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 .
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.
Claim(s) 1-2, 8-10, 13-15 and 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Deo et al. (US Pub No. 2021/0000449).
With regards to claims 1 and 13, Deo et al. disclose an apparatus and a computer implemented method of processing a sequence of ultrasound (US) images of an anatomical feature with periodic motions, the sequence of US images corresponding to different view planes of the anatomical feature (paragraphs [0029]-[0031], referring to analyzing >40000 echo studies, wherein each echo study consists of up to 70 videos collected from different viewpoints, wherein, by definition, a video comprises a recording of moving images, and therefore the videos (i.e. moving images) correspond to a sequence of US images corresponding to different view planes/viewpoints of the anatomical feature), the method comprising:
using a first machine learning (ML) model (i.e. “convolutional neural networks (CNNs)”) to label detections of the anatomical feature (i.e. heart) in the US images in the sequence according to view plane of the anatomical feature visible in each respective image (paragraph [0033], referring to convolutional neural networks (CNNs) being used for automatically determining echo views (i.e. apical 2-, 3-, and 4-chamber (A2c, A3c, and A4c), parasternal long axis (PLAX), parasternal short axis at the level of the PSAX and the IVC, etc.), wherein the classification/labeling of which view a particular set of images corresponds can be used later in the pipeline for identifying a structure in the heart; paragraph [0043], referring to block 192 wherein view classification is performed; paragraphs [0045]-[0050], referring to the convolutional neural network successfully discriminating echo views, wherein an initial model comprising of labeled different views is used for the CNN; Figures 1-2, 10);
obtaining a first cluster of consecutive images in the sequence that all correspond to a same first view plane of the different view planes of the anatomical feature, based on the labelling (paragraphs [0034]- [0035], referring to discarding of views with a low probability of being in a view, and thus it follows that images/views with a high probability of being in a view would be kept and grouped/clustered together and/or referring to, based on the identified views, videos can be routed to specific image segmentation models, thus resulting in a “clustering” of images in a sequence that all correspond to a first view plane; paragraph [0048], referring to the grouping of images corresponding to six different echocardiographic views; paragraph [0040], referring to parasternal long-axis (PLAX) videos being phased to identify images at the end of cardiac systole and diastole, wherein the resulting image pairs/clusters can be used to detect a disease; paragraph [0041], referring to images from a same part of the cycle can be bundled/”cluster[ed]” together; paragraph [0043], referring to block 198 wherein images can be phased to identify images corresponding to particular parts of the cardiac cycle; Figures 1-2, 10); and
using the first cluster as a first clip of the first view plane of the anatomical feature (paragraph [0035], referring to, based on the identified views, videos/clips can be routed to specific segmentation models; paragraph [0037], referring to sorting videos in a broad sense of what the view is, but also what structures are sufficiently visible for use in further measurements; paragraph [0040], referring to the view-classified videos/clips; Figures 1-2, 10);
characterized by:
repeating steps i), ii) and iii) to obtain a plurality of clips, each corresponding to a same respective view plane of the different view planes of the anatomical feature (paragraphs [0033], [0035], referring to the CNNs being used for automatically determining echo views which include a plurality of different views, such as the A2c, A3c, A4c, PLAX views, etc., and therefore the steps of using the ML model to label/classify detections of the anatomical feature, obtaining a first cluster, and using the first cluster as a first clip of the first view plane would be repeated for each of the plurality of different views; Figures 1-2, 10); and
selecting the first clip as a preferred clip of the anatomical feature from the plurality of clips, if the first clip comprises a cluster of consecutive images for which the respective labels are more statistically significant compared to other labels in the plurality of clips (paragraph [0034], referring to providing a quality score for the echo/video measurement, wherein a quality score can be determined using an average, median, or maximum assigned probability of a view across every video in the study, and if the best guess for a view still has a low probability, then the measurement might be discarded, and thus it would follow that a view having high probability would be selected as a preferred clip/video; paragraphs [0036]-[0037], referring to probabilities being assigned to the images and using separate classes for views with obscured structures and those with unobscured structures, wherein embodiments can compare the probabilities to determine whether the video should be used to estimate atrial size, and thus the video/clip that is determined to be used to estimate atrial size is viewed as the “preferred clip”; paragraph [0040], referring to, for the detection of a disease characterized by abnormal cardiac thickening, such as hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis, the videos corresponding to the specific parasternal long-axis (PLAX) view can be phased to identify images at the end of the cardiac systole and diastole in order to detect the disease, and therefore the PLAX view video/clip is selected as the preferred clip for the disease detection as it is implicitly “more statistically significant” for the disease detection compared to other labels/classifications in the plurality of clips; paragraph [0043], referring to the view classification providing a probability score (193) of an image being in each of a plurality of views; paragraph [0050]; Figures 1-2,10).
