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 Amendment
The amendment filed 10/31/2025 has been entered. Claims 4 and 17 are cancelled, claims 21-22 are added, and claims 1-3, 5-16, 18, and 20-22 remain pending in the application. Applicant’s amendments to the Claims have overcome each and every 101 and 112(b) rejections previously set forth in the Non-Final Office Action mailed 08/13/2025.
Response to Arguments
Applicant’s arguments filed 10/31/2025 with respect to claim 1 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.
Given the amendments to claim 1, reference to Ostvik is being relied upon to teach dependent claims 2-3 more-consistently with the instant claim language, as shown below.
Claim Objections
Claims 5 and 13 are objected to because of the following informalities:
In claim 5, the limitation “the third region corresponding to interventricular septum (IVS)” should be removed for clarity.
In claim 13, “…at least a part of the vertical line,” should be “…at least a part of the vertical line.”, replacing the comma with a period at the end of the claim.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 18, and 20-22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ostvik et al. (US 20200074625 A1, published March 5, 2020), hereinafter referred to as Ostvik.
Regarding claim 1, and similarly for claims 18 and 20, Ostvik teaches a method for detecting medical indices from medical images, the method performed by a processor of a computing device that is operably coupled to a memory storing executable instructions, the method comprising:
receiving, from an image source, a series of medical images depicting a heart over time and including frames at time t-1, time t, and time t+1, each frame constituting a medical image (Fig. 1, cardiac ultrasound images I at time ti-1, t, and ti+1);
applying an artificial neural network of the processor to the series of medical images to obtain a segmentation prediction for the frame at time t (see para. 0059 – “(ii) Segmentation: Utilizes a U-Net type of CNN [artificial neural network] classify the LV myocardium. The pixel map of the segmentation is processed and used to define regions of interest (ROI) and the basis of measurement kernels.”),
the artificial neural network configured to model temporal dependencies and having been trained using motion-propagated image pairs by estimating motion vector fields between adjacent frames among the frames at time t-1, time t, and time t+1 and by propagating images and image labels across the adjacent frames (see para. 0066 – “The networks are trained separately, in a schedule consisting of different synthetic datasets with a wide range of motion vector representations.”);
generating, by the processor, a segmentation mask according to a plurality of regions of the heart depicted at time t (Fig. 1; see para. 0059 – “The pixel map of the segmentation is processed and used to define regions of interest (ROI) and the basis of measurement kernels.”; see para. 0060 – “The output of the network is a segmentation mask Ω.”);
computing, by the processor, at least one reference line based on the segmentation mask, each of the at least one reference line corresponding to one region of the plurality regions (Fig. 1, centerline C as the reference line based on the segmentation mask Ω; see para. 0061 – “The contour of the segmentation Ω was used to define the endo- and epicardial borders, and further the centerline C=[(x,y)1, . . . , (x,y)N] was sampled between with N=120 equally spaced points along the myocard.”); and
determining, by the processor, at least one medical index based on the at least one reference line (see para. 0068 – “The updated centreline C′ is used to calculate the longitudinal ventricular length [medical index], t, i.e. the arc length, for each timestep t.”).
Furthermore, regarding claim 2, Ostvik further teaches wherein the at least one medical index comprises at least one of a length of the at least one reference line or a value determined based on the length of the at least one reference line (see para. 0068 – “The updated centreline C′ is used to calculate the longitudinal ventricular length, t, i.e. the arc length, for each timestep t.”).
Furthermore, regarding claim 3, Ostvik further teaches generating, by the processor, at least one reference point based on the segmentation mask, the at least one reference point being generated based on at least one center point of the plurality of regions, the at least one center point being computed from central moments in a corresponding region of the plurality of regions (Fig. 1, centerline C as the reference line based on the segmentation mask Ω; see para. 0059 – “The pixel map of the segmentation is processed and used to define regions of interest (ROI) and the basis of measurement kernels.”; see para. 0061 – “The contour of the segmentation Ω was used to define the endo- and epicardial borders, and further the centerline C=[(x,y)1, . . . , (x,y)N] was sampled between with N=120 equally spaced points [reference points] along the myocard.”).
