DETAILED ACTION
This action is in response to the original filing on 08/19/2024. Claims 1-20 are pending for examination.
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 Objections
Claim 9 is objected to because of the following informalities: “comprising an encoder, a decoder coupled”. The word “and” appears to be missing in place of a comma. Appropriate correction is required.
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 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 12, and 20
Step 1: Claims 1, 12, and 20 recite a device, a method, and a non-transitory computer-readable storage medium. Therefore, they are directed to the statutory categories of a machine, method, and non-transitory computer readable medium.
Step 2A Prong 1: The claims recite, inter alia: “compute a weighted sum of the first bio-signal and the second bio-signal” Under its broadest reasonable interpretation in light of the specification, this amounts to no more than a mental process of a mathematical computation.
The claims further recite: “generate a mixed signal based on the weighted sum”. Under its broadest reasonable interpretation in light of the specification, this limitation amounts to no more than a mental process, as the recitation of generation of a mixed signal is so broad that this also encompasses any sort of hand-drawn or otherwise human-produced signal, such as a sine wave made by a human with the aid of pen and paper.
The claims further recite: “compute a loss based on a comparison of the output signal with the first bio-signal; and” Under its broadest reasonable interpretation in light of the specification, this limitation amounts to no more than a mathematical computation.
The claims further recite: “train the DNN for a number of epochs until the computed loss is below a threshold.” Under its broadest reasonable interpretation, this amounts to no more than a mathematical relationship between values (loss and a threshold), wherein the training is defined by this mathematical relationship. As such, since the training requires a mathematical relationship, the limitation therefore encompasses a mathematical concept.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional element of “receive training data comprising a first bio-signal and a second bio-signal that is different from the first bio-signal;” amounts to no more than mere data gathering (see MPEP 2106.05(g)).
The claims also recite the additional element of: “generate an output signal based on application of a denoising neural network (DNN) on the mixed signal;”, which amounts to no more than a high level recitation of “Apply it”, as there is only the recitation of the outcome without details of how the neural network is applied to the mixed signal (see MPEP 2106.05(f)).
Step 2B: The claims do not contain significantly more than the judicial exception. The additional element of “receive training data comprising a first bio-signal and a second bio-signal that is different from the first bio-signal;” amounts to no more than mere data gathering (see MPEP 2106.05(g)).
The claims also recite the additional element of: “generate an output signal based on application of a denoising neural network (DNN) on the mixed signal;”, which amounts to no more than a high level recitation of “Apply it”, as there is only the recitation of the outcome without details of how the neural network is applied to the mixed signal (see MPEP 2106.05(f)).
Nothing in the claims provides significantly more than the abstract idea. As such, the claims are ineligible.
Claims 2 and 13
Step 1: Claims 2 and 13 recite a device and a method. Therefore, they are directed to the statutory categories of a machine and method.
Step 2A Prong 1: Claims 2 and 13 merely narrow the previously recited abstract limitations. For the reasons described above with respect to Claims 1 and 12, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claim above and do not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional element of: “wherein the first bio-signal is a magneto cardiogram (MCG) signal, and the second bio-signal is an electrocardiogram (ECG) signal” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)).
Step 2B: The claims do not contain significantly more than the judicial exception. The additional element of: “wherein the first bio-signal is a magneto cardiogram (MCG) signal, and the second bio-signal is an electrocardiogram (ECG) signal” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). Nothing in the claims provides significantly more than the abstract idea. As such, the claims are ineligible.
Claims 3 and 14
Step 1: Claims 3 and 14 recite a device and a method. Therefore, they are directed to the statutory categories of a machine and method.
Step 2A Prong 1: Claims 3 and 14 merely narrow the previously recited abstract limitations. For the reasons described above with respect to Claims 1 and 12, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claim above and do not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional element of: “wherein the first bio-signal is one of an electrocardiogram (ECG) signal, and the second bio-signal is a magneto cardiogram (MCG) signal.” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)).
Step 2B: The claims do not contain significantly more than the judicial exception. The additional element of: “wherein the first bio-signal is one of an electrocardiogram (ECG) signal, and the second bio-signal is a magneto cardiogram (MCG) signal.” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). Nothing in the claims provides significantly more than the abstract idea. As such, the claims are ineligible.
