to the inverters 232, 234, respectively. The detection devices 291, 292 are coupled to each of the traction motors 241-244. The detection devices 291-295 are configured to monitor one or more operating parameters of the traction motors 241-245. More specifically, the detection devices 291-295 may obtain measurements relating to the operating parameters to determine a performance relationship of the fraction motors 241-245. In some embodiments, the detection devices 291-295 are configured to detect a motor measurement or other measurements that may be used to calculate a motor measurement. Motor measurements may include excitation frequency of the current, a supply voltage, a motor current (e.g., induced current), a torque or horsepower, a motor speed or rotation rate of the rotor, axle or wheel diameters of the traction motor, a motor slip, impedance of the traction motor, or reactance of the traction motor. In some embodiments, the detection devices 291-295 may also be configured to detect torsional vibrations, vehicle speed (e.g., ground speed), wheel strain, axle strain, or dog-bone strain.”
}
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gillespey to incorporate the teachings of Daum to have the current drag losses value and operating point factor be sent from the e-machine to the processor because it would be obvious to try. It’s a matter of consolidated or distributed computing with the distributed computing not offering any clear advantage in the claimed invention.
Gillespey in view of Daum does not teach, values and factors for the operating curve are determined in real time
However Fatemi teaches values and factors for the operating curve are determined in real time
{Column 6 “As part of the present method 100, the controller 50 of FIG. 1 is programmed with predetermined or calibrated baseline electric drive losses, referred to hereinafter as power losses (“P.sub.L”), for the electric propulsion system 10 for the S-connected and P-connected configurations. The baseline power losses may be ascertained offline and stored in memory (M) of the controller 50 for each of the configurations shown in FIGS. 2A and 2B as calibrated values. The baseline losses may be extracted from memory and thereafter adjusted or scaled in real-time by the controller 50. For instance, a loss scaling factor could be calculated by the controller 50 based on various system feedback values, e.g., temperature. Likewise, baseline peak torque curves as described herein may be scaled in real-time based on such calculations to properly account for different operating conditions.”
}
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gillespey in view of Daum to incorporate the teachings of Fatemi to have the factors associated with a power loss curve determined in real time because it allows the power loss curve to account for different operating conditions the processor because it would be obvious to try. It’s a matter of consolidated or distributed computing with the distributed computing not offering any clear advantage in the claimed invention.
Regarding Claim 2, Gillespey in view of Daum and Fatemi teaches the method according to claim 1. Gillespey further teaches wherein a number of the drive axles is two.
{Fig. 1 where it it shows the vehicle has a front an rear axle
}
Regarding Claim 3, Gillespey in view of Daum and Fatemi teaches the method according to claim 2. Gillespey further teaches wherein the number n of e-machines is two.
{Para [0014] “One or more electric machines coupled to a gearbox may be referred to as a drive unit. A first drive unit 180 may include a first front-axle electric machine 160 and a second front-axle electric machine 162 coupled to a front-axle gearbox 116.”
Para [0015] “second drive unit 182 may include a first rear-axle electric machine 164 and a second rear-axle electric machine 166 coupled to a rear-axle gearbox 114.”
Where both a vehicle with 2 e-machines is contemplated as well as a vehicle with 4 e-machines.
}
Regarding Claim 4, Gillespey in view of Daum and Fatemi teaches The method according to claim 2. Gillespey further teaches wherein the number n of e-machines is four.
{ Para [0014] “One or more electric machines coupled to a gearbox may be referred to as a drive unit. A first drive unit 180 may include a first front-axle electric machine 160 and a second front-axle electric machine 162 coupled to a front-axle gearbox 116.”
Para [0015] “second drive unit 182 may include a first rear-axle electric machine 164 and a second rear-axle electric machine 166 coupled to a rear-axle gearbox 114.”
Where both a vehicle with 2 e-machines is contemplated as well as a vehicle with 4 e-machines.
}
Regarding claim 6, it recites A drive system having limitations similar to those of claim 1 and therefore is rejected on the same basis.
Regarding claim 7, it recites A drive system having limitations similar to those of claim 2 and therefore is rejected on the same basis.
Regarding claim 8, it recites A drive system having limitations similar to those of claim 3 and therefore is rejected on the same basis.
Regarding claim 9, it recites A drive system having limitations similar to those of claim 4 and therefore is rejected on the same basis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Kagoshima et al. (US 20040148817 A1) teaches in para [0089-0090] “ The battery power-loss characteristics determining unit 91 determines loss characteristics of the battery 63 against input power to the battery 63 using the temperature TEMP.sub.b of the battery 63 inputted from the battery temperature sensor 71 and the state-of-charge SOC of the battery 63 inputted from the battery state-of-charge detecting unit 74, and then outputs coefficients (a, b, c) of quadratic expression in an approximation of the determined characteristics by a quadratic expression at the generator/battery power distribution determining unit
For details, there are stored coefficients (a, b, c) of quadratic expression for state-of-charge SOC and temperature TEMP.sub.b of the battery 63 individually, which are obtained by an approximation of characteristics of power loss P2.sub.bross of the battery 63 against input power P2.sub.b to the battery 63 by a quadratic expression represented with the following expression, in a table (a memory unit) of the battery power-loss characteristics determining unit 91 as shown in FIG. 11 and FIG. 12, both of which are precedently obtained by executing experiments etc. In FIG. 11, (a, b, c)=(0.0025, 0.2032, 0).”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER MATTA whose telephone number is (571)272-4296. The examiner can normally be reached Mon - Fri 10:00-6:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James Lee can be reached on (571) 270-5965. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.G.M./Examiner, Art Unit 3668
/ABDHESH K JHA/Primary Examiner, Art Unit 3668
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