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 .
Response to Arguments
Applicant’s arguments with respect to claims 1, 9, and 16 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant argues on page 15, that the prior art of reference fails to teach the amended features.
However, newly found reference, Peters (Pub. No.: US 2023/0200664) discloses a computing device receiving intracardiac pressure data from invasive measurement device wherein intracardiac pressure may comprise left ventricular pressure, left atrial pressure, pulmonary capillary wedge pressure, pulmonary artery pressure, right atrial pressure and/or right ventricular pressure (paragraph [0196]). Peters further discloses analyzing using support vector machines (paragraph [0205]).
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 1-5, 7-12, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Salgo et al. (Pub. No. US 2018/0192987) in view of Khader et al. (Pub. No.: WO 2017/205836, Applicant’s IDS filed 05/15/2025) and further in view of Peters (Pub. No.: US 2023/0200664).
Consider claims 1, 9, Salgo discloses an ultrasound imaging system (paragraph [0012], Fig. 1, ultrasound imaging system 10) comprising:
a processor (paragraph [0014], Fig. 1, image processor 36) configured to:
receive ultrasound data of a heart, wherein the ultrasound data was acquired across at least a portion of a cardiac cycle of the heart (paragraph [0021], Fig. 2, acquiring an ultrasound image including the heart chamber (Step 56));
generate a heart based measurement based, at least in part on the ultrasound data (paragraph [0021], strain information of the heart tissue can also be acquired and tracked); and
Salgo further discloses residing in the software of the ultrasound system is also a variety of algorithms for calculating information about the patient's heart (paragraph [0017]).
Salgo does not specifically disclose generate a plurality of heart based measurements based, at least in part on the ultrasound data; and
analyze, using a correlation algorithm, the plurality of heart based measurements to determine a value of cardiac pressure.
Khader discloses generate a plurality of heart based measurements based, at least in part on the ultrasound data (paragraph [0049], a plurality of ultrasound gray-scale measurement images of the heart across a plurality of heartbeats is obtained); and
analyze, using a correlation algorithm, the plurality of heart based measurements to determine a value of cardiac pressure (paragraph [0049], a plurality of cardiac parameters are subjected to classifiers to determine the classification in the set of classifications of the heart to include the diastolic dysfunction of the heart, paragraph [0055]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to replace algorithm as disclosed by Salgo with the algorithm as taught by Khader to provide for categorization of the diastolic dysfunction of a heart into a classification in a set of classifications (Khader, paragraph [0049]).
The combination of Salgo and Khader does not specifically disclose different heart based measurements, wherein the plurality of different heart based measurements comprise at least a left atrial (LA) index and two or more other heart measurements and wherein the correlation algorithm comprises a support vector machine (SVM) model.
Peters discloses different heart based measurements, wherein the plurality of different heart based measurements comprise at least a left atrial (LA) index and two or more other heart measurements (paragraph [0196], computing device receiving intracardiac pressure data from invasive measurement device wherein intracardiac pressure may comprise left ventricular pressure, left atrial pressure, pulmonary capillary wedge pressure, pulmonary artery pressure, right atrial pressure and/or right ventricular pressure) and wherein the correlation algorithm comprises a support vector machine (SVM) model (paragraph [0205], support vector machines).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to replace algorithm as disclosed by the combination of Salgo Khader with the algorithm as taught by Peters to generate features from at least one or more portions of the non-invasive cardiac health data that fall within each of the one or more temporal windows (Peters, paragraph [0196]).
Consider claim 16, Salgo discloses at least one non-transitory computer-readable medium (paragraph [0030], a tangible and non-transitory computer readable medium) carrying instructions that, when executed by at least one processor (paragraph [0014], Fig. 1, image processor 36) of an ultrasound imaging system (paragraph [0012], Fig. 1, ultrasound imaging system 10), cause the ultrasound imaging system to:
receive ultrasound data of a heart, wherein the ultrasound data was acquired across at least a portion of a cardiac cycle of the heart (paragraph [0021], Fig. 2, acquiring an ultrasound image including the heart chamber (Step 56));
generate a heart based measurement based, at least in part on the ultrasound data (paragraph [0021], strain information of the heart tissue can also be acquired and tracked); and
Salgo further discloses residing in the software of the ultrasound system is also a variety of algorithms for calculating information about the patient's heart (paragraph [0017]).