Claims 4 and 15
Step 1: Claims 4 and 15 recite a device and a method. Therefore, they are directed to the statutory categories of a machine and method.
Step 2A Prong 1: The claims recite, inter alia: “wherein the loss between the output signal and the first bio-signal is computed based on a spectral loss function.”, under its broadest reasonable interpretation in light of the specification, this amounts to no more than a mathematical calculation or computation based on a specified function.
Step 2A Prong 2 and Step B: There are no additional elements recited, so the claim does not provide a practical application and is not considered to be significantly more. As such, the claims are patent ineligible.
Claims 5 and 16
Step 1: Claims 5 and 16 recite a device and a method. Therefore, they are directed to the statutory categories of a machine and method.
Step 2A Prong 1: The claims recite, inter alia: “wherein the spectral loss function is based on Short-Time Fourier Transform (STFT) or Gabor transform.”, under its broadest reasonable interpretation in light of the specification, this amounts to no more than merely specifying the aforementioned function, i.e. a mathematical relationship.
Step 2A Prong 2 and Step B: There are no additional elements recited, so the claim does not provide a practical application and is not considered to be significantly more. As such, the claims are patent ineligible.
Claims 6 and 17
Step 1: Claims 6 and 17 recite a device and a method. Therefore, they are directed to the statutory categories of a machine and method.
Step 2A Prong 1: The claims recite, inter alia: “wherein the computation of the weighted sum is based on a first weight for the first bio-signal and a second weight for the second bio-signal, and the second weight is more than the first weight.”, under its broadest reasonable interpretation in light of the specification, this amounts to no more than a mere mathematical computation, wherein a specified value (the second) is greater than another value (the first).
Step 2A Prong 2 and Step B: There are no additional elements recited, so the claim does not provide a practical application and is not considered to be significantly more. As such, the claims are patent ineligible.
Claim 7
Step 1: Claim 7 recites a device. Therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: Claim 7 merely narrows the previously recited abstract limitations. For the reasons described above with respect to Claim 6, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claim disclose similar limitations described for the independent claim above and does not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional element of “wherein the processor is further configured to set each of the first weight and the second weight based on a user input or a random function” amounts to no more than mere insignificant extra solution activity, especially when considering that the extra-solution limitation is well known (See MPEP 2106.05(g) Bilski v. Kappos, 561 U.S. 593, 611-12, 95 USPQ2d 1001, 1010 (2010) (well-known random analysis techniques to establish the inputs of an equation were token extra-solution activity)).
Step 2B: The claims do not contain significantly more than the judicial exception. The additional element of “wherein the processor is further configured to set each of the first weight and the second weight based on a user input or a random function” amounts to no more than merely receiving or transmitting data over a network, e.g., using the internet to gather data (See MPEP 2106.05(d)). Nothing in the claims provides significantly more than the abstract idea. As such, the claims are ineligible.
Claims 8 and 19
Step 1: Claims 8 and 19 recite a device and a method. Therefore, they are directed to the statutory categories of a machine and method.
Step 2A Prong 1: Claims 8 and 19 merely narrow the previously recited abstract limitations. For the reasons described above with respect to Claims 1 and 12, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claim above and do not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional element of “receive, from a sensor associated with a user, a bio-signal comprising a noise component via a single channel input or a muti-channel input; and” amounts to no more than merely specifying a particular data source or type to be manipulated (See MPEP 2106.05(g)).
The additional element of: “generate a denoised bio-signal based on application of the trained denoising neural network on the received bio-signal,” amounts to no more than a high-level recitation of the words “Apply it” or an equivalent, as no detail of the application of the trained denoising network is provided by the claimed limitation (See MPEP 2106.05(f)).
The additional element of: “wherein the denoised bio-signal is generated after a removal of the noise component from the received bio-signal” amounts to no more than a high-level recitation of the words “Apply it” or an equivalent, as no detail is provided for the removal of noise (See MPEP 2106.05(f)).
