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 .
Detailed Action
The following non-final action is in response to application 18/522,937 filed on 11/29/2023. The communication is the first action on the merits.
Status of Claims
Claims 1-12 are currently pending and have been rejected as follows.
Drawings
The drawings filed on 11/29/2023 are accepted.
Foreign Priority
An attempt by the Office to electronically receive, under the priority document exchange program, the foreign application 10-2023-0066499 to which priority is claimed has FAILED on 10/23/2024.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Claim 1 recites:
A method comprising:
generating, by a computing device, noise point data by converting a first vibration signal, measured at a noise point at a first time, into a frequency domain, wherein the first vibration signal is generated at the noise point;
generating sound point data by converting a second vibration signal measured at a sound point at the first time into a frequency domain, wherein the second vibration signal is based on propagation of the first vibration signal from the noise point to the sound point;
generating, based on deep learning using the noise point data and the sound point data, a noise prediction model configured to generate, using frequency data indicating a vibration at the sound point, frequency data indicating a vibration at the noise point;
predicting, based on the noise prediction model and based on a target vibration signal measured at the sound point at a second time, target noise point data indicating vibration at the noise point;
and diagnosing, based on the predicted target noise point data, a fault at the noise point.
Under Step 1 of the analysis, claim 1 does belong to a statutory category, namely it is a process claim. Claim 7 is a machine claim.
Under Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Under Step 2A, Prong One, the broadest reasonable interpretation consistent with the specification of the limitations recited in Claim 1 recite at least one judicial exception, that being a mathematical concept (mathematical calculations/relationships /formulas/ or equations).
According to the specification, generating noise point data by converting a first and second vibration signal into a frequency domain involves converting by means of the fast Fourier transform (FFT) [0040]. The claim limitation involves a mathematical calculation and thus falls into the category of mathematical concept.
According to the specification, generating a noise prediction model involves the prediction model training unit 200 providing the generated noise prediction model 2000 to the controller 400 by means of the deep learning [0115] where the generating of the noise prediction model further may comprise determining a weight value and bias of the kernel [0176] where the generating of the noise prediction model further may comprise calculating a loss function value by digitalizing a difference between a prediction value predicted by inputting the target sound point data to the noise prediction model and noise point data corresponding to the target sound point signal and training the plurality of calculation blocks to make the loss function value minimum [0177]. The claim limitation involves mathematical calculations, formulas and relationships and thus falls into the category of mathematical concept.
According to the specification, generating frequency data indicating a vibration at the noise point involves the noise prediction model 2000, which may be a transfer function, indicating a relationship which outputs frequency data indicating a vibration of the noise point [0044]. The claim limitation involves mathematical calculations and relationships and thus falls into the category of mathematical concept.
According to the specification, predicting the target noise point data indicating vibration at the noise point involves the noise prediction model, which may be a transfer function, predicting the target noise point data [0120, 0044]. The claim limitation involves mathematical calculations and relationships and thus falls into the category of mathematical concept.
According to the specification, diagnosing the fault at the noise point involves the diagnosis model training unit 300, which may train the plurality of fully connected layers 311 to 314 and the plurality of layers 3113 to 3115 to make a loss function value indicating the difference between the prediction data output from the layer 3115 and the labeling data minimum [0148], and also involves the confusion matrix of Figure 5. The claim limitation involves mathematical calculations and relationships and thus falls into the category of mathematical concept.
Claim 7 recites similar abstract ideas rejected in the claim 1 analysis.
Step 2A, Prong Two of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. 2019 PEG Section III(A)(2), 84 Fed. Reg. at 54-55.
The additional elements in the preambles of all independent claims are recited in generality and represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
Claim 1 recites additional elements:
“a computing device”
“a first vibrational signal measured at a noise point at a first time”
“wherein the first vibration signal is generated at the noise point”
“a second vibration signal measured at a sound point at the first time”
“wherein the second vibration signal is based on propagation of the first vibration signal from the noise point to the sound point”
“the noise prediction model”
“a target vibration signal measured at the sound point at a second time”
Claim 7 recites similar additional elements as claim 1 as well as:
“providing noise point data and sound point data to the prediction model training unit”
“receiving the trained noise prediction model”
These claim limitations generically recite collecting/outputting, by sensors/devices/displays, measurement data (all independent claims), which represents the insignificant extra-solution activity of mere data gathering/outputting results. According to the October update on 2019 SME Guidance such steps are “performed in order to gather data for the mental analysis step, and is a necessary precursor for all uses of the recited exception. It is thus extra-solution activity, and does not integrate the judicial exception into a practical application”.
