DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the application filed on 17 January 2025.
Claims 1-20 are currently pending and have been examined.
Priority
While acknowledgement is made of certified copies of priority documents, English translations for KR10-2022-0103921 and KR10-2023-0107866 as well as a statement that the translations are accurate is required. See 37 CFR 1.55 (g)(3)(iii) & 37 CFR 1.55 (g)(4). Also, an English translation for PCT/KR2023/012267 as well as a statement that the translation is accurate is required. See 37 CFR 1.55 (g)(3)(iii) & 37 CFR 1.55 (g)(4).
Failure to provide a certified translation may result in no benefit being accorded
for the non-English applications.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: S110 and S120. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
The claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of the patent eligible subject matter because the broadest interpretation of the computer-readable medium of claim 11 encompasses signals per se.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a process, (claims 1-10) and machine (claim 12).
INDEPENDENT CLAIMS
Step 2A Prong 1
Claim 1 recites steps of
the method being performed by a computing device including at least one processor, the method comprising: for a plurality of augmentation algorithms for augmenting bio-signal data by converting a form or rhythm of each bio-signal, generating a target algorithm based on an optimal augmentation intensity determined for each of the augmentation algorithms or by combining one or more of the plurality of augmentation algorithms; and
generating augmented data by augmenting basic data including a bio-signal according to the target algorithm.
Claims 11and 12 recite similar limitations as claim 1 but for the recitation of generic computer components.
These steps directed to augmenting a bio-signal, as drafted, under the broadest reasonable interpretation, includes mathematical concepts. That is, nothing in the claim element precludes the italicized portions from performing mathematical calculations through algorithm optimization and data manipulation for augmenting a bio-signal. This could be analogized to using a formula to convert geospatial coordinates into natural numbers. If a claim limitation, under its broadest reasonable interpretation, covers mathematical calculations but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, the additional elements non-italicized portions identified above for claims 1 and 11-12 do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as by a computing device including at least one processor; a computer program stored in a computer- readable storage medium, the computer program performing operations; and, a processor including at least one core; memory including program codes executable on the processor; and a network unit amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
Each of the above additional elements therefore only amounts to mere instructions to implement functions within the abstract idea using generic computer components or other machines within their ordinary capacity. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. These elements are therefore not sufficient to integrate the abstract idea into a practical application. Therefore, the above claims, as a whole, are directed to an abstract idea.
Step 2B
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to mere instructions to apply an exception in particular fields such as by a computing device including at least one processor; a computer program stored in a computer- readable storage medium, the computer program performing operations; and, a processor including at least one core; memory including program codes executable on the processor; and a network unit, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f);
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
DEPENDENT CLAIMS
Step 2A Prong 1
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-10 reciting particular aspects augmenting bio-signal data such as
[Claim 2] wherein generating the target algorithm comprises: for each of the augmentation algorithms, generating first experimental data by augmenting bio- signal data based on different augmentation intensities;
outputting a performance score of an artificial intelligence model for each augmentation intensity by inputting the first experimental data to the artificial intelligence model; and
determining an optimal augmentation intensity among augmentation intensities for each of the augmentation algorithms based on the performance score for each augmentation intensity;
wherein the artificial intelligence model is trained to predict a disease or bio-information based on a biosignal;
[Claim 3] wherein the augmentation intensity is calculated based on a signal-to- noise ratio (SNR) between the bio-signal data and the first experimental data;
[Claim 4] wherein the optimal augmentation intensity for each of the augmentation algorithms is an augmentation intensity corresponding to the first experimental data having a highest performance score of the artificial intelligence model;
