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 § 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) 1-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Konofagou et al. (US 2020/0163646) in view of Airaksinen et al. (WO 2017/060569)
Claim 1.
Konofagou teaches a method for characterizing activation/contraction of anatomical tissue, particularly cardiac tissue, including acquisition and processing of electrical and mechanical activity associated with cardiac activation/contraction.
Regarding acquiring, with an electrocardiogram, an activation signal representative of electrical activity of the anatomical tissue with respect to time and determining an activation time period, Konofagou teaches acquisition of ECG data synchronously with ultrasound channel data and using the synchronized information to identify cardiac systolic and diastolic phases associated with contraction and relaxation [0073].
Regarding acquiring consecutive images of the anatomical tissue at a high cadence in synchronization with the activation signal, Konofagou et al. teaches RF-based motion estimation during electrical activation of the myocardium using high-frame-rate ultrasound acquisition, including frame rates of approximately 500-2000 Hz [0101], and specifically teaches acquisition of RF frames at approximately 2000 fps together with retrospective ECG gating for temporal alignment of the high-frame-rate ultrasound, anatomical imaging and acquisition [0102].
Regarding segmenting the images into a plurality of pixels/points and, for each pixel, determining an activity signal representative of mechanical activity with respect to time between consecutive images, Konofagou teaches RF-based motion estimation and gradient operations for mapping transient myocardial deformation during electrical activation and determining displacement and incremental cardiac strain from consecutively acquired high-frame-rate data ([0101]-[0103]).
Regarding calculating spectral content of the mechanical activity signal and determination of a dominant frequency, Konofagou expressly teaches obtaining a frequency spectrum from incremental strain curves by Fast Fourier Transform (FFT), applying the FFT to strain curves associated with points within the atrial region, and detecting a dominant frequency from each resulting frequency spectrum [0106].
Konofagou therefore teaches acquiring a temporally resolved mechanical activity/strain signal associated with cardiac activation, calculating spectral content of the signal using FFT, and determining a dominant frequency from the resulting frequency spectrum.
However, Konofagou does not expressly teach performing the disclosed spectral analysis by dividing a signal activation time period into a plurality of elementary temporal time windows, and separately calculating the spectral content for each respective window.
However, at the time of the invention, it would have been obvious to one having ordinary skill in the signal-processing art to perform Konofagou’s disclosed FFT/dominant-frequency analysis over a plurality of successive temporal portions of the acquired mechanical activity signal, rather than only over the signal as a whole, in order to provide time-resolved spectral information and thereby permit temporal localization of changes in dominant-frequency content during the cardiac activation/contraction event.
Airaksinen et al., which is likewise directed to processing physiological signals for determining cardiac condition, teaches dividing a time-domain physiological signal into a plurality of temporal samples/windows, including overlapping samples, windowing each sample using a window function, and computing a discrete Fourier transform for each windowed sample, wherein the DFT may comprise an FFT. Airaksinen et al. further teaches dividing cardiac measurement data into successive temporal segments/time periods and performing FFT-based processing thereof (pp. 11).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the FFT-based spectral analysis of Konofagou et al. by dividing the acquired mechanical activity/stain signal into successive temporal windows and performing the FFT analysis separately for the respective windows, as taught by Airaksinen et al., in order to obtain temporally localized spectral information and thereby characterize changes in the frequency content of the cardiac mechanical activity over time. One or ordinary skill would have had a reasonable expectation of success in making the modification because both Konofagou et al. and Airaksinen et al. concern Fourier-based spectral processing of physiological/cardiac signals, and Airaksinen’s windowing technique represents a known manner of applying Fourier analysis to successive temporal portions of such a signal.
Applying Konofagou et al. expressly taught dominant frequency determination to the respective frequency spectra generated for the successive windows according to Airaksinen et al. would predictably result in determining a dominant frequency corresponding to each elementary time window. Further, because the respective dominant-frequency values correspond to successive temporal windows having respective positions within the activation period, arranging/considering those values according to their corresponding temporal positions yields the claimed time evolution of the dominant frequency with the position of the elementary windows.
Thus, the proposed modification does not require a change in Konofagou’s principle of operation. Rather, it applies Airaksinen’s known temporal-windowed Fourier-analysis technique to the same type of cardiac/physiological signal processing already performed by Konofagou et al. to obtain the predictable benefit of temporally localized spectral characterization. Under KSR, the combination constitutes the use of a known technique (Airaksinen’s temporal windowing and Fourier analysis of respective windows) to improve a similar method (Konofagou’s FFT/dominant-frequency analysis of cardiac mechanical activity signals) in the same way, by providing frequency information associated with respective temporal portions of the physiological signal. KSR Int’l Co. v. Teleflex Inc, 550 U.S. 398, 417 (2007).
Accordingly, it would have been obvious to one of ordinary skill in the art to combine the teachings of Konofagou et al. and Airaksinen et al. to arrive at the method of claim 1.
Claim 2.
…for each pixel, a contraction timing corresponding to a pixel peak time at which the peak of the dominant frequency occurs from an initial time of the activation period.
