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, see pages 9-12, filed 06 August 2025, with respect to 112 rejection have been fully considered and are persuasive in view of amendment. The 112 rejection of 07 May 2025 has been withdrawn.
Applicant's arguments filed 06 August 2025 have been fully considered but they are not persuasive.
With regards to 103 rejections, applicant argues that combination of Hare II and Bonnefous fail to teach specific workflow using multiple neural network sets as recited in the independent claims (page 10). Specifically, applicant argues that prior arts cannot be combined without substantial modification, since Hare II uses a neural network based image processing workflow, while Bonnefous employs 3D geometric modeling and flow simulation approach generating a 3D flow model of the mitral valve and orifice and focuses on flow dynamics simulation rather than automated image processing, and the examiner did not sufficiently establish to integrate Bonnefous’ 3D visualization system into Hare II’s neural network workflow, as two references address different problem domains, Hare II addressing automated image analysis across multiple cardiac structures, while Bonnefous targets 3D flow visualization for mitral regurgitation (page 10). The applicant further argues that combining a neural-network based automated workflow with a 3D geometric modeling system would require a fundamental redesign of both systems, and contrary to examiner’s assertion of “no change in their respective functions, combining references require redesigning neural network to incorporate 3D geometric modeling, creating new interfaces between fundamentally different processing paradigms and developing new algorithms to bridge the image processing and geometric modeling approaches, and would not yield merely predictable results (page 11). The combination fail to teach specific workflow of claimed invention, specifically for grading MR/TR severity, it would be non-obvious to combine two over the prior art (pages 11-12).
However, the examiner respectfully disagrees.
As indicated in the office action mailed on 07 May 2025, Hare II teaches most of the limitations of claim 1, except “a grade of MR or TR severity.” Hare II disclose a prognosis of condition of region of interest in heart ([0116]) using neural network, and the examiner had incorporated Bonnefous for disclosing “a grade of MR or TR severity” using the images ([0099]). Thus, Hare II already teaches neural network workflow analyzing images, to prognose a condition of heart, and both Hare II and Bonnefous are directed to image processing of the images to prognose a condition of the heart. The applicant’ assertion of fundamental different between two prior arts are not persuasive, since both prior arts are directed to use an image of heart, to diagnose a condition of the heart. Bonnefous’ teaching of grade of mitral regurgitation can be implemented in the image analysis workflow, without substantial modification, as one can use image of Hare II’s heart, to analyze the image data to grade the severity of MR, as taught by Bonnefous. The modification can be simply writing program code to analyze the image data to perform grade of MR severity, and does not change principle operation of Hare II at all.
Use of Neural network and 3D geometric modeling are not contradicting or conflicting with each other, rather 3D geometric modeling is part of image processing/analyzing to meaningfully interpret medical image to draw a prognostic and diagnostic conclusion. Since, both are directed to take measurement from ultrasonic image data, to diagnose a condition of heart, they are fundamentally directed to solving same problem of medical diagnose using measurement from ultrasonic image data. Thus, the examiner does not agree with assertion that combining two prior arts would create new interfaces, rather the modification would be implementing algorithm taught by Bonnefous of grade of MR severity into neural network workflow of Hare II of image classification, segmentation and calculation of cardiac measurements.
Therefore, absent any evidence to the contrary, the examiner maintains that the combination of reference teaches and/or makes obvious the claimed limitations.
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, 6-13, and 18-25 are rejected under 35 U.S.C. 103 as being unpatentable over “Hare, II et al.,” US 2020/0226757 (hereinafter Hare, II) and “Bonnefous et al.,” US 2024/0366182 (hereinafter Bonnefous, filed on 8/10/2022).
