DETAILED ACTION
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/12/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-22 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 “pre-trained” and “previously acquired” in claims 1, 2 and 16-19 is a relative term which renders the claim indefinite. The term “pre-trained” and “previously acquired” 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 purposes of examination the indefinite limitation has been deemed to claim that the training dataset is acquired prior to the receiving of the sound signal.
Regarding Claims 1, 2 and 16-19, the limitation “a training dataset of previously acquired and generated training 2D feature maps of a PH subject group and a non-PH subject group” renders the claim indefinite. It is unclear whether the training dataset is a positively recited element of the claimed system/method or a characteristic of the pre-trained neural network, and whether the method requires the step of acquiring or generating the training feature maps. For purposes of examination the indefinite limitation has been deemed to claim the indefinite limitation has been deemed to claim that the training dataset is a characteristics of the previously trained network and does not require any acquiring or generating of the training dataset.
Regarding claim 2, the limitation “generating one or more 2D feature maps comprising a 2D pulmonary feature map” and “applying a pre-trained neural network” render the claim indefinite. Claim 1 already recites generating 2D feature maps and applying a pre-trained neural network. It is unclear whether claim 2 recites the same step as claim 1 or separate, additional steps. For purposes of examination the indefinite limitation has been deemed to claim that claim 2 is further limiting the steps of claim 1, such that the one or more 2D feature maps of claim 1 comprise a 2D pulmonary feature map.
Regarding claim 5, the limitation “wherein each one or more 2D feature maps is combined as a channel of the multichannel input” renders the claim indefinite. It is unclear whether every one of the 2D feature maps must be combined as a channel, or whether one or more of them must be. For purposes of examination the indefinite limitation has been deemed to claim each generated 2D feature map is treated as forming a respective channel of the multichannel input.
The term “over-parameterized” in claim 10 is a relative term which renders the claim indefinite. The term “over-parameterized” 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 purposes of examination the indefinite limitation has been deemed to claim encompassing any deep neural network.
Regarding claim 14, the limitation “the heart sound signal” lacks proper antecedent basis.
Regarding claim 14, the limitation “cleaning the heart sound signal by removing spikes” renders the claim indefinite. It is unclear whether the cleaning is performed on the sound signal (S2) or on the aortic and pulmonary signals resulting from the splitting step. For purposes of examination the indefinite limitation has been deemed to claim where the cleaning is performed on the signals resulting from the splitting step.
Regarding claim 15, the limitation “the subject’s pulmonary spot” lacks proper antecedent basis.
Regarding claim 16, “for training a neural network” renders the claim indefinite. The preamble omits as article. Furthermore, it is unclear how the recited steps train a neural network, as the only recited steps are receiving a sound signal, generating 2D feature maps, and “applying a pre-trained neural network”, none of which trains a network and the last of which requires the network to have been trained before the method is performed. For purposes of examination the indefinite limitation has been deemed to claim the same subject matter as set forth in claim 1.
Regarding claim 17, the limitation “the heart sound (S2) signal” lacks proper antecedent basis.
Regarding claims 17 and 19, the limitation “applying a pre-trained neural network” renders the claim indefinite because claim 16 already recites this step.It is unclear whether claim 17 recites the same step as claim 16 or separate, additional steps. For purposes of examination the indefinite limitation has been deemed to claim that claim 17 is further limiting the steps of claim 16, such that the one or more 2D feature maps of claim 16 comprise a 2D pulmonary feature map.
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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Each of Claims 1-22 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 1-22 recites at least one step or instruction for using sound signals and neural networks to obtain an indicator of the presence of pulmonary hypertension, which is grouped as a mental process under the 2019 PEG or a certain method of organizing human activity under the 2019 PEG. Accordingly, each of Claims 1-22 recites an abstract idea.
Specifically, Claims 1, 16, 17 and 18 recite using a computer to take a heart sound recording, arrange the beats into a 2D map (time on one axis, individual heartbeats on the other), and run it through a pre-trained neural network to get a pulmonary hypertension indicator (observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG);
Further, dependent Claims 2-15 and 19-22 merely include limitations that either further define the abstract idea (and thus don’t make the abstract idea any less abstract) or amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they’re merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Accordingly, as indicated above, each of the above-identified claims recites an abstract idea.
