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 .
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.
Claim(s) 1-4 and 8-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20190111839 A1, hereinafter “Lee”) in view of Takahashi (US 20130182864 A1,hereinafter “Takahashi”).
Regarding claim 1, Lee teaches a virtual engine sound generating system using an engine vibration simulated signal for a vehicle to which an active sound design (ASD) function and an engine sound by engine vibration (ESEV) function are applied, the virtual engine sound generating system comprising: (see [0031]-[0034]: engine sound color control method)
a signal generation unit configured to receive a vibration simulated signal for an engine of the vehicle in the driving state from the stored generation model and to provide the vibration simulated signal to a control system having the ESEV; (see [0012], [0032]: engine sound generation includes a signal processing controller configured to receive an engine vibration signal according to operation of an engine.)
and an output control unit configured to control an output state of a virtual engine sound output to the vehicle in the driving state through the ASD function, when the signal generation unit inputs the vibration simulated signal to a control system having the ESEV function. (see [0050]-[0051]: engine dynamic characteristic reflection mode S30 may be implemented as a sensor signal detection configured to detect the engine state then engine vibration by producing output sound of the speaker 41 based on the virtual engine)
Lee is silent to an information input unit configured to receive driving-related information from the vehicle in a driving state and a random number generated randomly through a random number generator and to provide the driving-related information from the vehicle in the driving state and the random number to a stored generation model, the stored generation model being trained to generate a vibration simulated signal based on the driving-related information and the random number.
However, Takahashi teaches an information input unit configured to receive driving-related information from the vehicle in a driving state and a random number generated randomly through a random number generator and to provide the driving-related information from the vehicle in the driving state and the random number to a stored generation model, (see [0077], [0080], [0086]-[0087]: random sound data generation unit 92, vehicle data and random sound data which provides it to this generation model)
the stored generation model being trained to generate a vibration simulated signal based on the driving-related information and the random number. Lee teaches a trained model for generating vibration signals from vehicle/driving information (see [0040], [0061]). Takahashi teaches supplying both vehicle data and random data generated from a random number generator to a generation model. It would have been obvious to modify Lee’s trained model to additionally receive the random input taught by Takahashi because random inputs are known to increase variation/diversity in generated outputs while preserving conditioning on vehicle state.
Lee and Takahashi are considered to be analogous to the claimed invention because both are in the field of vehicle sound data and virtual engine sound generation. It would have been obvious to a person of ordinary skill in the art to have chosen to apply the broad teachings of Takahashi which is including some random information generated by the random number generator in the process of synthetic engine sound generation for more realistic sound effects to Lee in order to simulate synthetic engine sound through random generation, which can make it sound more realistic.
Regarding claim 2, Lee teaches the driving-related information input to the information input unit includes at least one of vehicle speed information, engine RPM information, and accelerator pedal sensor (APS) sensing information of the vehicle in the driving state. (see [0050]: engine RPM information, accelerator pedal position sensor is reflected in the engine dynamic characteristics)
Regarding claim 3, Lee teaches the output control unit controls an output size of the virtual engine sound output based on the vibration simulated signal. (see [0050]-[0051]: artificial intelligence implements visual effect considering the engine vibration by producing output sound based on the virtual engine RPM)
Regarding claim 4, Lee teaches comprising a model generation unit configured to generate the generation model that outputs the virtual vibration simulated signal using vehicle speed information, engine RPM information, APS sensing information(see [0050]: engine RPM information, accelerator pedal position sensor is reflected in the engine dynamic characteristics)
Lee is silent to the random number input thereto, as inputs while applying conditional GAN (cGAN) algorithm.
However, official notice is taken that it is well-known in the art that a conditional GAN algorithm generates data samples that are conditioned on specific input information, allowing control over the generated output, and therefore it would have been obvious to a person of ordinary skill to have used a conditional GAN algorithm to create a sequence of random numbers to produce sequences with desirable statistical properties.
Regarding claim 8-11, the claimed limitations are a method claim directly corresponding to the system claim 1-4; therefore, is rejected for the significant similar reasons as claim 1-4 as discussed above.
Claim(s) 5-7, and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20190111839 A1, hereinafter “Lee”) in view of Takahashi (US 20130182864 A1,hereinafter “Takahashi”) and Chang (US 20210407492 A1, hereinafter “Chang”).