Furthermore, regarding claim 21, Ostvik further teaches the instructions further causing the processor to generate at least one reference point based on the segmentation mask, the at least one reference point being generated based on at least one center point of the plurality of regions, the at least one center point being computed from central moments in a corresponding region of the plurality of regions (Fig. 1, centerline C as the reference line based on the segmentation mask Ω; see para. 0059 – “The pixel map of the segmentation is processed and used to define regions of interest (ROI) and the basis of measurement kernels.”; see para. 0061 – “The contour of the segmentation Ω was used to define the endo- and epicardial borders, and further the centerline C=[(x,y)1, . . . , (x,y)N] was sampled between with N=120 equally spaced points [reference points] along the myocard.”).
Furthermore, regarding claim 22, Ostvik further teaches wherein the at least one processor is further configured to generate at least one reference point based on the segmentation mask, the at least one reference point being generated based on at least one center point of the plurality of regions, the at least one center point being computed from central moments in a corresponding region of the plurality of regions (Fig. 1, centerline C as the reference line based on the segmentation mask Ω; see para. 0059 – “The pixel map of the segmentation is processed and used to define regions of interest (ROI) and the basis of measurement kernels.”; see para. 0061 – “The contour of the segmentation Ω was used to define the endo- and epicardial borders, and further the centerline C=[(x,y)1, . . . , (x,y)N] was sampled between with N=120 equally spaced points [reference points] along the myocard.”).
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.
Claims 5-16 are rejected under 35 U.S.C. 103 as being unpatentable over Ostvik in view of Deo et al. (US 20210000449 A1, published January 7, 2021), hereinafter referred to as Deo.
Regarding claim 5, Ostvik teaches all of the elements disclosed in claim 1 above.
Ostvik teaches a plurality of regions in a heart image, but does not explicitly teach where the plurality of regions corresponds to a left ventricle (LV), interventricular septum, LV posterior wall, right ventricle (RV), aorta, and left atrium (LA).
Whereas, Deo, in the same field of endeavor, teaches wherein the plurality of regions comprise at least one of a first region, a second region, a third region, a fourth region, a fifth region or a sixth region, the first region corresponding to left ventricle (LV), the second region corresponding to interventricular septum (IVS), the third region corresponding to LV posterior wall (LVPW), the fourth region corresponding to right ventricle (RV), the fifth region corresponding to aorta, and the sixth corresponding to left atrium (LA) (Fig. 3, PLAX view with six regions; see Table 1 — View: Parasternal long axis (PLAX), Segmented area: left atrium blood pool, Right ventricle blood pool, Aortic root, Outer cardiac boundary, Anterior septum, posterior wall).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a plurality of regions in a heart image, as disclosed in Ostvik, by having the plurality of regions correspond to a left ventricle (LV), interventricular septum, LV posterior wall, right ventricle (RV), aorta, and left atrium (LA), as disclosed in Deo. One of ordinary skill in the art would have been motivated to make this modification in order to detect a disease, e.g., characterized by abnormal cardiac thickening, such as hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis, as taught in Deo (see para. 0040).
Furthermore, regarding claim 6, Deo further teaches wherein: the plurality of regions are based on an echocardiographic image acquired in the parasternal long-axis (PLAX) view, the first region is located between the second region and the third region, the second region is located between the fourth region and the first region, the fourth region is located at a top of the echocardiography image, the fifth region is adjacent to a right side of the first region, or the sixth region is adjacent to the right side of the first region and is located below the fifth region. (Fig. 3, PLAX view with six regions; see Table 1 — View: Parasternal long axis (PLAX), Segmented area: left atrium blood pool, Right ventricle blood pool, Aortic root, Outer cardiac boundary, Anterior septum, posterior wall).
Furthermore, regarding claim 7, Deo further teaches wherein the computing the at least one reference line comprises: identifying a transversal line passing through a reference point of the first region and a center of a left boundary line of the first region and crossing the first region; identifying a first orthogonal line passing through the reference line and orthogonal to the transversal line; and generating a first reference line to include at least a part of the first orthogonal line (see para. 0128 — “For left ventricular mass (LVMI), we again took a sliding window approach, using the 90% percentile value for the LV outer (myocardial) area and computed LVMI using the Area-Length formula.”; see para. 0043 — “At block 195, metrics of cardiac structure (e.g., mass, length, and volume) can be performed using the segmentation results of the view-classified images.” Known in the art to measure length/volume/mass of regions in echocardiography using tracings/lines/points determined by user).
