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 .
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an applications filed in Korea:
KR10-2024-0021747 (Filing Date 02/15/2024)
KR10-2023-0106181 (Filing Date08/14/2023)
The examiner notes that copies of these documents were received from the International Bureau (WIPO) on 02/25/2026.
Response to Amendment
This action is in response to amendments and remarks filed on 04/17/2026. The examiner notes the following adjustments to the claims by the applicant:
Claims 1, 2, 5, 10, 12, 13, 15, and 20 are amended;
No claims are cancelled and no new claims added.
Therefore, Claims 1-20 are pending examination, in which Claims 1, 12 and 20 are independent claims.
In light of the instant amendments and arguments:
Regarding the rejection of Claims 1-4, 12-14 and 20 under 35 U.S.C. §102, the rejection is withdrawn.
Further examination resulted in a new rejection of Claims 1-20 under 35 U.S.C. §103, as detailed below.
THIS ACTION IS MADE FINAL. Necessitated by amendment.
Response to Arguments
Applicant presents the following arguments regarding the previous office action:
To overcome the 35 U.S.C. §102 rejection, the applicant has amended each independent claim to include the additional underlined limitations: "inputting operation data of a steering wheel…related to steering wheel operation wherein the artificial neural network model is trained by a loss function including a physics-based loss function reflecting dynamic characteristics of the column electric power steering (EPS) system.".
[A.] “Kim does not disclose each and every limitation recited in claim 1. For example, Kim fails to disclose "inputting operation data of a steering wheel related to steering wheel operation by a driver of the vehicle and state data indicating a state of the vehicle into an artificial neural network model to obtain at least one estimated value of an output of the motor from the artificial neural network model" as recited in claim 1 (emphasis added). The Office Action indicates that "Kim teaches ... inputting operation data related to steering wheel operation by a driver of the vehicle and state data indicating a state of the vehicle {"The ANN observer to estimate a fault in a motor in R-EPS is designed using the minimum sensor signal, the rotational angle of the motor measured by a rotational encoder.", Pg. 3, 3; see also input parameters in Fig. 1" (Office Action: pages 3-4; emphasis added).”;
[B.] “In Kim, it is silent of "detecting the abnormality of the motor of the EPS based on comparison result between the estimated value of the output of the motor estimated by the artificial neural network model and the measured value of the output of the motor sensed by the sensor" as recited in claim 1 (emphasis added). The Office Action contends that "Kim teaches ... detecting the abnormality of the motor of the EPS based on comparison result between the estimated value of the output of the motor estimated by the artificial neural network model and the measured value of the output of the motor sensed by the sensor {as represented in Fig. 1, and provided immediately below; '(iii) The ANN observer for estimating a fault, fa(t), in a motor is proposed in Section 4. In addition, two representative model-based approaches, an adaptive observer and a Kalman filter, are presented to compare the estimation performance with the one via proposed ANN observer.', Pg. 5}" (Office Action: pages 3-4).”;
[C.] “”Accordingly, in Kim, there is no identical or completely detailed disclosure of "detecting the abnormality of the motor of the EPS based on comparison result between the estimated value of the output of the motor estimated by the artificial neural network model and the measured value of the output of the motor sensed by the sensor" as recited in claim 1 (emphasis added).”.
Applicant's arguments A., B. and C. appear to be directed to the instantly amended subject matter. Accordingly, they have been addressed in the rejections below.
Claim Rejections - 35 USC §103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 12-14 and 20 are rejected under 35 U.S.C. §103 as being unpatentable over the combination of Kim et al. (“Fault Estimation of Rack-Driving Motor in Electrical Power Steering System Using an Artificial Neural Network Observer”, henceforth Kim) and Trimboli et al. (US 2014/0371989 A1, henceforth Trimboli).