Regarding to claim 1, Hare, II teaches a computer-implemented method for grading of mitral valve regurgitation (MR) or tricuspid valve regurgitation (TR) severity performed by an automated workflow engine executed by at least one processor (echo workflow engine automatically recognize and analyze images to perform automated measurements and the diagnosis, prediction and prognosis of heart disease [0058]), the method comprising:
receiving, from a memory, a plurality of echocardiogram images a heart (receiving a plurality of echocardiogram images [0059]);
separating the plurality of echocardiogram (echo) images according to 2D images and Doppler modality images (filter to separate the echocardiogram images according to 2D images and Doppler modality images [0060]);
classifying the 2D images by view type, including PLAX (parasternal long axis), apical 2-chamber (A2C), and apical 4-chamber (A4C) (classifying many different view types including parasternal long axis, apical 2,3,4-chamber [0062]);
classifying the Doppler modality images by region, including continuous wave of the mitral valve (CWMR) or continuous wave of the tricuspid valve (CWTR) (classifying continuous wave or pulse wave of mitral and tricuspid [0062]);
segmenting regions of interest in the 2D images to produce segmented 2D images, including PLAX segmented images, A2C segmented images, and A4C segmented images (segment region of interest in 2D images [0063]);
segmenting the Doppler modality images to generate waveform traces to produce segmented CW Doppler modality images, including CWMR or CWTR (segment classified Doppler modality region [0063]);
using both the segmented 2D images and the segmented CW Doppler modality images to calculate measurements of cardiac features of the heart (using both segmented 2D images and the segmented Doppler modality images, calculates for cardiac features of the heart [0065]);
generating a diagnosis by comparing a portion of the calculated measurements to cardiac guidelines ([0048] and [0111]); and
outputting at least one report showing ones of the calculated measurements that fall within or outside of the cardiac guidelines (report showing highlighted values that are outside the range of Internal guidelines Figure 14 [0112]).
Hare, II teaches scores for a prognosis of condition of region of interest in heart ([0116]), but does not explicitly teach diagnosis is a grade of MR or TR severity.
However, in the analogous field of ultrasound imaging diagnosis of heart, Bonnefous teaches ultrasound diagnostic system generating B-mode and Doppler images and the cardiac measurements that can be used to grade the severity of mitral regurgitation ([0099]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify diagnosis by comparing calculated measurements to cardiac guidelines as taught by Hare, II to incorporate teaching of Bonnefous, since a severity score/index for mitral regurgitation was well known in the art as taught by Bonnefous. One of ordinary skill in the art could have combined the elements as claimed by Hare, II with no change in their respective functions, determining a diagnosis to include a mitral regurgitation degree, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide a grade the severity of mitral regurgitation ([0099]), and there was reasonable expectation of success.
Regarding to claims 6-7, Hare, II and Bonnefous together teach all limitations of claim 1 as discussed above.
Hare, II further teaches following limitations of claim 6, wherein receiving, from a memory, the plurality of echo images, further comprises:
receiving the plurality of echo images directly from a local or remote source, including an ultrasound device (ultrasound device [0058]; echo images received from a local or remote storage [0068]);
storing the plurality of echo images in an image archive (store echo images into the image file archive [0068]); and
opening the stored echo images in the memory for processing (opening echo images from the patient study in memory of computer [0071]).
Hare, II further teaches following limitations of claim 7, wherein separating the plurality of echo images further comprises:
analyzing metadata incorporated in the echo images to distinguish between the 2D and the Doppler modality images (analyzing metadata [0060]);
separating the Doppler modality images into either pulse wave, continuous wave, PWTDI or m-mode groupings (separate echocardiogram images according to 2D images and Doppler modality images [0060]);
performing color flow analysis on extracted pixel data using a combination of metadata and color content within the echo images to separate views that contain color from those that do not (separate view that contain color from those that do not [0060]);
removing from the echo images any metatags that contain personal information and cropping the echo images to exclude any identifying information (removing metatags that contain personal information [0060]); and
extracting pixel data from the echo images and converting the pixel data to numpy arrays for further processing (extract the pixel data from images and converts the pixel data to numpy arrays for further processing [0060]).
Regarding to claim 8, Hare, II and Bonnefous together teach all limitations of claim 1 as discussed above.
Hare, II further teaches limitations of claim 8, wherein classifying the 2D images and the Doppler modality images is based on a majority voting scheme (majority voting scheme [0061]) comprising:
dividing a video of a 2D image of a Doppler modality image into frames (a video is divided to be classified to 2D image and Doppler modality images [0061]);
generating for the frames, classification labels that constitute votes (classification labels [0061]); and
applying a particular one of the classification labels receiving a highest number of the votes as the classification of the video (classification label receiving the most votes is applied [0061]).