Specifically, the “computing device”, “noise prediction model”, “the prediction model training unit”, and “the trained noise prediction model” are additional elements in the form of devices/units/models or computer components recited in generality and not meaningful and, therefore, are not qualified as particular machines to indicate a practical application.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. When re-evaluated under Step 2B, the claim limitations are found to be well-understood, routine, and conventional as explained by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a communication network) as referenced by Song and Yang.
Therefore, the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claims 1 and 7, amount to significantly more than the abstract idea.
With regards to dependent claims 2-6 and 8-12, they provide additional features/steps which are part of an expanded abstract idea of the independent claims (additionally comprising abstract idea steps) and, therefore, these claims are not eligible without meaningful additional elements that reflect a practical application and/or additional elements that qualify for significantly more for substantially similar reasons as discussed with regards to Claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Song (US 20210383211 A1) in view of Yang (CN 114689167 A).
Regarding claim 1, Song teaches
generating, by a computing device, noise point data by converting a first vibration signal, measured at a noise point at a first time, into a frequency domain, wherein the first vibration signal is generated at the noise point (…data collected for each channel associated with noise and vibration…to generate a learning dataset [0054]… the range may include a frequency range, a time range, and a value range, which correspond to a specific channel number of the noise source/the vibration source/the output [0081]… the dataset may be configured in an order of a frequency, a time, an input (e.g., a first noise source, a second noise source…, a first vibration source, a second vibration source, a third vibration source…), and an output (e.g., front seat noise or rear seat noise) [0058]… the dataset may be determined according to a frequency domain, a time domain, a frequency resolution, and a time resolution [0056]; Figure 6, element 610);
generating, based on deep learning using the noise point data…a noise prediction model configured to generate, using frequency data…frequency data indicating a vibration at the noise point ([Figure 6]; (…a vehicle NVH system based on deep learning [Figure 7, 0044]; the visualization device 140 may visualize and output the result of predicting the performance as a color map on a frequency domain [0065]… An NVH system model using an artificial neural network may be roughly composed of a noise source and a vibration source, a transfer system which is a trimmed body for transferring noise and vibration, and a response which is vehicle interior noise and vibration [0050]…As an example, the source may include engine noise, intake/exhaust noise, tire noise, vibration at the powertrain body, and street vibration [0051]… the dataset may be configured in an order of a frequency, an input (e.g., a first noise source, a second noise source, . . . , a first vibration source, a second vibration source, a third vibration source, . . . ),[0057-0058]).
Song also discloses an NVH system model that (is a term referring to all phenomena of vibration and noise of vehicles [0003]) and that (noise refers to a loud sound, which is displayed in decibel (dB), making the human emotion unpleasant, and is roughly classified as interior noise generated by vehicle parts and exterior noise generated from the outside of the vehicle [0004]) and that (NVH is an important element for determining emotional quality of the vehicle. The previous NVH research aims to make quiet vehicles by simply reducing sounds and vibration [0007]).
Song further discloses predicting, based on the noise prediction model and based on a target vibration signal data at the sound point (…the preprocessing dataset generator 220 may generate one dataset where learning data corresponding to the learning target channel selected by the user is configured with an input and an output on a frequency domain [0076]… the model structure variable may include the number of hidden layers for each of a noise layer, a vibration layer, and a target layer [0085]… In S780, the apparatus 100 for predicting the performance may perform performance prediction based on a test dataset and a previously learned model and may visualize and output the result of performing the performance prediction as a graph or the like [Figure 7, 0123]; Figure 6; In S780, the apparatus 100 for predicting the performance may perform performance prediction based on a test dataset and a previously learned model and may visualize and output the result of performing the performance prediction as a graph or the like [Figure 7, 0123] …a method for establishing a vehicle NVH system model, which is capable of providing an NVH system model indicating a vehicle characteristic and/or a part characteristic using data [0128]… a vehicle NVH system model and predicting performance of a vehicle NVH system model, which is capable of quickly and simply identifying characteristics and performance of the entire vehicle NVH system through one monitoring [0129]).
