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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/13/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claims 1, 21 and 31 have been fully 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.
Butler, et al., US 20200305822 A1 which teaches a sequence of angiographic images (abstract) has been introduced to modify the multiple set of CT contrast images ([0027]) of Zaharchuk, et al., US 20190108634 A1. Butler, et al., US 20200305822 A1 also teaches the limitations requiring a frame rate exceeding a cardiac rate ([0033]).
As best understood from the claims, the second and third sequences of angiographic images comprise input and output training data, respectively, for the neural network (see Zaharchuk’s [0024]), and the first sequence (Zaharchuk’s pre-contrast and low-dose contrast images in [0038]-[0040], with respect to figs. 6, 7, and 8) comprises data to be implemented in the trained neural network, an output of which comprise the synthesized full-dose contrast agent images 606, 706, and 806 of figs. 6, 7, and 8, respectively.
With that said, modified Zaharchuk teaches all the limitations of claims 1, 21 and 31 as claimed and the claims stand rejected.
Withdrawn Claim Rejections - 35 USC § 112
Pursuant of Applicant’s responses filed 07/13/2026, the rejection of claims 1,4-21,23-31 and 33-48 under 35 U.S.C. 112(a) have been withdrawn.
Pursuant of Applicant’s responses filed 07/13/2026, the rejection of claim 48 under 35 U.S.C. 112(b) has been withdrawn.
Pursuant of Applicant’s responses filed 07/13/2026, the rejection of claims 41-44, and 46-47 are rejected under 35 U.S.C. 112(d) have been withdrawn.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 4-12, 15-17, 20-21, 23-24, 25-27, 28, 30-31, 33-38, 40-48 are rejected under 35 U.S.C. 103 as being unpatentable over Zaharchuk, et al., US 20190108634 A1 in view of Butler, W.E. US 20200305822 A1, Lenga, et al., US 20250201408 A1, and Bagherzadeh, et al., US 20210015438 A1.
Regarding claims 1, 21, and 31, Zaharchuk teaches a method (see abstract for the method for diagnostic imaging with reduced contrast agent dose uses a deep learning network (DLN) [114] that has been trained using zero-contrast [100] and low-contrast images as input to the DLN and full-contrast images [104] as reference ground truth images. Also see fig. 1) as it relates to claim 1; A system comprising: one or more memory devices; and at least one processor coupled to the one or more memory devices, the at least one processor ([0010] discloses an imaging device) as it relates to claim 21; A computer program product comprising one or more non-transitory computer readable media having instructions stored thereon, the instructions executable by at least one processor ([0036] discloses software and hardware for the implementation of deep networks), as it relates to claim 31 comprising:
providing, as an input to a machine learning model, via a processor, a first sequence ([0027] discloses multiple sets of images for CT) of angiographic images of a subject obtained using a first dose of a chemical contrast agent and/or x-ray radiation ([0037] discloses that the trained model (see [0024]) is tested by feeding the trained model with several pre-contrast and low-dose contrast images as inputs according to [0038]-[0040], with respect to figs. 6, 7, and 8).
