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 Objections
Claim 18 is objected to because of the following informalities: There is a typo in the word leads is spelled "leadsc". 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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claim 1 recites a process. Claim 10 recites a product.
Step 2A, Prong 1
Claims 1 and 10 recite the limitations of generating a diagnostic result from ECG data. This step, given its broadest reasonable interpretation, can be practically performed in the human mind. Namely, a person could determine a diagnosis from an ECG result. Therefore, the claims recite a mental process abstract idea.
Step 2A, Prong 2
Claims 1 and 10 do not include any additional elements that integrate the abstract idea into a practical application.
Claims 1 and 10 include the additional elements of obtaining an ECG file, converting the file to lead traces, and generating integrated ECG data through zero-padding and stacking. These amount to merely data gathering. Claims 1 and 10 also include the additional element of using a deep learning model, which amounts to merely generic computer implementation of the abstract idea. Therefore, these elements do not amount to integrating the abstract idea into a practical application.
Step 2B
Claims 1 and 10 do not include any additional elements that are sufficient to amount to significantly more than the abstract idea.
Claims 1 and 10 include the additional elements of obtaining an ECG file, converting the file to lead traces, and generating integrated ECG data through zero-padding and stacking. These amount to merely data gathering. Claims 1 and 10 also include the additional element of using a deep learning model, which amounts to merely generic computer implementation of the abstract idea. As noted by Applicant, generating a compensated ECG is well-understood, routine and conventional in the art (Specification Par. 34: “In addition, based on the above description, those skilled in the art can understand how to generate the compensated ECG data of 12 leads according to similar operations. It is not described in details here”). Furthermore, Albert (U.S. Patent No. 5,046,504) states that “The individual digitized X, Y and Z signals from block 16 can be signal averaged as represented by a block 24 to produce high resolution electrocardiogram (HRECG) signals using well known stacking techniques” (Col. 5, lines 27-31) and “This is accomplished by "zero-padding": i.e. the input data to the FFT is augmented with zeros placed after the ECG data points.” (Col. 9, lines 49-52). Therefore, these elements do not amount to significantly more than the abstract idea itself.
Claims 2-5 and 11-14 only further describe generic computer implementation of the abstract idea.
Claims 6-9 and 15-18 only further describe generically linking the abstract idea to a particular technology or field of use and generic computer implementation of the abstract idea.
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 (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 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 1-6 and 10-15 are rejected under 35 U.S.C. 103 as being unpatentable over Fornwalt (U.S. Patent Application Publication No. 2021/0076960), in view of Denner (U.S. Patent Application Publication No. 2022/0386924).
0Regarding claims 1 and 10, Fornwalt discloses an electronic device and a method of diagnosing a heart state based on an electrocardiogram, comprising:
a storage device (Fig. 2: memory 220); and
a processor, coupled to the storage device (Fig. 2: processor 204), configured to:
obtaining an electrocardiogram file, wherein the electrocardiogram file is a first file format (Par. 107: “FIG. 3 is an example of raw ECG voltage input data 300.”);
converting the electrocardiogram file into a second file format to obtain electrocardiogram data corresponding to a plurality of leads, wherein the electrocardiogram data of each of the plurality of leads comprises a potential trace relative to time (Par. 175: “The underlying voltage data may be extracted from these PDFs by first converting the PDF to XML and then parsing the XML file for the underlying data points which make up each of the voltage-time traces.”);
generating a diagnostic result of a heart state according to the integrated electrocardiogram data and a deep learning model (Par. 12: “the trained model being trained to generate a risk score based on input electrocardiogram data associated with the electrocardiogram configuration and supplementary information associated with the patient”).
