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 NON-FINAL.
The examiner would like to note that this application is now being handled by examiner Jeffrey Chalhoub.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on September 5th, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without reciting significantly more. The claims are being rejected according to the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 5, p. 50-57 (January 7, 2019).
Step One: Does the Claim Fall Within a Statutory Category?
Yes. Claim 1 is directed towards a device (machine). Dependent claims 2-11 are also directed towards a device (machine). Claim 12 is directed towards a method (process). Dependent claims 13-19 are also directed towards a method (process). Finally, claim 20 is directed towards a non-transitory computer-readable storage medium (machine).
Step Two A, Prong One: Is a Judicial Exception Recited?
Yes. Taking into account claim 1 as one example, the claim recites obtaining the estimation value of the output variable from the artificial neural network model and detecting whether the brake motor is in an abnormal state based on a difference between the estimation value of the output variable and a measurement value of the output variable. These limitations, as drafted, are simple processes that, under their broadest reasonable interpretation, cover performance of the limitations in the mind. That is, nothing in the claim elements precludes the steps from practically being performed in the mind. For example, the claim encompasses a mechanic inspecting a vehicle brake, running a diagnosis by inputting the brake’s specifications into a diagnosis display in order to determine a malfunction of the brake, receiving a message on the display regarding a certain brake error, and alerting a driver of the vehicle about the brake’s malfunction. Thus, the claim recites a mental process.
Step Two A, Prong Two: Is the Abstract Idea Integrated into a Practical Application?
No. Claim 1 recites two additional elements – a memory and a processor. Both elements are recited at a high-level of generality (i.e., as means to transmit and receive data) such that they amount to no more than mere instructions to apply the exception using a generic memory and processor. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
On the other hand, claim 1 recites the additional element of inputting input data, associated with one or more operations of a vehicle, to an artificial neural network model configured to, in response to the input data, output an estimation value of an output variable related to an output of a brake motor configured to generate a driving force for moving a friction member of a wheel of the vehicle to generate a braking force.
The inputting step is recited at a high level of generality (i.e. as a general means of transmitting and receiving data), and amounts to mere data gathering, which is a form of insignificant pre-solution activity.
On the other hand, claims 12 and 20 recite the additional element of inputting input data, associated with one or more operations of a vehicle, to an artificial neural network model configured to, in response to the input data, output an estimation value of an output variable related to an output of a brake motor configured to generate a driving force for moving a friction member of a wheel of the vehicle to generate a braking force, and obtaining the estimation value of the output variable from the artificial neural network model.
The inputting step is recited at a high level of generality (i.e. as a general means of transmitting and receiving data), and amounts to mere data gathering, which is a form of insignificant pre-solution activity.
This type of abstract idea recited in claims 1-20 is a mental process.
Step Two B: Does the Claim Provide an Inventive Concept
No. Regarding claim 1, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a memory and a processor amount to no more than mere instructions to apply the exception using a generic memory and processor. Mere instructions to apply an exception using a memory and a processor cannot provide an inventive concept.
Dependent Claims
The dependent claims are merely further defining the abstract idea by providing field of use limitations on transmitting and receiving data and are not adding anything to the abstract idea set forth in the independent claims such that the invention will amount to significantly more than the abstract idea.
Claims 2-11 and 13-19 are merely field of use limitations which simply further limit the abstract idea set forth in claim 1 and 12, respectively. These claims do not contain further limitations that make them subject matter eligible.
For example, dependent claim 5 merely recites the well understood, routine and conventional computing functions of data transmission and gathering. These claims do not contain further limitations that make them subject matter eligible.
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 comprising: inputting input data, associated with one or more operations of a vehicle, to an artificial neural network model configured to, in response to the input data,, (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:
output an estimation value of an output variable related to an output of a brake motor configured to, (“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, and a braking force applied to each wheel 1 to 4. A vehicle braking force control device for controlling An abnormality detecting means 13 for detecting an abnormality when a braking force of a driving wheel to which a driving force is given by the braking / driving force generating means 6 among the wheels 1 to 4 is generated; For the wheels 1, (2-4) in which the abnormality is detected by the abnormality detecting means 13, the braking force for estimating the braking force generated due to the abnormality of the wheels 1, (2-4) according to a predetermined standard Estimating means 14; At the time of braking of the vehicle by the braking / driving force generating means 6, the sum of the braking force distributed to the wheels 1, (2-4) in which an abnormality is detected and the braking force estimated by the braking force estimating means 14 is: A braking force adjusting means 21 for adjusting a braking force distributed to any of the wheels 1 to 4 according to a defined rule when the threshold value is exceeded; Is provided. The predetermined standard and the predetermined rule are determined by the results of tests and simulations, respectively. According to this configuration, the abnormality detection means 13 detects an abnormality in which a braking force is generated for each wheel 1 to 4. The braking force estimator 14 determines the braking force generated due to the abnormality of the wheels 1, 2 to 4 (hereinafter sometimes referred to as “abnormal wheels”), that is, the abnormality cause. Estimate the braking force. 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, and the motor 6 is an in-wheel motor drive that is partially or entirely disposed in a wheel and includes the motor 6, a wheel bearing 16, and a speed reducer 15. The device IWM may be configured.” (Hirata: Description))