Regarding to claims 9-10, Hare, II and Bonnefous together teach all limitations of claim 1 as discussed above.
Hare, II further teaches limitations of claims 9-10, implementing the workflow engine to comprise
a set of one or more classification convolutional neural networks (CNNs) for view Classification ( CNN to classify images by view type [0061]),
a set of one or more segmentation CNNs for chamber segmentation and waveform mask/trace (segmentation CNNs [0089]),
a set of one or more prediction CNNs for disease prediction (prediction CNNs for disease prediction [0053]).
wherein the one or more segmentation CNNs are trained from hand-labeled real images or artificial images generated by general adversarial networks (GANSs) (segmentation CNNs trained from hand-labeled real images or artificial images generated by GANs [0064])
Regarding to claims 11-12, Hare, II and Bonnefous together teach all limitations of claim 1 as discussed above.
Hare, II further teaches limitations of claim 11, further comprising: maintaining classification confidence scores, annotation confidence scores, and measurement confidence scores during processing and filtering out ones of the confidence scores failing to meet a threshold (confidence scores for classification, annotation, and measurements [0084]; filtering out the echo images with classification confidence scores fails to meet a threshold [0083], annotation confidence score [0092]).
Hare, II further teaches limitations of claim 12, further comprising: for all non-filtered out data, selecting as best measurement data the measurements associated with cardiac chambers with largest volumes; and saving with the best measurement data, image location, classification, annotation and other measurement data associated with the best measurement data ( best measurements data the measurements associated with cardiac chambers with the largest volumes, saves the measurement data, image location, classification, annotation and other measurement [0104]-[0105]).
Regarding to claim 13, Hare, II teaches a system, comprising:
a memory storing a plurality of echocardiogram images of a heart (memory a plurality of echocardiograms [0059]);
at least one processor coupled to the memory; a workflow engine, which when executed by the at least one processor (echo workflow engine executed by computer processors [0038]) is configurable to:
receive, from a memory, a plurality of echocardiogram images a heart (receiving a plurality of echocardiogram images [0059]);
separate the plurality of echocardiogram (echo) images according to 2D images and Doppler modality images (filter to separate the echocardiogram images according to 2D images and Doppler modality images [0060]);
classify the 2D images by view type, including PLAX (parasternal long axis), apical 2-chamber (A2C), and apical 4-chamber (A4C) (classifying many different view types including parasternal long axis, apical 2,3,4-chamber [0062]);
classify the Doppler modality images by region, including continuous wave of the mitral valve (CWMR) or continuous wave of the tricuspid valve (CWTR) (classifying continuous wave or pulse wave of mitral and tricuspid [0062]);
segment regions of interest in the 2D images to produce segmented 2D images, including PLAX segmented images, A2C segmented images, and A4C segmented images (segment region of interest in 2D images [0063]);
segment the Doppler modality images to generate waveform traces to produce segmented CW Doppler modality images, including CWMR or CWTR (segment classified Doppler modality region [0063]);
use both the segmented 2D images and the segmented CW Doppler modality images to calculate measurements of cardiac features of the heart (using both segmented 2D images and the segmented Doppler modality images, calculates for cardiac features of the heart [0065]);
generate a diagnosis by comparing a portion of the calculated measurements to cardiac guidelines ([0048] and [0111]); and
output at least one report showing ones of the calculated measurements that fall within or outside of the cardiac guidelines (report showing highlighted values that are outside the range of Internal guidelines Figure 14 [0112]).
Hare, II teaches scores for a prognosis of condition of region of interest in heart ([0116]), but does not explicitly teach diagnosis is a grade of MR or TR severity.
However, in the analogous field of ultrasound imaging diagnosis system of heart, Bonnefous teaches ultrasound diagnostic system generating B-mode and Doppler images and the cardiac measurements that can be used to grade the severity of mitral regurgitation ([0099]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify diagnosis by comparing calculated measurements to cardiac guidelines as taught by Hare, II to incorporate teaching of Bonnefous, since a severity score/index for mitral regurgitation was well known in the art as taught by Bonnefous. One of ordinary skill in the art could have combined the elements as claimed by Hare, II with no change in their respective functions, determining a diagnosis to include a mitral regurgitation degree, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide a grade the severity of mitral regurgitation ([0099]), and there was reasonable expectation of success.
