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, 10, and 20 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.
On pages 13 and 14 of the remarks filed 04/24/2026, the applicant argues “the Examiner argues that Konofagou teaches a set of N frames of raw ultrasound data of the heart is acquired during a cardiac cycle. However, Konofagou merely discusses that N frames of raw ultrasound data can be acquired during a cardiac cycle at high frame rate (e.g., higher than 100 fps). See Konofagou, paragraph [0061]. While Konofagou discusses "interpolation," it is related to a "high- resolution Fourier transform can be performed using a generalized Goertzel algorithm for interpolation in Fourier space on 1.5-second long incremental strains signals for each individual pixel in the atria."” Therefore, Konofagou does not teach or suggest the amended feature.
Newly found reference Kim et al. (Pub. No.: US 2013/0123633) discloses at paragraphs [0053] and [0050] an interpolation process is carried out for frames included in all local periods such that the same number of frames is contained in each local period including a heartbeat period, see paragraph [0047]). Kim further discloses the ultrasound data reconstructing unit 137 may reconstruct the interpolated volume data to provide a 3-dimensional ultrasound image showing a figure of the heartbeat (paragraph [0054]).
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, 4, 8-10, 13, 14, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Salgo et al. (Pub. No. US 2018/0192987) in view of Kim et al. (Pub. No.: US 2013/0123633) and further in view of Tek et al. (Pub. No.: US 2019/0125295).
Consider claims 1, 10, Salgo discloses an ultrasound imaging system (paragraph [0012], Fig. 1, ultrasound imaging system 10) comprising:
a processor configured (paragraph [0014], Fig. 1, segmentation processor 42) to:
receive ultrasound data from a heart, wherein the ultrasound data was acquired across at least a portion of a cardiac cycle (paragraph [0021], Fig. 2, acquiring an ultrasound image including the heart chamber (Step 56)); and
analyze the ultrasound data by applying a correlation algorithm to determine a value of cardiac pressure (paragraph [0021], based on the image data and/or waveform generated by the ultrasound system, a static pressure (e.g., of the left ventricle) can be calculated using optimal pressure calculation algorithms, see paragraph [0025]).
Salgo does not specifically disclose wherein the cardiac cycle comprises a plurality of periods;
interpolate the ultrasound data to a pre-set number of frames for each of the plurality of periods of the cardiac cycle to generate interpolation results for each of the plurality of phases;
combine the interpolation results to generate interpolated ultrasound data.
Kim discloses wherein the cardiac cycle comprises a plurality of periods (paragraph [0048], multiple heartbeat periods);
interpolate the ultrasound data to a pre-set number of frames for each of the plurality of periods of the cardiac cycle to generate interpolation results for each of the plurality of phases (paragraphs [0053] and [0050] an interpolation process is carried out for frames included in all local periods such that the same number of frames is contained in each local period including a heartbeat period, see paragraph [0047]));
combine the interpolation results to generate interpolated ultrasound data (paragraph [0054], the ultrasound data reconstructing unit 137 may reconstruct the interpolated volume data to provide a 3-dimensional ultrasound image showing a figure of the heartbeat).
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 processor as disclosed by Salgo with the processor as taught by Kim to obtain total local periods (Kim, paragraph [0050]).
The combination of Salgo and Kim does not specifically disclose wherein the cardiac cycle comprises a plurality of periods includes phases and analyze the interpolated ultrasound data by applying a correlation algorithm to determine a value of cardiac pressure.
Tek discloses wherein the cardiac cycle comprises a plurality of periods includes phases (paragraph [0030], cardiac region at different periods or phases of the cardiac cycle) and analyze the interpolated ultrasound data by applying a correlation algorithm to determine a value of cardiac pressure (paragraph [0020], volumetric ultrasound data acquired in B-mode is analyzed and interpreted to automatically quantify pressure gradients, wherein the ultrasound data corresponds to a data set interpolated to a regular 3D grid, see paragraph [0030]).
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 processor as disclosed by the combination of Salgo and Kim with the processor as taught by Tek to automatically quantify volumetric flow through anatomical structures, such as heart valves or vessels, and to compute parameters like cardiac output (Tek, paragraph [0020]).
Consider claim 4, the combination of Salgo, Kim, and Tek discloses wherein the processor is further configured to filter the interpolated ultrasound data with a digital filter (Tek, paragraph [0041], deep learning learns filter kernels to be applied to the ultrasound data for classification).
Consider claims 8, 13, the combination of Salgo, Kim, and Tek discloses a strain processor configured to generate strain measurements based, at least in part on ultrasound signals received from the heart, wherein the strain measurements are included in the ultrasound data (paragraph [0021], strain information of the heart tissue can also be acquired and tracked).
