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 § 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 1-3, 5, 9, 11-14, 17-19, 23, 27, 34-35, 38, 40 and 48-50 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more.
Step 1: All claims are directed either to a method/process or to a system/machine.
Step 2A, Prong One: The claims recite a mental process including steps such as “determining … the cardiac condition measure” and “obtain a first result … obtain a second result …” which could be performed by the human mind and/or by a human with a physical aid such as pen and paper.
Step 2A, Prong Two: This judicial exception is not integrated into a practical application because the claims merely implement the mental process using generic processing technology and add insignificant extra-solution activity. Specifically: the steps of obtaining and collecting the physiological data is considered insignificant pre-solution activity of mere data gathering, since it merely collects the data necessary to carry out the mental process using conventional, generic sensors; the step of “providing … information” seen in e.g. claims 13-14 and 48-50 is considered insignificant post-solution activity since it merely outputs the result of the mental process using a generic output modality (such as a display). Furthermore, merely carrying out mental steps using generic computing technology such as “processing circuitry” and “a machine learning engine [having tiers and classifiers]” is well established to not amount to an integration into a practical application under the § 101 analysis. See, e.g., MPEP §§ 2106.04(a)(2)(III)(C) and 2106.04(d)(I) and 2106.05(f).
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only additional elements recited in the claims are generic processing/computing components and generic data collection and output components. The Examiner takes official notice that these are basic, generic components which are well-understood, routine and conventional in the medical diagnostic arts, and the claims here merely use them for their well-understood, routine and conventional functions. As such, those additional elements cannot be considered “significantly more” than the judicial exception in Step 2B of the § 101 analysis.
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.
Claims 1-3, 17-19, 34-35, 38 and 40 are rejected under 35 U.S.C. 102(a)(1) as being clearly anticipated by Liu et al. entitled “Multiple-feature-branch convolutional neural network for myocardial infarction diagnosis using electrocardiogram” (NPL No. 10 cited by Applicant in the IDS dated 04/28/2023 and a copy of which exists in this application file wrapper with that same date) (hereinafter “Liu”).
Regarding Claims 1 and 35, Liu teaches: A method for determining a cardiac condition measure of a patient wearing a wearable medical device based on machine learning analysis (see, e.g., the title and abstract and FIG. 2), the method comprising:
obtaining, by processing circuitry from the wearable medical device, physiological data representing a sample timeframe, the physiological data comprising ECG data representing a plurality of ECG signals of a patient, wherein the plurality of ECG signals was collected by at least two ECG electrodes of the wearable medical device monitoring a heart of the patient (see, e.g., “12-lead ECG” on page 24, left column, and in FIG. 2)
applying, by the processing circuitry, the physiological data to a machine learning engine (see, e.g., “Multiple-Feature-Branch Convolutional Neural Network (MFB-CNN)” in the abstract) to determine the cardiac condition measure of the patient, wherein applying comprises
applying the ECG data to a first tier of the machine learning engine comprising one or more first machine learning classifiers to obtain a first result (see generally Section 3.1 entitled “Independent feature branch”; also see the “feature branches” in FIG. 2), and
applying the first result along with i) clinical information regarding the patient and/or ii) physiological metrics determined from signals collected by the wearable medical device to a second tier of the machine learning engine comprising one or more second machine learning classifiers to obtain a second result (see generally Section 3.2 entitled “Global fully-connected layer”; also see the “fully-connected layers” in FIG. 2; the feature vectors themselves correspond to both the ”first result” as well as either “clinical information” and/or “physiological metrics”); and
determining, by the processing circuitry at least in part from the second result, the cardiac condition measure (see, e.g., the final output vector Y described in section 3.2; see table 1 on page 23 showing the various classes of cardiac conditions which are encompassed by the final output).
Regarding Claims 2-3, see, e.g., FIGS. 1-2, and page 24, last paragraph before section 3.1, and section 3.2.
