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 .
The examiner acknowledges receipt of remarks/amendments dated October 21, 2025 in which, the applicants amended claims 1, 6, 12, 15, 17 and 20, and cancelled claims 4-5, 11 and 19.
Claim Objections
Claim 20 is objected to because of the following informalities: Claim 20 is dependent on cancelled claim 19. Appropriate correction is required.
Response to Arguments
Applicant's arguments filed October 21, 2025 have been fully considered but they are not persuasive.
In regards to claims 1 and 17, the applicants state that “The combination of Abol and Ruijsink et al fails to disclose or teach the specific combination of a quality control algorithm for CMR images to enable the detection of cardiac biomarkers as now claimed.” The examiner respectfully traverses. Abol teaches quality control check of echocardiographic images before facilitating quantified clinical measurement of anatomical features (i.e., biomarkers). (See Abol Col. 14, line 58 to Col. 15, line 27, Col. 17, lines 22-37 and Col. 24, lines 27-52). Abol does not explicitly teach that the input images are CMR images or biomarkers. However, Ruijsink teaches CMR images and biomarkers. (See Ruijsink Abstract, page 685, Step 3: Parameter Calculation.). Therefore, the examiner submits that amended claims 1 and 17 are still rejected by Abol in view of Ruijsink.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 6-10, 12-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. 11,129,591 by Abolmaesumi et al. (hereinafter ‘Abol’) in view of “Fully Automated, Quality-Controlled Cardiac Analysis From CMR” by Ruijsink et al. (hereinafter ‘Ruijsink’).
In regards to claims 1, Abol teaches a computer-implemented method for characterizing images of a target area of the internal anatomy of a human or animal subject, the images having been obtained using a medical imaging modality, the method comprising: (See Abol Figure 4, Abol teaches images of human heart obtained using electrocardiograph.)
providing a plurality of images of the target area obtained using the medical imaging modality; (See Abol Col. 5, line 46 to Col. 6, line 12, Abol teaches providing electrocardiographic images.)
performing a first quality control check on the plurality of images, wherein the quality control check comprises: (i) classifying the plurality of images into one or more classes based on predefined metadata associated with each image; and (See Abol Col. 8, line 66 to Col. 9, line 18, Abol teaches categorizing electrocardiographic images.)
(ii) screening the classified images, based on image quality and image orientation, to select a first set of images for analysis, (See Abol Col. 14, line 58 to Col. 15, line 27, Abol teaches determining quality of images based on various image capture parameters.)
wherein the method further comprises analysing the selected first set of images to evaluate one or more characteristics associated with the said target area as discernible from the selected first set of images, and wherein the method comprises the use of one or more deep learning (DL) algorithms. (See Abol Col. 24, lines 27-52, and Col. 12, lines 31-62, Abol teaches further analyzing images for medical diagnosis and Abol teaches using neural networks.)
However, Abol does not expressly teach wherein the medical imaging modality is cardiovascular magnetic resonance imaging (CMR) and wherein the one or more characteristics associated with the target area comprise cardiac biomarkers.
Ruijsink teaches wherein the medical imaging modality is cardiovascular magnetic resonance imaging (CMR). (See Ruijsink Abstract, Ruijsink teaches CMR images.)
wherein the one or more characteristics associated with the target area comprise cardiac biomarkers. (See Ruijsink page 685, left column, 1st paragraph.).
It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify Abol to include CMR images as taught by Ruijsink. The determination of obviousness is predicated upon the following findings: One skilled in the art would have been motivated to modify Abol in this manner because/in order to be able non-invasively quantify cardiac volumes and ejection fraction.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Abol with Ruijsink to obtain the invention as specified in claim 1.
In regards to claim 2, Abol and Ruijsink teach all the limitations of claim 1. Abol teaches wherein the method further comprises a second, post-analysis, quality control step comprising screening the analysed images based on image orientation and coverage of the target area in the analysed image. (See Abol Col. 16, line 60 to Col. 17, lines 37).