Salgo does not specifically disclose generate a plurality of heart based measurements based, at least in part on the ultrasound data; and
analyze, using a correlation algorithm, the plurality of heart based measurements to determine a value of cardiac pressure.
Khader discloses generate a plurality of heart based measurements based, at least in part on the ultrasound data (paragraph [0049], a plurality of ultrasound gray-scale measurement images of the heart across a plurality of heartbeats is obtained); and
analyze, using a correlation algorithm, the plurality of heart based measurements to determine a value of cardiac pressure (paragraph [0049], a plurality of cardiac parameters are subjected to classifiers to determine the classification in the set of classifications of the heart to include the diastolic dysfunction of the heart, paragraph [0055]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to replace algorithm as disclosed by Salgo with the algorithm as taught by Khader to provide for categorization of the diastolic dysfunction of a heart into a classification in a set of classifications (Khader, paragraph [0049]).
The combination of Salgo and Khader does not specifically disclose different heart based measurements, wherein the plurality of different heart based measurements comprise at least a left atrial (LA) index and two or more other heart measurements and wherein the correlation algorithm comprises a support vector machine (SVM) model.
Peters discloses different heart based measurements, wherein the plurality of different heart based measurements comprise at least a left atrial (LA) index and two or more other heart measurements (paragraph [0196], computing device receiving intracardiac pressure data from invasive measurement device wherein intracardiac pressure may comprise left ventricular pressure, left atrial pressure, pulmonary capillary wedge pressure, pulmonary artery pressure, right atrial pressure and/or right ventricular pressure) and wherein the correlation algorithm comprises a support vector machine (SVM) model (paragraph [0205], support vector machines).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to replace algorithm as disclosed by the combination of Salgo Khader with the algorithm as taught by Peters to generate features from at least one or more portions of the non-invasive cardiac health data that fall within each of the one or more temporal windows (Peters, paragraph [0196]).
Consider claims 2, 10, 17, the combination of Salgo, Khader, and Peters discloses the SVM model is trained on one or more of a LA index, a left ventricular (LV) index, LA volume parameters, echo parameters, LV strain data, LA strain data, right atrial (RA) strain data, right ventricular (RV) strain data, ultrasound Doppler data, ultrasound pulsed wave Doppler data, ultrasound continuous wave Doppler data, ultrasound two dimensional color data, and ultrasound three dimensional color data (Peters, paragraph [0196], left ventricular pressure, left atrial pressure, pulmonary capillary wedge pressure, pulmonary artery pressure, right atrial pressure and/or right ventricular pressure).
Consider claims 3, 11, 18, the combination of Salgo, Khader, and Peters discloses wherein, he two or more other heart measurements comprise at least two of LA volume parameters, echo parameters, LV strain data, LA strain data, right atrial (RA) strain data, right ventricular (RV) strain data, ultrasound Doppler data, ultrasound pulsed wave Doppler data, ultrasound continuous wave Doppler data, ultrasound two dimensional color data, and ultrasound three dimensional color data (Peters, paragraph [0196], left ventricular pressure, left atrial pressure, pulmonary capillary wedge pressure, pulmonary artery pressure, right atrial pressure and/or right ventricular pressure).
Consider claims 4, 12, 19, the combination of Salgo, Khader, and Peters discloses wherein the processor is further configured to: weight one or more of the plurality of heart based measurements; and analyze, using the correlation algorithm, the weighted one or more of the plurality of heart based measurements to determine the value of cardiac pressure (Khader, paragraph [0055], Fi. 1, a weighted neighborhood scheme 56 that uses the cardiac parameter dataset 48 for a heart to determine a classification in the set of classifications as a second prediction result 58).
Consider claims 5, 20, the combination of Salgo, Khader, and Peters discloses wherein the plurality of heart based measurements comprises one or more global left ventricular (LV) strain curves, and wherein the one or more global LV strain curves comprise an average of a plurality of LV strain curves (paragraph [0079], magnitude of global left atrial strain (LAS) and the magnitude of global left ventricular strain (LVS) is obtained from these curves wherein AVS is calculated as the average of (i) half the sum of the first instantaneous maximal absolute values of LAS and LVS).