[Claim 5] wherein generating the target algorithm comprises determining an augmentation algorithm whose performance score exceeds a reference value to be the target algorithm based on the performance score generated as a result of reflecting therein the optimal augmentation intensity for each of the augmentation algorithms;
[Claim 6] wherein generating the target algorithm comprises: combining n (n is a natural number equal to or larger than 2) augmentation algorithms among the plurality of augmentation algorithms;
outputting a performance score of an artificial intelligence model by inputting second experimental data, generated according to the combined augmentation algorithms, to the artificial intelligence model; and
generating the target algorithm based on the performance score;
wherein the artificial intelligence model is trained to predict a disease or bio-information based on a bio- signal;
[Claim 7] wherein the performance score varies depending on a type of disease or bio-information predicted by the artificial intelligence model;
[Claim 8] wherein the plurality of augmentation algorithms comprise: a first augmentation algorithm for preserving a form of a bio-signal while converting a rhythm of the bio-signal; a second augmentation algorithm for converting a form of a bio-signal while preserving a rhythm of the bio-signal; a third augmentation algorithm for converting both a rhythm and form of a bio-signal; and a fourth augmentation algorithm for adding noise generated by a process of measuring a bio-signal;
[Claim 9] wherein the bio- signal comprises an electrocardiogram (ECG) signal;
[Claim 10] wherein the artificial intelligence model comprises an artificial neural network model trained to predict arrhythmia based on an electrocardiogram signal;
these italicized portions are mathematical calculations and mathematical formulas which fall under mathematical concepts. Additionally, the training of the generic artificial intelligence model has been treated as mathematical calculations which falls under mathematical concepts in light of the 2024 USPTO AI Guidance (MPEP 2106).
Step 2A Prong 2
Dependent claim 10 recites additional subject matter which amount to limitations consistent with the additional elements in the independent claims (the additional limitations in claims 10 (an artificial neural network model) amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
Dependent claim 10 recites additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea, e.g., requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), MPEP 2106.05(f). There is no indication that these additional elements improve the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation.
Therefore, in consideration of all the facts, this is a textbook USC 101 where the present invention is clearly not patent-eligible under USC 101. Additionally, it is evident that the present claims monopolize fundamental basic tools of scientific and technological work such as algorithms and mathematical concepts, restricting further innovation in this area without offering a specific, technical improvement to how the computer actually operates; “monopolization of those tools through the grant of a patent might tend to impede innovation more than it would tend to promote it.” Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980 (quoting Myriad, 569 U.S. at 589, 106 USPQ2d at 1978 and Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012)).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148
USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Raghu et al. (Data Augmentation for Electrocardiograms) in view of De Haan (US20200121262A1).
Regarding claim 1, Raghu discloses for a plurality of augmentation algorithms for augmenting bio-signal data by converting a form or rhythm of each bio-signal, generating a target algorithm based on an optimal augmentation intensity determined for each of the augmentation algorithms or by combining one or more of the plurality of augmentation algorithms ([pg. 287] “Based on prior work in time series and ECG data augmentation (Iwana and Uchida, 2021a; Mehari and Strodthoff, 2021) we use the following transformations in the TaskAug policy […] Random temporal warp […] The variance is the strength parameter, with higher variance indicating more warping. Baseline wander […] Gaussian wander […] Magnitude scale […] Time mask […] Random temporal displacement […].”)
and generating augmented data by augmenting basic data including a bio-signal according to the target algorithm ([pg. 285] “We define a set of operations S = {A1,...,AM}, each of which is an augmentation function of the form Ai(x,y;µ0,µ1), where x is the input data point to the augmentation function, y is the label, and {µ0,µ1} represent the augmentation strengths for datapoints of class label 0 and class label 1 respectively. […] The overall augmentation policy consists of a set of K stages, where at each stage we: (1) sample an augmentation function Ai to apply; and (2) apply it to the input signal to that stage. This allows composing combinations of operations in a stochastic manner.”)