Additionally, with regards to claim 13, Deo et al. disclose that the apparatus comprises a memory (72) comprising instruction data representing a set of instructions (paragraphs [0158]-[0160], referring to the execution of a plurality of instructions from system memory (72); Figure 11); and a processor (73) configured to communicate with the memory and to execute the set of instructions (paragraphs [0157]-[0160], referring to the central processor (73) which communicates with each subsystem and controls the execution of a plurality of instructions from system memory (72); Figure 11), wherein the set of instructions, when executed by the processor, cause the processor to perform the above steps as set forth in claim 1 [see rejection of claim 1].
With regards to claim 2, Deo et al. disclose determining a frequency of the periodic motions from the preferred clip (paragraphs [0041]-[0042], [0123], referring to structure measurements being plotted over time as a set of cycles (e.g, as a wave), and points along that curve can define different aspects of the cardiac cycle (e.g., peaks/maximum and valleys/minimum of the volume of a particular chamber), wherein such plotting of the cycle and tracking/defining of peaks or valleys of the cycle results in a determination of the rate at which the peaks/valleys are repeated over a particular period of time, which thus corresponds to a “frequency”, wherein such tracked peaks/valleys are representative of the periodic motions).
With regards to claim 8, Deo et al. disclose that the method further comprises converting each image in the first clip into a feature vector (i.e. size, mass, length, volume, etc.) to obtain a sequence of feature vectors (paragraph [0042], referring to structure measurements being plotted over time as a set of cycles, wherein, for instance, the view identification can provide an input to the segmentation module, so as to identify a chamber accurately, which then allows tracking its size, which then allows selecting a part of the cycle, e.g., where it is the largest or smallest; paragraph [0043], referring to metrics of cardiac structure (e.g., mass, length, volume) can be performed using the segmentation results of the view-classified images, wherein the variations in the metric can be used to identify images corresponding to particular positions in a cardiac cycle; note that the metrics plotted over time corresponds to a sequence of the feature vectors); determining correlations between the feature vectors in the sequence of feature vectors (paragraph [0042]-[0043], referring to metrics of cardiac structure (e.g., mass, length, volume) can be performed using the segmentation results of the view-classified images, wherein the variations in the metric can be used to identify images corresponding to particular positions in a cardiac cycle; note that determining variations, such as determining peaks/valleys in the metric, requires determining correlations (i.e. matching of peaks/valleys) between the feature vectors (i.e. metrics) plotted over time (i.e. sequence of feature vectors)); and using the correlations to determine a third subset of images from the first clip corresponding to one period of the periodic movements (paragraphs [0042]-[0043], referring to using the variations in the metric to identify images corresponding to particular positions in a cardiac cycle, which corresponds to periods of the periodic movements).
With regards to claim 9, Deo et al. disclose that the feature vector comprises an encoding of a spatial pattern in a respective image; and/or one or more features of: a histogram of oriented gradients in the respective image; a scale invariant feature transform of the respective image; and a local binary pattern of the respective image (paragraphs [0042]-[0043], referring to segmentation being used to identify metrics, such as size, which is used for the phasing; paragraph [0055], referring to segmentation being performed by identifying each pixel as being in a structure or not, e.g., as signified by 0 or 1, which corresponds to a local binary pattern of the respective image).
With regards to claim 10, Deo et al. disclose that step v) comprises selecting a first feature vector, fp, in the sequence of feature vectors; correlating the first feature vector fp with each of the other feature vectors in the sequence of feature vectors to obtain an N dimensional correlation vector c, wherein N is the number of images in the first clip (paragraphs [0042]-[0043], referring to identifying peaks/maximums and valleys/minimums of the metrics plotted over time, which would ultimately provide a N dimensional correlation vector c (i.e. feature vectors vs. time) with N being the number of images in the first clip (i.e. each time point correspond to an image) and wherein determining a peak or valley would inherently require a correlation/comparison/matching of a first selected feature vector (i.e. size measurement at one particular time point) to the other feature vectors (i.e. other size measurements) over time).
With regards to claim 14, Deo et al. disclose an ultrasound imaging system, comprising an ultrasound probe for transmitting ultrasound waves and receiving echo information (paragraphs [0026], [0045], referring to the ultrasound probe/transducer for acquiring the images/views); and an apparatus as in claim 13 for processing a sequence of US images of an anatomical feature with periodic movements obtained based on the received echo information [see rejection of claim 13].