generate a driving force for moving a friction member of a wheel of the vehicle to generate a braking force; obtaining the estimation value of the output variable from the artificial neural network model;, (“The braking force control apparatus for a vehicle according to the present invention includes a braking / driving force generating means capable of generating a driving force and a braking force on a plurality of wheels, and controls the braking force applied to each wheel. An abnormality detecting means for detecting an abnormality in which a braking force of a driving wheel to which a driving force is applied by the braking / driving force generating means among the wheels is detected, and an abnormality is detected by the abnormality detecting means. A braking force estimating means for estimating a braking force generated due to an abnormality of the wheel according to a predetermined standard, and a wheel in which an abnormality is detected during braking of the vehicle by the braking / driving force generating means. Braking force adjusting means for adjusting the braking force to be distributed to any of the wheels according to a predetermined rule when the sum of the braking force to be distributed and the braking force estimated by the braking force estimating means exceeds a threshold; Provided. For this reason, even when braking force is generated due to an abnormality in the drive wheels, running stability during braking does not decrease, and vehicle deceleration corresponding to the amount of depression of the brake pedal is generated, making it difficult to cause uncomfortable feeling during braking. Can do.” (Hirata: Description) Hirata further mentions “As shown in FIG. 1, when the driver operates the brake pedal 11 to brake, the vehicle control device 7 distributes the braking force requested by the driver from the pedal operation amount to the four wheels, and the friction brake 5 or the motor 6. The normal distribution braking force is generated in each of the wheels 1 to 4. In this case, the friction brake control device 9 controls each friction brake 5, the motor control device 8 controls each motor 6, and the friction brake 5 and the motor 6 cooperate to generate a braking force.” (Hirata: Description))
and detecting whether the brake motor is in an abnormal state based on a difference between the estimation value of the output variable and a measurement value of the output variable., (“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. The difference may be distributed to and added to the braking force of other wheels for which no abnormality is detected by the abnormality detection means 13. Thus, the vehicle deceleration corresponding to the depression amount of the brake pedal 11 can be realized by redistributing the portion corresponding to the difference reduced from the braking force allocated to the abnormal wheel to the healthy wheel. Therefore, even if an abnormal wheel is detected by the abnormality detection means 13, it is difficult for the driver to feel an uncomfortable feeling when operating the brake. The braking force adjusting means 21 distributes the ratio of distributing the difference between the sum of the braking forces and the threshold value to the braking force of another wheel in which no abnormality is detected, and the braking force of the other wheel before allocating the difference. May be equal to the ratio. In this case, for example, it is possible to shorten the braking distance of the vehicle rather than redistributing the difference evenly to the healthy wheels, 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)
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, when the driver operates the brake pedal 11 to brake, the vehicle control device 7 distributes the braking force requested by the driver from the pedal operation amount to the four wheels, and the friction brake 5 or the motor 6. The normal distribution braking force is generated in each of the wheels 1 to 4. In this case, the friction brake control device 9 controls each friction brake 5, the motor control device 8 controls each motor 6, 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, for example, a control area network (abbreviation: CAN) communication line, 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, 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 […], (“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, and a braking force applied to each wheel 1 to 4. A vehicle braking force control device for controlling An abnormality detecting means 13 for detecting an abnormality when a braking force of a driving wheel to which a driving force is given by the braking / driving force generating means 6 among the wheels 1 to 4 is generated; For the wheels 1, (2-4) in which the abnormality is detected by the abnormality detecting means 13, the braking force for estimating the braking force generated due to the abnormality of the wheels 1, (2-4) according to a predetermined standard Estimating means 14; At the time of braking of the vehicle by the braking / driving force generating means 6, the sum of the braking force distributed to the wheels 1, (2-4) in which an abnormality is detected and the braking force estimated by the braking force estimating means 14 is: A braking force adjusting means 21 for adjusting a braking force distributed to any of the wheels 1 to 4 according to a defined rule when the threshold value is exceeded; Is provided. The predetermined standard and the predetermined rule are determined by the results of tests and simulations, respectively. According to this configuration, the abnormality detection means 13 detects an abnormality in which a braking force is generated for each wheel 1 to 4. The braking force estimator 14 determines the braking force generated due to the abnormality of the wheels 1, 2 to 4 (hereinafter sometimes referred to as “abnormal wheels”), that is, the abnormality cause. Estimate the braking force. 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, and a braking force applied to each wheel 1 to 4. A vehicle braking force control device for controlling An abnormality detecting means 13 for detecting an abnormality when a braking force of a driving wheel to which a driving force is given by the braking / driving force generating means 6 among the wheels 1 to 4 is generated; For the wheels 1, (2-4) in which the abnormality is detected by the abnormality detecting means 13, the braking force for estimating the braking force generated due to the abnormality of the wheels 1, (2-4) according to a predetermined standard Estimating means 14; At the time of braking of the vehicle by the braking / driving force generating means 6, the sum of the braking force distributed to the wheels 1, (2-4) in which an abnormality is detected and the braking force estimated by the braking force estimating means 14 is: A braking force adjusting means 21 for adjusting a braking force distributed to any of the wheels 1 to 4 according to a defined rule when the threshold value is exceeded; Is provided. The predetermined standard and the predetermined rule are determined by the results of tests and simulations, respectively. According to this configuration, the abnormality detection means 13 detects an abnormality in which a braking force is generated for each wheel 1 to 4. The braking force estimator 14 determines the braking force generated due to the abnormality of the wheels 1, 2 to 4 (hereinafter sometimes referred to as “abnormal wheels”), that is, the abnormality cause. Estimate the braking force. 