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 Number 18/826,110 filed on 09/05/2024.
Claims 1-20 are currently pending and have been examined.
This action is made FINAL in response to the “Amendment” and “Remarks” filed on 06/10/2026.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-8, 12-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Fukuda (U.S. Pub. No. 2022/0004846 A1) in view of Hirata (JP 2016107690 A).
Regarding Claim 1:
Fukuda teaches:
A device comprising: a memory configured to one or more instructions; and a processor configured to execute the one or more instructions to perform operations comprising: by an artificial neural network model trained using, (See (Fukuda: Summary – 5th-8th paragraphs and Detailed Description – 16th-27th, 31st-38th, 47th, and 72nd-79th paragraphs))
Fukuda does not teach but Hirata teaches:
data obtained in a normal state of a brake motor configured to generate a driving force for moving a friction member of a wheel of a vehicle to generate a braking force, calculating, in response to input data associated with one or more operations of the vehicle, an estimation value of an output variable related to an output of the brake motor in the normal state of the brake motor; obtaining a measurement value of an output variable corresponding to a current state of the brake motor; detecting whether the brake motor is to be in an abnormal state based on a difference between the measurement value of the output variable corresponding to the current state of the brake motor and the estimation value, calculated by the artificial neural network model, of the output variable corresponding to the normal state of the brake motor;, (“The braking force control device for a vehicle according to the present invention includes a braking / driving force generating means 6 capable of generating a driving force and a braking force on a plurality of wheels 1 to 4, […] Can do.” (Hirata: Description) Hirata further mentions “As shown in FIG. 1, […] and the friction brake 5 and the motor 6 cooperate to generate a braking force.” (Hirata: Description))
and controlling the vehicle based on whether the brake motor is detected to be in the abnormal state., (“The braking force adjusting means 21 is the sum of the braking force distributed to the abnormal wheels 1 and (2-4) and the braking force caused by the abnormality (braking force) when the vehicle is braked by one or both of the friction brake 5 and the motor 6. […] so the present invention is not applied.” (Hirata: Description) Hirata further mentions “When an abnormality is detected (step S1: Yes), […] Thereafter, this process is terminated.” (Hirata: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda with these above aforementioned teachings from Hirata in order to create an effective device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Hirata’s braking force control device for a vehicle in order to output an estimation value of an output variable of a brake motor, generate a driving force for moving a vehicle to generate a braking force, and detect whether the brake motor is in an abnormal state. Combining Fukuda and Hirata would thus provide “a braking force control device for a vehicle that controls braking force applied to each wheel when an abnormality occurs in the driving wheel in an electric vehicle having an electric motor on the driving wheel.” (Hirata: Description)
Regarding Claim 2:
Fukuda in view of Hirata, as shown in the rejection above, discloses the limitations of claim 1. Fukuda further teaches:
The device of claim 1, wherein the input data, associated with the one or more operations of the vehicle, includes one or more of a speed of the vehicle, a lateral acceleration of the vehicle, an input displacement of a brake pedal, or a wheel speed of the vehicle., (See (Fukuda: Detailed Description – 33rd paragraph))
Regarding Claim 3:
Fukuda in view of Hirata, as shown in the rejection above, discloses the limitations of claim 1. Fukuda does not teach but Hirata teaches:
The device of claim 1, wherein the output variable includes a brake clamping force generated by the friction member., (“As shown in FIG. 1, […] and the friction brake 5 and the motor 6 cooperate to generate a braking force.” (Hirata: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda with these above aforementioned teachings from Hirata in order to create an effective device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Hirata’s braking force control device for a vehicle in order to output an estimation value of an output variable of a brake motor, generate a driving force for moving a vehicle to generate a braking force, and detect whether the brake motor is in an abnormal state. Combining Fukuda and Hirata would thus provide “a braking force control device for a vehicle that controls braking force applied to each wheel when an abnormality occurs in the driving wheel in an electric vehicle having an electric motor on the driving wheel.” (Hirata: Description)
Regarding Claim 4:
Fukuda in view of Hirata, as shown in the rejection above, discloses the limitations of claim 1. Fukuda further teaches:
The device of claim 1, wherein the processor is configured to obtain the input data, associated with the one or more operations of the vehicle,, (See (Fukuda: Summary – 5th-8th paragraphs and Detailed Description – 16th-27th, 31st-38th, 47th, and 72nd-79th paragraphs))
Fukuda does not teach but Hirata teaches:
[…] through a controller area network (CAN) of the vehicle., (“The motor control device 8 and the vehicle control device 7 are connected by, […] and information is transmitted to each other.” (Hirata: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda with these above aforementioned teachings from Hirata in order to create an effective device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Hirata’s braking force control device for a vehicle in order to output an estimation value of an output variable of a brake motor, generate a driving force for moving a vehicle to generate a braking force, and detect whether the brake motor is in an abnormal state. Combining Fukuda and Hirata would thus provide “a braking force control device for a vehicle that controls braking force applied to each wheel when an abnormality occurs in the driving wheel in an electric vehicle having an electric motor on the driving wheel.” (Hirata: Description)