Regarding Claim 1, Kim teaches the limitations: a device comprising: a memory configured to store one or more instructions; and a processor {ECU, ¶3 of Pg. 3, which is inherently associated with computerized system comprising a processor and memory} configured to detect an abnormality of a motor of a column electric power steering (EPS) {fault detection or estimation in the motor/actuator, to avoid a steering condition malfunction in an electrical power steering/EPS system, Pg. 1, 1. Introduction} configured to provide an auxiliary steering force {the aforementioned electrical power steering system inherently provides an auxiliary force to the steering column to assist the driver, as is well known in the art} to a steering column of the vehicle {system described in Abstract pertains to “an auxiliary axle steering for 10-ton class electro-hydraulic synchronous steering system”, Pg. 18, Conclusions} and execute the one or more instructions {fault detection, Abstract} comprising: inputting operational data {with regard to Fig. 2, input data (i.e., input vector, Pg. 6, ¶2) includes the angular rotation and velocity of the motor, and the motor torque} into an artificial neural network model {ANN represent pictorially in Fig. 2 on Pg. 6} to obtain at least one estimated value of an output of the motor from the artificial neural network model {output from ANN in Fig. 2}, comparing the estimated value of the output of the motor estimated by the artificial neural network model with at least one value {comparison of estimated fault is with a true reference, Fig. 5, Pg. 13 and Section 5.1, Pg. 10; the examiner notes the true-reference is not a measured value but rather a synthetic value, the measured value aspect is dealt with in the next reference} , and detecting the abnormality of the motor of the EPS {detection of a fault based on the parametric levels of the input parameters is evident in Fig. 6, Pg. 13}, wherein the artificial neural network model is trained by a loss function including a physics-based loss function reflecting dynamic characteristics of the column electric power steering (EPS) system {with regard to Section 4.1, Pg. 5-7, the training of the neural network includes the use of loss-function Eq. 21}.
Kim does not appear to explicitly recite the limitations: inputting operation data of a steering wheel related to steering wheel operation by a driver of the vehicle and state data indicating a state of the vehicle into an artificial neural network model to obtain at least one estimated value of an output of the motor from the artificial neural network model; comparing the estimated value of the output of the motor estimated by the artificial neural network model with at least one measured value of the output of the motor sensed by a sensor; detecting the abnormality of the motor of the EPS based on comparison result between the estimated value of the output of the motor estimated by the artificial neural network model and the measured value of the output of the motor sensed by the sensor.
However, Trimboli explicitly recites the limitations: inputting operation data of a steering wheel related to steering wheel operation by a driver of the vehicle {¶24, power-assisted steering system partially represented in Fig. 1, in which: electric servomotor 4, steering column 5, steering wheel 6 and torque sensor 7} and state data indicating a state of the vehicle into an artificial neural network model {input data to manager 10, such as torque and vehicle speed, described in ¶26} to obtain at least one estimated value of an output of the motor from the artificial neural network model {per ¶17, manager 10 may be a neural network}; comparing the estimated value of the output of the motor estimated by the artificial neural network model with at least one measured value of the output of the motor sensed by a sensor {per ¶26, manager 10 uses a model to predict the overall steering system response and compares it to measured values}; detecting the abnormality of the motor of the EPS based on comparison result between the estimated value of the output of the motor estimated by the artificial neural network model and the measured value of the output of the motor sensed by the sensor {the difference in calculated and measured values determined in ¶26, necessarily represents an undesirable outcome, as will be appreciated by in the art}.
Kim and Trimboli are analogous art because they both deal with using neural networks to evaluate the operation of power steering systems.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings Kim and Trimboli before them, to modify the teachings of Kim to include the teachings of Trimboli to detect.
Regarding Claim 2, the combination of Kim and Trimboli discloses all the limitations of Claim 1, as discussed supra. In addition, Kim explicitly recites the limitation: wherein the operation data of the steering wheel related to the steering wheel operation by the driver includes at least one of a steering angle of the steering wheel, a steering angular velocity of the steering wheel, and a steering torque of the steering wheel {“The ANN observer to estimate a fault in a motor in R-EPS is designed using the minimum sensor signal, the rotational angle of the motor measured by a rotational encoder.”, Pg. 3, ¶3; see also input parameters in Fig. 1}.