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, and a braking force applied to each wheel 1 to 4. A vehicle braking force control device for controlling An abnormality detecting means 13 for detecting an abnormality when a braking force of a driving wheel to which a driving force is given by the braking / driving force generating means 6 among the wheels 1 to 4 is generated; For the wheels 1, (2-4) in which the abnormality is detected by the abnormality detecting means 13, the braking force for estimating the braking force generated due to the abnormality of the wheels 1, (2-4) according to a predetermined standard Estimating means 14; At the time of braking of the vehicle by the braking / driving force generating means 6, the sum of the braking force distributed to the wheels 1, (2-4) in which an abnormality is detected and the braking force estimated by the braking force estimating means 14 is: A braking force adjusting means 21 for adjusting a braking force distributed to any of the wheels 1 to 4 according to a defined rule when the threshold value is exceeded; Is provided. The predetermined standard and the predetermined rule are determined by the results of tests and simulations, respectively. According to this configuration, the abnormality detection means 13 detects an abnormality in which a braking force is generated for each wheel 1 to 4. The braking force estimator 14 determines the braking force generated due to the abnormality of the wheels 1, 2 to 4 (hereinafter sometimes referred to as “abnormal wheels”), that is, the abnormality cause. Estimate the braking force. 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: inputting input data, associated with one or more operations of a vehicle, to an artificial neural network model configured to, in response to the input data,, (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:
output an estimation value of an output variable related to an output of a brake motor configured to, (“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, and a braking force applied to each wheel 1 to 4. A vehicle braking force control device for controlling An abnormality detecting means 13 for detecting an abnormality when a braking force of a driving wheel to which a driving force is given by the braking / driving force generating means 6 among the wheels 1 to 4 is generated; For the wheels 1, (2-4) in which the abnormality is detected by the abnormality detecting means 13, the braking force for estimating the braking force generated due to the abnormality of the wheels 1, (2-4) according to a predetermined standard Estimating means 14; At the time of braking of the vehicle by the braking / driving force generating means 6, the sum of the braking force distributed to the wheels 1, (2-4) in which an abnormality is detected and the braking force estimated by the braking force estimating means 14 is: A braking force adjusting means 21 for adjusting a braking force distributed to any of the wheels 1 to 4 according to a defined rule when the threshold value is exceeded; Is provided. The predetermined standard and the predetermined rule are determined by the results of tests and simulations, respectively. According to this configuration, the abnormality detection means 13 detects an abnormality in which a braking force is generated for each wheel 1 to 4. The braking force estimator 14 determines the braking force generated due to the abnormality of the wheels 1, 2 to 4 (hereinafter sometimes referred to as “abnormal wheels”), that is, the abnormality cause. Estimate the braking force. 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, and the motor 6 is an in-wheel motor drive that is partially or entirely disposed in a wheel and includes the motor 6, a wheel bearing 16, and a speed reducer 15. The device IWM may be configured.” (Hirata: Description))
generate a driving force for moving a friction member of a wheel of the vehicle to generate a braking force, and obtaining the estimation value of the output variable from the artificial neural network model;, (“The braking force control apparatus for a vehicle according to the present invention includes a braking / driving force generating means capable of generating a driving force and a braking force on a plurality of wheels, and controls the braking force applied to each wheel. An abnormality detecting means for detecting an abnormality in which a braking force of a driving wheel to which a driving force is applied by the braking / driving force generating means among the wheels is detected, and an abnormality is detected by the abnormality detecting means. A braking force estimating means for estimating a braking force generated due to an abnormality of the wheel according to a predetermined standard, and a wheel in which an abnormality is detected during braking of the vehicle by the braking / driving force generating means. Braking force adjusting means for adjusting the braking force to be distributed to any of the wheels according to a predetermined rule when the sum of the braking force to be distributed and the braking force estimated by the braking force estimating means exceeds a threshold; Provided. For this reason, even when braking force is generated due to an abnormality in the drive wheels, running stability during braking does not decrease, and vehicle deceleration corresponding to the amount of depression of the brake pedal is generated, making it difficult to cause uncomfortable feeling during braking. Can do.” (Hirata: Description) Hirata further mentions “As shown in FIG. 1, when the driver operates the brake pedal 11 to brake, the vehicle control device 7 distributes the braking force requested by the driver from the pedal operation amount to the four wheels, and the friction brake 5 or the motor 6. The normal distribution braking force is generated in each of the wheels 1 to 4. In this case, the friction brake control device 9 controls each friction brake 5, the motor control device 8 controls each motor 6, and the friction brake 5 and the motor 6 cooperate to generate a braking force.” (Hirata: Description))
and detecting whether the brake motor is in an abnormal state based on a difference between the estimation value of the output variable and a measurement value of the output variable., (“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. The difference may be distributed to and added to the braking force of other wheels for which no abnormality is detected by the abnormality detection means 13. Thus, the vehicle deceleration corresponding to the depression amount of the brake pedal 11 can be realized by redistributing the portion corresponding to the difference reduced from the braking force allocated to the abnormal wheel to the healthy wheel. Therefore, even if an abnormal wheel is detected by the abnormality detection means 13, it is difficult for the driver to feel an uncomfortable feeling when operating the brake. The braking force adjusting means 21 distributes the ratio of distributing the difference between the sum of the braking forces and the threshold value to the braking force of another wheel in which no abnormality is detected, and the braking force of the other wheel before allocating the difference. May be equal to the ratio. In this case, for example, it is possible to shorten the braking distance of the vehicle rather than redistributing the difference evenly to the healthy wheels, 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)