Fornwalt does not disclose generating an integrated electrocardiogram data associated with the plurality of leads based on the electrocardiogram data of the plurality of leads through a zero-padding operation and a stacking operation. However, Denner, in the same field of endeavor of ECG diagnostics, discloses generating an integrated electrocardiogram data associated with the plurality of leads based on the electrocardiogram data of the plurality of leads through a zero-padding operation and a stacking operation (Par. 147: “One example of a convolution operation is visualized in FIG. 5. Stacking multiple convolutional layers allows a network to learn more and higher level features. For example, in image processing the first convolution layers learn edges and corners, and then later layers learn full objects. By way of example, in FIG. 5 a 3×3 kernel is convolved over a 5×5 input (blue) zero padding and unit strides, resulting in the output (green).”). Therefore, it would have been obvious to someone of ordinary skill in the art, before the effective filing date of the claimed invention, to include a zero-padding operation and stacking operation, as taught and suggested by Denner, because it “allows a network to learn more and higher level features” (Denner, Par. 147).
Regarding claim 2 and 11, Fornwalt, in view of Denner, discloses the method of diagnosing the heart state based on the electrocardiogram as claimed in claims 1 and 10, as previously stated. Fornwalt further discloses wherein the first file format comprises a portable document format (PDF) file format, and the second file format comprises a scalable sector graphics (SVG) file format (Par. 175: “Example code is presented below in APPENDIX A for converting from PDF to SVG format and from SVG to parsed data points.”).
Regarding claims 3 and 12, Fornwalt, in view of Denner, discloses the method of diagnosing the heart state based on the electrocardiogram as claimed in claims 2 and 11. Fornwalt further discloses wherein the step of converting the electrocardiogram file into the second file format and obtaining the electrocardiogram data corresponding to the plurality of leads comprises:
according to a label defined by the second file format, obtaining the trace description coordinate data corresponding to each of the plurality of leads from a file converted to the second file format, wherein the trace description coordinate data of each of the plurality of leads is used to describe the potential trace of each of the plurality of leads (Par. 175: “by first converting the PDF to XML and then parsing the XML file for the underlying data points which make up each of the voltage-time traces. The XML may also be parsed to determine the patient's age, sex, nine continuous numerical measurements output by the ECG machine (QRS duration, QT, QTC, PR interval, ventricular rate, average RR interval and P, Q and T-wave axes) and thirty categorical ECG patterns”); and
converting the trace description coordinate data of each of the plurality of leads to the electrocardiogram data corresponding to each of the plurality of leads (Par. 117: “The ECG voltage input data 428 can be transformed into ECG waveforms.”).
Regarding claims 4 and 13, Fornwalt, in view of Denner, discloses the method of diagnosing the heart state based on the electrocardiogram as claimed in claims 1 and 10, as previously stated. Fornwalt further discloses wherein the step of generating the diagnostic result of the heart state according to the integrated electrocardiogram data and the deep learning model comprises: inputting the integrated electrocardiogram data and patient data into the deep learning model, so that the deep learning model outputs the diagnostic result of the heart state (Fig. 13: provide at least a portion of patient data to trained model 1308, output risk score 1316).
Regarding claims 5 and 14, Fornwalt, in view of Denner, discloses the method of diagnosing the heart state based on the electrocardiogram as claimed in claims 4 and 13, as previously stated. Fornwalt discloses the method further comprising: obtaining the patient data recorded by the electrocardiogram file according to a label defined by the second file format (Par. 159: “At 1304, the process 1300 can receive patient data including ECG data. The ECG data can be associated with the patient. In some embodiments, the ECG data can include the ECG voltage input data 300.”).
Regarding claims 6 and 15, Fornwalt, in view of Denner, discloses the method of diagnosing the heart state based on the electrocardiogram as claimed in claims 1 and 10, as previously stated. Fornwalt further discloses the plurality of leads comprise at least two of lead I, lead II, lead III, lead aVR, lead aVL, lead aVF, lead V1, lead V2, lead V3, lead V4, lead V5, and lead V6 (Par. 19: “In the method, the plurality of leads may include a lead I, a lead V2, a lead V4, a lead V3, a lead V6, a lead II, a lead VI, and a lead V5”).