With regards to claim 15, Deo et al. disclose a computer program product comprising a computer readable medium, the computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is caused to perform the method as claimed in claim 1 (paragraphs [0005], [0007], referring to the computer readable media associated with the methods; see rejection of claim 1).
With regards to claim 17, Deo et al. disclose that the method further comprises displaying, on a display associated with an ultrasound system, real-time feedback to a user regarding the view plane of the anatomical feature currently being imaged (paragraph [0034], referring to analyzing the video in real time, wherein an expected location of the device can be displayed to a user and feedback can generate a alert which can indicate a problem or provide specific guidance on how to move the device based on identifying a best view).
Claim(s) 3 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deo et al. as applied to claim 1 above, and further in view of Guracar (US Pub No. 2015/0031995).
With regards to claim 3, as discussed above, Deo et al. meet the limitations of claim 1. However, they do not specifically disclose that their method further comprises determining a minimum intensity image, Imin, from the first cluster of images in the first clip, wherein the intensity of each image component in the minimum intensity image is determined as the minimum intensity of image components in equivalent positions in each of the images in the first cluster of images.
Guracar discloses suppressing motion artifacts in ultrasonic imaging, wherein motion tracking is performed to derive parameter values over time, wherein for each voxel, a value is elected as a function of data from each of the frames of data (Abstract; paragraphs [0064]-[0066]; Figure 1). As an example, a minimum or other data in relation to data of the selected frames is selected based on comparison, wherein the frames of the selected subset are combined into a persisted frame or single frame (paragraph [0066], note that by combining the minimum values from each of the frames to form a persisted, single frame, a minimum intensity image is determined).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the method of the above combined references further comprise determining a minimum intensity image, Imin, from the first cluster of images in the first clip, wherein the intensity of each image component in the minimum intensity image is determined as the minimum intensity of image components in equivalent positions in each of the images in the first cluster of images, as taught by Guracar, in order to suppress motion artifacts and obtain a persisted image (Abstract; paragraph [0066]).
With regards to claim 20, Guracar discloses registering each image in the first cluster with respect to a common frame using planar transformations prior to determining the minimum intensity image (paragraphs [0055], [0059]-[0063], referring to the registration performed prior to determining the minimum intensity image).
Claim(s) 4-7 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deo et al. in view of Guracar as applied to claim 3 above, and further in view of Kasahara et al. (US Pub No. 2010/0262006).
With regards to claim 4, as discussed above, the above combined references meet the limitations of claim 3. However, though Guracar does disclose that the Imin image serves as a reference image as it is the image from which parameter values are derived (paragraphs [0064]-[0068]; Figure 2), the above combined references do not specifically disclose that the method further comprises determining a first image, Ipivot, in the first clip that represents a turning point in the periodic motion, by comparing each image in the clip to Imin and selecting Ipivot as an image having either minimal or maximal intensity deviations from Imin.
Kasahara et al. disclose an ultrasound diagnostic apparatus for forming display images of an object in periodic motion, wherein a virtual period setting unit detects peak values (local maximum values) of the mutual difference values and determines an interval (heartbeat, HB) between peak values to be a period of the heart (period of a heartbeat), wherein the mutual difference values are determined by determining the difference in pixel values of two adjacent sets of tomographic image data in the Z-axis direction (Abstract; paragraphs [0038]-[0039], [0042], note that the “peak values” correspond to maximal intensity deviations from a reference image, wherein in the above combined references, the reference image correspond to Imin). The mutual difference values allows for the dilation and contraction (i.e. turning points in periodic motion of the heart) of the heart to be distinguished from one another (paragraph [0047]; Figures 1-4). Images can be searched/selected using the virtual period, wherein tomographic image data corresponding to the position wherein the mutual difference value becomes a maximum can be selected as a representative base image which is then used to select new base images (paragraphs [0050]-[0055], note that the selected tomographic image data corresponds to the claimed Ipivot image which represents a “turning point in the periodic motion”; Figure 5).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the method of the above combined references further comprise determining a first image, Ipivot, in the first clip that represents a turning point in the periodic motion, by comparing each image in the clip to Imin and selecting Ipivot as an image having either minimal or maximal intensity deviations from Imin, as taught by Kasahara et al., in order to distinguish different periodic events, such as dilation/contraction of the heart, from one another, thus providing a useful diagnostic aid (Abstract; paragraph [0038]).