Song also mentions using back-propagation in the learning algorithm [0086].
Song does not explicitly teach using frequency data to indicate a vibration at the sound point…
generating and using sound point data by converting a second vibration signal measured at a sound point at the first time into a frequency domain wherein the second vibration signal is based on propagation of the first vibration signal from the noise point to the sound point…
predicting, based on the noise prediction model and based on a target vibration signal measured at the sound point at a second time, target noise point data indicating vibration at the noise point…
diagnosing, based on the predicted target noise point data, a fault at the noise point.
Yang teaches using frequency data to indicate a vibration at the sound point… generating and using sound point data by converting a second vibration signal measured at a sound point at the first time into a frequency domain wherein the second vibration signal is based on propagation of the first vibration signal from the noise point to the sound point… (…determining the occurrence time of the low-frequency sound problem…processing the in-vehicle noise signal at the occurrence time, determining the first frequency characteristics the noise signal in the vehicle…used for processing the vibration signals of the vehicle outside the occurrence time, determining the second frequency characteristics…based on the first frequency characteristics the second frequency characteristics determining the low-frequency sound problem position… processing the time domain and frequency domain to the vibration signals of the outside of the occurrence time, obtain frequency of characteristics component out of the vehicle…the corresponding parts are determined as the low frequency sound problem position…an optimizing module for optimizing the position of the low-frequency sound problem based on the position of the low-frequency sound problem; or determining the propagation path of the low-frequency sound problem position to the vehicle body; based on the propagation path, optimizing on the propagation path…the frequency spectrum characterizes the vibration noise problem from the randomness frequency domain in an integrated analysis, effectively determining the key influence factor caused by the low-frequency sound problem, p.8).
It also would be obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify Song in view of Yang to use frequency data to indicate a vibration at the sound point to pinpoint specific faults (like bearing failure, imbalance, or misalignment) by identifying the exact frequency components at the sound point and to generate and use sound point data by converting a second vibration signal measured at a sound point at the first time into a frequency domain wherein the second vibration signal is based on propagation of the first vibration signal from the noise point to the sound point to analyze how the vibration propagates from the "noise point" to the "sound point".
It also would be obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify Song in view of Yang to use machine learning to generate a prediction model that diagnoses faults at the noise point as a reverse algorithm compared to the prediction model that generates data at the sound point as discussed in Song and Yang.
It also would be obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify Song in view of Yang to predict, based on the noise prediction model and based on a target vibration signal measured at the sound point at a second time, target noise point data indicating vibration at the noise point to enable real-time monitoring of noise, vibration, and harshness (NVH) levels without placing sensors directly in hard-to-reach, high-noise, or high-temperature locations and to diagnose, based on the predicted target noise point data, a fault at the noise point to allow for proactive, high-precision maintenance even in loud, complex operational environments, thus making the vehicle quieter and improving emotional quality for the driver.
Regarding claim 2, Song teaches
generating subpoint data by converting a vibration signal acquired from at least one subpoint, between the noise point and the sound point, into a frequency domain, wherein the generating the noise prediction model comprises:
training, based on the noise point data and the subpoint data a first model configured to generate, based on the frequency data indicating a vibration at the sound point, frequency data indicating a vibration at the subpoint;
(…learning data collected for each channel associated with noise and vibration during any driving for a long time to generate a learning dataset [0054]…the preprocessing device 110 may obtain an autopower spectrum at each channel and each time through fast Fourier transform (FFT) and may configure each separate data as one big learning data [0055]… as an example, respective points at the same point on a color map may be combined into one dataset, and datasets may be generated by a number obtained by multiplying the number of frequency variables by the number of time variables on the color map. Herein, a size of the dataset may be determined according to a frequency domain, a time domain, a frequency resolution, and a time resolution [0056]).
and training, based on the subpoint data and the sound point data, a second model configured to generate, based on the frequency data indicating a vibration of the subpoint, frequency data indicating a vibration at the noise point (…the preprocessed learning data using an artificial neural network, and predicting performance using a vehicle NVH system model formed through the learned model [Figure 7, Abstract]).