Step 2B: The claims do not contain significantly more than the judicial exception. The additional element of “receive, from a sensor associated with a user, a bio-signal comprising a noise component via a single channel input or a muti-channel input; and” amounts to no more than merely receiving or transmitting data over a network, e.g., over the internet (See MPEP 2106.05(d)).
The additional element of: “generate a denoised bio-signal based on application of the trained denoising neural network on the received bio-signal,” amounts to no more than a high-level recitation of the words “Apply it” or an equivalent, as no detail of the application of the trained denoising network is provided by the claimed limitation (See MPEP 2106.05(f)).
The additional element of: “wherein the denoised bio-signal is generated after a removal of the noise component from the received bio-signal” amounts to no more than a high-level recitation of the words “Apply it” or an equivalent, as no detail is provided for the removal of noise (See MPEP 2106.05(f)). Nothing in the claims provides significantly more than the abstract idea. As such, the claims are ineligible.
Claim 9
Step 1: Claim 9 recites a device. Therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: Claim 9 merely narrows the previously recited abstract limitations. For the reasons described above with respect to Claim 1, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claim disclose similar limitations described for the independent claim above and does not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional element of “wherein the DNN is an encoder-decoder network comprising an encoder, a decoder coupled to an output of the encoder” amounts to no more than merely linking a limitation to a particular field of use or technological environment, similar to specifying that the abstract idea of using advertising as currency is used on the Internet, because this narrowing limitation is merely an attempt to limit the use of the abstract idea to a particular technological environment (See MPEP 2106.05(h)).
Step 2B: The claims do not contain significantly more than the judicial exception. The additional element of “wherein the DNN is an encoder-decoder network comprising an encoder, a decoder coupled to an output of the encoder” amounts to no more than merely linking a limitation to a particular field of use or technological environment, similar to specifying that the abstract idea of using advertising as currency is used on the Internet, because this narrowing limitation is merely an attempt to limit the use of the abstract idea to a particular technological environment (See MPEP 2106.05(h)). Nothing in the claims provides significantly more than the abstract idea. As such, the claims are ineligible.
Claim 10
Step 1: Claim 10 recites a device. Therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: Claim 10 merely narrows the previously recited abstract limitations. For the reasons described above with respect to Claim 1, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claim disclose similar limitations described for the independent claim above and does not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional element of “wherein the DNN is a denoising autoencoder or a variant of the denoising autoencoder” amounts to no more than merely linking a limitation to a particular field of use or technological environment, similar to specifying that the abstract idea of using advertising as currency is used on the Internet, because this narrowing limitation is merely an attempt to limit the use of the abstract idea to a particular technological environment (See MPEP 2106.05(h)).
Step 2B: The claims do not contain significantly more than the judicial exception. The additional element of “wherein the DNN is a denoising autoencoder or a variant of the denoising autoencoder” amounts to no more than merely linking a limitation to a particular field of use or technological environment, similar to specifying that the abstract idea of using advertising as currency is used on the Internet, because this narrowing limitation is merely an attempt to limit the use of the abstract idea to a particular technological environment (See MPEP 2106.05(h)). Nothing in the claims provides significantly more than the abstract idea. As such, the claims are ineligible.
Claim 11
Step 1: Claim 11 recites a device. Therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: Claim 11 merely narrows the previously recited abstract limitations. For the reasons described above with respect to Claim 1, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claim disclose similar limitations described for the independent claim above and does not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional element of “wherein the DNN is a U-net or a variant of the U-net.” amounts to no more than merely linking a limitation to a particular field of use or technological environment, similar to specifying that the abstract idea of using advertising as currency is used on the Internet, because this narrowing limitation is merely an attempt to limit the use of the abstract idea to a particular technological environment (See MPEP 2106.05(h)).
Step 2B: The claims do not contain significantly more than the judicial exception. The additional element of “wherein the DNN is a U-net or a variant of the U-net.” amounts to no more than merely linking a limitation to a particular field of use or technological environment, similar to specifying that the abstract idea of using advertising as currency is used on the Internet, because this narrowing limitation is merely an attempt to limit the use of the abstract idea to a particular technological environment (See MPEP 2106.05(h)). Nothing in the claims provides significantly more than the abstract idea. As such, the claims are ineligible.
