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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “significant congenital heart disease” in claim 1 (and similarly claims 6 and 11) is a relative term which renders the claim indefinite. The term “significant” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For the purposes of examination, any abnormality in the electrocardiogram (ECG) of an infant will be construed as any congenital heart disease.
5. Claims 2-5, 7-10, and 12-15 are rejected at least because they depend from a claim(s) which is indefinite.
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, 3-15 are rejected under 35 U.S.C 103 as being unpatentable over Sen et al. (US Pub.: 2026/0026756 A1) and further in view of Li et al. (US Pub.: 2020/0312459 A1) and further in view of Zimmerman (US Pub.: 2023/0028783 A1).
Regarding claim 1, Sen teaches a prediction system of a significant congenital heart disease in infants (e.g. paragraphs 0002-0003), comprising:
a storage device configured to store at least one instruction and at least one electrocardiogram (e.g. paragraph 0015 – Holter monitors and/or cardiac event monitors have built-in memory),
and an original format of the at least one electrocardiogram being a first format (e.g. paragraph 0015 – ECG data is collected/stored as an analog signal);
and a processor coupled to the storage device (e.g. paragraph 0015 – processing unit), and the processor configured to access and execute the at least one instruction for:
converting the first format into a second format, so that the at least one electrocardiogram has the second format (e.g. paragraph 0015, – analog ECG data is converted into multi-lead ECG waveforms (i.e. digital signals));
performing a continuous wavelet transformation on the at least one electrocardiogram having the second format to obtain at least one processed electrocardiogram (e.g. paragraph 0170 – tsfresh python package is used to perform a wavelet transform. The wavelet transformation is a continuous wavelet transform as evidenced by tsfresh python package documentation (attached, see pg. 11)).
However, Sen does not explicitly teach performing an oversampling on the at least one processed electrocardiogram to obtain a plurality of electrocardiogram segments;
and using a transfer learning through a plurality of pre-trained models based on the plurality of the electrocardiogram segments to establish a significant congenital heart disease model.
Li, in a same field of endeavor of electrocardiogram analysis systems discloses performing an oversampling on the at least one processed electrocardiogram to obtain a plurality of electrocardiogram segments (e.g. paragraphs 0034, 0036, – synthetic minority oversampling technique (SMOTE) is an oversampling technique).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Sen to incorporate performing an oversampling on the at least one processed electrocardiogram to obtain a plurality of electrocardiogram segments, as taught and suggested by Li, for the purpose of synthesizing additional samples of the minority class data in order to balance the ECG data before training of the machine learning model commences (Li, paragraph 0036).
However, Sen in view of Li does not explicitly teach using a transfer learning through a plurality of pre-trained models based on the plurality of the electrocardiogram segments to establish a significant congenital heart disease model.
Zimmerman, in a same field of endeavor of electrocardiogram systems, discloses using a transfer learning through a plurality of pre-trained models based on the plurality of the electrocardiogram segments to establish a significant congenital heart disease model (e.g. paragraphs 0037-0039).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Sen and Li to include using a transfer learning through a plurality of pre-trained models based on the plurality of the electrocardiogram segments to establish a significant congenital heart disease model, as taught and suggested by Zimmerman, because it reduces the time and effort required for training the model and allows for the usage of fewer ECG leads to assess risk of a cardiac event (Zimmerman, paragraph 0039).
Regarding claim 3, Sen in view of Li in view of Zimmerman teaches the prediction system of the significant congenital heart disease in the infants of claim 1 as discussed above, and Li further teaches wherein the oversampling divides the at least one processed electrocardiogram into the plurality of electrocardiogram segments in each time unit of a predetermined period (e.g. paragraphs 0036, 0070).
Regarding claim 4, Sen in view of Li in view of Zimmerman teaches the prediction system of the significant congenital heart disease in the infants of claim 3 as discussed above, and Sen further teaches wherein a number of electrocardiogram segments is five times a number of processed electrocardiograms (e.g. paragraphs 0023, 0174), and the processor accesses and executes the at least one instruction for: performing a five-fold cross validation on the plurality of the electrocardiogram segments, thereby ensuring a stability of the plurality of the pre-trained models (e.g. paragraph 0174).
Regarding claim 5, Sen in view of Li in view of Zimmerman teaches the prediction system of the significant congenital heart disease in the infants of claim 1 as discussed above, and Zimmerman further teaches wherein the processor accesses and executes the at least one instruction for: training the plurality of the pre-trained models through the transfer learning to obtain a plurality of trained models (e.g. paragraphs 0039, 0089); and selecting one trained model with a highest accuracy rate from the plurality of trained models, so as to designate the one trained model as the significant congenital heart disease model (e.g. paragraphs 0039, 0089).
Regarding claim 6, Sen teaches an operation method of a prediction system of a significant congenital heart disease in infants (e.g. paragraphs 0002-0003), and the operation method, comprising steps of:
performing a continuous wavelet transformation on at least one electrocardiogram to obtain at least one processed electrocardiogram (e.g. paragraph 0170 – tsfresh python package is used to perform a continuous wavelet transform).