Regarding Claim 3, the combination of Kim and Trimboli discloses all the limitations of Claim 1, as discussed supra. In addition, Kim explicitly recites the limitation: wherein the state data indicating the state of the vehicle includes at least one of a speed of the vehicle, a lateral acceleration of the vehicle, a yaw rate of the vehicle, and a wheel speed of the vehicle {“In the first phase, according to a given representative fault scenario, collecting and preprocessing the related data are conducted to secure the input data for the reference output.”, Pg. 6, Section 4.1; and “the angular position of motor, the angular rate and the control torque as well as the absolute error between desire trajectory and actual angular position of motor.”, Pg. 7, second paragraph; and Fig. 2 on Pg. 7}.
Regarding Claim 4, the combination of Kim and Trimboli discloses all the limitations of Claim 1, as discussed supra. In addition, Kim explicitly recites the limitation: wherein the operation data and the state data comprise signals that are obtainable through a controller area network (CAN) of the vehicle {“The ANN observer to estimate a fault in a motor in R-EPS is designed using the minimum sensor signal, the rotational angle of the motor measured by a rotational encoder.”, Pg. 3, ¶3; see also input parameters in Fig. 1, wherein one skilled in the art will appreciate that use of a CAN-bus is an industry standard}.
Regarding Claim 12, Kim teaches the limitations: a method detecting an abnormality of a motor of a column electric power steering (EPS) {fault detection or estimation in the motor/actuator, to avoid a steering condition malfunction in an electrical power steering/EPS system, Pg. 1, 1. Introduction} configured to provide an auxiliary steering force {the aforementioned electrical power steering system inherently provides an auxiliary force to the steering column to assist the driver, as is well known in the art}to a steering column of a vehicle {system described in Abstract pertains to “an auxiliary axle steering for 10-ton class electro-hydraulic synchronous steering system”, Pg. 18, Conclusions}, the method comprising: obtaining, by a processor {ECU, ¶3 of Pg. 3, which is inherently associated with computerized system comprising a processor and memory}, at least one estimated value of an output of the motor from the artificial neural network model {output from ANN in Fig. 2} by inputting operation data {with regard to Fig. 2, input data (i.e., input vector, Pg. 6, ¶2) includes the angular rotation and velocity of the motor, and the motor torque} into an artificial neural network model {ANN represent pictorially in Fig. 2 on Pg. 6}; detecting, by the processor, the abnormality of the motor EPS {detection of a fault based on the parametric levels of the input parameters is evident in Fig. 6, Pg. 13} by comparing the estimated value of the output of the motor estimated by the artificial neural network model with at least one value {comparison of estimated fault is with a true reference, Fig. 5, Pg. 13 and Section 5.1, Pg. 10; the examiner notes the true-reference is not a measured value but rather a synthetic value, the measured value aspect is dealt with in the next reference}, and detecting the abnormality of the motor of the EPS {detection of a fault based on the parametric levels of the input parameters is evident in Fig. 6, Pg. 13}, wherein the artificial neural network model is trained by a loss function including a physics-based loss function reflecting dynamic characteristics of the column electric power steering (EPS) system {with regard to Section 4.1, Pg. 5-7, the training of the neural network includes the use of loss-function Eq. 21}.
Kim does not appear to explicitly recite the limitations: obtaining at least one estimated value of an output of the motor from the artificial neural network model by inputting operation data of the steering wheel related to steering wheel operation by a driver of the vehicle and state data indicating a state of the vehicle into the artificial neural network model; detecting, by the processor, the abnormality of the motor by comparing the estimated value of the output of the motor estimated by the artificial neural network model with at least one measured value of the output of the motor sensed by a sensor; wherein the artificial neural network model is trained by a loss function including a physics-based loss function reflecting dynamic characteristics of the column electric power steering (EPS) system.
However, Trimboli explicitly recites the limitations: obtaining at least one estimated value of an output of the motor from the artificial neural network model {per ¶17, manager 10 may be a neural network} by inputting operation data of the steering wheel related to steering wheel operation by a driver of the vehicle {¶24, power-assisted steering system partially represented in Fig. 1, in which: electric servomotor 4, steering column 5, steering wheel 6 and torque sensor 7} and state data indicating a state of the vehicle into the artificial neural network model {input data to manager 10, such as torque and vehicle speed, described in ¶26}; detecting, by the processor, the abnormality of the motor {the difference in calculated and measured values determined in ¶26, necessarily represents an undesirable outcome, as will be appreciated by in the art} by comparing the estimated value of the output of the motor estimated by the artificial neural network model with at least one measured value of the output of the motor sensed by a sensor {per ¶26, manager 10 uses a model to predict the overall steering system response and compares it to measured values}.