Claim Rejections - 35 USC § 103
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 1-3 and 6-14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhai et al. (US 20240325822 A1, hereinafter Zhai) in view of TONG et al. (CN 115389865 A, hereinafter Tong) in further view of DECH et al. (US 20240298950 A1, hereinafter Dech).
Regarding Claim 1, Zhai teaches an electronic device, comprising: a processor configured to: (Paragraph [0022] Furthermore, a computer program product on a computer-readable storage medium (e.g., non-transitory) having processor-executable instructions (e.g., computer software) embodied in the storage medium.)
receive training data comprising a first bio-signal and a second bio-signal that is different from the first bio-signal; (Paragraph [0046] The method may include receiving data (e.g., data 405). The data may be in the form of a signal or representative of the signal (e.g., an electrocardiogram 402). Paragraph [0068] For example, the first neural network (e.g., network 412) may be trained on data that is based on electrocardiogram signals with distortion and signals without distortion. Paragraph [0095] The first training data and the second training data may be determined based on noise-free or clean electrocardiograms. For example, the first training data and/or the second training data may include distorted, corrupted, or noisy electrocardiogram signals or data. (Training data for a model is received, comprising bio-signal (electrocardiogram) features that are distinct from each other. As an example, the first signal group has no noise while the second group of signals does))
compute a loss based on a comparison of the output signal with the first bio-signal; and (Paragraph [0053] A moving average (e.g., mean value of the electrocardiogram) over a window (e.g., previous 0.2 seconds of segment 404) may be determined in layer 706. That is, for each data point over time, the value may be replaced by a mean of the previous 0.2 seconds of the segment 404 The adjusted 30-second segment having the averaged windows may be used as an input to a differential layer 708 and activation layer 709. The differential layer 708 may be based on or defined by Equations 1 and 2. [Equation 00001] where for the forward pass, x is the input (e.g., adjusted 30-second segment with averaged windows over each 0.2 window) to the differential layer 708 and z is the output (e.g., feature map outputted to the subsequent layer). For the backward pass, “is the backpropagated loss from the depth concatenation 705 and” is the calculated loss to backpropagate and dz/dx is the local gradient. (Calculation of a loss of received signals of the neural network via backpropagation (i.e. through comparison of output to input bio-signal)))
train the DNN for a number of epochs until the computed loss is below a threshold. (Paragraph [0085] The network (e.g., network 406, network 412, network 416, network 432, etc.) may be trained until a certain quantity of epochs or error rate is achieved. In step 1108, the quantity of epochs or error rate may be compared with a threshold or set point to determine if the network (e.g., network 406, network 412, network 416, network 432, etc.) is satisfactorily trained. (Retraining the network in order to minimize the loss))
Zhai does not teach: compute a weighted sum of the first bio-signal and the second bio-signal; generate a mixed signal based on the weighted sum;
In the same field of endeavor, Tong teaches: compute a weighted sum of the first bio-signal and the second bio-signal; (Paragraph [n0009] A neural network incorporating the propagation mechanism of acoustic vibration is used to extract feature vectors from the acoustic vibration data to obtain multiple feature vectors. The multiple feature vectors are then subjected to adaptive weight calculation and weighted fusion to obtain a hybrid feature vector. Paragraph [n0158] Figure 11 shows a comparison before and after using the power equipment fault diagnosis method based on cloud-edge-device collaborative architecture of the present invention. It shows the process of extracting features from acoustic vibration signals and performing fusion processing. (Multiple signals are represented as feature vectors and a weighted summation is computed))
generate a mixed signal based on the weighted sum; (Paragraph [n0053] Based on the fusion weights of each feature vector among the multiple feature vectors, the multiple feature vectors obtained by the identity scaling process are fused to obtain a mixed feature vector of the voiceprint vibration data. Paragraph [n0158] It shows the process of extracting features from acoustic vibration signals and performing fusion processing. (Feature vectors representing signals are fused (mixed))).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have combined generating a mixed signal based on the weighted sum of a first and second bio-signal as taught by Tong into Zhai as both are in the related field of removing noise from observed signals, and doing so would be advantageous by allowing for the increased detection of faults in the subject of observation, in the case of Tong that being electrical signals (Tong Paragraph [n0003])
The combination of Zhai and Tong does not teach: generate an output signal based on application of a denoising neural network (DNN) on the mixed signal;
In the same field of endeavor, Dech teaches: generate an output signal based on application of a denoising neural network (DNN) on the mixed signal; (Paragraph [0044] In other embodiments, the bandpass and notch filtering operations may be implemented using wavelet analysis or machine learning techniques, e.g., neural networks. Paragraph [0075] The method 1000 includes, at operation 1040, generating a denoised combined heart signal by performing the denoising operation on the combined heart signal. (Maternal and fetal heart signals are combined, then run through a denoising neural network to remove noise))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have combined generation of an output signal based on application of a denoising network on a mixed or combined signal as taught by Dech into the combination of Zhai and Tong as all three references are in the related field of removal of noise from observed inputs. Doing so would be desirable and represent a clear improvement in the state of the art by allowing for the increased detection of and increased physiological observation of any issues that may present themselves in development of an observed signal source, in the case of Dech that being the observation and noise reduction of conflicting heart rates between an expectant mother and a developing fetus (Dech Paragraphs [0003]-[0004]).