Regarding claim 3, Song teaches
the noise prediction model comprises a plurality of calculation blocks and at least one fully connected block; (…the artificial neural network may be a deep neural network (DNN) including a plurality of hidden layers between an input layer and an output layer [0019]).
a kernel is applied to…the plurality of calculation blocks;
and the generating the noise prediction model further comprises: determining a weight value and bias of the kernel (…the learning variable may include epochs (the learned number of all datasets), save-epochs (the number of epochs when saving a model), batch size (the number of datasets where weights are updated and occurrence of back-propagation per batch size), or validation split (a rate at which it will be used as validation data, which is 0 to 1.0) [0086]… the training setting may include an activation function, a weight initial value, a bias initial value, a learning algorithm, a learning rate, and a loss function [0088]).
Song teaches training settings applied to the calculation layers including a weight initial value and bias initial value, but does not explicitly teach applying a kernel to each of the plurality of calculation blocks.
Before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to modify Song to apply a kernel to each of the plurality of calculation blocks to enable parallel processing, improve computational efficiency, and extract diverse features.
Regarding claim 4, Song teaches
the generating the noise prediction model further comprises:
calculating a loss function value by digitalizing a difference between:
a prediction value predicted by inputting target sound point data to the noise prediction model, and noise point data corresponding to the target sound point data; training the plurality of calculation blocks… ([Figure 5]; …the training setting may include…a loss function [0088]…where the loss function may be log(cosh(x)) [0089]…the preprocessing dataset generator 220 may generate one dataset where learning data corresponding to the learning target channel selected by the user is configured with an input and an output on a frequency domain [0076]… the model structure variable may include the number of hidden layers for each of a noise layer, a vibration layer, and a target layer [0085]).
Song does not explicitly teach training the plurality of calculation blocks to minimize the loss function value.
Before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to modify Song to minimize the loss function value to maximize the accuracy and performance of the learning model.
Regarding claim 5, Song teaches based on the deep learning, a training dataset and a test dataset, (a test dataset generator 430 [0094]… generating one learning dataset [0018]).
generating a…diagnosis model configured to predict, based on frequency data indicating the vibration at the noise point…(…a method for establishing a vehicle NVH system model, which is capable of providing an NVH system model indicating a vehicle characteristic and/or a part characteristic using data [0128]… a vehicle NVH system model and predicting performance of a vehicle NVH system model, which is capable of quickly and simply identifying characteristics and performance of the entire vehicle NVH system through one monitoring [Figure 7, 0129]).
Song does not explicitly teach a fault diagnosis model configured to predict the fault at the noise point.
These limitations in claim 5 related to fault prediction and diagnosis via the model are rejected for similar reasons according to the claim 1 analysis.
Regarding claim 6, Song teaches wherein each of the training dataset and the test dataset comprise data labeled with…information…in accordance with the noise point data (in a model which should include a noise source and a vibration source together, the artificial neural network may be implemented such that paths of the noise source 611 (air-borne) and the vibration source 612 (structure-borne) are divided and learned to the Nth hidden layer of the artificial neural network and are then combined and learned from the N+1th hidden layer by reflecting a characteristic when a noise and vibration transfer path is divided into the air-borne and the structure-borne to transfer noise and vibration [0111]…a method for establishing a vehicle NVH system model, which is capable of providing an NVH system model indicating a vehicle characteristic and/or a part characteristic using data [0128]… a vehicle NVH system model and predicting performance of a vehicle NVH system model, which is capable of quickly and simply identifying characteristics and performance of the entire vehicle NVH system through one monitoring [0129]) and wherein the generating of the…diagnosis model comprises: training, based on the training dataset, the…model; and testing, based on the test dataset, the…model (In S780, the apparatus 100 for predicting the performance may perform performance prediction based on a test dataset and a previously learned model and may visualize and output the result of performing the performance prediction as a graph or the like…[Figure 7, 0123]…the learning variable may include epochs (the learned number of all datasets), save-epochs (the number of epochs when saving a model), batch size (the number of datasets where weights are updated and occurrence of back-propagation per batch size), or validation split (a rate at which it will be used as validation data, which is 0 to 1.0) [0086]… the training setting may include an activation function, a weight initial value, a bias initial value, a learning algorithm, a learning rate, and a loss function [0088]).
Song does not explicitly teach a fault information label indicating a fault at a noise point and a fault diagnosis model.
It also would be obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify Song in view of Yang to also use fault information label in the fault diagnosis model as known in the art of supervised learning.