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, when the driver operates the brake pedal 11 to brake, the vehicle control device 7 distributes the braking force requested by the driver from the pedal operation amount to the four wheels, and the friction brake 5 or the motor 6. The normal distribution braking force is generated in each of the wheels 1 to 4. In this case, the friction brake control device 9 controls each friction brake 5, the motor control device 8 controls each motor 6, 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, 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 […], (“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, and a braking force applied to each wheel 1 to 4. A vehicle braking force control device for controlling An abnormality detecting means 13 for detecting an abnormality when a braking force of a driving wheel to which a driving force is given by the braking / driving force generating means 6 among the wheels 1 to 4 is generated; For the wheels 1, (2-4) in which the abnormality is detected by the abnormality detecting means 13, the braking force for estimating the braking force generated due to the abnormality of the wheels 1, (2-4) according to a predetermined standard Estimating means 14; At the time of braking of the vehicle by the braking / driving force generating means 6, the sum of the braking force distributed to the wheels 1, (2-4) in which an abnormality is detected and the braking force estimated by the braking force estimating means 14 is: A braking force adjusting means 21 for adjusting a braking force distributed to any of the wheels 1 to 4 according to a defined rule when the threshold value is exceeded; Is provided. The predetermined standard and the predetermined rule are determined by the results of tests and simulations, respectively. According to this configuration, the abnormality detection means 13 detects an abnormality in which a braking force is generated for each wheel 1 to 4. The braking force estimator 14 determines the braking force generated due to the abnormality of the wheels 1, 2 to 4 (hereinafter sometimes referred to as “abnormal wheels”), that is, the abnormality cause. Estimate the braking force. 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, and a braking force applied to each wheel 1 to 4. A vehicle braking force control device for controlling An abnormality detecting means 13 for detecting an abnormality when a braking force of a driving wheel to which a driving force is given by the braking / driving force generating means 6 among the wheels 1 to 4 is generated; For the wheels 1, (2-4) in which the abnormality is detected by the abnormality detecting means 13, the braking force for estimating the braking force generated due to the abnormality of the wheels 1, (2-4) according to a predetermined standard Estimating means 14; At the time of braking of the vehicle by the braking / driving force generating means 6, the sum of the braking force distributed to the wheels 1, (2-4) in which an abnormality is detected and the braking force estimated by the braking force estimating means 14 is: A braking force adjusting means 21 for adjusting a braking force distributed to any of the wheels 1 to 4 according to a defined rule when the threshold value is exceeded; Is provided. The predetermined standard and the predetermined rule are determined by the results of tests and simulations, respectively. According to this configuration, the abnormality detection means 13 detects an abnormality in which a braking force is generated for each wheel 1 to 4. The braking force estimator 14 determines the braking force generated due to the abnormality of the wheels 1, 2 to 4 (hereinafter sometimes referred to as “abnormal wheels”), that is, the abnormality cause. Estimate the braking force. 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: inputting input data, associated with one or more operations of a vehicle, to an artificial neural network model configured to, in response to the input data,, (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:
output an estimation value of an output variable related to an output of a brake motor configured to, (“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, and a braking force applied to each wheel 1 to 4. A vehicle braking force control device for controlling An abnormality detecting means 13 for detecting an abnormality when a braking force of a driving wheel to which a driving force is given by the braking / driving force generating means 6 among the wheels 1 to 4 is generated; For the wheels 1, (2-4) in which the abnormality is detected by the abnormality detecting means 13, the braking force for estimating the braking force generated due to the abnormality of the wheels 1, (2-4) according to a predetermined standard Estimating means 14; At the time of braking of the vehicle by the braking / driving force generating means 6, the sum of the braking force distributed to the wheels 1, (2-4) in which an abnormality is detected and the braking force estimated by the braking force estimating means 14 is: A braking force adjusting means 21 for adjusting a braking force distributed to any of the wheels 1 to 4 according to a defined rule when the threshold value is exceeded; Is provided. The predetermined standard and the predetermined rule are determined by the results of tests and simulations, respectively. According to this configuration, the abnormality detection means 13 detects an abnormality in which a braking force is generated for each wheel 1 to 4. The braking force estimator 14 determines the braking force generated due to the abnormality of the wheels 1, 2 to 4 (hereinafter sometimes referred to as “abnormal wheels”), that is, the abnormality cause. Estimate the braking force. 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, and the motor 6 is an in-wheel motor drive that is partially or entirely disposed in a wheel and includes the motor 6, a wheel bearing 16, and a speed reducer 15. The device IWM may be configured.” (Hirata: Description))