Regarding Claims 12 and 16, these are method and non-transitory computer readable medium claims similar to Claim 1. As such, they are rejected for the same reasons as above.
Regarding Claim 2, the combination of Zhai, Tong, and Dech teaches all of the limitations of Claim 1, including: wherein the first bio-signal is a magneto cardiogram (MCG) signal, and (Dech Paragraph [0004] Embodiments of the disclosed technology relate to methods, systems, and devices for fetal magnetocardiography (fMCG). (An MCG signal))
the second bio-signal is an electrocardiogram (ECG) signal. (Zhai Paragraph [0067] In step 902, first data may be received. The first data may be based on an electrocardiogram (ECG) signal (e.g., electrocardiogram 402) or another signal. (A signal which is an ECG signal))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have combined the concept of a first bio-signal comprising a magneto cardiogram signal (MCG) as taught by Dech into the combination of Zhai and Tong as all three references are in the related field of noise removal from observational data. Doing so would prove desirable by allowing for the increased detection of heart rates having differing clinical objectives, such as the differing heart rates between an expectant mother and a developing fetus (Dech Paragraph [0003]).
Regarding Claims 13, this is a method claim corresponding to Claim 2. As such, it is rejected for the same reasons as above.
Regarding Claim 3, the combination of Zhai, Tong, and Dech teaches all of the limitations of Claim 1, including: wherein the first bio-signal is one of an electrocardiogram (ECG) signal, and (Zhai Paragraph [0067] In step 902, first data may be received. The first data may be based on an electrocardiogram (ECG) signal (e.g., electrocardiogram 402) or another signal. (A signal which is an ECG signal))
the second bio-signal is a magneto cardiogram (MCG) signal. (Dech Paragraph [0004] Embodiments of the disclosed technology relate to methods, systems, and devices for fetal magnetocardiography (fMCG). (An MCG signal))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have combined the concept of a second bio-signal comprising a magneto cardiogram signal (MCG) as taught by Dech into the combination of Zhai and Tong as all three references are in the related field of noise removal from observational data. Doing so would prove desirable by allowing for the increased detection of heart rates having differing clinical objectives, such as the differing heart rates between an expectant mother and a developing fetus (Dech Paragraph [0003]).
Regarding Claims 14, this is a method claim corresponding to Claim 3. As such, it is rejected for the same reasons as above.
Regarding Claim 6, the combination of Zhai, Tong, and Dech teaches all of the limitations of Claim 1, including: wherein the computation of the weighted sum is based on a first weight for the first bio-signal and a second weight for the second bio-signal, and the second weight is more than the first weight. (Tong Paragraph [n0036] Based on the loss functions L1, L2, L3, and L4, different weights are assigned to them and summed to obtain the total loss function Loss: (The total weighted sum is based on individual signal weights. The weights are variable, meaning the second could hold higher weight depending on a number of factors))
Regarding Claims 17, this is a method claim corresponding to Claim 6. As such, it is rejected for the same reasons as above.