the machine learning model having been trained ([0024] discloses training a convolutional neural network) using
(a) a second set of angiographic images obtained or simulated to have been obtained using a second dose of the chemical contrast agent and/or x-ray radiation and provided to the machine learning model as a training input ([0024] describes an inputting multi-contrast images, stating that “The input to the deep learning network is a zero-contrast dose image 100 and low-contrast dose image 102, while the output of the network is a synthesized prediction of a full-contrast dose image 116”. The images are taught to be angiographic images according to [0012] which states that the low contrast agent dose is preferably less than 10% of a full contrast agent dose. The diagnostic imaging may be angiography, fluoroscopy, computed tomography (CT), ultrasound, or magnetic resonance imaging. For example, performing diagnostic imaging may include performing magnetic resonance imaging where the full contrast agent dose is at most 0.1 mmol/kg Gadolinium MRI contrast. Furthermore, [0027] discloses that for CT there can be multiple set of images with different contrast visualization (possibly the same dosage but different visual appearance) by using multiple energy radiation. For CT, the ground-truth is a set of images acquired with 100% full-dose of CT contrast.), and
(b) a third set of angiographic images obtained using a third dose of the chemical contrast agent and/or x-ray radiation and provided to the machine learning model as a known output ([0024] states that “The input to the deep learning network is a zero-contrast dose image 100 and low-contrast dose image 102, while the output of the network is a synthesized prediction of a full-contrast dose image 116. During training, a reference full contrast image 104 is compared with the synthesized image 116 using a loss function to train the network using error backpropagation.”),
wherein the third dose is greater than the first and second doses (the full contrast image has higher dose compared to the low-dose [0025]); and
obtaining, from the machine learning model, via the processor, an output comprising a segmented angiographic image corresponding to an image of interest in the first sequence of angiographic images ([0011] states that “applying the low-contrast image and the zero-contrast image as input to a deep learning network (DLN) to generate as output of the DLN a synthesized full-dose contrast agent image of the subject; where the DLN has been trained by applying zero-contrast images and low-contrast images as input and full-contrast images as reference ground-truth images”. [0037] discloses testing of the trained model by feeding the trained model with several pre-contrast and low-dose contrast images as inputs and synthesized images comparable to full-dose segmented images as outputs according to [0038]-[0040], with respect to figs. 6, 7, and 8, with [0037] stating that demonstrating that the method can generate synthesized full-dose contrast-enhanced MM with consistent enhancement but using lower dose (e.g., 1/10 full dose)),
Zaharchuk fails to teach that the first and third sequences of angiographic images are obtained fluoroscopically at a frame rate exceeding the cardiac rate; and the second sequence of angiographic images are obtained or simulated to be obtained fluoroscopically at a frame rate exceeding the cardiac rate.
However, within the same field of endeavor, Butler teaches reconstructing cardiac frequency phenomena from a sequence of angiographic images, i.e., two-dimensional projection images acquired at faster than cardiac rate (greater than two-fold), and analyzed to provide a spatiotemporal reconstruction of moving vascular pulse waves according to that projection (see abstract), with [0033] indicating that For reconstructing a moving vascular pulse wave, raw data is acquired via a fluoroscopic angiogram imaging system at a rate higher than cardiac frequency (e.g., images may be acquired at a rate up to 30 Hz). In aspects, and according to the Nyqvist Sampling Theorem, images are acquired by the system at over twice as fast as the highest frequency component of the cardiac signal. Given an angiogram obtained at faster than cardiac rate, the images may be processed according to the techniques provided herein to generate a time varying spatial reconstruction of the cardiac frequency angiographic phenomena and hence teaching sequence of angiographic images of a subject obtained fluoroscopically at a frame rate exceeding the cardiac rate.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Zaharchuk, to provide a sequence of angiographic images of a subject obtained fluoroscopically at a frame rate exceeding the cardiac rate, as taught by Butler, allowing the spatiotemporal cardiac frequency phenomenon to be amplified and superimposed on the angiographic signal ([0077]).
Zaharchuk in view of Butler fails to teach wherein the first and third sequences of angiographic images are obtained using non-zero doses of the contrast agent, and wherein the second sequence of angiographic images are obtained or simulated to be obtained using a non-zero dose of the contrast agent.