However, Sen does not explicitly teach performing an oversampling on the at least one processed electrocardiogram to obtain a plurality of electrocardiogram segments;
and using a transfer learning through a plurality of pre-trained models based on the plurality of the electrocardiogram segments to establish a significant congenital heart disease model.
Li, in a same field of endeavor of electrocardiogram analysis methods, discloses performing an oversampling on the at least one processed electrocardiogram to obtain a plurality of electrocardiogram segments (e.g. paragraphs 0034, 0036, – synthetic minority oversampling technique (SMOTE) is an oversampling technique).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Sen to incorporate performing an oversampling on the at least one processed electrocardiogram to obtain a plurality of electrocardiogram segments, as taught and suggested by Li, for the purpose of synthesizing additional samples of the minority class data in order to balance the ECG data before training of the machine learning model commences (Li, paragraph 0036).
However, Sen in view of Li does not explicitly teach using a transfer learning through a plurality of pre-trained models based on the plurality of the electrocardiogram segments to establish a significant congenital heart disease model.
Zimmerman, in a same field of endeavor of electrocardiogram systems, discloses using a transfer learning through a plurality of pre-trained models based on the plurality of the electrocardiogram segments to establish a significant congenital heart disease model (e.g. paragraphs 0037-0039).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Sen and Li to include using a transfer learning through a plurality of pre-trained models based on the plurality of the electrocardiogram segments to establish a significant congenital heart disease model, as taught and suggested by Zimmerman, because it reduces the time and effort required for training the model and allows for the usage of fewer ECG leads to assess risk of a cardiac event (Zimmerman, paragraph 0039).
Regarding claim 7, Sen in view of Li in view of Zimmerman teaches the operation method of claim 6 as discussed above, and Sen further teaches wherein an original format of the at least one electrocardiogram is a first format (e.g. paragraph 0015 – ECG data is collected/stored as an analog signal), and the step of performing the continuous wavelet transformation on the at least one electrocardiogram to obtain the at least one processed electrocardiogram (e.g. paragraphs 0015; 0170) comprises: converting the first format into a second format, so that the at least one electrocardiogram has the second format (e.g. paragraph 0015, – analog ECG data is converted into multi-lead ECG waveforms (i.e. digital signals)); and performing the continuous wavelet transformation on the at least one electrocardiogram having the second format to obtain the at least one processed electrocardiogram (e.g. paragraph 0170 – tsfresh python package is used to perform a continuous wavelet transform).
Regarding claim 8, Sen in view of Li in view of Zimmerman teaches the operation method of claim 6 as discussed above, and Li further teaches wherein the step of performing the oversampling on the at least one processed electrocardiogram to obtain the plurality of electrocardiogram segments (e.g. paragraphs 0036, 0070) comprises: dividing the at least one processed electrocardiogram into the plurality of electrocardiogram segments in each time unit of a predetermined period (e.g. paragraphs 0036, 0070).
Regarding claim 9, Sen in view of Li in view of Zimmerman teaches the operation method of claim 8 as discussed above, and Sen further teaches wherein the step of using the transfer learning through the plurality of pre-trained models based on the plurality of the electrocardiogram segments to establish the significant congenital heart disease model (e.g. paragraphs 0023) comprises: performing a five-fold cross validation on the plurality of the electrocardiogram segments, thereby ensuring a stability of the plurality of the pre-trained models (e.g. paragraph 0174), wherein a number of electrocardiogram segments is five times a number of processed electrocardiograms (e.g. paragraphs 0023, 0174).
Regarding claim 10, Sen in view of Li in view of Zimmerman teaches the operation method of claim 6 as discussed above, and Zimmerman further teaches wherein the step of using the transfer learning through the plurality of pre-trained models based on the plurality of the electrocardiogram segments to establish the significant congenital heart disease model (e.g. paragraph 0039) comprises: training the plurality of the pre-trained models through the transfer learning to obtain a plurality of trained models (e.g. paragraphs 0039, 0089); and selecting one trained model with a highest accuracy rate from the plurality of trained models, so as to designate the one trained model as the significant congenital heart disease model (e.g. paragraphs 0039, 0089).
Regarding claim 11, Sen teaches a non-transitory computer readable medium to store a plurality of instructions for commanding a computer to execute an operation method (e.g. paragraph 0110), and the operation method comprising steps of:
performing a continuous wavelet transformation on at least one electrocardiogram to obtain at least one processed electrocardiogram (e.g. paragraph 0170 – tsfresh python package is used to perform a continuous wavelet transform).
However, Sen does not explicitly teach performing an oversampling on the at least one processed electrocardiogram to obtain a plurality of electrocardiogram segments;
and using a transfer learning through a plurality of pre-trained models based on the plurality of the electrocardiogram segments to establish a significant congenital heart disease model.