Regarding Claim 13, the combination of Kim and Trimboli discloses all the limitations of Claim 12, as discussed supra. In addition, Kim explicitly recites the limitation: wherein the operation data related to the steering wheel operation by the driver includes at least one of a steering angle, a steering angular velocity, and a steering torque {“The ANN observer to estimate a fault in a motor in R-EPS is designed using the minimum sensor signal, the rotational angle of the motor measured by a rotational encoder.”, Pg. 3, ¶3; see also input parameters in Fig. 1}.
Regarding Claim 14, the combination of Kim and Trimboli discloses all the limitations of Claim 12, as discussed supra. In addition, Kim explicitly recites the limitation: wherein the state data indicating the state of the vehicle includes at least one of a speed of the vehicle, a lateral acceleration of the vehicle, a yaw rate of the vehicle, and a wheel speed of the vehicle {“In the first phase, according to a given representative fault scenario, collecting and preprocessing the related data are conducted to secure the input data for the reference output.”, Pg. 6, Section 4.1; and “the angular position of motor, the angular rate and the control torque as well as the absolute error between desire trajectory and actual angular position of motor.”, Pg. 7, second paragraph; and Fig. 2 on Pg. 7}.
Regarding Claim 20, Kim teaches the limitations: non-transitory computer-readable medium configured to store at least one instruction {ECU, ¶3 of Pg. 3, which is inherently associated with computerized system comprising a processor, memory and executable computer coding}, that when executed by a processor, causes the processor to perform operations of detecting an abnormality of a motor {fault detection or estimation in the motor/actuator, to avoid a steering condition malfunction in an electrical power steering/EPS system, Pg. 1, 1. Introduction} of a column electric power steering (EPS) configured to provide an auxiliary steering force {the aforementioned electrical power steering system inherently provides an auxiliary force to the steering column to assist the driver, as is well known in the art} to a steering column of a vehicle {system described in Abstract pertains to “an auxiliary axle steering for 10-ton class electro-hydraulic synchronous steering system”, Pg. 18, Conclusions}, the operations comprising: obtaining at least one estimated value of an output of the motor from the artificial neural network model {output from ANN in Fig. 2} by inputting operation data {with regard to Fig. 2, input data (i.e., input vector, Pg. 6, ¶2) includes the angular rotation and velocity of the motor, and the motor torque} into an artificial neural network model {ANN represent pictorially in Fig. 2 on Pg. 6}; detecting, by the processor, the abnormality of the motor EPS {detection of a fault based on the parametric levels of the input parameters is evident in Fig. 6, Pg. 13} by comparing the estimated value of the output of the motor estimated by the artificial neural network model with at least one value {comparison of estimated fault is with a true reference, Fig. 5, Pg. 13 and Section 5.1, Pg. 10; the examiner notes the true-reference is not a measured value but rather a synthetic value, the measured value aspect is dealt with in the next reference} , and detecting the abnormality of the motor of the EPS {detection of a fault based on the parametric levels of the input parameters is evident in Fig. 6, Pg. 13}, wherein the artificial neural network model is trained by a loss function including a physics-based loss function reflecting dynamic characteristics of the column electric power steering (EPS) system {with regard to Section 4.1, Pg. 5-7, the training of the neural network includes the use of loss-function Eq. 21}.
Kim does not appear to explicitly recite the limitations: obtaining at least one estimated value of an output of the motor from the artificial neural network model by inputting operation data of the steering wheel related to steering wheel operation by a driver of the vehicle and state data indicating a state of the vehicle into the artificial neural network model; detecting, by the processor, the abnormality of the motor by comparing the estimated value of the output of the motor estimated by the artificial neural network model with at least one measured value of the output of the motor sensed by a sensor; wherein the artificial neural network model is trained by a loss function including a physics-based loss function reflecting dynamic characteristics of the column electric power steering (EPS) system.