However, within the same field of endeavor, Lenga teaches training a machine learning model and using the trained model to predict representations of an examination area after applications of different amounts of a contrast agent (see abstract), implemented for pluralities of CT images ([0069]-[0070]). [0011]-[0014] state that “The training method comprises: [0012] receiving training data, [0013] where the training data comprise representations .sup.TR.sub.1 to .sup.TR.sub.n of an examination region for a multiplicity of examination objects, where n is an integer greater than two, [0014] where each representation .sup.TR.sub.i represents the examination region after administration of an amount a.sub.i of a contrast agent, where i is an integer in the range from 1 to n, where the amounts a.sub.1 to a.sub.n form a sequence of preferably increasing amounts”. [0099] then states that “FIG. 1 shows, by way of example and in schematic form, four real-space representations of an examination region after administration of different amounts of a contrast agent…. The four representations .sup.TR.sub.1, .sup.TR.sub.2, .sup.TR.sub.3 and .sup.TR.sub.4 are lined up along an axis indicating the amount a of concentration agent that has been administered in each case to the examination object… The first amount a.sub.1 may be greater than or equal to zero, and the second amount a.sub.2, the third amount a.sub.3 and the fourth amount a.sub.4 in the present example are each greater than zero. In the present example, the second amount a.sub.2 is greater than the first amount a.sub.1, the third amount a.sub.3 is greater than the second amount a.sub.2 and the fourth amount a.sub.4 is greater than the third amount a.sub.3: 0≤a.sub.1<a.sub.2<a.sub.3<a.sub.4”, hence teaching “wherein the first and third sequences of angiographic images are obtained using non-zero doses of the contrast agent, and wherein the second sequence of angiographic images are obtained or simulated to be obtained using a non-zero dose of the contrast agent”.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Zaharchuk, as modified by Butler, wherein the first and third sequences of angiographic images are obtained using non-zero doses of the contrast agent, and wherein the second sequence of angiographic images are obtained or simulated to be obtained using a non-zero dose of the contrast agent, as taught by Lenga, to increase the accuracy of predicting the region of interest based on the amount of contrast in the region of interest ([0132]).
Zaharchuk in view of Butler and Lenga, fails to teach wherein the first sequence of angiographic images is provided to the machine learning model as a single vector.
However, within the same field of endeavor, Bagherzadah teaches a machine-learned model, such as a model trained with deep learning, that generates perfusion examination information from CT scans of the patient for decision support based on perfusion in medical imaging (see abstract). [0043] states that “In act 14, the image processor generates perfusion examination information. The CT data representing the contrast agent over time in the patient with or without other information (e.g., patient-specific information and/or values for injection protocol parameters) is input to a machine-learned model, which outputs the perfusion examination information in response to the input” and [0046] states that “the deep learning is to train a machine-learned model to estimate the myocardial perfusion or other perfusion examination information. For example, each sample input vector includes CT perfusion sequential images, patient-specific information, and a value or values for one or more injection protocol parameters” and hence teaching wherein the first sequence of angiographic images is provided to the machine learning model as a single vector.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Zaharchuk, as modified by Butler and Lenga, wherein the first sequence of angiographic images is provided to the machine learning model as a single vector, as taught by Bagherzadeh, as such modification allows for perfusion examination decision support using a shorter scan time and/or less dose ([0021]).
Regarding claim 4, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claims 1, 21 and 31, respectively above.
Zaharchuk further teaches wherein the first and second sequences of angiographic images each consist of an odd number of angiographic images ([0028] states that “Mask can be applied to calculate the average, max, min, percentile or the distribution to get better normalization performance to match different set of images. This step is performed because the transmit and receive gains used for 3 sequences of the 3 different scans are not guaranteed to be the same”).
Regarding claims 5, 23, and 33, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claims 4, 21 and 31, respectively above.
Zaharchuk teaches wherein the image of interest is in a middle of the first sequence of angiographic images ([0036] states that “In a clinical setting, the trained deep learning network is used to synthesize a full-dose image from zero-dose and low-dose images. The co-registered and normalized non-contrast (zero-dose) and low-dose images are loaded from DICOM files and input to the trained network. With efficient forward-passing, which takes around 0.1 sec per 512-by-512 image, the synthesized full-dose image is generated. The process is then conducted for each 2D slice to generate entire 3D volume and stored in a new DICOM folder for further evaluation”).
Regarding claims 6, 25, and 35, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claims 1, 21 and 31, respectively above.