Regarding Claim 5:
Fukuda in view of Hirata, as shown in the rejection above, discloses the limitations of claim 1. Fukuda further teaches:
The device of claim 1, wherein the artificial neural network model is comprised in a generative adversarial network (GAN) including a generator configured to receive the input data, associated with the one or more operations of the vehicle,, (See (Fukuda: Summary – 5th-8th paragraphs and Detailed Description – 16th-27th, 31st-38th, 47th, and 72nd-79th paragraphs))
[…] and a discriminator configured to, in response to the input data, associated with the one or more operations of the vehicle, and the measurement value of the output variable corresponding to the current state of the brake motor, output a measurement related discrimination value., (See (Fukuda: Summary – 7th-8th paragraphs and Detailed Description – 22nd-31st paragraphs))
Fukuda does not teach but Hirata teaches:
[…] and generate the estimation value of the output variable corresponding to the normal state of the brake motor […], (“The braking force control device for a vehicle according to the present invention includes a braking / driving force generating means 6 capable of generating a driving force and a braking force on a plurality of wheels 1 to 4, […] For example, the abnormality-induced braking force is estimated from the difference between the braking force or driving force requested by the driver and the value obtained by multiplying the vehicle longitudinal acceleration measured by the vehicle acceleration sensor 23 by the vehicle weight.” (Hirata: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda with these above aforementioned teachings from Hirata in order to create an effective device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Hirata’s braking force control device for a vehicle in order to output an estimation value of an output variable of a brake motor, generate a driving force for moving a vehicle to generate a braking force, and detect whether the brake motor is in an abnormal state. Combining Fukuda and Hirata would thus provide “a braking force control device for a vehicle that controls braking force applied to each wheel when an abnormality occurs in the driving wheel in an electric vehicle having an electric motor on the driving wheel.” (Hirata: Description)
Regarding Claim 6:
Fukuda in view of Hirata, as shown in the rejection above, discloses the limitations of claim 5. Fukuda further teaches:
The device of claim 5, wherein the discriminator is further configured to, in response to the input data, associated with the one or more operations of the vehicle,, (See (Fukuda: Summary – 7th-8th paragraphs and Detailed Description – 22nd-31st and 33rd paragraphs))
Fukuda does not teach but Hirata teaches:
[…] and the estimation value of the output variable, output an estimation related discrimination value., (“The braking force control device for a vehicle according to the present invention includes a braking / driving force generating means 6 capable of generating a driving force and a braking force on a plurality of wheels 1 to 4, […] For example, the abnormality-induced braking force is estimated from the difference between the braking force or driving force requested by the driver and the value obtained by multiplying the vehicle longitudinal acceleration measured by the vehicle acceleration sensor 23 by the vehicle weight.” (Hirata: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda with these above aforementioned teachings from Hirata in order to create an effective device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Hirata’s braking force control device for a vehicle in order to output an estimation value of an output variable of a brake motor, generate a driving force for moving a vehicle to generate a braking force, and detect whether the brake motor is in an abnormal state. Combining Fukuda and Hirata would thus provide “a braking force control device for a vehicle that controls braking force applied to each wheel when an abnormality occurs in the driving wheel in an electric vehicle having an electric motor on the driving wheel.” (Hirata: Description)
Regarding Claim 7:
Fukuda in view of Hirata, as shown in the rejection above, discloses the limitations of claim 6. Fukuda further teaches:
The device of claim 6, wherein: the artificial neural network model is configured to alternately perform learning of the generator and the discriminator; and the input data and the measurement value of the output variable used for the learning of the generator and the discriminator are obtained, (See (Fukuda: Summary – 5th-8th paragraphs and Detailed Description – 16th-31st, 32nd-38th, 47th, and 72nd-79th paragraphs))
Fukuda does not teach but Hirata teaches:
[…] when the vehicle and the brake motor are in a normal state., (“if a friction brake is operated in the same manner as in a normal state in a state where braking force is generated on a drive wheel” (Hirata: Description) Hirata further mentions “The braking force control device for a vehicle according to the present invention includes a braking / driving force generating means 6 capable of generating a driving force and a braking force on a plurality of wheels 1 to 4, […] For example, the abnormality-induced braking force is estimated from the difference between the braking force or driving force requested by the driver and the value obtained by multiplying the vehicle longitudinal acceleration measured by the vehicle acceleration sensor 23 by the vehicle weight.” (Hirata: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda with these above aforementioned teachings from Hirata in order to create an effective device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Hirata’s braking force control device for a vehicle in order to output an estimation value of an output variable of a brake motor, generate a driving force for moving a vehicle to generate a braking force, and detect whether the brake motor is in an abnormal state. Combining Fukuda and Hirata would thus provide “a braking force control device for a vehicle that controls braking force applied to each wheel when an abnormality occurs in the driving wheel in an electric vehicle having an electric motor on the driving wheel.” (Hirata: Description)