However, Trimboli explicitly recites the limitations: obtaining at least one estimated value of an output of the motor from the artificial neural network model {per ¶17, manager 10 may be a neural network} by inputting operation data of the steering wheel related to steering wheel operation by a driver of the vehicle {¶24, power-assisted steering system partially represented in Fig. 1, in which: electric servomotor 4, steering column 5, steering wheel 6 and torque sensor 7} and state data indicating a state of the vehicle into the artificial neural network model {input data to manager 10, such as torque and vehicle speed, described in ¶26}; detecting, by the processor, the abnormality of the motor {the difference in calculated and measured values determined in ¶26, necessarily represents an undesirable outcome, as will be appreciated by in the art} by comparing the estimated value of the output of the motor estimated by the artificial neural network model with at least one measured value of the output of the motor sensed by a sensor {per ¶26, manager 10 uses a model to predict the overall steering system response and compares it to measured values}.
Claims 5-11 and 15-19 are rejected under 35 U.S.C. §103 as being unpatentable over the combination of Kim, Trimboli and Lee (KR 20230017677 A).
Regarding Claim 5, the combination of Kim and Trimboli discloses all the limitations of Claim 5, as discussed supra. The combination of Kim and Trimboli does not appear to explicitly recite the limitations: wherein the artificial neural network model includes a generative adversarial network (GAN) including a generator configured to receive the operation data of the steering wheel related to the steering wheel operation by the driver and the state data indicating the state of the vehicle and generate the estimated value of the output of the motor.
However, Lee explicitly recites the limitation: wherein the artificial neural network model includes a generative adversarial network (GAN) {“The present invention relates to a method for evaluating a vehicle controller based on a generative adversarial network.”, Abstract} including a generator configured to receive the operation data of the steering wheel related to the steering wheel operation by the driver and the state data indicating the state of the vehicle and generate the estimated value of the output of the motor {“In the method for evaluating a vehicle controller based on a generative adversarial network according to an embodiment of the present invention, a step of generating a real vehicle simulation signal using a time-series generative adversarial network (S110) and determining whether the vehicle controller is abnormal through abnormality detection (S120) is included.”, ¶41, associated with Fig. 2}.
Kim and Lee are analogous art because they both deal with detecting abnormalities in normal vehicle operation.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Kim and Lee before them, to modify the teachings of Kim to include the teachings of Lee to increase the reliability of a fault detection system {¶38}.
Regarding Claim 6, the combination of Kim, Trimboli and Lee discloses all the limitations of Claim 5, as discussed supra. The combination of Kim and Trimboli does not appear to explicitly recite the limitations: wherein the artificial neural network model further includes a discriminator configured to receive measurement data including the operation data, the state data, and the measured value of the output of the motor and output a discrimination value for the measurement data.
However, Lee explicitly recites the limitation: wherein the artificial neural network model further includes a discriminator configured to receive measurement data including the operation data, the state data, and the measured value of the output of the motor and output a discrimination value for the measurement data {“The discrimination unit 108 to which the encoder structure is applied distinguishes the generated simulated input value from the actual vehicle input, and classification is performed by the classification unit 109, so that learning is possible to distinguish between the actual vehicle signal and the simulated generated signal. is carried out”, ¶53}.
Regarding Claim 7, the combination of Kim, Trimboli and Lee discloses all the limitations of Claim 6, as discussed supra. The combination of Kim and Trimboli does not appear to explicitly recite the limitations: wherein the processor is configured to input error data related to a difference between the estimated value of the output of the motor estimated by the artificial neural network model and the measured value of the output of the motor sensed by the sensor into an abnormality detection model to detect the abnormality of the motor .
However, Lee explicitly recites the limitation: wherein the processor is configured to input error data related to a difference between the estimated value of the output of the motor estimated by the artificial neural network model and the measured value of the output of the motor sensed by the sensor into an abnormality detection model {“abnormality detection network”, Abstract, and 200 Fig. 4, in which the output from GAN 100 is input into 200} to detect the abnormality of the motor {“step (a) embeds the actual vehicle input data to obtain a latent code, restores the size of the feature reduced in the embedding process, and learns the network to enable regeneration of the actual vehicle input data. The step (a) generates a random vehicle input using a random vector, and learns to distinguish between a simulated input value and an actual vehicle input.”, ¶10-11}.