Claims 7-9 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Fornwalt (U.S. Patent Application Publication No. 2021/0076960), in view of Denner (U.S. Patent Application Publication No. 2022/0386924), further in view of Albert (U.S. Patent No. 5,046,504).
Regarding claims 7 and 16, Fornwalt, in view of Denner, discloses the method of diagnosing the heart state based on the electrocardiogram as claimed in claims 1 and 10. Fornwalt further discloses the plurality of leads comprise a first lead and a second lead (Par. 8: “ECGs can be acquired using a minimum of 2 body surface potential recordings”). Fornwalt, in view of Denner, does not disclose wherein the steps of generating the integrated electrocardiogram data associated with the plurality of leads based on the electrocardiogram data of the plurality of leads through the zero-padding operation and the stacking operation comprise: based on a first time span corresponding to the electrocardiogram data of the first lead, performing the zero-padding operation in at least one second time span other than the first time span to generate a compensated electrocardiogram data of the first lead, wherein the third time span corresponding to the electrocardiogram data of the second lead comprises the first time span and the at least one second time span.
However, Albert, in the same field of endeavor of electrocardiogram diagnostics, discloses “The individual digitized X, Y and Z signals from block 16 can be signal averaged as represented by a block 24 to produce high resolution electrocardiogram (HRECG) signals using well known stacking techniques” (Col. 5, lines 27-31) and “This is accomplished by "zero-padding": i.e. the input data to the FFT is augmented with zeros placed after the ECG data points.” (Col. 9, lines 49-52). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include a stacking operation and a zero-padding operation, as taught and suggested by Albert, in order to “significantly enhance the signal-to-noise ratio of each signal.” (Col. 5, lines 36-37).
Regarding claim 8 and 17, Fornwalt, in view of Denner, in further view of Albert, discloses the method of diagnosing the heart state based on the electrocardiogram as claimed in claims 7 and 16. Fornwalt further discloses the plurality of leads comprise a third lead (Par. 8: “ECGs can be acquired using a minimum of 2 body surface potential recordings (such that a voltage difference can be calculated from the subtraction of the two electrical potentials)… there is no limit to the number of different “leads” that can be acquired for an ECG.”). Albert further discloses the steps of generating the integrated electrocardiogram data associated with the plurality of leads based on the electrocardiogram data of the plurality of leads through the zero-padding operation and the stacking operation comprise: based on the fourth time span corresponding to the electrocardiogram data of the third lead, performing the zero-padding operation in at least one fifth time span other than the fourth time span to generate the compensated electrocardiogram data of the third lead, wherein the at least one second time span partially not overlap with the at least one fifth time span (Col. 5, lines 27-31: “The individual digitized X, Y and Z signals from block 16 can be signal averaged as represented by a block 24 to produce high resolution electrocardiogram (HRECG) signals using well known stacking techniques”; Col. 9, lines 49-52: “This is accomplished by "zero-padding": i.e. the input data to the FFT is augmented with zeros placed after the ECG data points.”).
Regarding claims 9 and 18, Fornwalt, in view of Denner, in further view of Albert, discloses the method of diagnosing the heart state based on the electrocardiogram as claimed in claims 7 and 16. Albert further discloses wherein the step of generating the integrated electrocardiogram data associated with the plurality of leads based on the electrocardiogram data of the plurality of leads through the zero-padding operation and the stacking operation comprises: generating the integrated electrocardiogram data associated with the plurality of leads by stacking the compensated electrocardiogram data of the first lead and the electrocardiogram data of the second lead (Col. 5, lines 27-31: “The individual digitized X, Y and Z signals from block 16 can be signal averaged as represented by a block 24 to produce high resolution electrocardiogram (HRECG) signals using well known stacking techniques”; Col. 9, lines 49-52: “This is accomplished by "zero-padding": i.e. the input data to the FFT is augmented with zeros placed after the ECG data points.”).
Conclusion
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/A.M./Examiner, Art Unit 3792
/ALLEN PORTER/Primary Examiner, Art Unit 3796
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