Regarding Claim 8:
Fukuda in view of Hirata, as shown in the rejection above, discloses the limitations of claim 5. Fukuda further teaches:
The device of claim 5, wherein the generator comprises a multivariate transformer., (See (Fukuda: Summary – 5th-8th paragraphs and Detailed Description – 29th-32nd paragraphs))
Regarding Claim 12:
Fukuda teaches:
A method comprising: by an artificial neural network model trained using, (See (Fukuda: Summary – 5th-8th paragraphs and Detailed Description – 16th-27th, 31st-38th, 47th, and 72nd-79th paragraphs))
Fukuda does not teach but Hirata teaches:
data obtained in a normal state of a brake motor configured to generate a driving force for moving a friction member of a wheel of a vehicle to generate a braking force, calculating, in response to input data associated with one or more operations of the vehicle, an estimation value of an output variable related to an output of the brake motor in the normal state of the brake motor; obtaining a measurement value of an output variable corresponding to a current state of the brake motor; detecting whether the brake motor is to be in an abnormal state based on a difference between the measurement value of the output variable corresponding to the current state of the brake motor and the estimation value, calculated by the artificial neural network model, of the output variable corresponding to the normal state of the brake motor;, (“The braking force control device for a vehicle according to the present invention includes a braking / driving force generating means 6 capable of generating a driving force and a braking force on a plurality of wheels 1 to 4, […] Can do.” (Hirata: Description) Hirata further mentions “As shown in FIG. 1, […] and the friction brake 5 and the motor 6 cooperate to generate a braking force.” (Hirata: Description))
and controlling the vehicle based on whether the brake motor is detected to be in the abnormal state., (“The braking force adjusting means 21 is the sum of the braking force distributed to the abnormal wheels 1 and (2-4) and the braking force caused by the abnormality (braking force) when the vehicle is braked by one or both of the friction brake 5 and the motor 6. […] so the present invention is not applied.” (Hirata: Description) Hirata further mentions “When an abnormality is detected (step S1: Yes), […] Thereafter, this process is terminated.” (Hirata: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda with these above aforementioned teachings from Hirata in order to create an effective device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Hirata’s braking force control device for a vehicle in order to output an estimation value of an output variable of a brake motor, generate a driving force for moving a vehicle to generate a braking force, and detect whether the brake motor is in an abnormal state. Combining Fukuda and Hirata would thus provide “a braking force control device for a vehicle that controls braking force applied to each wheel when an abnormality occurs in the driving wheel in an electric vehicle having an electric motor on the driving wheel.” (Hirata: Description)
Regarding Claim 13:
Fukuda in view of Hirata, as shown in the rejection above, discloses the limitations of claim 12. Fukuda further teaches:
The method of claim 12, wherein the input data, associated with the one or more operations of the vehicle, includes one or more of a speed of the vehicle, a lateral acceleration of the vehicle, an input displacement of a brake pedal, or a wheel speed of the vehicle., (See (Fukuda: Detailed Description – 33rd paragraph))
Regarding Claim 14:
Fukuda in view of Hirata, as shown in the rejection above, discloses the limitations of claim 12. Fukuda does not teach but Hirata teaches:
The method of claim 12, wherein the output variable includes a brake clamping force generated by the friction member., (“As shown in FIG. 1, […] and the friction brake 5 and the motor 6 cooperate to generate a braking force.” (Hirata: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda with these above aforementioned teachings from Hirata in order to create an effective device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Hirata’s braking force control device for a vehicle in order to output an estimation value of an output variable of a brake motor, generate a driving force for moving a vehicle to generate a braking force, and detect whether the brake motor is in an abnormal state. Combining Fukuda and Hirata would thus provide “a braking force control device for a vehicle that controls braking force applied to each wheel when an abnormality occurs in the driving wheel in an electric vehicle having an electric motor on the driving wheel.” (Hirata: Description)
Regarding Claim 15:
Fukuda in view of Hirata, as shown in the rejection above, discloses the limitations of claim 12. Fukuda further teaches:
The method of claim 12, wherein the artificial neural network model is comprised in a generative adversarial network (GAN) including a generator configured to receive the input data, associated with the one or more operations of the vehicle,, (See (Fukuda: Summary – 5th-8th paragraphs and Detailed Description – 16th-27th, 31st-38th, 47th, and 72nd-79th paragraphs))
[…] and a discriminator configured to, in response to the input data, associated with the one or more operations of the vehicle, and the measurement value of the output variable corresponding to the current state of the brake motor, output a measurement related discrimination value., (See (Fukuda: Summary – 7th-8th paragraphs and Detailed Description – 22nd-31st paragraphs))
Fukuda does not teach but Hirata teaches:
[…] and generate the estimation value of the output variable corresponding to the normal state of the brake motor […], (“The braking force control device for a vehicle according to the present invention includes a braking / driving force generating means 6 capable of generating a driving force and a braking force on a plurality of wheels 1 to 4, […] For example, the abnormality-induced braking force is estimated from the difference between the braking force or driving force requested by the driver and the value obtained by multiplying the vehicle longitudinal acceleration measured by the vehicle acceleration sensor 23 by the vehicle weight.” (Hirata: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda with these above aforementioned teachings from Hirata in order to create an effective device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Hirata’s braking force control device for a vehicle in order to output an estimation value of an output variable of a brake motor, generate a driving force for moving a vehicle to generate a braking force, and detect whether the brake motor is in an abnormal state. Combining Fukuda and Hirata would thus provide “a braking force control device for a vehicle that controls braking force applied to each wheel when an abnormality occurs in the driving wheel in an electric vehicle having an electric motor on the driving wheel.” (Hirata: Description)
Regarding Claim 16:
Fukuda in view of Hirata, as shown in the rejection above, discloses the limitations of claim 15. Fukuda further teaches:
The method of claim 15, wherein: the artificial neural network model is configured to alternately perform learning of the generator and the discriminator; and the method further comprises obtaining the input data and the measurement value of the output variable used for performing the learning of the generator and the discriminator, (See (Fukuda: Summary – 5th-8th paragraphs and Detailed Description – 16th-31st, 32nd-38th, 47th, and 72nd-79th paragraphs))
Fukuda does not teach but Hirata teaches:
[…] when the vehicle and the brake motor are in a normal state., (“if a friction brake is operated in the same manner as in a normal state in a state where braking force is generated on a drive wheel” (Hirata: Description) Hirata further mentions “The braking force control device for a vehicle according to the present invention includes a braking / driving force generating means 6 capable of generating a driving force and a braking force on a plurality of wheels 1 to 4, […] For example, the abnormality-induced braking force is estimated from the difference between the braking force or driving force requested by the driver and the value obtained by multiplying the vehicle longitudinal acceleration measured by the vehicle acceleration sensor 23 by the vehicle weight.” (Hirata: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda with these above aforementioned teachings from Hirata in order to create an effective device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Hirata’s braking force control device for a vehicle in order to output an estimation value of an output variable of a brake motor, generate a driving force for moving a vehicle to generate a braking force, and detect whether the brake motor is in an abnormal state. Combining Fukuda and Hirata would thus provide “a braking force control device for a vehicle that controls braking force applied to each wheel when an abnormality occurs in the driving wheel in an electric vehicle having an electric motor on the driving wheel.” (Hirata: Description)
Regarding Claim 20:
Fukuda teaches:
A non-transitory computer-readable storage medium configured to store instructions that when executed by one or more processors, cause the one or more processors to perform operations comprising: by an artificial neural network model trained using, (See (Fukuda: Summary – 5th-8th paragraphs and Detailed Description – 16th-27th, 31st-38th, 47th, and 72nd-79th paragraphs))
Fukuda does not teach but Hirata teaches:
data obtained in a normal state of a brake motor configured to generate a driving force for moving a friction member of a wheel of a vehicle to generate a braking force, calculating, in response to input data associated with one or more operations of the vehicle, an estimation value of an output variable related to an output of the brake motor in the normal state of the brake motor; obtaining a measurement value of an output variable corresponding to a current state of the brake motor; detecting whether the brake motor is to be in an abnormal state based on a difference between the measurement value of the output variable corresponding to the current state of the brake motor and the estimation value, calculated by the artificial neural network model, of the output variable corresponding to the normal state of the brake motor;, (“The braking force control device for a vehicle according to the present invention includes a braking / driving force generating means 6 capable of generating a driving force and a braking force on a plurality of wheels 1 to 4, […] Can do.” (Hirata: Description) Hirata further mentions “As shown in FIG. 1, […] and the friction brake 5 and the motor 6 cooperate to generate a braking force.” (Hirata: Description))
and controlling the vehicle based on whether the brake motor is detected to be in the abnormal state., (“The braking force adjusting means 21 is the sum of the braking force distributed to the abnormal wheels 1 and (2-4) and the braking force caused by the abnormality (braking force) when the vehicle is braked by one or both of the friction brake 5 and the motor 6. […] so the present invention is not applied.” (Hirata: Description) Hirata further mentions “When an abnormality is detected (step S1: Yes), […] Thereafter, this process is terminated.” (Hirata: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda with these above aforementioned teachings from Hirata in order to create an effective device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Hirata’s braking force control device for a vehicle in order to output an estimation value of an output variable of a brake motor, generate a driving force for moving a vehicle to generate a braking force, and detect whether the brake motor is in an abnormal state. Combining Fukuda and Hirata would thus provide “a braking force control device for a vehicle that controls braking force applied to each wheel when an abnormality occurs in the driving wheel in an electric vehicle having an electric motor on the driving wheel.” (Hirata: Description)
Claims 9, 11, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Fukuda (U.S. Pub. No. 2022/0004846 A1) in view of Hirata (JP 2016107690 A) in further view of Kikuchi (TW I691420 B).