Step 2A, Prong 2
The above-identified abstract idea in each of independent Claims 1, 16, 17 and 18 (and their respective dependent claims) is not integrated into a practical application under 2019 PEG because the additional elements (identified above in independent Claims 1, 16, 17 and 18), either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use. More specifically, the additional elements of: a computer are generically recited computer elements in independent Claims 1, 16, 17 and 18 (and their respective dependent claims) which do not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea identified above in independent Claims 1, 16, 17 and 18 (and their respective dependent claims) is not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process and certain method of organizing human activity) using rules (e.g., computer instructions) executed by a computer (e.g., computer as claimed). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in independent Claims 1, 16, 17 and 18 (and their respective dependent claims) is not integrated into a practical application under the 2019 PEG.
Accordingly, independent Claims 1, 16, 17 and 18 (and their respective dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B
None of Claims 1-22 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
The above-identified additional elements are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Accordingly, in light of Applicant’s specification, the claimed term computer is reasonably construed as a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for the computer. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications).
The recitation of the above-identified additional limitations in Claims 1-22 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the method and system of Claims 1-22 are directed to applying an abstract idea as identified above on a general purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. None of Claims 1-22 provides meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in independent Claims 1, 16, 17 and 18 (and their dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 1-22 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 1-22 amounts to significantly more than the abstract idea itself. Accordingly, Claims 1-22 are not patent eligible and rejected under 35 U.S.C. 101.
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(s) 1, 6-10, 15, 16, 18, 20, 21 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20180368723 A1 to Ibáñez Català et al. (hereinafter, Ibáñez) in view of Estimation of pulmonary arterial pressure by a neural network analysis using features based on time-frequency representations of the second heart sound to Tranulis et al. (hereinafter, Tranulis).
Ibáñez discloses a computer-implemented method for non-invasive estimation of Pulmonary Hypertension (“PH”), from heart sound signals, comprising the steps:
receiving a sound signal (S2) acquired from a beating heart of a subject over a predetermined time period (beat detector 24 is “positioned and configured for obtaining biosignal data related to a user’s heart activity” [0017] and the lead “is divided into a plurality of non-overlapping segments p of a pre-determined temporal length or duration (404), such as ten minutes or 500 heartbeats” [0024], FIGS. 1 and 4);
generating one or more 2D feature maps comprising a 2D feature map with the received signal, wherein a first axis of the map is arranged over time and a second axis of the map is arranged over individual heartbeats (the user application “defines a beat matrix (508), with each row of the beat matrix corresponding to a successive heartbeat 34 in the associated segment p, within a window of pre-determined temporal length (such as 520 milliseconds for example) around the temporal position 36 of each heartbeat 34,” the matrix “defined using the formula Beat=[nb.sub.Beats(p)×nb.sub.samples], where nb.sub.Beats(p) is the number of detected heartbeats 34 in the segment p and nb.sub.samples is the number of samples taken by the user application 40 around the position of a given heartbeat 34” [0025], FIG. 5) and
applying a pre-trained neural network to relate the generated one or more 2D feature maps with a training dataset of previously acquired and generated training 2D feature maps of a first subject group and a second subject group to obtain an indicator of the presence of a cardiac condition (classifier 256 is “an artificial neural network (“ANN”)” [0017] which “utilizes a reservoir computing technique – specifically, Echo State Networks (“ESN”)” [0032]; the ECG matrix “is then transmitted to the at least one classifier 26 (324) together with the appropriate label for each beat (i.e., 0 and 1—for the SVE and VE classes, respectively) which, in turn, uses the at least one vector matrix to train itself” [0030], and the trained classifier thereafter “analyzes the real-time matrix to determine whether it contains supraventricular ectopic (“SVE”) or ventricular ectopic (“VE”) heartbeats 34 (606)” [0031], FIGS. 3 and 6);
(claims 6 and 21) further comprising segmenting the acquired signal into a plurality of time windows of a predetermined duration, wherein each time window comprises a heartbeat signal peak (the columns of the beat matrix are populated with samples taken within a window of pre-determined temporal length (such as 520 milliseconds for example) around the temporal position 36 of each heartbeat 34,” wherein “the number of samples taken is 130, with 50 samples immediately preceding the reference point of the heartbeat 34 and 80 samples immediately after the reference point of the heartbeat 34” [0025], FIG. 5);