Raghu does not explicitly disclose however De Haan teaches performed by a computing device including at least one processor ([0022] “a computer program product, which, when executed by a processor” [0076] “The device 10 for processing physiological signals of the subject 100 comprises”)
Therefore, it would have been obvious to one of ordinary still in the art to include in the data augmentation technique of Raghu a computing device including at least one processor as taught by De Haan since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2, Raghu discloses wherein generating the target algorithm comprises: for each of the augmentation algorithms, generating first experimental data by augmenting bio- signal data based on different augmentation intensities ([pg. 287] “Since the value of data augmentation can depend on the amount of training data, we train on different dataset sizes. For the nonhemodynamic tasks (Datasets A and B), we generate development datasets with 1000, 2500, and 5000 ECGs.” [pg. 288] “For the number of model optimization steps P (defined in Section 4.2.2), we evaluate both P = 1 and P = 5, and select the best performing setting based on validation set loss.”)
outputting a performance score of an artificial intelligence model for each augmentation intensity by inputting the first experimental data to the artificial intelligence model ([pg. 288] “As evaluation, we compute the AUROC of the best performing model on the held-out testing set, and report mean/standard error across runs.” Also, see Figure 1.)
and determining an optimal augmentation intensity among augmentation intensities for each of the augmentation algorithms based on the performance score for each augmentation intensity ([pg. 289] “Augmentations do not worsen performance however, and some tasks (STTC, CD) benefit a small amount, ∼+1%AUROC.”)
wherein the artificial intelligence model is trained to predict a disease or bio-information based on a biosignal ([pg. 288] “Given that performance improvements are most evident in the lowest sample regimes for both datasets (N = 1000), we focus on this setting with results shown in Table 2.” Also, see Table 2 Augmentation strategies improve AUROC on detecting most cardiac abnormalities in the low-sample regime (N=1000))
Regarding claim 3, Raghu does not explicitly disclose however De Haan teaches wherein the augmentation intensity is calculated based on a signal-to- noise ratio (SNR) between the bio-signal data and the first experimental data ([0102] “The quality of the ensemble-averaged DCA waveforms can be assessed by using a signal-to-noise ratio (SNR) metric. […] Hereby, the power of the true signal can be computed as the variance of the DCA cycle after adaptive band-pass filtering and spectral truncation to the first 8 cardiac frequency bands.”)
Therefore, it would have been obvious to one of ordinary still in the art to include in the data augmentation technique of Raghu wherein the augmentation intensity is calculated based on a signal-to- noise ratio (SNR) between the bio-signal data and the first experimental data as taught by De Haan since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 4, Raghu does not explicitly disclose however De Haan teaches wherein the optimal augmentation intensity for each of the augmentation algorithms is an augmentation intensity corresponding to the first experimental data having a highest performance score of the artificial intelligence model ([0100] “For improved robustness against sporadic interferences or non-representative cycles, for example ectopic beats or arrhythmia episodes, an optional confidence scheme can be applied for example for reducing motion artifacts whereby e.g. relative weights are assigned to individual cardiac cycles based on a trust metric or trust score derived from correlation with neighboring cycles.” [0125] “The lower graphs in FIG. 14 illustrate the trust weights w and the resulting weighted displacement waveform signal DCA·×w. As can be seen from the given example, the incoming time-varying DCA signal is polluted by a strong motion artifact between samples 3000 and 3400.”)
Therefore, it would have been obvious to one of ordinary still in the art to include in the data augmentation technique of Raghu wherein the optimal augmentation intensity for each of the augmentation algorithms is an augmentation intensity corresponding to the first experimental data having a highest performance score of the artificial intelligence model as taught by De Haan since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 5, Raghu discloses wherein generating the target algorithm comprises determining an augmentation algorithm whose performance score exceeds a reference value to be the target algorithm based on the performance score generated as a result of reflecting therein the optimal augmentation intensity for each of the augmentation algorithms ([pg. 288] “TaskAug almost always improves on the NoAugs baseline, and even boosts performance on some tasks where other augmentations worsen performance (AFib).” Also, see Table 4 The best-performing method is bolded, and the second best is underlined, and ∗ indicates statistically significant improvement at the p < 0.05 level. TaskAug is the only method to obtain significant improvements in performance on both tasks.”)