Furthermore, regarding claim 8, Deo further teaches wherein the computing the at least one reference line further comprises: identifying a second orthogonal line passing through a point of contact between the first reference line and a segmentation contour of the second region and orthogonal to a long axis of the second region; and generating a second reference line to include at least a part of the second orthogonal line (see para. 0128 — “For left ventricular mass (LVMI), we again took a sliding window approach, using the 90% percentile value for the LV outer (myocardial) area and computed LVMI using the Area-Length formula.”; see para. 0043 — “At block 195, metrics of cardiac structure (e.g., mass, length, and volume) can be performed using the segmentation results of the view classified images.” Known in the art to measure length/volume/mass of regions in echocardiography using tracings/lines/points determined by user).
Furthermore, regarding claim 9, Deo further teaches wherein the computing the at least one reference line further comprises: identifying a third orthogonal line passing through a point of contact between the first reference line and a segmentation contour of the third region and orthogonal to a long axis of the third region; and generating a third reference line to include at least a part of the third orthogonal line (see para. 0128 — “For left ventricular mass (LVMI), we again took a sliding window approach, using the 90% percentile value for the LV outer (myocardial) area and computed LVMI using the Area-Length formula.”; see para. 0043 — “At block 195, metrics of cardiac structure (e.g., mass, length, and volume) can be performed using the segmentation results of the view-classified images.” Known in the art to measure length/volume/mass of regions in echocardiography using tracings/lines/points determined by user).
Furthermore, regarding claim 10, Deo further teaches wherein the computing the at least one reference line further comprises: identifying a center line of a short axis of a region corresponding to the second region; identifying a first vertical line perpendicular to a boundary line of the second region at a point where the center line and the boundary line of the region meet; and generating a first reference line to include at least a part of the first vertical line, and wherein the first reference line is used to measure a diameter of the first region (see para. 0128 — “For left ventricular mass (LVMI), we again took a sliding window approach, using the 90% percentile value for the LV outer (myocardial) area and computed LVMI using the Area-Length formula.”; see para. 0043 — “At block 195, metrics of cardiac structure (e.g., mass, length, and volume) can be performed using the segmentation results of the view-classified images.” Known in the art to measure length/volume/mass of regions in echocardiography using tracings/lines/points determined by user).
Furthermore, regarding claim 11, Deo further teaches wherein the computing the at least one reference line further comprises: identifying a second vertical line perpendicular to a boundary line of the third region at a point where the first reference line and the boundary line of the third region meet; and generating a second reference line to include at least a part of the second vertical line, and wherein the second reference line is used to measure a thickness of the third region (see para. 0128 — “For left ventricular mass (LVMI), we again took a sliding window approach, using the 90% percentile value for the LV outer (myocardial) area and computed LVMI using the Area-Length formula.”; see para. 0043 — “At block 195, metrics of cardiac structure (e.g., mass, length, and volume) can be performed using the segmentation results of the view-classified images.” Known in the art to measure length/volume/mass of regions in echocardiography using tracings/lines/points determined by user).
Furthermore, regarding claim 12, Deo further teaches wherein the computing the at least one reference line further comprises: identifying a closest point closest to the fourth region among points on a boundary line of the fifth region; identifying a parallel line including the closest point and parallel to a junction of the first region and the fifth region; and generating a third reference line to include at least a part of the parallel line (see para. 0128 — “For left ventricular mass (LVMI), we again took a sliding window approach, using the 90% percentile value for the LV outer (myocardial) area and computed LVMI using the Area-Length formula.”; see para. 0043 — “At block 195, metrics of cardiac structure (e.g., mass, length, and volume) can be performed using the segmentation results of the view-classified images.” Known in the art to measure length/volume/mass of regions in echocardiography using tracings/lines/points determined by user).
Furthermore, regarding claim 13, Deo further teaches wherein the generating the at least one reference line comprises: identifying a sinus point on a boundary line of the fifth region, the sinus point occurring at a high point or a low point of a convex part at an end of the boundary line of the fifth region; identifying a vertical line including a point closest to the sinus point on a boundary line of a region corresponding to the sixth region, parallel to a vertical axis of the medial image and penetrating the inside of the region corresponding to the sixth region; and generating a fourth reference line to include at least a part of the vertical line (see para. 0128 — “For left ventricular mass (LVMI), we again took a sliding window approach, using the 90% percentile value for the LV outer (myocardial) area and computed LVMI using the Area Length formula.”; see para. 0043 — “At block 195, metrics of cardiac structure (e.g., mass, length, and volume) can be performed using the segmentation results of the view-classified images.” Known in the art to measure length/volume/mass of regions in echocardiography using tracings/lines/points determined by user).