Regarding Claim 7, the combination of Zhai, Tong, and Dech teaches all of the limitations of Claim 6, including: wherein the processor is further configured to set each of the first weight and the second weight based on a user input or a random function. (Tong Paragraph [n0035] Where L1, L2, L3, and L4 are the four constructed loss functions, N<sub>1</sub> and N<sub>2</sub> are the number of values randomly sampled within the range of variable values, x<sub>i</sub>, y<sub>i</sub>, and z<sub>i</sub> are a certain value of the x, y, and z position variables, respectively, t<sub>i</sub> is a certain value of the time variable, is the expression for the displacement u of the sound wave vibration when the independent variable takes a specific value, are the second-order partial derivatives of the displacement u of the sound wave vibration with respect to the x, y, and z position variables, respectively (The weights are based on a random sampling))
Regarding Claims 18, this is a method claim corresponding to Claim 7. As such, it is rejected for the same reasons as above.
Regarding Claim 8, the combination of Zhai, Tong, and Dech teaches all of the limitations of Claim 1, including: wherein the processor is further configured to: receive, from a sensor associated with a user, a bio-signal comprising a noise component via a single channel input or a muti-channel input; and (Zhai Paragraph [0046] The method may include receiving data (e.g., data 405). The data may be in the form of a signal or representative of the signal (e.g., an electrocardiogram 402). The network 406 may be used to classify the signal into classifications based on a signal-to-noise ratio. For example, a first class may include the electrocardiogram signals that have a signal-to-noise value of greater than zero dB value, which would not require distortion or noise reduction. A second class may include the electrocardiogram signals that have a signal-to-noise value of between negative six and zero dB value, which would allow for noise or distortion reduction with network 412. Paragraph [0049] The input data (e.g., data 405) may have a size of N×1×C, where N is the length of the segment 404 (e.g., 30 seconds) and C is the number of channels of the segment. (An audio signal from a sensor associated with a user (ECG signal) is received, along with associated noise in a multi-channel input))
generate a denoised bio-signal based on application of the trained denoising neural network on the received bio-signal, (Zhai Paragraph [0046] That is, instructions 410 may be configured to discard the data (e.g., data 405) or pass the data onto one or more other neural networks (e.g., denoising network 412, categorization network 432). Paragraph [0047] The denoising network 412 may reduce distortion imparted in the electrocardiogram signal as it is collected. Distortion may be caused by various sources (e.g., movement, conversion, sensitivity, etc.). Through denoising, the data (e.g., data 405) is converted to denoised or clean data (e.g., data 414). The data (e.g., data 414) may represent a clean electrocardiogram signal or an electrocardiogram signal with reduced distortion. Paragraph [0048] The data (e.g., data 414) may be classified again to determine whether the data (e.g., data 414) can be classified into the correct category. For example, a network (e.g., neural network 416) may be used to evaluate the data 414 to determine whether another neural network could correctly categorize the data after denoising, reducing false positives, or negatives, categorized by the categorization network 432. Paragraph [0068] In step 904, second data may be generated (e.g., data 414). The second data may be generated by a first neural network (e.g., network 412). The second data may be based on an electrocardiogram (e.g., electrocardiogram 402). The generated data may reduce the distortion contained therein. For example, the second data may be generated by reducing the distortion. (A bio-signal is passed to a trained neural network when deemed to have appropriate levels of noise that can be removed. The signal is cleaned and re-generated, acting as a second data (and signal) based on the ECG that is reclassified.))
wherein the denoised bio-signal is generated after a removal of the noise component from the received bio-signal. (Zhai Paragraph [0047] The denoising network 412 may reduce distortion imparted in the electrocardiogram signal as it is collected. Distortion may be caused by various sources (e.g., movement, conversion, sensitivity, etc.). Through denoising, the data (e.g., data 405) is converted to denoised or clean data (e.g., data 414). (The signal is considered denoised after removal of noise))
Regarding Claims 19, this is a method claim corresponding to Claim 8. As such, it is rejected for the same reasons as above.