Regarding to claims 18-19, Hare, II and Bonnefous together teach all limitations of claim 13 as discussed above.
Hare, II further teaches following limitations of claim 18, wherein the workflow engine receives from the plurality of echo images directly from a local or remote source, including an ultrasound device (ultrasound device [0058]; echo images received from a local or remote storage [0068]); stores the plurality of echo images in an image archive (store echo images into the image file archive [0068]); and opens the stored echo images in the memory for processing (opening echo images from the patient study in memory of computer [0071]).
Hare, II further teaches following limitations of claim 19, wherein the workflow engine separates uses metadata incorporated in the echo images to distinguish between the 2D and the Doppler modality images )metadata [0060]);
separates the Doppler modality images into either pulse wave, continuous wave, PWTDI or m-mode groupings (separate echocardiogram images according to 2D images and Doppler modality images [0060]);
performs color flow analysis on extracted pixel data using a combination of metadata and color content within the echo images to separate views that contain color from those that do not (separate view that contain color from those that do not [0060]);
removes from the echo images any metatags that contain personal information and cropping the echo images to exclude any identifying information (removing metatags that contain personal information [0060]); and
extracts pixel data from the echo images and converting the pixel data to numpy arrays for further processing (extract the pixel data from images and converts the pixel data to numpy arrays for further processing [0060]).
Regarding to claim 20, Hare, II and Bonnefous together teach all limitations of claim 13 as discussed above.
Hare, II further teaches limitations of claim 20, wherein the workflow engine classifies the 2D images and the Doppler modality images is based on a majority voting scheme (majority voting scheme [0061]) wherein the workflow engine divides a video of a 2D image of a Doppler modality image into frames (a video is divided to be classified to 2D image and Doppler modality images [0061]); generates for the frames, classification labels that constitute votes (classification labels [0061]); and applies a particular one of the classification labels receiving a highest number of the votes as the classification of the video (classification label receiving the most votes is applied [0061]).
Regarding to claims 21-22, Hare, II and Bonnefous together teach all limitations of claim 13 and 19 as discussed above.
Hare, II further teaches limitations of claims 21-22, wherein the workflow engine is implemented to comprise:
a set of one or more classification convolutional neural networks (CNNs) for view Classification ( CNN to classify images by view type [0061]),
a set of one or more segmentation CNNs for chamber segmentation and waveform mask/trace (segmentation CNNs [0089]),
a set of one or more prediction CNNs for disease prediction (prediction CNNs for disease prediction [0053]).
wherein the one or more segmentation CNNs are trained from hand-labeled real images or artificial images generated by general adversarial networks (GANSs) (segmentation CNNs trained from hand-labeled real images or artificial images generated by GANs [0064])
Regarding to claims 23-24, Hare, II and Bonnefous together teach all limitations of claim 13 and 22 as discussed above.
Hare, II further teaches limitations of claim 23, wherein the workflow engine maintains classification confidence scores, annotation confidence scores, and measurement confidence scores during processing and filtering out ones of the confidence scores failing to meet a threshold (confidence scores for classification, annotation, and measurements [0084]; filtering out the echo images with classification confidence scores fails to meet a threshold [0083], annotation confidence score [0092]).
Hare, II further teaches limitations of claim 24, wherein for all non-filtered out data, selecting as best measurement data the measurements associated with cardiac chambers with largest volumes; and saving with the best measurement data, image location, classification, annotation and other measurement data associated with the best measurement data ( best measurements data the measurements associated with cardiac chambers with the largest volumes, saves the measurement data, image location, classification, annotation and other measurement [0104]-[0105]).