Li, in a same field of endeavor of electrocardiogram analysis, discloses performing an oversampling on the at least one processed electrocardiogram to obtain a plurality of electrocardiogram segments (e.g. paragraphs 0034, 0036).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the non-transitory computer readable medium of Sen to incorporate performing an oversampling on the at least one processed electrocardiogram to obtain a plurality of electrocardiogram segments, as taught and suggested by Li, for the purpose of synthesizing additional samples of the minority class data in order to balance the ECG data before training of the machine learning model commences (Li, paragraph 0036).
However, Sen in view of Li does not explicitly teach using a transfer learning through a plurality of pre-trained models based on the plurality of the electrocardiogram segments to establish a significant congenital heart disease model.
Zimmerman, in a same field of endeavor of electrocardiogram systems, discloses using a transfer learning through a plurality of pre-trained models based on the plurality of the electrocardiogram segments to establish a significant congenital heart disease model (e.g. paragraphs 0037-0039).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Sen and Li to include using a transfer learning through a plurality of pre-trained models based on the plurality of the electrocardiogram segments to establish a significant congenital heart disease model, as taught and suggested by Zimmerman, because it reduces the time and effort required for training the model and allows for the usage of fewer ECG leads to assess risk of a cardiac event (Zimmerman, paragraph 0039).
Regarding claim 12, Sen in view of Li in view of Zimmerman teaches the non-transitory computer readable medium of claim 11 as discussed above, and Sen further teaches wherein an original format of the at least one electrocardiogram is a first format (e.g. paragraph 0015 – ECG data is collected/stored as an analog signal), and the step of performing the continuous wavelet transformation on the at least one electrocardiogram to obtain the at least one processed electrocardiogram (e.g. paragraphs 0015; 0170) comprises: converting the first format into a second format, so that the at least one electrocardiogram has the second format (e.g. paragraph 0015, – analog ECG data is converted into multi-lead ECG waveforms (i.e. digital signals)); and performing the continuous wavelet transformation on the at least one electrocardiogram having the second format to obtain the at least one processed electrocardiogram (e.g. paragraph 0170).
Regarding claim 13, Sen in view of Li in view of Zimmerman teaches the non-transitory computer readable medium of claim 11 as discussed above, and Li further teaches wherein the step of performing the oversampling on the at least one processed electrocardiogram to obtain the plurality of electrocardiogram segments (e.g. paragraphs 0036, 0070) comprises: dividing the at least one processed electrocardiogram into the plurality of electrocardiogram segments in each time unit of a predetermined period (e.g. paragraphs 0036, 0070).
Regarding claim 14, Sen in view of Li in view of Zimmerman teaches the non-transitory computer readable medium of claim 13 as discussed above, and Sen further teaches wherein the step of using the transfer learning through the plurality of pre-trained models based on the plurality of the electrocardiogram segments to establish the significant congenital heart disease model (e.g. paragraphs 0023) comprises: performing a five-fold cross validation on the plurality of the electrocardiogram segments, thereby ensuring a stability of the plurality of the pre-trained models (e.g. paragraph 0174), wherein a number of electrocardiogram segments is five times a number of processed electrocardiograms (e.g. paragraphs 0023, 0174).
Regarding claim 15, Sen in view of Li in view of Zimmerman teaches the non-transitory computer readable medium of claim 11 as discussed above, and Zimmerman further teaches wherein the step of using the transfer learning through the plurality of pre-trained models based on the plurality of the electrocardiogram segments to establish the significant congenital heart disease model (e.g. paragraph 0039) comprises: training the plurality of the pre-trained models through the transfer learning to obtain a plurality of trained models (e.g. paragraphs 0039, 0089); and selecting one trained model with a highest accuracy rate from the plurality of trained models, so as to designate the one trained model as the significant congenital heart disease model (e.g. paragraphs 0039, 0089).
Claims 2 is rejected under 35 U.S.C 103 as being unpatentable over Sen and further in view of Li and further in view of Zimmerman and further in view of Zheng et al. (NPL reference, “A 12-lead electrocardiogram database for arrhythmia research…”, published February 2020).
Regarding claim 2, Sen in view of Li in view of Zimmerman teaches the prediction
system of the significant congenital heart disease in the infants of claim 1 as discussed above. However, Sen in view of Li in view of Zimmerman does not explicitly teach wherein the first format is an extensible markup language format, and the second format is a comma-separated values format.
Zheng, in a same field of endeavor of electrocardiogram analysis systems, discloses wherein the first format is an extensible markup language format, and the second format is a comma-separated values format (e.g. pg. 3, first paragraph).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Sen, Li, and Zimmerman to include wherein the first format is an extensible markup language format, and the second format is a comma-separated values format, as taught and suggested by Zheng, because XML and CSV allow for ECG data to be better organized and ensures interoperability between different clinical information systems and programming languages (i.e. Python, MATLAB) (Zheng, pg. 7).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL TEHRANI whose telephone number is (571)270-0697. The examiner can normally be reached 9:00am-5:00pm.
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/D.T./Examiner, Art Unit 3792
/Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792
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