Regarding Claim 17, Liu teaches sample timeframes which include “at least 30 seconds” (see Section 2, first paragraph). The recitation of “at least 30 seconds” is interpreted to include at least some sample timeframes between 30 seconds and 60 seconds (i.e. overlapping with several of Applicant’s claimed ranges), since if all the timeframes were at least 60 seconds (for example), then Liu would simply have said “at least 60 seconds.” The reference to “30 seconds” in particular necessarily implies that at least some of the sample timeframes were down near that value.
Regarding Claims 18-19, as noted in the rejection of claim 1 above, the feature vectors themselves correspond to both the “clinical information” and/or “physiological metrics.” As such, both of claims 18-19 are met as well, since each of these claims only further modifies one of these alternatives. Those further modifications only need to be shown if that specific alternative is relied upon. So, for claim 18, which further limits the “physiological metrics” to be one among a list, the claim can be met either by showing one from among that list or by showing the “clinical information” recited earlier in claim 1. In this case, as noted above, the “clinical information” recited earlier in claim 1 is met, and thus so is claim 18. Similarly, but in a mirrored fashion, claim 19 further limits the “clinical information” but can also be met by the “physiological metrics” recited in claim 1. In this case, as noted above, the “physiological metrics” recited earlier in claim 1 is met, and thus so is claim 19.
Regarding Claim 34, see, e.g., page 29, Section 4.4, and FIGS. 8-9 and Table 13.
Regarding Claim 38, see e.g. FIG. 2 (each tier has multiple stages of classifiers).
Regarding Claim 40, see e.g. Table 2, column entitled “Kernel size.”
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.
The factual inquiries 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.
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 40 is rejected under 35 U.S.C. 103 as being unpatentable over Liu.
Regarding Claim 40, this claim is anticipated by Liu as discussed above. Furthermore, it would have been obvious to one of ordinary skill in the art as of the filing date of Applicant’s invention to engage in routine experimentation to discover the optimal size(s) of the convolutional filters. See MPEP § 2144.05(II)(A)( “[W]here the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation”) (citing In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955)). Liu also notes that this parameter can be experimented with and determined via trial and error (see Section 4.1: “there are also some parameters determined by trial and error, such as the kernel size …”).
Claims 5, 9, 13-14, 18-19, 23, 27 and 48-50 are rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of US 2019/0282178 A1 to Volosin et al. (hereinafter “Volosin”).
Regarding Claim 5, Liu teaches ECG trained machine learning classifier (see discussion of machine learning engine above), but fails to explicitly teach a cardio-vibrational trained machine learning classifier. Another reference, Volosin, teaches an analogous system in which both ECG and cardio-vibrational data (see “cardio-vibration” throughout Volosin; in e.g. Para. 93, it is seen that “bio-vibration” may include “cardio-vibrations”) may be used as training data for a machine learning model for cardiac condition measures (see, e.g., Para. 267). It would have been obvious to one of ordinary skill in the art as of Applicant's effective filing date to modify Liu to also incorporate cardio-vibrational data into the training of the machine learning engine as well as a subsequent input to the model because doing so would predictably and advantageously increase the accuracy of the overall determination.
Regarding Claim 9, Volosin teaches an analogous system in which the determined cardiac condition measure can include a NYHA I, II, III or IV heart failure classification (see, e.g., Paras. 176-180 and Table 9 of Volosin). It would have been obvious to one of ordinary skill in the art as of Applicant's effective filing date to modify Liu to determine a cardiac condition measure as one of the NYHA heart failure classifications, as taught by Volosin, because this would predictably and advantageously yield diagnostically useful information in a scoring/classification system that is already known and understood to medical professionals, thereby allowing for easier interpretation of the data.
Regarding Claims 13-14 and 48-50, it is extraordinarily well known to output the data and results of a diagnostic analysis to a medical professional’s device, such as on a display screen of a remote computing device. As one example, Volosin teaches this in an analogous system (see, e.g., Paras. 74, 107 and 181). It would have been obvious to one of ordinary skill in the art as of Applicant's effective filing date to modify Liu to provide the data and results to an output device, including a visual display, for review by a medical professional or caregiver etc. because this is extremely commonplace in the medical diagnostic arts and because doing so would allow the data to be properly utilized and put into practical use.