Regarding to claim 25, Hare, II teaches an executable software product (software [0136]) stored on a non-transitory computer-readable medium containing program instructions for implementing automated workflow for grading of mitral valve regurgitation (MR) or tricuspid valve regurgitation (TR) severity (echo workflow engine automatically recognize and analyze images to perform automated measurements and the diagnosis, prediction and prognosis of heart disease [0058]), when executed by a set of one or more processors (workflow engine implemented in processors [0038]), are configurable to cause the set of one or more processors to perform operations comprising:
receiving, from a memory, a plurality of echocardiogram images a heart (receiving a plurality of echocardiogram images [0059]);
separating the plurality of echocardiogram (echo) images according to 2D images and Doppler modality images (filter to separate the echocardiogram images according to 2D images and Doppler modality images [0060]);
classifying the 2D images by view type, including PLAX (parasternal long axis), apical 2-chamber (A2C), and apical 4-chamber (A4C) (classifying many different view types including parasternal long axis, apical 2,3,4-chamber [0062]);
classifying the Doppler modality images by region, including continuous wave of the mitral valve (CWMR) or continuous wave of the tricuspid valve (CWTR) (classifying continuous wave or pulse wave of mitral and tricuspid [0062]);
segmenting regions of interest in the 2D images to produce segmented 2D images, including PLAX segmented images, A2C segmented images, and A4C segmented images (segment region of interest in 2D images [0063]);
segmenting the Doppler modality images to generate waveform traces to produce segmented CW Doppler modality images, including CWMR or CWTR (segment classified Doppler modality region [0063]);
using both the segmented 2D images and the segmented CW Doppler modality images to calculate measurements of cardiac features of the heart (using both segmented 2D images and the segmented Doppler modality images, calculates for cardiac features of the heart [0065]);
generating a diagnosis by comparing a portion of the calculated measurements to cardiac guidelines ([0048] and [0111]); and
outputting at least one report showing ones of the calculated measurements that fall within or outside of the cardiac guidelines (report showing highlighted values that are outside the range of Internal guidelines Figure 14 [0112]).
Hare, II teaches scores for a prognosis of condition of region of interest in heart ([0116]), but does not explicitly teach diagnosis is a grade of MR or TR severity.
However, in the analogous field of ultrasound imaging diagnosis of heart, Bonnefous teaches ultrasound diagnostic system generating B-mode and Doppler images and the cardiac measurements that can be used to grade the severity of mitral regurgitation ([0099]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify diagnosis by comparing calculated measurements to cardiac guidelines as taught by Hare, II to incorporate teaching of Bonnefous, since a severity score/index for mitral regurgitation was well known in the art as taught by Bonnefous. One of ordinary skill in the art could have combined the elements as claimed by Hare, II with no change in their respective functions, determining a diagnosis to include a mitral regurgitation degree, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide a grade the severity of mitral regurgitation ([0099]), and there was reasonable expectation of success.
Claims 2-3 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Hare, II and Bonnefous as applied to claims 1 and 13 above, and further in view of “Grayburn et al.,” “Quantitation of Mitral Regurgitation,” Circulation 126, 2012 (hereinafter Grayburn).
Regarding to claims 2 and 14, Hare, II and Bonnefous together teach all limitations of claims 1 and 13 as discussed above.
Hare, II provides extensive examples of cardiac measurements, in Measurement Table ([0100]) including various measurements related to mitral and tricuspid valve in PLAX, A2C and A4 segmented images.
Bonnefous further specifically discloses vena contracta area, vena contracta width ([0099]).
Hare, II and Bonnefous do not explicitly disclose ii) mitral regurgitation jet area to left atrial area ratio (JAR); and iii) MR CW Doppler Density (CWDD).
However, in the analogous field of endeavor in quantification of severity of mitral regurgitation Grayburn teaches quantification of mitral regurgitation including ii) mitral regurgitation jet area to left atrial area ratio (JAR) (Jet area indexed for LA area , Col. 1 under Color Doppler Jet Area, page 2007); and iii) MR CW Doppler Density (CWDD) (density of CW Doppler, under Adjunctive Findings CW Doppler Col. 1 page 2012).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify diagnosis by comparing calculated measurements to cardiac guidelines for mitral and tricuspid valve as taught by Hare, II and Bonnefous to incorporate teaching of Grayburn, since a jet area index to LA and CW Doppler density were well known in the art as taught by Grayburn. One of ordinary skill in the art could have combined the elements as claimed by Hare, II and Bonnefous with no change in their respective functions, adding a jet area indexed to LA and CW doppler density to quantify degree of severity of mitral regurgitation, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide an improved assessment of MR severity (Col. 1 Summary page 2015), and there was reasonable expectation of success.