Consider claim 7, the combination of Salgo, Khader, and Peters discloses wherein the processor is further configured to: analyze a sequence of two-dimensional ultrasound images with a machine learning model to determine a border of a chamber of the heart in individual ones of the two - dimensional ultrasound images; and calculate volumes of the chamber, based, at least in part, on the borders of the individual ones of the two-dimensional ultrasound images, wherein the volumes of the chamber are included in the ultrasound data (Khader, paragraph [00107], Two-dimensional cardiac performance analysis software was used for assessment of of left ventricular (LV) and left atrial (LA) speckles in apical 4-chamber and 2-chamber views. endocardial borders of both the LA and LV were traced and the instantaneous changes in volume were obtained).
Consider claim 8, the combination of Salgo, Khader, and Peters discloses wherein the processor is further configured to: analyze a sequence of three-dimensional ultrasound images with a machine learning model to determine a border of a chamber of the heart in individual ones of the three - dimensional ultrasound images; and calculate volumes of the chamber, based, at least in part, on the borders of the individual ones of the three-dimensional ultrasound images, wherein the volumes of the chamber are included in the ultrasound data (Khader, paragraph [00107] and paragraph [0067], three-dimensional full-volume dataset that is acquired by a fully sampled matrix array transducer such as the X5-1/X3-1 (Philips Medical Systems or 4V, GE Healthcare)).
Consider claim 15, the combination of Salgo, Khader, and Peters discloses generating a classifier associated with the value of the cardiac pressure (Khader, paragraph [0057], set of classifications constitute degrees of diastolic dysfunction to include mild (grade I) to severe (grade III) with increasing likelihood of symptomatic heart failure).
Claims 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Salgo, Khader, and Peters in view of Konofagou et al. (Pub. No.: US 2016/0249880) and further in view of Olivier (Pub. No.: US 2014/0128691).
Consider claim 13, the combination of Salgo, Khader, and Peters does not specifically disclose wherein the processor is further configured to filter the ultrasound data with a digital filter.
Konofagou discloses wherein the processor is further configured to filter the ultrasound data with a digital filter (paragraph [0086], slow motions of the tissues were separated using a digital filter).
Therefore, it would have been obvious one having ordinary skill in the art before the effective filing date of the claimed invention to replace processor as disclosed by the combination of Salgo, Khader, and Peters with the processor as taught by Konofagou in order to separate slow motions of the tissues (Konofagou, paragraph [0086]).
The combination of Salgo, Khader, Peters, and Konofagou does not specifically disclose wherein the digital filter includes a Savitsky-Golay filter with a cubic polyfit.
Olivier discloses wherein the digital filter includes a Savitsky-Golay filter with a cubic polyfit (paragraph [0050], Savitsky-Golay filter).
Therefore, it would have been obvious one having ordinary skill in the art before the effective filing date of the claimed invention to replace the digital filter as disclosed by the combination of Salgo, Khader, Peters, and Konofagou with the Savitsky-Golay filter as taught by Olivier in order to increase the signal-to-noise (S/N) ratio (Olivier, paragraph [0050]).
Consider claim 14, the combination of Salgo, Khader, and Peters does not specifically disclose wherein the processor is further configured to interpolate the ultrasound data to a pre-set number of frames across the at least the portion of the cardiac cycle.
Konofagou discloses wherein the processor is further configured to interpolate the ultrasound data to a pre-set number of frames across the at least the portion of the cardiac cycle (paragraph [0061], a set of N frames of raw ultrasound data of the heart is acquired during a cardiac cycle).
Therefore, it would have been obvious one having ordinary skill in the art before the effective filing date of the claimed invention to replace processor as disclosed by the combination of Salgo, Khader, and Peters with the processor as taught by Konofagou to be useful for imaging the propagation of electromechanical waves in the heart (Konofagou, paragraph [0061]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/Gerald Johnson/
Primary Examiner, Art Unit 3797
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