E.G. Konofagou teaches determining displacement/stain and frequency information at respective spatial points within the cardiac tissue and detecting a dominant frequency for the resulting frequency spectrum ([0101]-[0106]).
E.G. Airaksinen further teaches performing Fourier analysis over respective temporally windowed portions of a cardiac signal (pp. 11).
It would have been obvious to determine the temporal position at which the dominant-frequency peak occurs for each respective pixel, thereby providing a corresponding contraction timing relative to the beginning of the activation period, as a predictable use of the time-resolved dominant-frequency information produced by the combination.
…displaying a pattern of contraction by attributing a display parameter to the contraction timing of each pixel.
E.G. Konofagou teaches generating spatial maps based upon electromechanical/frequency information determined at respective spatial points within the cardiac tissue ([0106]-[0107]).
It would have been obvious to similarly display the determined contraction timing associated with each pixel to provide a spatial representation of cardiac mechanical activation/contraction.
Claim 3.
…determining a tissue peak time at which peaks of dominant frequency are reached in a largest number of pixels comprised in the tissue…
E.G. Konofagou teaches determining strain/frequency information at a plurality of spatial points and generating spatial maps from the resulting information ([0106]-[0107]), while Airaksinen teaches time-resolved/windowed Fourier analysis of cardiac signals (pp. 11).
It would have been obvious to identify the time at which the greatest number of spatial locations exhibit a determined dominant-frequency peak to provide a representative tissue-level timing of the contraction event.
…refining the activation time period with respect to the tissue peak time and defining the peak of dominant frequency for each pixel as a first local maximum within the refined activation time period.
It would have been obvious to identify the time at which the greatest number of spatial locations exhibit a determined dominant-frequency peak to provide a representative tissue-level timing of the contraction event.
…refining the activation time period with respect to the tissue peak time and defining the peak of dominant frequency for each pixel as a first local maximum within the refined activation time period.
It would have been obvious to temporally restrict the analysis relative to the identified tissue event and select the relevant local maximum within the interval to distinguish the contraction-associated spectral peak from temporally remote/non-representative maxima. Such processing represents the predictable application of known temporal selection and peak-detection operations to the time-resolved spectral data generated by the combined teachings.
Claim 4.
…beginning the activation time period at a time interval before the tissue peak time…
E.G. Konofagou teaches temporally characterizing myocardial contraction and relaxation, including determining the onset of systole based upon myocardial wall displacement [0073].
It would have been obvious to begin the analysis interval before the identified tissue peak so that the analyzed period encompasses the mechanical activity preceding and leading to the contraction peak, thereby permitting temporal characterization of the contraction event.
Claim 5.
…measuring a mechanical parameter representative of the mechanical activity of each pixel on each consecutive image…
Konofagou teaches RF-based motion estimation and gradient operations for mapping transient myocardial deformation and estimating displacement and cardiac strain from consecutively acquired high-frame-rate image data ([0101]-[0103]).
Claim 6.
…mechanical parameter being selected from displacement of the pixel and strain of the pixel…
E.G. Konofagou expressly teaches determining myocardial displacement and cardiac/incremental strain from the acquired ultrasound data ([0073], [0101]-[0103]).
Claim 7.
…high cadence being N images per second, wherein N≥500 images…
E.G. Konofagou expressly teaches frame rates between approximately 500 Hz and 2000 Hz for displacement and cardiac-strain estimation [0101] and specifically teaches high-frame-rate acquisition at approximately 2000 fps [0102].
…dividing the activation time period Ta into successive elementary time windows Tf…
E.G. Airaksinen teaches splitting a time-domain cardiac signal into overlapping samples, windowing the samples with a suitable window function, and computing a DFT/FFT for each windowed sample (pp. 11).
…Tf being between Ta/3 and Ta/12 and successive windows being shifted by at most 0.5*N Tf images
E.G. the claimed window duration and degree of overlap constitute selection of workable parameters of the known windowed spectral-analysis technique
It would have been obvious to optimize the duration and overlap of the successive windows according to the desired temporal and frequency resolution, absent evidence that the claimed values produce a critical or unexpected result.
Claim 8.
…calculating the spectral content using a Short-Time Fourier Transform
E.G. Airaksinen expressly teaches splitting a time-domain signal into overlapping temporal samples, applying a window function to each sample, and computing a discrete Fourier transform, including an FFT, for each windowed sample (pp. 11).
The disclosed successive windowing and Fourier transformation constitutes or, at minimum, renders obvious implementation of a Short-Time Fourier Transform for determining spectral content as a function of time.
Claim 9.
…acquiring images by an ultrasound modality, including images of a plane in the tissue thickness.
E.G. Konofagou teaches acquisition and reconstruction of RF/B-mode ultrasound images of cardiac tissue using a phased-array ultrasound transducer ([0101]-[0103]).
Claim 10.
The ultrasound modality being implemented in electromechanical wave imaging…
E.G. Konofagou teaches high-frame-rate ultrasound-based electromechanical characterization/mapping of myocardial activation using RF-based motion estimation and gradient operations to map transient deformation/strain occurring during electrical activation of the myocardium ([0101]-[0103]).