generate a driving force for moving a friction member of a wheel of the vehicle to generate a braking force, and obtaining the estimation value of the output variable from the artificial neural network model;, (“The braking force control apparatus for a vehicle according to the present invention includes a braking / driving force generating means capable of generating a driving force and a braking force on a plurality of wheels, and controls the braking force applied to each wheel. An abnormality detecting means for detecting an abnormality in which a braking force of a driving wheel to which a driving force is applied by the braking / driving force generating means among the wheels is detected, and an abnormality is detected by the abnormality detecting means. A braking force estimating means for estimating a braking force generated due to an abnormality of the wheel according to a predetermined standard, and a wheel in which an abnormality is detected during braking of the vehicle by the braking / driving force generating means. Braking force adjusting means for adjusting the braking force to be distributed to any of the wheels according to a predetermined rule when the sum of the braking force to be distributed and the braking force estimated by the braking force estimating means exceeds a threshold; Provided. For this reason, even when braking force is generated due to an abnormality in the drive wheels, running stability during braking does not decrease, and vehicle deceleration corresponding to the amount of depression of the brake pedal is generated, making it difficult to cause uncomfortable feeling during braking. Can do.” (Hirata: Description) Hirata further mentions “As shown in FIG. 1, when the driver operates the brake pedal 11 to brake, the vehicle control device 7 distributes the braking force requested by the driver from the pedal operation amount to the four wheels, and the friction brake 5 or the motor 6. The normal distribution braking force is generated in each of the wheels 1 to 4. In this case, the friction brake control device 9 controls each friction brake 5, the motor control device 8 controls each motor 6, and the friction brake 5 and the motor 6 cooperate to generate a braking force.” (Hirata: Description))
and detecting whether the brake motor is in an abnormal state based on a difference between the estimation value of the output variable and a measurement value of the output variable., (“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. The difference may be distributed to and added to the braking force of other wheels for which no abnormality is detected by the abnormality detection means 13. Thus, the vehicle deceleration corresponding to the depression amount of the brake pedal 11 can be realized by redistributing the portion corresponding to the difference reduced from the braking force allocated to the abnormal wheel to the healthy wheel. Therefore, even if an abnormal wheel is detected by the abnormality detection means 13, it is difficult for the driver to feel an uncomfortable feeling when operating the brake. The braking force adjusting means 21 distributes the ratio of distributing the difference between the sum of the braking forces and the threshold value to the braking force of another wheel in which no abnormality is detected, and the braking force of the other wheel before allocating the difference. May be equal to the ratio. In this case, for example, it is possible to shorten the braking distance of the vehicle rather than redistributing the difference evenly to the healthy wheels, 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)
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, and a braking force applied to each wheel 1 to 4. A vehicle braking force control device for controlling An abnormality detecting means 13 for detecting an abnormality when a braking force of a driving wheel to which a driving force is given by the braking / driving force generating means 6 among the wheels 1 to 4 is generated; For the wheels 1, (2-4) in which the abnormality is detected by the abnormality detecting means 13, the braking force for estimating the braking force generated due to the abnormality of the wheels 1, (2-4) according to a predetermined standard Estimating means 14; At the time of braking of the vehicle by the braking / driving force generating means 6, the sum of the braking force distributed to the wheels 1, (2-4) in which an abnormality is detected and the braking force estimated by the braking force estimating means 14 is: A braking force adjusting means 21 for adjusting a braking force distributed to any of the wheels 1 to 4 according to a defined rule when the threshold value is exceeded; Is provided. The predetermined standard and the predetermined rule are determined by the results of tests and simulations, respectively. According to this configuration, the abnormality detection means 13 detects an abnormality in which a braking force is generated for each wheel 1 to 4. The braking force estimator 14 determines the braking force generated due to the abnormality of the wheels 1, 2 to 4 (hereinafter sometimes referred to as “abnormal wheels”), that is, the abnormality cause. Estimate the braking force. 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. The difference may be distributed to and added to the braking force of other wheels for which no abnormality is detected by the abnormality detection means 13. Thus, the vehicle deceleration corresponding to the depression amount of the brake pedal 11 can be realized by redistributing the portion corresponding to the difference reduced from the braking force allocated to the abnormal wheel to the healthy wheel. Therefore, even if an abnormal wheel is detected by the abnormality detection means 13, it is difficult for the driver to feel an uncomfortable feeling when operating the brake. The braking force adjusting means 21 distributes the ratio of distributing the difference between the sum of the braking forces and the threshold value to the braking force of another wheel in which no abnormality is detected, and the braking force of the other wheel before allocating the difference. May be equal to the ratio. In this case, for example, it is possible to shorten the braking distance of the vehicle rather than redistributing the difference evenly to the healthy wheels, 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. When a power generating brake is used, the main motor 60a, the main motor 60b and the resistor 70 constitute a closed circuit, which further converts the electric power of the main motor into heat energy. On the other hand, when the regenerative brake is used, electric power generated by the main motor 60 a and the main motor 60 b is transmitted from the pantograph 80 toward the overhead line 90. Alternatively, when a battery is mounted in the vehicle, the battery may be charged using the generated electric power. In this way, in the regenerative brake, the main motor 60a and the main motor 60b are used as generators, and the kinetic energy is converted into electric power, 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. In this embodiment, an air brake is assumed as a braking device, and an abnormality detection model of the air brake is generated. In the case of a vehicle formation, an abnormality detection model of the braking device may be separately generated for vehicles provided with a braking device. Instead of separately generating an abnormality detection model for each braking device, an abnormality detection model common to a plurality of braking devices may be generated. In addition, the model generating unit 140 generates an abnormality detection model of a braking system (hereinafter referred to as a group brake). The group brake includes brake devices provided in a plurality of vehicles, and the abnormality detection model of the group brake is the overall abnormality detection model of these brake devices. Various abnormality detection models generated by the model generation unit 140 are stored in the model database 102. In the operation mode, the abnormality detection unit 150 uses the abnormality detection model of the deceleration performance to perform abnormality detection of the deceleration performance. In addition, the abnormal detection of the air brake is performed using the abnormal detection model of the air brake. In addition, the abnormal detection of the group brake is performed using the abnormal detection model of the group brake. The so-called anomaly detection refers to determining whether there is an anomaly. Anomaly detection is also called anomaly determination. 