Consider claims 9, 14, the combination of Salgo, Kim, and Tek discloses wherein the processor is further configured to generate a classifier associated with the value of the cardiac pressure (paragraph [0017], a value can compare to the statistical data to identify whether congestive heart failure is likely).
Consider claim 19, the combination of Salgo, Kim, and Tek discloses where the ultrasound data is from at least one of a left atrium or a left ventricle of the heart (paragraph [0022], Fig. 3, left atrium).
Consider claim 20, Salgo discloses a non-transitory computer-readable medium containing instructions (paragraph [0030], a tangible and non-transitory computer readable medium), that when executed, causes an imaging system to:
receive ultrasound data from a heart, wherein the ultrasound data was acquired across at least a portion of a cardiac cycle (paragraph [0021], Fig. 2, acquiring an ultrasound image including the heart chamber (Step 56)); and
analyze the ultrasound data by applying a correlation algorithm to determine a value of cardiac pressure (paragraph [0021], based on the image data and/or waveform generated by the ultrasound system, a static pressure (e.g., of the left ventricle) can be calculated using optimal pressure calculation algorithms, see paragraph [0025]).
Salgo does not specifically disclose interpolate the ultrasound data to a pre-set number of frames for each of a plurality of periods of the cardiac cycle to generate interpolation results for each of the plurality of phases;
combine the interpolation results to generate interpolated ultrasound data.
Kim discloses wherein the cardiac cycle comprises a plurality of periods (paragraph [0048], multiple heartbeat periods);
interpolate the ultrasound data to a pre-set number of frames for each of the plurality of periods of the cardiac cycle to generate interpolation results for each of the plurality of phases (paragraphs [0053] and [0050] an interpolation process is carried out for frames included in all local periods such that the same number of frames is contained in each local period including a heartbeat period, see paragraph [0047]));
combine the interpolation results to generate interpolated ultrasound data (paragraph [0054], the ultrasound data reconstructing unit 137 may reconstruct the interpolated volume data to provide a 3-dimensional ultrasound image showing a figure of the heartbeat).
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 processor as disclosed by Salgo with the processor as taught by Kim to obtain total local periods (Kim, paragraph [0050]).
The combination of Salgo and Kim does not specifically disclose a plurality of periods includes phases, filter the interpolated ultrasound data with a digital filter after interpolating; and analyze the interpolated ultrasound data by applying a correlation algorithm to determine a value of cardiac pressure.
Tek discloses a plurality of periods includes phases (paragraph [0030], cardiac region at different periods or phases of the cardiac cycle), filter the interpolated ultrasound data with a digital filter after interpolating (paragraph [0041], deep learning learns filter kernels to be applied to the ultrasound data for classification); and analyze the interpolated and filtered ultrasound data by applying a correlation algorithm to determine a value of cardiac pressure (paragraph [0020], volumetric ultrasound data acquired in B-mode is analyzed and interpreted to automatically quantify pressure gradients, wherein the ultrasound data corresponds to a data set interpolated to a regular 3D grid, see paragraph [0030]).
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 processor as disclosed by the combination of Salgo and Kim with the processor as taught by Tek to automatically quantify volumetric flow through anatomical structures, such as heart valves or vessels, and to compute parameters like cardiac output (Tek, paragraph [0020]).
Claims 2, 16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Salgo, Kim, and Tek in view of Hunziker et al. (Pub. No.: US 2014/0276071).
Consider claims 2, 16, the combination of Salgo, Kim, and Tek does not specifically disclose wherein the correlation algorithm comprises at least one of a partial least squares model or a long short-term memory network.
Hunziker discloses wherein the correlation algorithm comprises at least one of a partial least squares model or a long short-term memory network (paragraph [0076], Fig. 2, step 210, a least-square optimal fashion).
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 optimal pressure calculation algorithms as disclosed by the combination of Salgo, Kim, and Tek with the least-square optimal fashion as taught by Hunziker to determine a mathematical model that transforms the physiologic input data into the target cardiac output (Hunziker, paragraph [0076]).
Consider claim 18, the combination of Salgo, Kim, and Tek does not specifically disclose wherein the analyzing comprises applying a transfer function including at least one regression coefficient to the ultrasound data.
Hunziker discloses wherein the analyzing comprises applying a transfer function including at least one regression coefficient to the ultrasound data (paragraph [0075], multitude of CO measurement samples taken for each subject is examined using e.g., regression to the mean).
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, Kim, and Tek with the processor as taught by Hunziker to identify an average value (Hunziker, paragraph [0075]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Salgo, Kim, Tek and Konofagou in view of Olivier (Pub. No.: US 2014/0128691).