Regarding claim 7, Song teaches
An NVH modeling system, comprising:
an input unit configured to receive: (input layer [0062]).
a first vibration signal measured at a noise point at a first time, wherein the first vibration signal is generated at the noise point,
a second vibration signal measured at a sound point at the first time, wherein the second vibration signal is based on propagation of the first vibration signal from the noise point to the sound point,
and a target vibration signal measured at the sound point at a second time;
a prediction model training unit configured to, via deep learning, generate and train a noise prediction model configured to generate, based on frequency data indicating a vibration at the sound point, frequency data indicating a vibration at the noise point;
and a controller configured to: (transfer system [0108, Figure 6]).
provide noise point data and sound point data to the prediction model training unit, wherein the noise point data is generated by converting the first vibration signal into a frequency domain and the sound point data is generated by converting the second vibration signal into the frequency domain;
receive the trained noise prediction model; (…the preprocessed learning data using an artificial neural network, and predicting performance using a vehicle NVH system model formed through the learned model [Abstract]).
predict, based on the trained noise prediction model and on target sound point data generated by converting the target vibration signal into a frequency domain, target noise point data indicating a vibration at the noise point at the second time;
and diagnose, based on the target noise point data, …the noise point.
Song does not explicitly teach a fault diagnosis system and diagnosing a fault at the noise point. These elements of the claim limitation are addressed in the claim 5 analysis.
The remaining limitations of Claim 7 are addressed and rejected for similar reasons according to the claim 1 analysis.
Regarding claim 8, Song teaches
the input unit is configured to acquire a third vibration signal measured at the first time at at least one subpoint between the noise point and the sound point, the controller is configured to generate subpoint data by converting the third vibration signal into the frequency domain,
(the range may include a frequency range, a time range, and a value range, which correspond to a specific channel number of the noise source/the vibration source/the output [0081]… the dataset may be configured in an order of a frequency, a time, an input (e.g., a first noise source, a second noise source…, a first vibration source, a second vibration source, a third vibration source…), and an output (e.g., front seat noise or rear seat noise) [0058]… the dataset may be determined according to a frequency domain, a time domain, a frequency resolution, and a time resolution [0056]…
…learning data collected for each channel associated with noise and vibration during any driving for a long time to generate a learning dataset [0054]…the preprocessing device 110 may obtain an autopower spectrum at each channel and each time through fast Fourier transform (FFT) and may configure each separate data as one big learning data [0055]… as an example, respective points at the same point on a color map may be combined into one dataset, and datasets may be generated by a number obtained by multiplying the number of frequency variables by the number of time variables on the color map. Herein, a size of the dataset may be determined according to a frequency domain, a time domain, a frequency resolution, and a time resolution [0056]).
the noise prediction model comprises:
a first model, based on the noise point data and the subpoint data, configured to generate, based on frequency data indicating a vibration at the sound point, frequency data indicating a vibration at the subpoint;
and a second model, based on the subpoint data and the sound point data, configured to generate frequency data indicating a vibration of the noise point from frequency data indicating a vibration of the subpoint.
The remaining limitations of Claim 8 are addressed and rejected for similar reasons according to the claim 2 analysis.
Regarding claim 9, the claim limitations are addressed and rejected for similar reasons according to the claim 3 analysis.
Regarding claim 10, the claim limitations are addressed and rejected for similar reasons according to the claim 4 analysis.
Regarding claim 11, the claim limitations are addressed and rejected for similar reasons according to the analysis of claims 5 and 1.
Regarding claim 12, the claim limitations are addressed and rejected for similar reasons according to the analysis of claims 6 and 1.
Pertinent Prior Art
Alharbi, F.; Luo, S.; Zhang, H.; Shaukat, K.; Yang, G.; Wheeler, C.A.; Chen, Z. A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models. Sensors 2023, 23, 1902. https://doi.org/10.3390/s23041902
This paper discloses supervised learning and presents a recent review of acoustic and vibration signal-based fault detection for a noise source using ML models. It also discusses major steps in the approaches, such as data collection, signal processing, feature extraction and selection, and ML model construction. Additionally, the paper provides an overview of the main components of belt conveyor systems, sources of defects in idlers, and a brief introduction to ML models.
Conclusion
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/LOGAN D COONS/Examiner, Art Unit 2857
/ALEXANDER SATANOVSKY/Primary Examiner, Art Unit 2857