Regarding Claim 9, the combination of Zhai, Tong, and Dech teaches all of the limitations of Claim 1, including: wherein the DNN is an encoder-decoder network comprising an encoder, a decoder coupled to an output of the encoder. (Zhai Paragraph [0052] The network 412 may be an encoder-decoder network. For example, layers 602, 604, 606, 608, 610, 612, 614, 616, 618, 620 may encode the data (e.g., data 405) and layers 622, 624, 626, 628, 630, 632, 634, 636, 638, 640, 642, 644 may decode the encoded data, reducing distortion present in the data, or underlying signal. (The DNN is an encoder-decoder network))
Regarding Claim 10, the combination of Zhai, Tong, and Dech teaches all of the limitations of Claim 1, including: wherein the DNN is a denoising autoencoder or a variant of the denoising autoencoder. (Zhai Paragraph [0068] The data may be encoded and then decoded and compared with data without distortion to train the first neural network (e.g., network 412) to remove distortion. As such, distortion may be removed from the second data (e.g., data 405) by a network and result in data 414). The first neural network may comprise an encoder-decoder network. (The neural network is a variant of an encoder))
Regarding Claim 11, the combination of Zhai, Tong, and Dech teaches all of the limitations of Claim 1, including: wherein the DNN is a U-net or a variant of the U-net. (Zhai Paragraph [0052] In FIG. 6, an example network 412 in accordance with one or more implementations of the present disclosure is shown. The example network 412 may be trained, implemented, or executed on one or more computing device described herein (e.g., computer 122, cloud computing 302, etc.). The network 412 may include instructions 308 depicted as layers (e.g., layers 602, 604) and weights. The example network 412 may be based on a U-Net configuration. (The DNN is a U-net or U-net variant))
Claims 4-5 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Zhai in view of Tong in further view of Dech, as applied in Claims 1 and 12 above, and further in view of Wichern et al. (US 20250088796 A1, hereinafter Wichern).
Regarding Claim 4, the combination of Zhai, Tong, and Dech teaches all of the limitations of Claim 1. The combination does not teach: wherein the loss between the output signal and the first bio-signal is computed based on a spectral loss function.
In the same field of endeavor, Wichern teaches: wherein the loss between the output signal and the first bio-signal is computed based on a spectral loss function. (Paragraph [0165] Returning to FIG. 6C, the set of loss functions may include a reconstruction loss function 616. In an example, the reconstruction loss function 616 may include two components, namely, spectral loss and time domain spatial covariance loss. (Loss computed based on spectral loss))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have combined the concept of computing a loss based on a spectral loss function as taught by Wichern into the combination of Zhai, Tong, and Dech as all four references are in the related field of removing noise from observed signals, and doing so would represent a clear improvement in the state of the art by allowing for the clear detection of abnormal functionalities in observed objects which provide these signals (Wichern Paragraphs [0003]-[0004])
Regarding Claims 15, this is a method claim corresponding to Claim 4. As such, it is rejected for the same reasons as above.
Regarding Claim 5, the combination of Zhai, Tong, Dech, and Wichern teaches all of the limitations of Claim 4, including: wherein the spectral loss function is based on Short-Time Fourier Transform (STFT) or Gabor transform. (Wichern Paragraph [0153] The training audio mixture 610, i.e., the spectral features of the training audio mixture 610 including a subset of complex short-time Fourier transform spectrograms, inter-channel phase difference spectrograms, and target phase correlation spectrograms for the known locations of the training audio sources 608a and 608b, may be processed with the neural network 202 to extract isolated training audio signals of the training audio sources 608a and 608b. (The loss function is based on STFT))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have combined the concept of a spectral loss function that is based on either Short-Time Fourier Transform (STFT) or Gabor transform used to calculate a loss as taught by Wichern into the combination of Zhai, Tong, and Dech as all of these references are in the related field of removing noise from observed signals, and doing so would represent a clear improvement in the state of the art by allowing for the clear detection of abnormal functionalities in observed objects which provide these signals (Wichern Paragraphs [0003]-[0004]).
Regarding Claims 16, this is a method claim corresponding to Claim 5. As such, it is rejected for the same reasons as above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Von Rosenburg et al. (US 20210093216 A1) teaches a combination of signals and noise reduction.
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/JUSTIN CARDOSO/
Patent Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