Regarding claim 5, Lee teaches the model generation unit includes: a data collection unit configured to receive driving-related information in a driving state from a data acquisition vehicle, to which the ADS function and the ESEV function are applied with an acceleration sensor mounted to an engine thereof, and a vibration signal from the acceleration sensor to be collected in a database; (see [0050]-[0053]: detect, or sense, the acceleration and collect driving-related information from vehicle)
and a learning processing unit configured to learn an operation of the generation unit for generating vibration simulated signals in such a manner as to minimize a classification accuracy (see [0035]: artificial intelligence algorithm based on deep learning configured to input and output control data)
Lee is silent to a generation unit implemented as a convolutional neural network and having a decoder structure, the generation unit being configured to generate a vibration simulated signal corresponding to the data acquisition vehicle when receiving the driving-related information from the data collection unit together with the random number generated randomly through the random number generator;
a classification unit implemented as a convolutional neural network and having a classification network structure, the classification unit configured to output a result of classifying the vibration signal, which is a real signal, and the vibration simulated signal, which is a simulated signal, when receiving the driving-related information and the vibration signal from the data collection unit and the vibration simulated signal from the generation unit;
Takahashi teaches the generation unit being configured to generate a vibration simulated signal corresponding to the data acquisition vehicle when receiving the driving-related information from the data collection unit together with the random number generated randomly through the random number generator; (see [0077], [0080], [0086]-[0087]: random sound data generation unit 92, vehicle data and random sound data which provides it to this generation model)
Chang teaches a generation unit implemented as a convolutional neural network and having a decoder structure, (see[0038]-[0039]: convolutional neural network algorithm)
a classification unit implemented as a convolutional neural network and having a classification network structure, the classification unit configured to output a result of classifying the vibration signal, which is a real signal, and the vibration simulated signal, which is a simulated signal, when receiving the driving-related information and the vibration signal from the data collection unit and the vibration simulated signal from the generation unit; (see [0036]-[0042]: convolutional neural network and a program for classifying and storing a plurality of driving sounds in categories stored in the database)
and to learn a classification operation in such a manner that the classification unit increases a classification accuracy against the generation unit in an adversarial way by applying cGAN algorithm. (see [0038]-[0039]: applying a Generative Adversarial Networks (GAN) algorithm)
Lee and Takahashi are considered to be analogous to the claimed invention because both are in the field of vehicle sound data and virtual engine sound generation. It would have been obvious to a person of ordinary skill in the art to have chosen to apply the broad teachings of Takahashi to Lee in order to simulate synthetic engine sound through random generation then classify the types of signals, which can make it sound more realistic. It is a well-known and obvious step for not only driving related information but including some generation of random information for realistic sound effects.
Lee and Chang are considered to be analogous to the claimed invention because both are in the field of vehicle sound data and virtual engine sound generation. It would have been obvious to a person of ordinary skill in the art to have chosen to apply the broad teachings of Chang to Lee in order to recognize patterns in data and generate new data to make this generation realistic, enabling tasks like data augmentation for training, and creating synthetic images for fields needing more data.
Regarding claim 6, Lee teaches wherein the learning processing unit controls the generation unit and the classification unit to perform adversarial iterative learning in such a manner that the generation unit is trained after the classification unit is trained. (see [0035]: artificial intelligence algorithm based on deep learning configured to input and output control data for training)
Regarding claim 7, Lee teaches the signal generation unit applies a learning model according to a learning result of the generation unit as the generation model. (see [0035]: artificial intelligence algorithm based on deep learning configured to input and output control data)
Regarding claim 12-13, the claimed limitations are a method claim directly corresponding to the system claim 5 and 7; therefore, is rejected for the significant similar reasons as claim 5 and 7 as discussed above.
Response to Arguments
Applicant's arguments filed March 10, 2026 have been fully considered but they are not persuasive.
On page 7-8 of applicant’s remarks, applicant mainly argues that the art of record fails to disclose the limitations of “generating a vibration simulated signal based on a random number. Rather, Takahashi teaches generating random sound based on random sound data prepared in advance and stored in the system.” The Examiner disagrees and maintains as pointed out in the rejection above, Lee in view of Takahashi clearly teaches an information input unit configured to receive driving-related information from the vehicle in a driving state and a random number generated randomly through a random number generator and to provide the driving-related information from the vehicle in the driving state and the random number to a stored generation model, (see [0077], [0080], [0086]-[0087]: random sound data generation unit 92, vehicle data and random sound data which provides it to this generation model). The claim recites generating a signal “based on” the driving related information and random number, but does not require the random number itself be directly input to the generation model. Because the random sound data is derived from the random number and is used by the generation model to generate the output, the reference teaches or at least suggests generating the signal based on the driving-related information and random number
For the amended claim language “the stored generation model being trained to generate a vibration simulated signal based on the driving-related information and the random number,” Lee teaches a trained model for generating vibration signals from vehicle/driving information (see [0040], [0061]). Takahashi teaches supplying both vehicle data and random data generated from a random number generator to a generation model. It would have been obvious to modify Lee’s trained model to additionally receive the random input taught by Takahashi because random inputs are known to increase variation/diversity in generated outputs while preserving conditioning on vehicle state.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNABELLE KANG whose telephone number is (571)270-3403. The examiner can normally be reached Monday-Thursday 8:00-5:00.
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/ANNABELLE KANG/Examiner, Art Unit 2695
/PAUL KIM/Primary Examiner, Art Unit 2695
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