Regarding claim 6, Raghu discloses wherein generating the target algorithm comprises: combining n (n is a natural number equal to or larger than 2) augmentation algorithms among the plurality of augmentation algorithms ([pg. 287] “Based on prior work in time series and ECG data augmentation (Iwana and Uchida, 2021a; Mehari and Strodthoff, 2021) we use the following transformations in the TaskAug policy […] Random temporal warp […] The variance is the strength parameter, with higher variance indicating more warping. Baseline wander […]”)
outputting a performance score of an artificial intelligence model by inputting second experimental data, generated according to the combined augmentation algorithms, to the artificial intelligence model;
([pg. 288] “As evaluation, we compute the AUROC of the best performing model on the held-out testing set, and report mean/standard error across runs.”)
and generating the target algorithm based on the performance score ([pg. 286] “Optimize the model parameters θ for P steps: at each step, sample a batch (x,y) of data from D(train), augment the batch with the augmentation policy to obtain (˜x,y), compute the predicted label ˆ y, and update the model parameters using gradient descent: θ ← θ −η∇L(y,ˆy)” Also, see Figure 3. We focus on the probability of selecting each transformation in both augmentation stages (left) and the optimized temporal warp strengths in the first stage (right). We show the mean/standard error of these optimized policy parameters over 15 runs.”)
wherein the artificial intelligence model is trained to predict a disease or bio-information based on a bio- signal ([pg. 288] “Given that performance improvements are most evident in the lowest sample regimes for both datasets (N = 1000), we focus on this setting with results shown in Table 2.” Also, see Table 2 Augmentation strategies improve AUROC on detecting most cardiac abnormalities in the low-sample regime (N=1000))
Regarding claim 7, Raghu discloses wherein the performance score varies depending on a type of disease or bio-information predicted by the artificial intelligence model ([pg. 289, Table 2] See the columns of Dataset A and Dataset B which disclose the type of disease or bio-information followed by scores underneath)
Regarding claim 8, Raghu discloses wherein the plurality of augmentation algorithms comprise: a first augmentation algorithm for preserving a form of a bio-signal while converting a rhythm of the bio-signal ([pg. 287] “Baseline wander: A low-frequency sinusoidal component is added to the signal, with the amplitude of the sinusoid representing the strength.”)
a second augmentation algorithm for converting a form of a bio-signal while preserving a rhythm of the bio-signal ([pg. 287] “Magnitude scale: The signal amplitude is scaled by a number drawn from a scaled uniform distribution, with the scale being the strength parameter.”)
a third augmentation algorithm for converting both a rhythm and form of a bio-signal ([pg. 287] “Random temporal displacement: The entire signal is translated forwards or backwards in time by a random temporal offset, drawn from a uniform distribution scaled by a strength parameter.”)
and a fourth augmentation algorithm for adding noise generated by a process of measuring a bio-signal ([pg. 287] “Gaussian noise: IID Gaussian noise is added to the signal, with the strength parameter represent ing the variance of the Gaussian.”)
Regarding claim 9, Raghu discloses wherein the bio- signal comprises an electrocardiogram (ECG) signal ([pg. 286] “To investigate these questions, we consider a range of settings that cover three different 12-lead ECG datasets and eight prediction tasks of varying difficulty, class imbalance, and training set sizes.”)
Regarding claim 10, Raghu discloses wherein the artificial intelligence model comprises an artificial neural network model trained to predict […] based on an electrocardiogram signal ([pg. 284] “Let f(x;θ) → ˆy be a neural network model with parameters θ that outputs a predicted label ˆy given x as input.”)
Raghu does not explicitly disclose however De Haan teaches […] arrhythmia […] ([0100] “arrhythmia episodes.”)