Zaharchuk further teaches wherein the first sequence of angiographic images consists of a same number of angiographic images as the second sequence of angiographic images ([0024] states that “The images are pre-processed to perform image co-registration 106, to produce multi-contrast images 108, and data augmentation 110 to produce normalized multi-contrast image patches 112. Rigid or non-rigid co-registration may be used to adjust multiple image slices or volumes to match the pixels and voxels to each other. Since there can be arbitrary scaling differences between different volumes, normalization is used to match the intensity of each image/volume”).
Regarding claim 7, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claim 1 above.
Zaharchuk further teaches wherein the first and second sequences of angiographic images each consist of five angiographic images ([0050] states that “there are five images with five different sequences acquired. The training uses a reference ground truth T1w image acquired with full contrast agent dose. The images are pre-processed to co-register and normalize them to adjust for acquisition and scaling differences.”).
Regarding claims 8, 24, and 34, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claims 1, 21, and 31, respectively above.
Zaharchuk further teaches wherein the first sequence of angiographic images is one of a plurality of input sub-sequences, wherein each of the plurality of input sub-sequences was extracted from a different part of a larger sequence of angiographic images obtained using the first dose of the chemical contrast agent and/or x-ray radiation, and further comprising providing, via the at least one processor, the plurality of input sub-sequences to the machine learning model as inputs and obtaining, via the at least one processor, from the machine learning model, outputs comprising a plurality of segmented angiographic images corresponding to a plurality of angiographic images of interest in the plurality of input sub-sequences ([0037] states “In tests of the technique, the synthesized full-dose CE-MRI images contain consistent contrast uptake and similar enhancement. Detailed comparisons for several representative cases are discussed below, demonstrating that the method can generate synthesized full-dose contrast-enhanced MM with consistent enhancement but using lower dose (e.g., 1/10 full dose)” and then goes on to explain the testing using figs. 6-8 as examples of implementation of the process).
Regarding claim 9, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claim 1 above.
Zaharchuk further teaches wherein the second sequence of angiographic images is one of a plurality of training sub-sequences used to train the machine-learning model ([0030] states that “During training, this network implicitly learns the guided denoising of the noisy contrast uptake extracted from the difference signal between low-dose and non-contrast (zero-dose) images, which can be scaled to generate the contrast enhancement of a full-dose image”), and
wherein each of the plurality of training sub-sequences was extracted from a different part of a larger training sequence of angiographic images obtained using the second dose of the chemical contrast agent and/or x-ray radiation ([0024] states that “Since there can be arbitrary scaling differences between different volumes, normalization is used to match the intensity of each image/volume. Brain and anatomy masks are used optionally to extract the important regions of interests in each image/volume. Reference images 104 are also processed to perform co-registration and normalization 118.”).
Regarding claim 10, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claim 1 above.
Zaharchuk further teaches wherein each of the angiographic images in the first sequence of angiographic images is temporally contiguous with at least one other angiographic image in the first sequence, and wherein each of the angiographic images in the second sub- sequence of angiographic images is temporally contiguous with at least one other angiographic image in the second sequence ([0027]-[0028] sequences of temporal scans).
Regarding claim 11, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claim 1 above.
Zaharchuk further teaches wherein the first and second doses are the same ([0027] states that “For CT there can be multiple set of images with different contrast visualization (possibly the same dosage but different visual appearance) by using multiple energy radiation”).
Regarding claim 12, 28, and 38, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claims 1, 21, and 31, respectively above.
Zaharchuk further teaches wherein the first and second doses are less than a dose required to obtain diagnostically useful images from an x-ray fluoroscopic imaging device, and wherein the third dose is no less than the dose required to obtain diagnostically useful images from the x-ray fluoroscopic imaging device (see fig. 2 for the zero dose, low dose and full dose scans).
Regarding claims 15, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claim 1 above.