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, and a braking force applied to each wheel 1 to 4. A vehicle braking force control device for controlling An abnormality detecting means 13 for detecting an abnormality when a braking force of a driving wheel to which a driving force is given by the braking / driving force generating means 6 among the wheels 1 to 4 is generated; For the wheels 1, (2-4) in which the abnormality is detected by the abnormality detecting means 13, the braking force for estimating the braking force generated due to the abnormality of the wheels 1, (2-4) according to a predetermined standard Estimating means 14; At the time of braking of the vehicle by the braking / driving force generating means 6, the sum of the braking force distributed to the wheels 1, (2-4) in which an abnormality is detected and the braking force estimated by the braking force estimating means 14 is: A braking force adjusting means 21 for adjusting a braking force distributed to any of the wheels 1 to 4 according to a defined rule when the threshold value is exceeded; Is provided. The predetermined standard and the predetermined rule are determined by the results of tests and simulations, respectively. According to this configuration, the abnormality detection means 13 detects an abnormality in which a braking force is generated for each wheel 1 to 4. The braking force estimator 14 determines the braking force generated due to the abnormality of the wheels 1, 2 to 4 (hereinafter sometimes referred to as “abnormal wheels”), that is, the abnormality cause. Estimate the braking force. 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, and the motor 6 is an in-wheel motor drive that is partially or entirely disposed in a wheel and includes the motor 6, a wheel bearing 16, and a speed reducer 15. 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, and a threshold value (hereinafter, group brake threshold) related to the deviation of the predicted value of the self-prediction model value). In this embodiment, an air brake is assumed as a braking device, and a prediction model of the total air brake pressure is assumed. If the value is based on the braking force of a plurality of braking devices, other values such as a statistical value such as an average value or an intermediate value may be used instead of the total of the braking force. 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. In addition, in the following description, when it is described as a prediction model, it may represent any one of a deceleration model, an air brake pressure model, and a group brake model. Regarding the threshold value, any one of the deceleration threshold value, the individual brake threshold value, and the group brake threshold value can be expressed in the same manner. Here, as a method of using the threshold value, when the difference between the predicted value of the target variable calculated by the prediction model (for example, the predicted value of deceleration) and the measured value of the deceleration (actually measured value) exceeds the threshold value, Make an abnormal decision. Making an abnormal decision is also called detecting abnormality. The difference between the predicted value and the measured value is called divergence. There may be both a case where the actual measured value is greater than the predicted value and a case where the actual measured value is less than the predicted value, so the value of the deviation may adopt the sign of either positive or negative. When focusing on the absolute value of the distance from the self-predicted value, and the sign is not a problem, the absolute value of the difference can also be defined as a deviation. FIG. 8 shows an example of the determination method using the threshold value of the normal distribution. The graph of FIG. 8 shows the normal distribution 401 of divergence, the horizontal axis is the divergence, and the vertical axis is the probability density. The deviations between the predicted values and the measured values of the plurality of prediction models are obtained, and it is assumed that the plurality of deviations are distributed according to the normal distribution, and a normal distribution 401 is prepared. The data used to obtain multiple deviations may be data samples used to generate the prediction model, test data, other driving information not related to the generation of the prediction model, or any combination of these. When the deviation from the deviation is larger, the normal distribution 402 and the normal distribution 403 indicated by the broken lines become distributions whose edges further expand. Using the normal distribution 401, a threshold value for the prediction model is set. As an example, if the standard deviation is set to σ, the value of a constant multiple of the standard deviation such as 2σ or 3σ is set as the threshold. When 2σ is set as the threshold value, if the deviation exceeds 2σ, an abnormality is detected in the abnormality detection. If such a threshold value is set, about 95% of the measured value is judged as no abnormality (normal). As another setting example of the threshold value, a deviation value corresponding to a predetermined probability (for example, upper X percentage points or lower X percentage points) or its absolute value may be set as the threshold value. The method of determining the threshold value described here is an example, and does not exclude the use of other methods. For example, it may be assumed that a distribution other than the normal distribution determines the threshold, and maintenance personnel, drivers, and others may also set the threshold based on experience. 