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 .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2, 5, 7, 9, 10, 12, 15, 16, and 18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Semeni et al. (US PGPUB 2014/0350421 – in IDS).
Regarding claims 1, 9, and 15, Semeni discloses a device, method, and non-transitory computer-readable storage medium for extracting a fetal ECG (e.g. ¶ 15, 16), comprising: processing circuitry (e.g. ¶ 107) configured to: determine a coherence between a maternal ECG and an abdominal ECG (e.g. ¶ 38, 101, Fig. 8; in order to analyze the fetal cardiac signal, signals recorded from the body surface of a pregnant woman (abdominal ECG) are processed to obtain a purified fetal cardiac signal; signal quality of the ECG electrode recordings can be assessed by comparing temporal and spectral coherence between recorded channels; periodic components are extracted from the dataset using maternal and fetal ECG beat synchronization), attenuate the maternal ECG independent of the coherence (e.g. ¶ 95, 99; as in the attenuation of the maternal signal, at each iteration, one, a subset, or the full N-dimensional space may be processed; Fig. 8B shows the maternal components are extracted from the ECG), and extract a fetal ECG from the abdominal ECG based on the coherence and the attenuated maternal ECG (e.g. ¶ 38, 95, 101, Fig. 8D; the maternal cardiac interference is removed (attenuated) and the desired fetal signals are extracted; in this case, rather than identifying the maternal heartbeats, the fetal heartbeats are identified in the remaining signal; in this enhancement phase, a subspace, which exhibits periodicity at the fetal heartrate, is enhanced; as in the attenuation of the maternal signal, one or more iterations may be performed, and at each iteration, one, a subset, or the full N-dimensional space may be processed; periodic components are extracted from the dataset using maternal and fetal ECG beat synchronization).
Regarding claims 2, 10, and 16, Semeni discloses dividing the maternal ECG and the abdominal ECG into epochs and extracting the fetal ECG for each of the epochs (e.g. Fig. 11 and ¶ 34, 66).
Regarding claims 5, 12, and 18, Semeni discloses the maternal ECG is attenuated based on a periodogram of the maternal ECG and a periodogram of the abdominal ECG (e.g. Fig. 8D and ¶ 101).
Regarding claim 7, Semeni discloses determining a signal quality of the fetal ECG using machine learning (e.g. ¶ 96; the fetal cardiac signal is enhanced by using a deflation method trained over the fetal R-peaks).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 3, 4, 8, 11, 14, 17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Semeni et al. in view of Wang et al. (CN 111434305 – in IDS).
Regarding claims 3, 11, and 17, Semeni discloses the claimed invention except for determining the coherence between the maternal ECG and the abdominal ECG in the frequency domain.
Wang teaches it is known to determine the coherence between the maternal ECG and the abdominal ECG in the frequency domain (e.g. page 2, ¶ 17 and page 6, ¶ 10; the electrocardio components of the mother and the electrocardio components of the fetus are overlapped in a frequency domain; a fetal electrocardiogram extraction method based on a convolutional encoding and decoding neural network comprises the following steps: 1) data preprocessing, collecting a path of signal on the abdomen of the mother body by using an electrode, wherein the sampling frequency is 250Hz). It would have been obvious to one having ordinary skill in the art to modify the determining the coherence as taught by Semeni with doing so in the frequency domain as taught by Want, since such a modification would provide the predictable results of providing a fetal electrocardiogram extraction system which is based on a convolutional coding and decoding neural network in order to improve the extraction efficiency and the extraction accuracy and a method using the system.
Regarding claim 4, Semeni discloses using machine learning as discussed above, but doesn’t explicitly recite using machine learning to determine the maternal ECG from the abdominal ECG.
Wang teaches it is known to determine the maternal ECG from the abdominal ECG using machine learning (e.g. page 2, ¶ 17 and page 6, ¶ 10; a fetal electrocardiogram extraction method based on a convolutional encoding and decoding neural network comprises the following steps: 1) data preprocessing, collecting a path of signal on the abdomen of the mother body by using an electrode, wherein the sampling frequency is 250Hz). It would have been obvious to one having ordinary skill in the art to modify the determining of the maternal ECG as taught by Semeni with using machine learning to do so as taught by Wang, since such a modification would provide the predictable results of providing a fetal electrocardiogram extraction system which is based on a convolutional coding and decoding neural network in order to improve the extraction efficiency and the extraction accuracy and a method using the system.
Regarding claims 8, 14, and 20, Semeni discloses using machine learning as discussed above, but doesn’t explicitly recite using machine learning to determine at least one fetal cardiac time interval based on the fetal ECG.
Wang teaches it is known to use machine learning to determine at least one fetal cardiac time interval based on the fetal ECG (e.g. page 6, ¶ 10-12; data preprocessing, collecting a path of signal on the abdomen of the mother body by using an electrode, wherein the sampling frequency is 250Hz, the signal samples are uniformly cropped to a fixed size that matches the size of the convolutional codec neural network input used in the method or the signal is acquired directly at the time of acquiring the signal, e.g., 2 or 4 seconds in length). It would have been obvious to one having ordinary skill in the art to modify the invention as taught by Semeni with the machine learning to determine at least one fetal cardiac time interval as taught by Wang, since such a modification would provide the predictable results of providing a fetal electrocardiogram extraction system which is based on a convolutional coding and decoding neural network in order to improve the extraction efficiency and the extraction accuracy and a method using the system.
Claim(s) 6, 13, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Semeni et al. in view of Yuan et al. (CN 112826513 – in IDS).
Regarding claims 6, 13, and 19, Semeni discloses the claimed invention except for calculating a loss function for the fetal ECG based on a number of missed heartbeats and a number of false heartbeats.
Yuan teaches it is known to calculate a loss function for the fetal ECG based on a number of missed heartbeats and a number of false heartbeats (e.g. page 3, ¶ 23 and page 7, ¶ 8; loss function score; obtained heartbeat probability sequence; ACCU/accuracy obtained by 14 electrocardiograms of the coordination data set is 90.2%, after the sliding window detection, the heart beats of misrecognition (false hearts) and missed recognition can be corrected according to the prior knowledge, and finally, the results of the fetal heart rate measured by 4 signals are selected). It would have been obvious to one having ordinary skill in the art to modify the invention as taught by Semeni with the calculating as taught by Yuan, since such a modification would provide the predictable results of improving a loss function, and enabling the network to be more concentrated on samples which are difficult to classify in the training process by reducing the weight of the loss function of samples which are easy to classify.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Cheng et al. (US PGPUB 2016/0198969) discloses fetal heart rate extraction from maternal abdominal ECG recordings.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH M DIETRICH whose telephone number is (571)270-1895. The examiner can normally be reached Mon - Fri 8:00-5:00.
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/JOSEPH M DIETRICH/Primary Examiner, Art Unit 3796