With regards to claim 5, Kasahara et al. disclose that the method further comprises determining a first subset of images in the first clip corresponding to one period of the periodic movements, as images lying between the first image, and a second image representing the next consecutive turning point in the periodic motion (paragraphs [0050]-[0055]; Figure 5, in particular, see Figure 5C, wherein the arrows of dotted lines show the positions of a plurality of base images which depict boundaries for the division bases and represent different image groups corresponding to a different cardiac periods, wherein the first dotted arrow line can represent the “first image” and the next dotted arrow line can represent the “second image”).
With regards to claim 6, Kasahara et al. disclose that the method further comprises determining an image number of a third image at a predefined phase of the periodic motion in the clip; and determining a relative location of the third image in the sequence compared to the first and second images; and determining a second subset of images in the first clip that start and end at the predefined phase of the motion in the clip by selecting the second subset of images relative to the first subset of images, shifted by the relative location of the third image (paragraphs [0050]-[0055]; Figure 5, in particular, see Figure 5C, wherein the arrows of dotted lines show the positions of a plurality of base images which depict boundaries for the division bases and represent different image groups corresponding to a different cardiac periods/phases, wherein the third (or greater than third) dotted arrow line can represent the “third image” which is at a relative location in the sequence compared to the first and second images, represented respectively by the first and second dotted arrows).
With regards to claim 7, Deo et al. disclose that the anatomical feature is a heart (Abstract; paragraphs [0033], [0036]-[0038]), and the method further comprises repeating steps i), ii) and iii) for a plurality of different predefined phases of the periodic motion; and/or repeating steps i), ii) and iii) for a plurality of different view planes to obtain a plurality single cycle clips that are all synchronized to a common cardiac phase for display to a user (paragraphs [0040]-[0042], referring to the videos being phased to identify images at the end of cardiac systole and diastole, wherein segmentation and structure information over cycles is used to identify which part (stage/phase) of a cycle a given image corresponds (systole or diastole), and thus steps i), (ii) and (iii) would necessarily be repeated for a plurality of the different predefined phases (i.e. systole/diastole) of the periodic motion of the heart; paragraphs [0033], [0035], referring to the CNNs being used for automatically determining echo views which include a plurality of different views, such as the A2c, A3c, A4c, PLAX views, etc., and therefore the steps of using the ML model to label/classify detections of the anatomical feature, obtaining a first cluster, and using the first cluster as a first clip of the first view plane would be repeated for each of the plurality of different views; Figures 1-2, 10).
With regard to claim 12, as discussed above, the above combined references meet the limitations of claim 1. However, though Dao et al. disclose that the anatomical feature is an object in periodic motion, such as a heart (Abstract; paragraphs [0033], [0036]-[0038]), Dao et al. do not specifically disclose that the anatomical feature is fetal heart.
Kasahara et al. disclose an ultrasound diagnostic apparatus for forming display of images of an object in periodic motion, wherein ultrasound images are formed of an object in unstable periodic motion, such as the heart of a fetus (Abstract; paragraphs [0007]-[0008], [0098]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to substitute the object in periodic motion of Dao et al. with an object in periodic motion comprising of a fetal heart, as taught by Kasahara et al., as the substitution of one known object in periodic motion for another yields predictable results (i.e. effective imaging of an anatomical object exhibiting periodic motion) to one of ordinary skill in the art. One of ordinary skill in the art would have been able to carry out such a substitution and the results are reasonably predictable.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dao et al. as applied to claim 8 above, and further in view of Florin et al. (US Pub No. 2006/0251307).
With regards to claim 11, as discussed above, Dao et al. meet the limitations of claim 8. Further, Dao et al. disclose that step vi) of the method comprises determining an average number of images in a period of the periodic motions, from the detected peaks (paragraphs [0042]-[0043], referring to phasing being performed by defining peaks/valleys of the curve defining different aspects of the cardiac cycle; paragraphs [0065], [0102]-[0103], referring to measurements can be averaged across every cardiac cycle of every relevant video).
However, though Dao et al. do disclose that the method comprises performing segmentation/edge detection to detect peaks in the periodic signal by detecting peaks of the correlations (paragraphs [0042]-[0043]), Dao et al. do not specifically disclose that detecting the peaks is performed by determining zero-crossings in a one-dimensional Laplacian domain of the correlations.