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. The difference may be distributed to and added to the braking force of other wheels for which no abnormality is detected by the abnormality detection means 13. Thus, the vehicle deceleration corresponding to the depression amount of the brake pedal 11 can be realized by redistributing the portion corresponding to the difference reduced from the braking force allocated to the abnormal wheel to the healthy wheel. Therefore, even if an abnormal wheel is detected by the abnormality detection means 13, it is difficult for the driver to feel an uncomfortable feeling when operating the brake. The braking force adjusting means 21 distributes the ratio of distributing the difference between the sum of the braking forces and the threshold value to the braking force of another wheel in which no abnormality is detected, and the braking force of the other wheel before allocating the difference. May be equal to the ratio. In this case, for example, it is possible to shorten the braking distance of the vehicle rather than redistributing the difference evenly to the healthy wheels, 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, and a braking force applied to each wheel 1 to 4. A vehicle braking force control device for controlling An abnormality detecting means 13 for detecting an abnormality when a braking force of a driving wheel to which a driving force is given by the braking / driving force generating means 6 among the wheels 1 to 4 is generated; For the wheels 1, (2-4) in which the abnormality is detected by the abnormality detecting means 13, the braking force for estimating the braking force generated due to the abnormality of the wheels 1, (2-4) according to a predetermined standard Estimating means 14; At the time of braking of the vehicle by the braking / driving force generating means 6, the sum of the braking force distributed to the wheels 1, (2-4) in which an abnormality is detected and the braking force estimated by the braking force estimating means 14 is: A braking force adjusting means 21 for adjusting a braking force distributed to any of the wheels 1 to 4 according to a defined rule when the threshold value is exceeded; Is provided. The predetermined standard and the predetermined rule are determined by the results of tests and simulations, respectively. According to this configuration, the abnormality detection means 13 detects an abnormality in which a braking force is generated for each wheel 1 to 4. The braking force estimator 14 determines the braking force generated due to the abnormality of the wheels 1, 2 to 4 (hereinafter sometimes referred to as “abnormal wheels”), that is, the abnormality cause. Estimate the braking force. 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. When a power generating brake is used, the main motor 60a, the main motor 60b and the resistor 70 constitute a closed circuit, which further converts the electric power of the main motor into heat energy. On the other hand, when the regenerative brake is used, electric power generated by the main motor 60 a and the main motor 60 b is transmitted from the pantograph 80 toward the overhead line 90. Alternatively, when a battery is mounted in the vehicle, the battery may be charged using the generated electric power. In this way, in the regenerative brake, the main motor 60a and the main motor 60b are used as generators, and the kinetic energy is converted into electric power, 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. In this embodiment, an air brake is assumed as a braking device, and an abnormality detection model of the air brake is generated. In the case of a vehicle formation, an abnormality detection model of the braking device may be separately generated for vehicles provided with a braking device. Instead of separately generating an abnormality detection model for each braking device, an abnormality detection model common to a plurality of braking devices may be generated. In addition, the model generating unit 140 generates an abnormality detection model of a braking system (hereinafter referred to as a group brake). The group brake includes brake devices provided in a plurality of vehicles, and the abnormality detection model of the group brake is the overall abnormality detection model of these brake devices. Various abnormality detection models generated by the model generation unit 140 are stored in the model database 102. In the operation mode, the abnormality detection unit 150 uses the abnormality detection model of the deceleration performance to perform abnormality detection of the deceleration performance. In addition, the abnormal detection of the air brake is performed using the abnormal detection model of the air brake. In addition, the abnormal detection of the group brake is performed using the abnormal detection model of the group brake. The so-called anomaly detection refers to determining whether there is an anomaly. Anomaly detection is also called anomaly determination. 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, and a braking force applied to each wheel 1 to 4. A vehicle braking force control device for controlling An abnormality detecting means 13 for detecting an abnormality when a braking force of a driving wheel to which a driving force is given by the braking / driving force generating means 6 among the wheels 1 to 4 is generated; For the wheels 1, (2-4) in which the abnormality is detected by the abnormality detecting means 13, the braking force for estimating the braking force generated due to the abnormality of the wheels 1, (2-4) according to a predetermined standard Estimating means 14; At the time of braking of the vehicle by the braking / driving force generating means 6, the sum of the braking force distributed to the wheels 1, (2-4) in which an abnormality is detected and the braking force estimated by the braking force estimating means 14 is: A braking force adjusting means 21 for adjusting a braking force distributed to any of the wheels 1 to 4 according to a defined rule when the threshold value is exceeded; Is provided. The predetermined standard and the predetermined rule are determined by the results of tests and simulations, respectively. According to this configuration, the abnormality detection means 13 detects an abnormality in which a braking force is generated for each wheel 1 to 4. The braking force estimator 14 determines the braking force generated due to the abnormality of the wheels 1, 2 to 4 (hereinafter sometimes referred to as “abnormal wheels”), that is, the abnormality cause. Estimate the braking force. 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, and the motor 6 is an in-wheel motor drive that is partially or entirely disposed in a wheel and includes the motor 6, a wheel bearing 16, and a speed reducer 15. 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, and a threshold value (hereinafter, group brake threshold) related to the deviation of the predicted value of the self-prediction model value). In this embodiment, an air brake is assumed as a braking device, and a prediction model of the total air brake pressure is assumed. If the value is based on the braking force of a plurality of braking devices, other values such as a statistical value such as an average value or an intermediate value may be used instead of the total of the braking force. 