Regarding Claim 8, the combination of Kim, Trimboli and Lee discloses all the limitations of Claim 7, as discussed supra. The combination of Kim and Trimboli does not appear to explicitly recite the limitations: wherein the abnormality detection model uses a one-class support vector machine (OCSVM) algorithm.
However, Lee explicitly recites the limitation: wherein the abnormality detection model uses a one-class support vector machine (OCSVM) algorithm {anomaly detection process, implementation and algorithms: “In the method for evaluating a vehicle controller based on a generative adversarial network according to an embodiment of the present invention, a step of generating a real vehicle simulation signal using a time-series generative adversarial network (S110) and determining whether the vehicle controller is abnormal through abnormality detection (S120) is included.”, ¶41, and “The anomaly detection network 200 is used to determine whether input/output data is similar to a data set measured in a real vehicle.”, ¶58]; see also ¶37}.
Regarding Claim 9, the combination of Kim, Trimboli and Lee discloses all the limitations of Claim 7, as discussed supra. The combination of Kim and Trimboli does not appear to explicitly recite the limitations: the at least one estimated value of the output of the motor estimated by the artificial neural network model comprises a plurality of estimated values, the at least one measured value of the output of the motor sensed by the sensor comprises a plurality of measured values, and a plurality of data sets include the operation data, the state data, the plurality of measured values, and the plurality of estimated values; and the error data input into the abnormality detection model includes a mean and standard deviation of errors between the plurality of measured values and the plurality of estimated values that are obtained from the plurality of data sets, a maximum absolute error among the errors between the plurality of measured values and the plurality of estimated values of the plurality of data sets, and a discrimination value of the discriminator for the measurement data.
However, Lee explicitly recites the limitations: the at least one estimated value of the output of the motor estimated by the artificial neural network model comprises a plurality of estimated values, the at least one measured value of the output of the motor sensed by the sensor comprises a plurality of measured values, and a plurality of data sets include the operation data, the state data, the plurality of measured values, and the plurality of estimated values {the combined GAN 100 and abnormality detection network 220 is represented in Fig. 4; one skilled in the art will appreciate that a plurality of input and output data is standard for robust results from neural network type systems}; and the error data input into the abnormality detection model includes a mean and standard deviation of errors between the plurality of measured values and the plurality of estimated values that are obtained from the plurality of data sets, a maximum absolute error among the errors between the plurality of measured values and the plurality of estimated values of the plurality of data sets {with respect to Fig. 4, output from time-series GAN is directed to abnormality/anomaly detection network 200; one skilled in the art will appreciated that manipulating data in the form of calculating mean-values, standard deviations and maximum values is well known in the art} and a discrimination value of the discriminator for the measurement data {operation of a discrimination unit as part of adversarial neural network generation described in ¶51-54}.
Regarding Claim 10, the combination of Kim, Trimboli and Lee discloses all the limitations of Claim 6, as discussed supra. The combination of Kim and Trimboli does not appear to explicitly recite the limitations: wherein the discriminator is configured to additionally receive estimate data including the operation data of the steering wheel related to the steering wheel operation, the state data indicating the state of the vehicle, and the estimated value of the output of the motor estimated by the artificial neural network model, and additionally output discrimination value for the estimate data.
However, Lee explicitly recites the limitations: wherein the discriminator {discrimination unit 108, Fig. 4 and ¶53} is configured to additionally receive estimate data including the operation data of the steering wheel related to the steering wheel operation, the state data indicating the state of the vehicle {with respect to Fig. 4, vehicle input data reproduction unit 101 inputs data to time-series GAN 200}, and the estimated value of the output of the motor estimated by the artificial neural network model, and additionally output discrimination value for the estimate data {with respect to Fig. 4, output from time-series GAN is directed to abnormality/anomaly detection network 200}.