Regarding Claim 9:
Fukuda in view of Hirata, as shown in the rejection above, discloses the limitations of claim 5. Fukuda does not teach but Hirata teaches:
The device of claim 5, wherein the processor is configured to input error data related to the difference between the estimation value of the output variable and the measurement value of the output variable, (“The braking force control device for a vehicle according to the present invention includes a braking / driving force generating means 6 capable of generating a driving force and a braking force on a plurality of wheels 1 to 4, […] For example, the abnormality-induced braking force is estimated from the difference between the braking force or driving force requested by the driver and the value obtained by multiplying the vehicle longitudinal acceleration measured by the vehicle acceleration sensor 23 by the vehicle weight.” (Hirata: Description) Hirata further mentions “The braking force adjusting means 21 calculates the difference between the braking force distributed to the wheel in which an abnormality is detected and the sum of the braking force generated due to the abnormality of the wheel and the threshold when braking the vehicle. […] and it is possible to prevent the rear wheel side healthy wheels from slipping and ensure vehicle stability.” (Hirata: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda with these above aforementioned teachings from Hirata in order to create an effective device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Hirata’s braking force control device for a vehicle in order to output an estimation value of an output variable of a brake motor, generate a driving force for moving a vehicle to generate a braking force, and detect whether the brake motor is in an abnormal state. Combining Fukuda and Hirata would thus provide “a braking force control device for a vehicle that controls braking force applied to each wheel when an abnormality occurs in the driving wheel in an electric vehicle having an electric motor on the driving wheel.” (Hirata: Description)
Fukuda in view of Hirata does not teach but Kikuchi teaches:
[…] to an abnormality detection model and determine whether the brake motor is in the abnormal state based on an output of the abnormality detection model., (“The vehicle of FIG. 3 is equipped with a main motor 60a and a main motor 60b. […] thereby ensuring the braking force.” (Kikuchi: Description) Kikuchi further mentions “In addition, the model generation unit 140 generates an abnormality detection model of the braking device of the vehicle based on the travel information. […] The abnormality detection results of the deceleration performance, the air brake, and the group brake are stored in the detection result database 103.” (Kikuchi: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda in view of Hirata with these above aforementioned teachings from Kikuchi in order to create an accurate device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Kikuchi’s anomaly diagnosis device, method, and computer program in order to input error data related to an abnormality detection model and determine whether a brake motor of a vehicle is in an abnormal state based on an output of the abnormality detection model, the error data including an average, a standard deviation, and a maximum absolute error of errors between measurement values of output variables and estimation values of output variables. Combining Fukuda and Kikuchi would thus provide “an abnormality diagnosis device, abnormality diagnosis method, and computer program that realize high-accuracy diagnosis.” (Kikuchi: Description)
Regarding Claim 11:
Fukuda in view of Hirata, as shown in the rejection above, discloses the limitations of claim 9. Fukuda further teaches:
The device of claim 9, wherein: each of a plurality of data sets includes the input data,, (See (Fukuda: Summary – 5th-8th paragraphs and Detailed Description – 16th-27th, 31st-38th, 47th, and 72nd-79th paragraphs))
[…] and discrimination values of the discriminator for the input data and the measurement values of the output variables of the plurality of data sets., (See (Fukuda: Summary – 6th-8th paragraphs and Detailed Description – 16th-31st paragraphs))
Fukuda does not teach but Hirata teaches:
[…] the measurement value of the output variable, and the estimation value of the output variable; […], (“The braking force control device for a vehicle according to the present invention includes a braking / driving force generating means 6 capable of generating a driving force and a braking force on a plurality of wheels 1 to 4, […] For example, the abnormality-induced braking force is estimated from the difference between the braking force or driving force requested by the driver and the value obtained by multiplying the vehicle longitudinal acceleration measured by the vehicle acceleration sensor 23 by the vehicle weight.” (Hirata: Description) Hirata further mentions “The vehicle includes a motor 6 as the braking / driving force generating means, […] The device IWM may be configured.” (Hirata: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda with these above aforementioned teachings from Hirata in order to create an effective device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Hirata’s braking force control device for a vehicle in order to output an estimation value of an output variable of a brake motor, generate a driving force for moving a vehicle to generate a braking force, and detect whether the brake motor is in an abnormal state. Combining Fukuda and Hirata would thus provide “a braking force control device for a vehicle that controls braking force applied to each wheel when an abnormality occurs in the driving wheel in an electric vehicle having an electric motor on the driving wheel.” (Hirata: Description)
Fukuda in view of Hirata does not teach but Kikuchi teaches:
[…] and the error data includes an average and a standard deviation of errors between measurement values of output variables of the plurality of data sets and estimation values of output variables of the plurality of data sets, a maximum absolute error between the measurement values of the output variables of the plurality of data sets and the estimation values of the output variables of the plurality of data sets, […], (“The abnormal detection model of the group brake includes a prediction model (hereinafter, group brake model) based on the braking force values of a plurality of braking devices, […] Specifically, the group brake threshold is used in order to compare with the deviation that is the total difference between the predicted value of the group brake model and the actual measured value of the air brake pressure.” (Kikuchi: Description) Kikuchi further mentions “Next, a method of determining the threshold value set for the prediction model will be described. […] Any one of the deceleration threshold, the individual brake threshold, and the group brake threshold can be determined by the method described above.” (Kikuchi: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda in view of Hirata with these above aforementioned teachings from Kikuchi in order to create an accurate device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Kikuchi’s anomaly diagnosis device, method, and computer program in order to input error data related to an abnormality detection model and determine whether a brake motor of a vehicle is in an abnormal state based on an output of the abnormality detection model, the error data including an average, a standard deviation, and a maximum absolute error of errors between measurement values of output variables and estimation values of output variables. Combining Fukuda and Kikuchi would thus provide “an abnormality diagnosis device, abnormality diagnosis method, and computer program that realize high-accuracy diagnosis.” (Kikuchi: Description)
Regarding Claim 17:
Fukuda in view of Hirata, as shown in the rejection above, discloses the limitations of claim 15. Fukuda further teaches:
[…] inputting the input data and the measurement value of the output variable to the discriminator and obtaining the measurement related discrimination value generated by the discriminator; and inputting the measurement related discrimination value […], (See (Fukuda: Summary – 6th-8th paragraphs and Detailed Description – 16th-31st and 33rd paragraphs))
Fukuda does not teach but Hirata teaches:
The method of claim 15, wherein the detecting of whether the brake motor is in the abnormal state includes:, (“The braking force adjusting means 21 calculates the difference between the braking force distributed to the wheel in which an abnormality is detected and the sum of the braking force generated due to the abnormality of the wheel and the threshold when braking the vehicle. […] and it is possible to prevent the rear wheel side healthy wheels from slipping and ensure vehicle stability.” (Hirata: Description))
[…] and error data including values related to the difference between the estimation value of the output variable and the measurement value of the output variable […], (“The braking force control device for a vehicle according to the present invention includes a braking / driving force generating means 6 capable of generating a driving force and a braking force on a plurality of wheels 1 to 4, […] For example, the abnormality-induced braking force is estimated from the difference between the braking force or driving force requested by the driver and the value obtained by multiplying the vehicle longitudinal acceleration measured by the vehicle acceleration sensor 23 by the vehicle weight.” (Hirata: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda with these above aforementioned teachings from Hirata in order to create an effective device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Hirata’s braking force control device for a vehicle in order to output an estimation value of an output variable of a brake motor, generate a driving force for moving a vehicle to generate a braking force, and detect whether the brake motor is in an abnormal state. Combining Fukuda and Hirata would thus provide “a braking force control device for a vehicle that controls braking force applied to each wheel when an abnormality occurs in the driving wheel in an electric vehicle having an electric motor on the driving wheel.” (Hirata: Description)
Fukuda in view of Hirata does not teach but Kikuchi teaches:
[…] to an abnormality detection model and obtaining an output of the abnormality detection model., (“The vehicle of FIG. 3 is equipped with a main motor 60a and a main motor 60b. […] thereby ensuring the braking force.” (Kikuchi: Description) Kikuchi further mentions “In addition, the model generation unit 140 generates an abnormality detection model of the braking device of the vehicle based on the travel information. […] The abnormality detection results of the deceleration performance, the air brake, and the group brake are stored in the detection result database 103.” (Kikuchi: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda in view of Hirata with these above aforementioned teachings from Kikuchi in order to create an accurate device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Kikuchi’s anomaly diagnosis device, method, and computer program in order to input error data related to an abnormality detection model and determine whether a brake motor of a vehicle is in an abnormal state based on an output of the abnormality detection model, the error data including an average, a standard deviation, and a maximum absolute error of errors between measurement values of output variables and estimation values of output variables. Combining Fukuda and Kikuchi would thus provide “an abnormality diagnosis device, abnormality diagnosis method, and computer program that realize high-accuracy diagnosis.” (Kikuchi: Description)
Regarding Claim 19:
Fukuda in view of Hirata, as shown in the rejection above, discloses the limitations of claim 17. Fukuda further teaches:
The method of claim 17, wherein: each of a plurality of data sets includes the input data,, (See (Fukuda: Summary – 5th-8th paragraphs and Detailed Description – 16th-27th, 31st-38th, 47th, and 72nd-79th paragraphs))
[…] and discrimination values of the discriminator for the input data and the measurement values of the output variables of the plurality of data sets., (See (Fukuda: Summary – 6th-8th paragraphs and Detailed Description – 16th-31st paragraphs))
Fukuda does not teach but Hirata teaches:
[…] the measurement value of the output variable, and the estimation value of the output variable; […], (“The braking force control device for a vehicle according to the present invention includes a braking / driving force generating means 6 capable of generating a driving force and a braking force on a plurality of wheels 1 to 4, […] For example, the abnormality-induced braking force is estimated from the difference between the braking force or driving force requested by the driver and the value obtained by multiplying the vehicle longitudinal acceleration measured by the vehicle acceleration sensor 23 by the vehicle weight.” (Hirata: Description) Hirata further mentions “The vehicle includes a motor 6 as the braking / driving force generating means, […] The device IWM may be configured.” (Hirata: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda with these above aforementioned teachings from Hirata in order to create an effective device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Hirata’s braking force control device for a vehicle in order to output an estimation value of an output variable of a brake motor, generate a driving force for moving a vehicle to generate a braking force, and detect whether the brake motor is in an abnormal state. Combining Fukuda and Hirata would thus provide “a braking force control device for a vehicle that controls braking force applied to each wheel when an abnormality occurs in the driving wheel in an electric vehicle having an electric motor on the driving wheel.” (Hirata: Description)
Fukuda in view of Hirata does not teach but Kikuchi teaches:
[…] and the error data includes an average and a standard deviation of errors between measurement values of output variables of the plurality of data sets and estimation values of output variables of the plurality of data sets, a maximum absolute error between the measurement values of the output variables of the generator and the discriminator and the estimation values of the output variables of the plurality of data sets, […], (“The abnormal detection model of the group brake includes a prediction model (hereinafter, group brake model) based on the braking force values of a plurality of braking devices, […] Specifically, the group brake threshold is used in order to compare with the deviation that is the total difference between the predicted value of the group brake model and the actual measured value of the air brake pressure.” (Kikuchi: Description) Kikuchi further mentions “Next, a method of determining the threshold value set for the prediction model will be described. […] Any one of the deceleration threshold, the individual brake threshold, and the group brake threshold can be determined by the method described above.” (Kikuchi: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda in view of Hirata with these above aforementioned teachings from Kikuchi in order to create an accurate device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Kikuchi’s anomaly diagnosis device, method, and computer program in order to input error data related to an abnormality detection model and determine whether a brake motor of a vehicle is in an abnormal state based on an output of the abnormality detection model, the error data including an average, a standard deviation, and a maximum absolute error of errors between measurement values of output variables and estimation values of output variables. Combining Fukuda and Kikuchi would thus provide “an abnormality diagnosis device, abnormality diagnosis method, and computer program that realize high-accuracy diagnosis.” (Kikuchi: Description)
Claims 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Fukuda (U.S. Pub. No. 2022/0004846 A1) in view of Hirata (JP 2016107690 A) in further view of Kikuchi (TW I691420 B) in even further view of Nakao (JP 2018028845 A).
Regarding Claim 10:
Fukuda in view of Hirata, as shown in the rejection above, discloses the limitations of claim 9. Fukuda in view of Hirata does not teach but Kikuchi teaches:
The device of claim 9, wherein the abnormality detection model is configured to use, (“In addition, the model generation unit 140 generates an abnormality detection model of the braking device of the vehicle based on the travel information. […] The abnormality detection results of the deceleration performance, the air brake, and the group brake are stored in the detection result database 103.” (Kikuchi: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda in view of Hirata with these above aforementioned teachings from Kikuchi in order to create an accurate device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Kikuchi’s anomaly diagnosis device, method, and computer program in order to input error data related to an abnormality detection model and determine whether a brake motor of a vehicle is in an abnormal state based on an output of the abnormality detection model, the error data including an average, a standard deviation, and a maximum absolute error of errors between measurement values of output variables and estimation values of output variables. Combining Fukuda and Kikuchi would thus provide “an abnormality diagnosis device, abnormality diagnosis method, and computer program that realize high-accuracy diagnosis.” (Kikuchi: Description)
Fukuda in view of Hirata in further view of Kikuchi does not teach but Nakao teaches:
[…] a one-class support vector machine (OCSVM) algorithm., (“Nowadays, with the development of data collection infrastructure, it has become easy to collect data constantly from sensors attached to equipment. […] However, it cannot cope with a case in which a state that can be regarded as normal by a single class support vector machine determiner changes with time (for example, the number of rotations changes with time and the vibration pattern changes accordingly).” (Nakao: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda in view of Hirata in further view of Kikuchi with these above aforementioned teachings from Nakao in order to create a precise device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Nakao’s abnormality sign detection system and method in order to provide an abnormality detection model that is configured to use a one-class support vector machine (OCSVM) algorithm. Combining Fukuda and Nakao would thus provide “an abnormal sign detection system and an abnormal sign detection method capable of creating an appropriate class set even when the number of normal classes and the normal range are unknown.” (Nakao: Description)
Regarding Claim 18:
Fukuda in view of Hirata, as shown in the rejection above, discloses the limitations of claim 17. Fukuda in view of Hirata does not teach but Kikuchi teaches:
The method of claim 17, wherein the abnormality detection model uses, (“In addition, the model generation unit 140 generates an abnormality detection model of the braking device of the vehicle based on the travel information. […] The abnormality detection results of the deceleration performance, the air brake, and the group brake are stored in the detection result database 103.” (Kikuchi: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda in view of Hirata with these above aforementioned teachings from Kikuchi in order to create an accurate device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Kikuchi’s anomaly diagnosis device, method, and computer program in order to input error data related to an abnormality detection model and determine whether a brake motor of a vehicle is in an abnormal state based on an output of the abnormality detection model, the error data including an average, a standard deviation, and a maximum absolute error of errors between measurement values of output variables and estimation values of output variables. Combining Fukuda and Kikuchi would thus provide “an abnormality diagnosis device, abnormality diagnosis method, and computer program that realize high-accuracy diagnosis.” (Kikuchi: Description)