Florin et al. disclose that intensity peaks of a curve may be detected by using a zero-crossing of the Laplacian (paragraphs [0036]-[0041]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to substitute the technique for detecting peaks of Dao et al. with a technique for detecting peaks comprising of determining zero-crossings in a one-dimensional Laplacian domain of the correlations, as taught by Florin et al., as the substitution of one known technique for detecting peaks for another yields predictable results (i.e. provide effective peak detection) to one of ordinary skill in the art. One of ordinary skill in the art would have been able to carry out such a substitution and the results are reasonably predictable.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deo et al. as applied to claim 1 above, and further in view of Park et al. (US Pub No. 2009/0088640).
With regards to claim 16, as discussed above, Deo et al. meet the limitations of claim 1. However, Deo et al. do not specifically disclose that prior to step i), using a second machine learning model to classify each image in the sequence as either comprising an image of the anatomical feature or not comprising an image of the anatomical feature.
Park et al. disclose classifying with a first machine learn multi-class local structure classifier, each of a plurality of echocardiographic data sets associated with different search positions of a window on an image as a cardiac standard view and selecting, for each of the echocardiographic data sets, a first machine learnt local structure detector as a function of the classified cardiac standard view, different first local structure detectors being available for each of the cardiac standard views (paragraph [0008]). The instructions also include detecting, with the selected first local structure detector and for each echocardiographic data set, whether the echocardiographic data sets include local structure and ceasing processing of each of the echocardiographic data sets where the local structure is not detected (paragraph [0008], note that a machine learning model is used to determine whether the image comprising an image of the anatomical feature (i.e. local structure) or not).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the method of Deo et al. comprise that prior to step i), using a second machine learning model to classify each image in the sequence as either comprising an image of the anatomical feature or not comprising an image of the anatomical feature, as taught by Park et al., in order to cease processing of the data when the image does not comprise the anatomical feature (paragraph [0008]), thus avoiding performing unnecessary additional steps.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deo et al. as applied to claim 1 above, and further in view of Redmon et. al. (“You Only Look Once: Unfied, Real-time Object Detection”, 2016), as cited by Applicant.
With regards to claim 18, as discussed above, the above combined references meet the limitations of claim 1. However, Deo et al. do not specifically disclose that the first ML model comprises a You Only Look Once (YOLO) network.
Redmon et al. disclose YOLO, a new approach to object detection which uses a single network and thus can be optimized end-to-end di rectly on detection performance, thus outperforming other detection methods (Abstract; pg. 1, right column, 3rd-4th paragraphs; pg. 8, Section 6. Conclusion).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the first ML model comprising a YOLO network, as taught by Redmon, in order to provide a fast machine-learning model which outperforms other models and successfully provides object detection (Abstract).
Allowable Subject Matter
Claim 19 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: With regards to claim 19, the prior art does not teach or suggest computing a statistical confidence score based on a standard deviation of differences between consecutive peak locations in the correlations, wherein the confidence score indicates a quality of the determined period of the periodic motions, in combination with the other claimed elements.
Response to Arguments
Applicant's arguments filed May 21, 2026 have been fully considered but they are not persuasive.
With regards to Deo, Applicant argues that Deo does not disclose or suggest a single input sequence containing images of different view planes and parsing that sequence to cluster consecutive same-view images into clips as Deo’s input is a pre-organized collection of separate view planes ---not a mixed view sequence. Applicant asserts that because Deo starts with an echo study already organized as multiple separate videos and classifies/routes those pre-existing videos, rather than parsing a single continuous mixed-view image sequence into clusters of consecutive same-view frames, Deo does not disclose or suggest each and every feature of claim 1.
Examiner respectfully disagrees and points to paragraphs [0029]-[0031] of Deo, referring to analyzing >40000 echo studies, wherein each echo study consists of up to 70 videos collected from different viewpoints, wherein, by definition, a video comprises a recording of moving images, and therefore the videos (i.e. moving images) correspond to a sequence of US images corresponding to different view planes/viewpoints of the anatomical feature. The echo studies/echocardiograms comprising of images (i.e. moving “images” forming the videos) from different viewpoints/view planes are input into the CNN for automatically determining and classifying echo views. The input of the echo studies/echocardiograms images (i.e. moving images of the videos) therefore correspond to a sequence of US images corresponding to different view planes of the anatomical feature (i.e. a mixed view sequence). Examiner further notes that Deo does disclose that the input comprising of moving images of different view points (i.e. echo studies/echocardiograms) are essentially parsed into clusters of same-view frames via the view classification as described in paragraph [0033] of Deo. The claims therefore remain rejected under Deo.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KATHERINE L FERNANDEZ/Primary Examiner, Art Unit 3798
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