Regarding Claim 11, the combination of Kim, Trimboli and Lee discloses all the limitations of Claim 10, as discussed supra. The combination of Kim and Trimboli does not appear to explicitly recite the limitations: the artificial neural network model has the generator and the discriminator alternately performing learning, and the artificial neural network model is configured to perform the learning using the operation data, the state data, and the measured value which are obtained when the motor is in a normal state.
However, Lee explicitly recites the limitation: the artificial neural network model has the generator {generation unit 106, Fig. 4 and ¶52} and the discriminator {discrimination unit 108, Fig. 4 and ¶53} alternately performing learning {a time-series GAN system is represented in Figs. 3-4 (100) by elements 103-109, one skilled in the art will appreciated that training a neural network is inherently iterative} and the artificial neural network model is configured to perform the learning using the operation data, the state data, and the measured value which are obtained when the motor is in a normal state {operation of time-series GAN portion of Fig. 4 is described in ¶50-54}.
Regarding Claim 15, the combination of Kim, Trimboli and Lee discloses all the limitations of Claim 12, as discussed supra. The combination of Kim and Trimboli does not appear to explicitly recite the limitations: wherein the artificial neural network model comprises a generative adversarial network (GAN) including: a generator configured to receive the operation data of the steering wheel related to the steering wheel operation by the driver and the state data indicating the state of the vehicle and generate the estimated value of the output of the motor, and a discriminator configured to receive measurement data including the operation data, the state data, and the measured value of the output of the motor and output a discrimination value for the measurement data.
However, Lee explicitly recites the limitation: wherein the artificial neural network model includes a generative adversarial network (GAN) {“The present invention relates to a method for evaluating a vehicle controller based on a generative adversarial network.”, Abstract} including a generator configured to receive the operation data of the steering wheel related to the steering wheel operation by the driver and the state data indicating the state of the vehicle and generate the estimated value of the output of the motor {“In the method for evaluating a vehicle controller based on a generative adversarial network according to an embodiment of the present invention, a step of generating a real vehicle simulation signal using a time-series generative adversarial network (S110) and determining whether the vehicle controller is abnormal through abnormality detection (S120) is included.”, ¶41, associated with Fig. 2}, and a discriminator {discrimination unit 108, Fig. 4 and ¶53} configured to receive measurement data including the operation data, the state data {with respect to Fig. 4, vehicle input data reproduction unit 101 inputs data to time-series GAN 200}, and the measured value of the output of the motor and output a discrimination value for the measurement data {with respect to Fig. 4, output from time-series GAN is directed to abnormality/anomaly detection network 200}.
Regarding Claim 16, the combination of Kim, Trimboli and Lee discloses all the limitations of Claim 15, as discussed supra. The combination of Kim and Trimboli does not appear to explicitly recite the limitations: wherein the obtaining of the at least one estimated value of the output of the motor from the artificial neural network model comprises inputting the operation data and the state data into the generator and obtaining the estimated value of the output of the motor generated by the generator.
However, Lee explicitly recites the limitation: wherein the obtaining of the at least one estimated value of the output of the motor from the artificial neural network model comprises inputting the operation data and the state data {with respect to Fig. 4, vehicle input data reproduction unit 101 inputs data to time-series GAN 200} into the generator and obtaining the estimated value of the output of the motor generated by the generator {with respect to Fig. 4, the input and output from generating unit 106 of GAN 100; also see ¶51-54 for additional description of GAN 100}.
Regarding Claim 17, the combination of Kim, Trimboli and Lee discloses all the limitations of Claim 16, as discussed supra. The combination of Kim and Trimboli does not appear to explicitly recite the limitations: wherein the detecting of the abnormality of the motor includes: inputting, by the processor, measurement data including the operation data, the state data, and the measured value of the output of the motor into the discriminator and obtaining, by the processor, the discrimination value for the measurement data generated by the discriminator; and inputting, by the processor, error data including the discrimination value for the measurement data and a value related to a difference between the estimated value and the measured value into an abnormality detection model and obtaining, by the processor, an output of the abnormality detection model.