Regarding to claims 3 and 15, Hare, II, Bonnefous and Grayburn together disclose claim 2 and 14 as discussed above.
Hare, II, Bonnefous and Grayburn collectively disclose parameters including VC width, jet area indexed to LA, and DW Doppler density.
Bonnefous further discloses the disclosure can be used for regurgitation associated with tricuspid valve ([0178]).
Claims 4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Hare, II, Bonnefous and Grayburn as applied to claims 3 and 15 above, and further in view of “Haddad et al.,” “Grading of mitral regurgitation based on intensity analysis of continuous wave Doppler signal,” Heart 2017; 103:190-197 (hereinafter Haddad).
Regarding to claims 4 and 16, Hare, II, Bonnefous and Grayburn together disclose claims 3 and 15 as discussed above.
Hare, II, Bonnefous and Grayburn do not further teach calculating parameters for a CW image and its details.
However, in the analogous field of endeavor in continuous wave Doppler imaging in mitral regurgitation evaluation, Haddad teaches analyzing CW image (1st paragraph, Col.1, page 191), by generating a mask (CW envelope contour, 2nd paragraph, Col.1 pg. 191); assigning a numerical values in grayscale to pixels comprising the mask (greyscale conversion, 2nd paragraph, Col.1 pg. 191); normalizing the numerical values to a brightness of the entire CW waveform image to generate normalized brightness values (PI histogram, with ranges fixed from 0-255 au, 2nd paragraph, Col.1 pg. 191); and calculating the MR CWDD or TR CWDD parameter as an average of the normalized brightness values (grading MR severity, 2nd paragraph, Col.1 pg. 191).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify diagnosis by comparing calculated measurements to cardiac guidelines for mitral and tricuspid valve as taught by Hare, II and Bonnefous to incorporate teaching of Haddad, since using average pixel intensity for grading of mitral regurgitation was well known in the art as taught by Haddad. One of ordinary skill in the art could have combined the elements as claimed by Hare, II and Bonnefous with no change in their respective functions, configuring determination of degree of MR or TR by average pixel intensity algorithm on CW Doppler images, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide a fast, easy, and reproducible way to grade MR severity using quantification of CW pixel intensity (Discussion, Col. 2page 194) and there was reasonable expectation of success.
Claims 5 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hare, II, Bonnefous and Grayburn as applied to claims 3 and 15 above, and further in view of “Zhou et al.,” US 2024/0180513 (hereinafter Zhou).
Regarding to claims 5 and 17, Hare, II, Bonnefous, and Grayburn together disclose claims 3 and 15 as discussed above.
In the analogous field of endeavor in monitoring regurgitation, Zhou teaches wherein generating the grade of MR or TR severity further comprises classifying the MR or TR severity by determining a respective grade for each of the MR or TR parameters, wherein a highest grade for each of the MR or TR parameters is used to determine a final MR or TR severity grade (processing device obtain a plurality of results of the regurgitation degree of the target position based on various regurgitation information, the result with a higher regurgitation degree may be designated as the regurgitation degree of the target position [0084]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify diagnosis by comparing calculated measurements to cardiac guidelines for mitral and tricuspid valve as taught by Hare, II and Bonnefous to incorporate teaching of Zhou, since results with higher regurgitation degree designated as the regurgitation degree of the target position were well known in the art as taught by Zhou. One of ordinary skill in the art could have combined the elements as claimed by Hare, II and Bonnefous with no change in their respective functions, configuring determination of degree of MR or TR by designating higher regurgitation degree of different results, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide regurgitation degree based on plurality of results on various regurgitation information ([0084]), and there was reasonable expectation of success.
Conclusion
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 PATRICIA J PARK whose telephone number is (571)270-1788. The examiner can normally be reached Monday-Thursday 8 am - 3 pm.
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/PATRICIA J PARK/Primary Examiner, Art Unit 3798