Furthermore, regarding claim 14, Deo further teaches wherein the computing the at least one reference line further comprises: identifying a sinus point on a boundary line of the fifth region, the sinus point occurring at a high point or a low point of a convex part at an end of the boundary line of the fifth region; identifying a vertical line including a point closest to the sinus point on a boundary line of a region corresponding to the sixth region, perpendicular to a long axis of a region corresponding to the sixth region and penetrating the inside of the region corresponding to the sixth region; and determining a fourth reference line to include at least a part of the vertical line (see para. 0128 — “For left ventricular mass (LVMI), we again took a sliding window approach, using the 90% percentile value for the LV outer (myocardial) area and computed LVMI using the Area-Length formula.”; see para. 0043 — “At block 195, metrics of cardiac structure (e.g., mass, length, and volume) can be performed using the segmentation results of the view-classified images.” Known in the art to measure length/volume/mass of regions in echocardiography using tracings/lines/points determined by user).
Furthermore, regarding claim 15, Deo further teaches wherein the computing the at least one reference line further comprises: identifying a vertical line including one point on a junction of the fifth region and the first region and perpendicular to a center line of a long axis of the fourth region; and generating a fifth reference line to include at least a part of the vertical line (see para. 0128 — “For left ventricular mass (LVMI), we again took a sliding window approach, using the 90% percentile value for the LV outer (myocardial) area and computed LVMI using the Area-Length formula.”; see para. 0043 — “At block 195, metrics of cardiac structure (e.g., mass, length, and volume) can be performed using the segmentation results of the view-classified images.” Known in the art to measure length/volume/mass of regions in echocardiography using tracings/lines/points determined by user).
Furthermore, regarding claim 16, Deo further teaches wherein the computing the at least one reference line further comprises: identifying a vertical line including one point on a junction of the fifth region and the first region and perpendicular to a vertical axis of the medical image at time t; and generating a fifth reference line to include at least a part of the vertical line (see para. 0128 — “For left ventricular mass (LVMI), we again took a sliding window approach, using the 90% percentile value for the LV outer (myocardial) area and computed LVMI using the Area-Length formula.”; see para. 0043 — “At block 195, metrics of cardiac structure (e.g., mass, length, and volume) can be performed using the segmentation results of the view-classified images.” Known in the art to measure length/volume/mass of regions in echocardiography using tracings/lines/points determined by user).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sheehan et al. (US 6106466 A, published August 22, 2000) discloses yielding an output that defines the shape of the endocardium and epicardium of the left Ventricle or other portion of the patient's heart, in three dimensions, for use in determining cardiac parameters.
Chen et al. (US 20200111214 A1, published April 9, 2020) discloses inputting images to neural networks, the images in a given group of MR images may correspond to a cycle and have a temporal order, and segmenting the images and outputting a probability map from the neural networks.
Lu et al. (US 20190223725 A1, published July 25, 2019) discloses the trained convolution units and LSTM units are applied to the scan data or derived feature values to extract the corresponding features and output the binary mask for the object for each of the input images to segment regions of MR heart image.
Arnaout (US 20220012875 A1, published January 13, 2022 with a priority date of September 18, 2018) discloses target views can include multiple image frames (e.g., frames of the heart in ventricular systole and diastole), which can be used for measurements such as (but not limited to) a fractional area change calculation and/or plots of chamber area over time.
Contijoch et al. (US 20230289972 A1, published September 14, 2023 with a priority date of July 20, 2020) discloses performing, using a convolutional neural network, a segmentation operation and a slicing operation on each of the first plurality of input image frames of cardiac CT images to generate each of a plurality of output image frames comprising results of the segmentation operation and the slicing operation.
Rothberg et al. (US 20170360411 A1, published December 21, 2017) discloses capturing an ultrasound image of the subject that contains the particular anatomical view of the heart, and analyzing the captured ultrasound image to identify medical information about the subject.
D. Zhang et al, “A MULTI-LEVEL CONVOLUTIONAL LSTM MODEL FOR THE SEGMENTATION OF LEFT VENTRICLE MYOCARDIUM IN INFARCTED PORCINE CINE MR IMAGES”, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 470-473, April 2018 discloses inputting cine MR images to a neural network model to obtain the segmentation result as a probability map, where each pixel is assigned a value between 0 and 1 indicating the probability for the pixel to belong to the myocardium.
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 Nyrobi Celestine whose telephone number is 571-272-0129. The examiner can normally be reached on Monday - Thursday, 7:00AM - 5:00PM EST.
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/Nyrobi Celestine/Examiner, Art Unit 3798