(claims 7 and 22) further comprising aligning the segmented sound signal time windows by aligning the heartbeat sound signal peaks of the segmented sound signal time windows (“he temporal position 36 of each heartbeat 34 is adjusted to be at the largest local extrema in the QRS complex of said heartbeat 34,” wherein “[t]he adjusted heartbeats 34 correspond to the position of the maximum peak of the squared temporal waveform within a small window around the original position” [0023], such that the windows forming the rows of the beat matrix are aligned on the respective peaks, FIG. 3);
(claim 8) further comprising calculating a saliency attribution corresponding to the generated one or more 2D feature maps (The user application “generates a reference beat (516) by calculating an average between the rows of the normalized beat matrix having the relatively lowest ration between the fourth column and fifth column values,” then computes “a correlation coefficient between the reference beat and the corresponding row of the beat matrix (522)” [0026]-[0027]);
(claim 9) further comprising pre-processing the acquired sound signal by filtering, spike removal, normalizing, alignment, or segmentation, or a combination thereof (the recording is resampled to a common sampling rate, where “the common sampling rate is 250 Hz” [0022]; “the ECG recording 32 is filtered to remove any unwanted high frequency noise and to correct the baseline (312),” wherein “the user application 40 utilizes a second order Butterworth high-pass filter with a cutoff frequency of 0.5 Hz and a finite impulse response filter of 12th order with 35 Hz at 3 decibels” [0023]; the heartbeat positions are adjusted as set forth above in claim 7 [0023]; the lead is divided into segments as set forth above in claim 1 [0024]; and “the beat matrix is normalized (“Beatnorm”) such that all rows of the beat matrix are normalized to have a mean of zero and a standard deviation of one (510)” [0025], FIGS. 3 and 5);
(claim 10) wherein the neural network is an extreme learning machine (Echo state network and Wout, [0032], [0053], [0054], [0111]);
Ibáñez discloses the claimed invention except for expressly disclosing that the signal is a sound signal (S2) acquired from a beating heart and that the two subject groups are a PH subject group and a non-PH subject group, acquiring the heart sound signal at the subject’s pulmonary spot, and a digital stethoscope for acquiring the beating heart sound signal, wherein the digital stethoscope is connected to the electronic data processor and configured to transmit the acquired beating heart sound signal.
However, Tranulis teaches a non-invasive method for estimating pulmonary arterial pressure from the second heart sound using a neural network (p. 205). Tranulis extracts a plurality of individual S2 sounds per subject, wherein “256 ms PCG segments containing S2 sounds were automatically extracted from the PCG recordings based on the timing reference of the ECG” and “[b]etween 15 and 50 S2 sounds were thus obtained for each PAP stage” (pp. 206-207), trains the network on two-thirds of the data and tests it on the remaining one-third (p. 205), and classifies subjects having normal pulmonary artery pressure or pulmonary hypertension using stated pressure thresholds (p. 205). The heart sounds of Tranulis are acquired at the subject’s pulmonary spot (“microphone was positioned and fixed on the thorax… at the pulmonary area” p. 206). Tranulis teaches a digital stethoscope for acquiring the beating heart sound signal, wherein the digital stethoscope is connected to the electronic data processor and configured to transmit the acquired beating heart sound signal (“ARMACO 1306 electret microphone” p. 206).
One having an ordinary skill in the art at the time the invention was filed would have found it obvious to organize the plurality of S2 sounds of Tranulis into the beat matrix of Ibáñez and the apply the trained classifier thereto, because Ibáñez expressly contemplates biosignals other than the electrocardiogram, providing the beat detector may be “any other type of device, sensor or combination thereof, now know or later developed, capable of substantially carrying out the functionality described herein” [0017], and states that the classifier is not limited to the disclosed beat classes [0022]. Tranulis already isolates and aligns a plurality of individual heartbeat sounds per subject but analyzes them individually rather than as a single two-dimensional map. Arranging those sounds as rows of a beat matrix is the application of a known technique to a known method ready for improvement, yielding the predictable result of classifying a subject as having or not having pulmonary hypertension from the plurality of isolated S2 sounds.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEAN PATRICK DOUGHERTY whose telephone number is (571)270-5044. The examiner can normally be reached 8am-5pm (Pacific Time).
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/SEAN P DOUGHERTY/ Primary Examiner, Art Unit 3791
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