Fukuda in view of Hirata in further view of Kikuchi does not teach but Nakao teaches:
[…] a one-class support vector machine (OCSVM) algorithm., (“Nowadays, with the development of data collection infrastructure, it has become easy to collect data constantly from sensors attached to equipment. […] However, it cannot cope with a case in which a state that can be regarded as normal by a single class support vector machine determiner changes with time (for example, the number of rotations changes with time and the vibration pattern changes accordingly).” (Nakao: Description))
It would have been obvious to one of ordinary skill in the art at the time of filing, before the effective filing date of the claimed invention, to modify Fukuda in view of Hirata in further view of Kikuchi with these above aforementioned teachings from Nakao in order to create a precise device and method for detecting an abnormality of a brake motor. At the time the invention was filed, one of ordinary skill in the art would have been motivated to incorporate Fukuda’s system for forecasting multivariate time series data with Nakao’s abnormality sign detection system and method in order to provide an abnormality detection model that is configured to use a one-class support vector machine (OCSVM) algorithm. Combining Fukuda and Nakao would thus provide “an abnormal sign detection system and an abnormal sign detection method capable of creating an appropriate class set even when the number of normal classes and the normal range are unknown.” (Nakao: Description)
Response to Arguments
The 35 U.S.C. 101 rejection set forth in the Non-Final Rejection mailed on March 10th, 2026 has been withdrawn as the “Amendments” and “Remarks” filed by the Applicant on June 10th, 2026 satisfactorily overcome this rejection.
Applicant’s arguments filed on June 10th, 2026 with regard to the 35 U.S.C. 103 rejection have been fully considered but are not persuasive.
With regard to the 35 U.S.C. 103 rejection, the limitations are taught in the combination of Fukuda and Hirata as has been set forth above, contrary to the Applicant’s assertions. Therefore, the Applicant’s amendments and arguments are insufficient to overcome these prior art rejections.
More specifically, See (Fukuda: Summary – 5th-8th paragraphs and Detailed Description – 16th-27th, 31st-38th, 47th, and 72nd-79th paragraphs) In doing so, Anthony addresses the Applicant’s limitation of “by an artificial neural network model trained using” as set forth in claim 1 and similarly in claims 12 and 20.
Moreover, Hirata mentions “The braking force control device for a vehicle according to the present invention includes a braking / driving force generating means 6 capable of generating a driving force and a braking force on a plurality of wheels 1 to 4, […] Can do.” (Hirata: Description) Hirata further mentions “As shown in FIG. 1, […] and the friction brake 5 and the motor 6 cooperate to generate a braking force.” (Hirata: Description) Furthermore, Hirata states “The braking force adjusting means 21 is the sum of the braking force distributed to the abnormal wheels 1 and (2-4) and the braking force caused by the abnormality (braking force) when the vehicle is braked by one or both of the friction brake 5 and the motor 6. […] so the present invention is not applied.” (Hirata: Description) Hirata further mentions “When an abnormality is detected (step S1: Yes), […] Thereafter, this process is terminated.” (Hirata: Description) In doing so, Hirata addresses the Applicant’s limitations of “data obtained in a normal state of a brake motor configured to generate a driving force for moving a friction member of a wheel of a vehicle to generate a braking force, calculating, in response to input data associated with one or more operations of the vehicle, an estimation value of an output variable related to an output of the brake motor in the normal state of the brake motor; obtaining a measurement value of an output variable corresponding to a current state of the brake motor; detecting whether the brake motor is to be in an abnormal state based on a difference between the measurement value of the output variable corresponding to the current state of the brake motor and the estimation value, calculated by the artificial neural network model, of the output variable corresponding to the normal state of the brake motor” and “and controlling the vehicle based on whether the brake motor is detected to be in the abnormal state” as set forth in claim 1 and similarly in claims 12 and 20.
As a result, the combination of Fukuda and Hirata addresses “by an artificial neural network model trained using data obtained in a normal state of a brake motor configured to generate a driving force for moving a friction member of a wheel of a vehicle to generate a braking force, calculating, in response to input data associated with one or more operations of the vehicle, an estimation value of an output variable related to an output of the brake motor in the normal state of the brake motor; obtaining a measurement value of an output variable corresponding to a current state of the brake motor; detecting whether the brake motor is to be in an abnormal state based on a difference between the measurement value of the output variable corresponding to the current state of the brake motor and the estimation value, calculated by the artificial neural network model, of the output variable corresponding to the normal state of the brake motor; and controlling the vehicle based on whether the brake motor is detected to be in the abnormal state” as set forth by the Applicant in claim 1 and similarly in claims 12 and 20.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jeffrey Chalhoub whose telephone number is (571) 272-9754. The examiner can normally be reached Mon-Fri 8:30-5:30. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Angela Ortiz can be reached on (571) 272-1206. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.R.C./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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