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. In addition, in the following description, when it is described as a prediction model, it may represent any one of a deceleration model, an air brake pressure model, and a group brake model. Regarding the threshold value, any one of the deceleration threshold value, the individual brake threshold value, and the group brake threshold value can be expressed in the same manner. Here, as a method of using the threshold value, when the difference between the predicted value of the target variable calculated by the prediction model (for example, the predicted value of deceleration) and the measured value of the deceleration (actually measured value) exceeds the threshold value, Make an abnormal decision. Making an abnormal decision is also called detecting abnormality. The difference between the predicted value and the measured value is called divergence. There may be both a case where the actual measured value is greater than the predicted value and a case where the actual measured value is less than the predicted value, so the value of the deviation may adopt the sign of either positive or negative. When focusing on the absolute value of the distance from the self-predicted value, and the sign is not a problem, the absolute value of the difference can also be defined as a deviation. FIG. 8 shows an example of the determination method using the threshold value of the normal distribution. The graph of FIG. 8 shows the normal distribution 401 of divergence, the horizontal axis is the divergence, and the vertical axis is the probability density. The deviations between the predicted values and the measured values of the plurality of prediction models are obtained, and it is assumed that the plurality of deviations are distributed according to the normal distribution, and a normal distribution 401 is prepared. The data used to obtain multiple deviations may be data samples used to generate the prediction model, test data, other driving information not related to the generation of the prediction model, or any combination of these. When the deviation from the deviation is larger, the normal distribution 402 and the normal distribution 403 indicated by the broken lines become distributions whose edges further expand. Using the normal distribution 401, a threshold value for the prediction model is set. As an example, if the standard deviation is set to σ, the value of a constant multiple of the standard deviation such as 2σ or 3σ is set as the threshold. When 2σ is set as the threshold value, if the deviation exceeds 2σ, an abnormality is detected in the abnormality detection. If such a threshold value is set, about 95% of the measured value is judged as no abnormality (normal). As another setting example of the threshold value, a deviation value corresponding to a predetermined probability (for example, upper X percentage points or lower X percentage points) or its absolute value may be set as the threshold value. The method of determining the threshold value described here is an example, and does not exclude the use of other methods. For example, it may be assumed that a distribution other than the normal distribution determines the threshold, and maintenance personnel, drivers, and others may also set the threshold based on experience. 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. In this embodiment, an air brake is assumed as a braking device, and an abnormality detection model of the air brake is generated. In the case of a vehicle formation, an abnormality detection model of the braking device may be separately generated for vehicles provided with a braking device. Instead of separately generating an abnormality detection model for each braking device, an abnormality detection model common to a plurality of braking devices may be generated. In addition, the model generating unit 140 generates an abnormality detection model of a braking system (hereinafter referred to as a group brake). The group brake includes brake devices provided in a plurality of vehicles, and the abnormality detection model of the group brake is the overall abnormality detection model of these brake devices. Various abnormality detection models generated by the model generation unit 140 are stored in the model database 102. In the operation mode, the abnormality detection unit 150 uses the abnormality detection model of the deceleration performance to perform abnormality detection of the deceleration performance. In addition, the abnormal detection of the air brake is performed using the abnormal detection model of the air brake. In addition, the abnormal detection of the group brake is performed using the abnormal detection model of the group brake. The so-called anomaly detection refers to determining whether there is an anomaly. Anomaly detection is also called anomaly determination. 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. It is possible to construct a more accurate judgment criterion for abnormalities / normality directly based on the collected large amount of data. However, it is usually difficult to collect all abnormal data of machinery and equipment. Therefore, a method can be considered in which a boundary surface in a normal range is constructed from only normal data, and the equipment is considered to be abnormal based on the data deviating from the boundary surface or the ratio of the deviating data. For example, a one-class support vector machine (one-class SVM; hereinafter sometimes referred to as OCSVM) may be applied to such a one-class identification problem. 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. In this embodiment, an air brake is assumed as a braking device, and an abnormality detection model of the air brake is generated. In the case of a vehicle formation, an abnormality detection model of the braking device may be separately generated for vehicles provided with a braking device. Instead of separately generating an abnormality detection model for each braking device, an abnormality detection model common to a plurality of braking devices may be generated. In addition, the model generating unit 140 generates an abnormality detection model of a braking system (hereinafter referred to as a group brake). The group brake includes brake devices provided in a plurality of vehicles, and the abnormality detection model of the group brake is the overall abnormality detection model of these brake devices. Various abnormality detection models generated by the model generation unit 140 are stored in the model database 102. In the operation mode, the abnormality detection unit 150 uses the abnormality detection model of the deceleration performance to perform abnormality detection of the deceleration performance. In addition, the abnormal detection of the air brake is performed using the abnormal detection model of the air brake. In addition, the abnormal detection of the group brake is performed using the abnormal detection model of the group brake. The so-called anomaly detection refers to determining whether there is an anomaly. Anomaly detection is also called anomaly determination. 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))
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. It is possible to construct a more accurate judgment criterion for abnormalities / normality directly based on the collected large amount of data. However, it is usually difficult to collect all abnormal data of machinery and equipment. Therefore, a method can be considered in which a boundary surface in a normal range is constructed from only normal data, and the equipment is considered to be abnormal based on the data deviating from the boundary surface or the ratio of the deviating data. For example, a one-class support vector machine (one-class SVM; hereinafter sometimes referred to as OCSVM) may be applied to such a one-class identification problem. 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)
Conclusion
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/J.R.C./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663