In regards to claim 3, Abol and Ruijsink teach all the limitations of claim 1. Abol teaches wherein the one or more DL algorithms comprise: Convolutional Neural Network (CNN), Fully Convolutional Network (FCN), CNN-Long short term memory (LSTM) network, and no- new-net (nnU-net) network. (See Abol Col. 12, lines 31-62).
In regards to claim 6, Abol and Ruijsink teach all the limitations of claim 1. Ruijsink teaches wherein the plurality of images comprise cine CMR images of the heart of the human or animal subject. (See Ruijsink section ‘Image Analysis Pipeline’).
In regards to claim 7, Abol and Ruijsink teach all the limitations of claim 6. Ruijsink teaches wherein the one or more classes is based on cardiac imaging planes used in cine CMR. (See Ruijsink section ‘Image Analysis Pipeline’).
In regards to claim 8, Abol and Ruijsink teach all the limitations of claim 7. Ruijsink teaches wherein, in the first quality control check, the screening based on image quality comprises screening the classified images for motion artefacts. (See Ruijsink section ‘Automated QC’).
In regards to claim 9, Abol and Ruijsink teach all the limitations of claim 7. Ruijsink teaches wherein, in the first quality control check, the screening based on image orientation comprises screening the classified images for off-axis orientations. (See Ruijsink section ‘Automated QC’).
In regards to claim 10, Abol and Ruijsink teach all the limitations of claim 8. Ruijsink teaches wherein, in the first quality control check, the said screening is performed using a binary classifier. (See Ruijsink section ‘Step 1: Pre-Analysis Image QC (QC1)’).
In regards to claim 12, Abol and Ruijsink teach all the limitations of claim 1. Ruijsink teaches wherein analysing the selected first set of images comprises segmenting left ventricle and right ventricle areas in the first set of images using a DL algorithm. (See Ruijsink section ‘Step 2: Image Segmentation’).
In regards to claim 13, Abol and Ruijsink teach all the limitations of claim 12. Ruijsink teaches wherein the said segmenting is performed using a no-new-net (nnU-net) network. (See Ruijsink section ‘Step 2: Image Segmentation’).
In regards to claim 14, Abol and Ruijsink teach all the limitations of claim 13. Ruijsink teaches wherein the one or more characteristics associated with the target area comprise cardiac biomarkers including Mitral and tricuspid valve annular plane systolic excursion (MAPSE and TAPSE) biomarkers and early diastolic velocities (MAPDv, TAPDv) biomarkers. (See Ruijsink section ‘Step 3: Parameter Calculation’)
In regards to claim 15, Abol and Ruijsink teach all the limitations of claim 1. Ruijsink teaches wherein post-analysis quality control step is performed using a CNN-LSTM network. (See Ruijsink section “Step 4: Post-Analysis QC (QC2)’).
In regards to claim 16, Abol and Ruijsink teach all the limitations of claim 15. Ruijsink teaches wherein the CNN- LSTM network is configured to receive the full cardiac cycle as input and detect unphysiological curves in the analysed images. (See Ruijsink section “Step 4: Post-Analysis QC (QC2)’).
Claims 17-18 recite limitations that are similar to that of claims 1-2, respectively. Therefore, claims 17-18 and 19 are rejected similarly as claims 1-2, respectively.
In regards to claim 20, Abol and Ruijsink teach all the limitations of claim 17. Ruijsink teaches wherein the plurality of images comprise cine CMR images of the heart of the human or animal subject, wherein the one or more classes is based on cardiac imaging planes used in cine CMR, and wherein, in the first quality control check, the screening based on image quality comprises screening the classified images for motion artefacts. (See Ruijsink section ‘Image Analysis Pipeline’, Ruijsink teaches determining quality based on motion artifacts.)
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 UTPAL D SHAH whose telephone number is (571)272-5729. The examiner can normally be reached M-F: 7:30-5:30.
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/UTPAL D SHAH/Primary Examiner, Art Unit 2668