Claim 11.
…acquiring images using intracorporeal ultrasound configured to emit an ultrasound signal and receive echoes of a reflected signal…
E.G. Konofagou teaches intracardiac echocardiographic acquisition and high-frame-rate ultrasound using a phased-array transducer, including emission of ultrasound waves and beamforming of raw signals received by the probe elements ([0073], [0102]).
Claim 12.
…the anatomical tissue being cardiac tissue…
E.G. Konofagou expressly teaches applying the disclosed high-frame-rate imaging, displacement/strain estimation, and spectral processing to myocardial/atrial cardiac tissue ([0073], [0101]-[0106]).
Claim 13.
….beginning the activation time period at a time interval corresponding to a duration of isovolumetric contraction of the cardiac tissue.
E.G. Konofagou teaches temporally identifying cardiac contraction phases from synchronized ECG and myocardial displacement information, including determining the onset of systole based upon movement of the cardiac walls [0073].
It would have been obvious to define the temporal analysis interval relative to a recognized phase of cardiac contraction in order to encompass the physiologically relevant mechanical activation/contraction event being characterized.
Claim 14.
…N≥1000, N≥1500, N≥2000 images
E.G. Konofagou et al. teaches frame rates ranging from approximately 500 to 2000 Hz and expressly exemplifies acquisition at 2000 fps [0102].
…successive elementary windows being shifted by 0.5*N Tf, 0.25*N Tf, or 0.1*N Tf images
E.G. Airaksinen teaches overlapping temporal samples/windows subjected individually to Fourier analysis (pp. 11).
It would have been obvious to select the particular degree over overlap/shift between successive windows as a workable parameter of Airaksinen’s known overlapping-windowing technique based upon the desired temporal sampling density, frequency resolution, and computational requirements, absent evidence of critically or unexpected results associated with the claimed values.
Claim 15.
…the anatomical tissue being cardiac tissue…
E.G. Konofagou expressly teaches imaging and analyzing myocardial/atrial cardiac tissue ([0073], [0101]-[0106]).
…Ta being between 30 ms and 120 ms…
E.G. Konofagou teaches temporarily analyzing myocardial activity relative to cardiac electrical/mechanical activation and recognizes acquisition duration and temporal sampling as parameters of the cardiac analysis ([0073], [0101]-[0102] & [0106]).
It would have been obvious to select a workable activation interval corresponding to the cardiac contraction event being characterized, with the particular duration representing optimization of the temporal interval for the physiological event of interest, absent evidence of critically or unexpected results attributable to the claimed range.
Response to Arguments
Applicant's arguments filed June 3, 2026 have been fully considered but they are not persuasive: The applicant argues that the primary reference, Konofagou et al. performs a global dominant-frequency/cycle-length analysis and does not disclose dividing a single activation period into successive time windows, determining a dominant frequency for each window, and tracking the dominant frequency over time. The examiner acknowledges that Konofagou et al. does not expressly disclose the claimed temporal windowing arrangement. Accordingly the previous §102 rejection has been withdrawn.
However, Konofagou et al. teaches ECG-synchronized high-frame-rate cardiac imaging, myocardial displacement/strain determination, FFT spectral analysis, and determination of dominant frequency ([0073], [0101]-[0106]).
Airaksinen et al. further teaches dividing a cardiac/physiological time-domain signal into overlapping temporal samples, windowing each sample, and performing Fourier analysis on each windowed sample (pp. 11).
It would have been obvious to apply Airaksinen’s known Fourier-analysis technique to Konofagou’s strain/activity signals to obtain temporally localized spectral information. Applying Konofagou’s dominant-frequency determination to the resulting windowed spectra predictably provides dominant-frequency values over time from which a peak may be identified.
Applicant’s contention that Konofagou et al. teaches away is not persuasive. Although Konofagou et al. recognizes the relationship between signal duration and frequency resolution [0106], it does not criticize or discourage windowed temporal analysis. Airaksinen expressly demonstrates that such windowed Fourier analysis was known for cardiac/physiological signals (pp. 11).
Accordingly, while Applicant’s arguments distinguish the amended claims from Konofagou et al. alone, they do not overcome the rejection based on the combined teachings of Konofagou et al. and Airaksinen et al., for the reasons set forth above.
Applicant’s arguments, filed June 3, 2026, with respect to the claim objections and the 101 claim rejections have been fully considered and are persuasive and have been withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The following reference is considered pertinent to the claimed invention: Chan (CA 2253760), which teaches frequency-domain analysis of cardiac signals using Fourier transform/FFT techniques and selection of an analysis window size for spectral analysis. The reference is considered pertinent for its disclosure of window-based spectral processing of cardiac signals but is not relied upon in the rejection of the claims.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICOLE F JOHNSON whose telephone number is (571)270-5040. The examiner can normally be reached Monday-Friday 8:00am-5:00pm EST.
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/NICOLE F JOHNSON/Primary Examiner, Art Unit 3796