Therefore, it would have been obvious to one of ordinary still in the art to include in the data augmentation technique of Raghu arrhythmia as taught by De Haan since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 11, Raghu discloses for a plurality of augmentation algorithms for augmenting bio-signal data by converting a form or rhythm of each bio-signal, generating a target algorithm based on an optimal augmentation intensity determined for each of the augmentation algorithms or by combining one or more of the plurality of augmentation algorithms ([pg. 287] “Based on prior work in time series and ECG data augmentation (Iwana and Uchida, 2021a; Mehari and Strodthoff, 2021) we use the following transformations in the TaskAug policy […] Random temporal warp […] The variance is the strength parameter, with higher variance indicating more warping. Baseline wander […] Gaussian wander […] Magnitude scale […] Time mask […] Random temporal displacement […].”)
and generating augmented data by augmenting basic data including a bio-signal according to the target algorithm ([pg. 285] “We define a set of operations S = {A1,...,AM}, each of which is an augmentation function of the form Ai(x,y;µ0,µ1), where x is the input data point to the augmentation function, y is the label, and {µ0,µ1} represent the augmentation strengths for datapoints of class label 0 and class label 1 respectively. […] The overall augmentation policy consists of a set of K stages, where at each stage we: (1) sample an augmentation function Ai to apply; and (2) apply it to the input signal to that stage. This allows composing combinations of operations in a stochastic manner.”)
Raghu does not explicitly disclose however De Haan teaches a computer program stored in a computer- readable storage medium, the computer program performing operations for augmenting bio-signal data when executed on one or more processors ([0022] “a computer program product, which, when executed by a processor” [0076] “The device 10 for processing physiological signals of the subject 100 comprises”) ([0022] “a non-transitory computer-readable recording medium that stores therein a computer program product, which, when executed by a processor, causes the method”)
Therefore, it would have been obvious to one of ordinary still in the art to include in the data augmentation technique of Raghu a computer program stored in a computer- readable storage medium, the computer program performing operations for augmenting bio-signal data when executed on one or more processors as taught by De Haan since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 12, Raghu discloses for a plurality of augmentation algorithms for augmenting bio-signal data by converting a form or rhythm of each bio-signal, generating a target algorithm based on an optimal augmentation intensity determined for each of the augmentation algorithms or by combining one or more of the plurality of augmentation algorithms ([pg. 287] “Based on prior work in time series and ECG data augmentation (Iwana and Uchida, 2021a; Mehari and Strodthoff, 2021) we use the following transformations in the TaskAug policy […] Random temporal warp […] The variance is the strength parameter, with higher variance indicating more warping. Baseline wander […] Gaussian wander […] Magnitude scale […] Time mask […] Random temporal displacement […].”)
and generating augmented data by augmenting basic data including a bio-signal according to the target algorithm ([pg. 285] “We define a set of operations S = {A1,...,AM}, each of which is an augmentation function of the form Ai(x,y;µ0,µ1), where x is the input data point to the augmentation function, y is the label, and {µ0,µ1} represent the augmentation strengths for datapoints of class label 0 and class label 1 respectively. […] The overall augmentation policy consists of a set of K stages, where at each stage we: (1) sample an augmentation function Ai to apply; and (2) apply it to the input signal to that stage. This allows composing combinations of operations in a stochastic manner.”)
Raghu does not explicitly disclose however De Haan teaches a computing device for augmenting bio- signal data, the computing device comprising: a processor including at least one core; memory including program codes executable on the processor; and a network unit for obtaining basic data including a bio-signal; wherein the processor ([0022] “a computer program product, which, when executed by a processor” [0076] “The device 10 for processing physiological signals of the subject 100 comprises”) ([0022] “a non-transitory computer-readable recording medium that stores therein a computer program product, which, when executed by a processor, causes the method”)
Therefore, it would have been obvious to one of ordinary still in the art to include in the data augmentation technique of Raghu a computing device for augmenting bio- signal data, the computing device comprising: a processor including at least one core; memory including program codes executable on the processor; and a network unit for obtaining basic data including a bio-signal; wherein the processor as taught by De Haan since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Prior Art Cited but Not Relied Upon
Nonaka, N., & Seita, J. (2020). Data augmentation for electrocardiogram classification with deep neural network. arXiv preprint arXiv:2009.04398.
This reference is relevant because it conceptually discloses the applicant’s invention for data augmentation.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WINSTON FURTADO whose telephone number is (571)272-5349. The examiner can normally be reached Monday-Friday 8:00 AM to 4:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WINSTON R FURTADO/Primary Examiner, Art Unit 3687
Prosecution Insights