Consider claim 5, the combination of Salgo, Kim, and Tek 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, Kim, Tek 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]).
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Salgo, Kim, and Tek in view of Haslam et al. (Pub. No: US 2021/0335041).
Consider claims 6, 7, Salgo does not specifically disclose 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.
Haslam 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 (paragraph [0193], predetermined orientation and spacing between each 2D medical image (associated with chamber of the heart, see paragraph [0212]) within the new set of 2D medical images are determined using machine learning wherein each channel represents a 2D image corresponding to a slice position within the 3D space of the 3D image, see paragraph [0202]); 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 (paragraphs [0211] to [0212], determined from the generated 3D image are volume of the heart, volume of blood in each chamber of the heart, thickness of the different layers of the heart wall).
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 processor as disclosed by the combination of Salgo, Kim, and Tek with the processor as disclosed by Haslam to determine from the generated 3D image, parameters of the patient anatomic feature (Haslam, paragraph [0211]).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Salgo, Kim, and Tek in view of Olivier (Pub. No.: US 2014/0128691).
Consider claim 12, the combination of Salgo, Kim, and Tek discloses filtering the ultrasound data with a digital filter prior to the analyzing (paragraph [0013], signal processor 26 can process the received echo signals by bandpass filtering).
The combination of Salgo, Kim, and Tek does not specifically disclose wherein the digital filter comprises a Savitsky-Golay filter with a cubic polyfit.
Olivier discloses wherein the digital filter comprises 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, Kim, and Tek with the Savitsky-Golay filter as taught by Olivier in order to increase the signal-to-noise (S/N) ratio (Olivier, paragraph [0050]).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Salgo, Kim, and Tek in view of Shuros et al. (Pub. No.: US 2010/0100144).
Consider claim 15, the combination of Salgo, Kim, and Tek does not specifically disclose wherein the classifier is a binary classifier and the binary classifier has a first level when the value of the pressure is below a threshold value and a second level when the value of the cardiac pressure is equal to or above the threshold value.
Shuros discloses wherein the classifier is a binary classifier and the binary classifier has a first level when the value of the pressure is below a threshold value and a second level when the value of the cardiac pressure is equal to or above the threshold value (paragraph [0068], claim 3, comparing the hemodynamic measurement with respective thresholds to determine if it exceeds or falls below the threshold).
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 processor as disclosed by the combination of Salgo, Kim, and Tek with the processor as taught by Shuros to avoid inappropriate therapy, such as defibrillation shocking (Shuros, paragraph [0068]).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Salgo, Kim, Tek and Hunziker in view of Hope et al. (Pub. No.: WO 2018/127497) and further in view of Srinivasa et al. (Pub. No.: WO 2020/020770).
Consider claim 17, the combination of Salgo, Kim, Tek and Hunziker does not specifically disclose training the model with a training data set, wherein the training data set comprises an ultrasound dataset labeled with a value of the cardiac pressure acquired from a catheter.
Hope discloses training the model with a training data set, wherein the training data set comprises an ultrasound dataset labeled with a value of the cardiac pressure acquired from a catheter (paragraph [037], neural network may be trained to identify and localize flowing flood from the 2D echo image data when the neural network operates on echoes acquired from an array supported on an intravascular ultrasound (IVUS) imaging catheter associated with a cardiac chamber, see paragraph [050]).
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 model as disclosed by the combination of Salgo, Kim, Tek and Hunziker with the model as taught by Hope to distinguish different tissue types on the basis of the spatial and temporal characteristics of the echoes from multiple transmit events (Hope, paragraph [037]).
The combination of Salgo, Kim, Tek, Hunziker, and Hope does not specifically disclose wherein training the model further comprises: training a starting architecture with the training data; and
iterating the training of the starting architecture until a target error margin is reached.
Srinivasa discloses wherein training the model further comprises: training a starting architecture with the training data (paragraph [053], Fig. 4, starting architecture 412 and training data 414 are provided to a training engine 410 for training the model); and
iterating the training of the starting architecture until a target error margin is reached (paragraph [053], Fig. 4, sufficient number of iterations (e.g., when the model performs consistently within an acceptable error), the model 420 is said to be trained and ready for deployment).
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 model as disclosed by the combination of Salgo, Kim, Tek, Hunziker, and Hope with the model as taught by Srinivasa to classify the unknown images in accordance with the training of the model to output a prediction (Srinivasa, paragraph [053]).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GERALD JOHNSON whose telephone number is (571)270-7685. The examiner can normally be reached Monday-Friday 8am-5pm EST.
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/Gerald Johnson/
Primary Examiner, Art Unit 3797
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