However, Lee explicitly recites the limitations: wherein the detecting of the abnormality of the motor includes: inputting, by the processor, measurement data including the operation data, the state data {with respect to Fig. 4, vehicle input data reproduction unit 101 inputs data to time-series GAN 200}, and the measured value of the output of the motor into the discriminator and obtaining, by the processor, the discrimination value for the measurement data generated by the discriminator {“The discrimination unit 108 to which the encoder structure is applied distinguishes the generated simulated input value from the actual vehicle input, and classification is performed by the classification unit 109, so that learning is possible to distinguish between the actual vehicle signal and the simulated generated signal. is carried out”, ¶53}; and inputting, by the processor, error data including the discrimination value for the measurement data and a value related to a difference between the estimated value and the measured value into an abnormality detection model {“abnormality detection network”, Abstract, and 200, Fig. 4, in which the output from GAN 100 is input into 200} and obtaining, by the processor, an output of the abnormality detection model {“step (a) embeds the actual vehicle input data to obtain a latent code, restores the size of the feature reduced in the embedding process, and learns the network to enable regeneration of the actual vehicle input data. The step (a) generates a random vehicle input using a random vector, and learns to distinguish between a simulated input value and an actual vehicle input.”, ¶10-11}.
Regarding Claim 18, the combination of Kim, Trimboli and Lee discloses all the limitations of Claim 17, as discussed supra. The combination of Kim and Trimboli does not appear to explicitly recite the limitations: wherein: the at least one estimated value of the output of the motor estimated by the artificial neural network model comprises a plurality of estimated values, the at least one measured value of the output of the motor sensed by the sensor comprises a plurality of measured values, and a plurality of data sets include the operation data, the state data, the plurality of estimated values, and the plurality of measured values; and the error data input into the abnormality detection model includes a mean and standard deviation of errors between the plurality of measured values and the plurality of estimated values that are obtained from the plurality of data sets, a maximum absolute error among the errors between the plurality of measured values and the plurality of estimated values of the plurality of data sets, and a discrimination value of the discriminator for the measurement data
However, Lee explicitly recites the limitations: wherein: the at least one estimated value of the output of the motor estimated by the artificial neural network model comprises a plurality of estimated values, the at least one measured value of the output of the motor sensed by the sensor comprises a plurality of measured values, and a plurality of data sets include the operation data, the state data, the plurality of estimated values, and the plurality of measured values {the combined GAN 100 and abnormality detection network 220 is represented in Fig. 4; one skilled in the art will appreciate that a plurality of input and output data is standard for robust results from neural network type systems}; and the error data input into the abnormality detection model includes a mean and standard deviation of errors between the plurality of measured values and the plurality of estimated values that are obtained from the plurality of data sets, a maximum absolute error among the errors between the plurality of measured values and the plurality of estimated values of the plurality of data sets {with respect to Fig. 4, output from time-series GAN is directed to abnormality/anomaly detection network 200; one skilled in the art will appreciated that manipulating data in the form of calculating mean-values, standard deviations and maximum values is well known in the art} and a discrimination value of the discriminator for the measurement data {operation of a discrimination unit as part of adversarial neural network generation described in ¶51-54}.
Regarding Claim 19, the combination of Kim, Trimboli and Lee discloses all the limitations of Claim 15, as discussed supra. The combination of Kim and Trimboli does not appear to explicitly recite the limitations: the artificial neural network model has the generator and the discriminator alternately performing learning, and the artificial neural network model is configured to perform the learning using the operation data, the state data, and the measured value which are obtained when the motor is in a normal state.
However, Lee explicitly recites the limitation: the artificial neural network model has the generator {generation unit 106, Fig. 4 and ¶52} and the discriminator {discrimination unit 108, Fig. 4 and ¶53} alternately performing learning {a time-series GAN system is represented in Figs. 3-4 (100) by elements 103-109, one skilled in the art will appreciated that training a neural network is inherently iterative} and the artificial neural network model is configured to perform the learning using the operation data, the state data, and the measured value which are obtained when the motor is in a normal state {operation of time-series GAN portion of Fig. 4 is described in ¶50-54}.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/R.E.G./Examiner, Art Unit 3665
*************** /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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