Regarding Claims 18-19, these claims are anticipated by Liu alone as noted above. However, in the interest of being thorough, Volosin teaches an analogous system in which multiple of the metrics and/or information in claims 18-19 can be factored into the analysis (see, e.g., Paras. 93 and 148 discussing EMAT and LVST and SDI, and Paras. 149 and 255 discussing age, gender, and medical history). It would have been obvious to one of ordinary skill in the art as of Applicant's effective filing date to modify Liu to input one or more of these types of data into the machine learning engine because doing so would predictably and advantageously increase the accuracy of the result.
Regarding Claim 23, Volosin teaches an analogous system in which the analysis is carried out on data collected over one or more timeframes overlapping with Applicant’s ranges here (see, e.g., Paras. 7, 297 and claim 3). It would have been obvious to one of ordinary skill in the art as of Applicant's effective filing date to modify Liu to utilize data collected from any desired suitable prior timeframe, such as those taught in Volosin, since any of them would be predictably suitable for this analysis with predictable benefits and drawbacks to various lengths of time.
Regarding Claim 27, Volosin teaches an analogous system in which historic cardiac condition measures are compared to current ones to identify changes (see, e.g., Paras. 61, last two sentences, 64, 99, 283, 307). It would have been obvious to one of ordinary skill in the art as of Applicant's effective filing date to modify Liu to continually measure and identify changes in the patient’s cardiac condition because this would be clearly useful and valuable diagnostic information.
Claims 11-12 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of US 2020/0397313 A1 to Attia et al. (hereinafter “Attia”).
Regarding Claims 11-12, Liu teaches the method of claim 1 as discussed above but fails to explicitly teach that the cardiac condition measure is an ejection fraction classification, and having multiple classifications as greater than and less than a certain percentage such as those listed in claim 12 here. Another reference, Attia, teaches an analogous machine learning method in which ECG data is input to obtain an ejection fraction classification (see, e.g., the title and abstract) including separate classifications for above and below a particular ejection fraction percentage, such as above and below 30%, 35%, 40% or 45% (see, e.g., Para. 36, 52 and 74). It would have been obvious to one of ordinary skill in the art as of Applicant's effective filing date to modify Liu to determine an ejection fraction classification as above and below one or more threshold percentages as the cardiac condition measure, as taught in Attia, because this is a known measure for evaluating aspects of a patient’s cardiovascular health (see, e.g., Paras. 3-5 of Attia) and thus determining it would predictably and advantageously provide useful diagnostic information.
Regarding Claim 17, Liu teaches sample timeframes of “at least 30 seconds” and “typically of ~2 min” (see Section 2, first paragraph). The recitation of “at least 30 seconds” is interpreted to include at least some sample timeframes between 30 seconds and e.g. 60 seconds, since if all the timeframes were at least 60 seconds, then Liu would simply have said “at least 60 seconds.” The reference to “30 seconds” necessarily implies that at least some of the sample timeframes were down near that value. Nevertheless, in the interest of being thorough, Attia teaches an analogous system which uses a variety of possible ECG sample timeframes overlapping with Applicant’s claimed values here (see, e.g., Paras. 55, 74). It would have been obvious to one of ordinary skill in the art as of Applicant's effective filing date to modify Liu to utilize a sample timeframe overlapping with Applicant’s claimed ranges here, as seen in Attia, because Attia demonstrates that these are known suitable timeframes for collecting ECG data to yield a diagnostically relevant result via a machine learning analysis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN R DOWNEY whose telephone number is (571)270-7247. The examiner can normally be reached Monday-Friday 8:30am-5:00pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, NIKETA PATEL can be reached at (571)-272-4156. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN R DOWNEY/Primary Examiner, Art Unit 3792
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