Zaharchuk further teaches wherein the second dose of chemical contrast agent and/or x-ray radiation is greater than the first dose of chemical contrast agent and/or x-ray radiation, and wherein the angiographic images in the second sequence are angiographic images that have been modified to simulate having been obtained using less than the second dose of chemical contrast agent and/or x-ray radiation ([0049] states that “if the training dataset has a sufficient number of paired scans with two distinct dose levels, the network can be trained to automatically generalized to improve the lower dose d.sub.low to the higher dose d.sub.high, where the higher dose level is a full dose or less, and the lower dose level is less than the high dose, i.e., 0≤d.sub.low<d.sub.high≤1”).
Regarding claims 16, 27, and 37, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claims 1, 21, and 31, respectively above.
Zaharchuk further teaches wherein the second dose of chemical contrast agent and/or x-ray radiation is greater than the first dose of chemical contrast agent and/or x-ray radiation, and wherein the second sequence of angiographic images have been modified to simulate having been obtained using less than the second dose of chemical contrast agent and/or x-ray radiation (see fig. 2 and [0036] states that “In a clinical setting, the trained deep learning network is used to synthesize a full-dose image from zero-dose and low-dose images. The co-registered and normalized non-contrast (zero-dose) and low-dose images are loaded from DICOM files and input to the trained network”).
Regarding claim 17, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claim 1 above.
Zaharchuk further teaches wherein the second sequence of angiographic images is modified by adding randomly generated noise to at least some pixels of the second sequence of angiographic images ([0030] states that “During training, this network implicitly learns the guided denoising of the noisy contrast uptake extracted from the difference signal between low-dose and non-contrast (zero-dose) images, which can be scaled to generate the contrast enhancement of a full-dose image”. The training is based on added noise).
Regarding claims 20, 30 and 40, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claims 1, 21, and 31, respectively above.
Zaharchuk further teaches displaying, via the processor, the segmented angiographic image corresponding to the image of interest on a display (see images in figs. 6-8).
Regarding claim 26, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claim 21 above.
Zaharchuk further teaches wherein the third dose of chemical contrast agent and/or x-ray radiation is greater than the second dose of chemical contrast agent and/or x-ray radiation(the full contrast image has higher dose compared to the low-dose and pre-contrast images [0025]).
Regarding claim 36, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claim 31 above.
Zaharchuk further teaches wherein the third dose of chemical contrast agent and/or x-ray radiation is greater than the second dose of chemical contrast agent and/or x-ray radiation(the full contrast image has higher dose compared to the low-dose and pre-contrast images [0025]).
Regarding claims 41-44 and 46-47, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claims 1, 21, and 31, respectively above.
Zaharchuk fails to teach wherein the plurality of angiographic images in the second sequence of angiographic images are fluoroscopically obtained; and wherein the plurality of angiographic images in the second sequence of angiographic images are based on one or more images that are fluoroscopically obtained.
However, Butler further teaches wherein the plurality of angiographic images in the second sequence of angiographic images are fluoroscopically obtained; and wherein the plurality of angiographic images in the second sequence of angiographic images are based on one or more images that are fluoroscopically obtained ([0034] discloses that referring first to FIGS. 1A and 1B, a rotational x-ray system 28 is illustrated that may be employed for obtaining an angiogram at faster than cardiac rate, such as via fluoroscopic angiography. Also see [0033]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Zaharchuk, wherein the plurality of angiographic images in the second sequence of angiographic images are fluoroscopically obtained; and wherein the plurality of angiographic images in the second sequence of angiographic images are based on one or more images that are fluoroscopically obtained, as taught by Butler, allowing the spatiotemporal cardiac frequency phenomenon to be amplified and superimposed on the angiographic signal ([0077]).
Regarding claims 45 and 48, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claims 21, and 31, respectively above.
Zaharchuk fails to teach wherein each of the angiographic images in the first sequence of angiographic images is temporally contiguous with at least one other angiographic image in the first sequence, and wherein each of the angiographic images in the second sequence of angiographic images is temporally contiguous with at least one other angiographic image in the second sequence; and wherein each of the angiographic images in the first sequence of angiographic images is temporally contiguous with at least one other angiographic image in the first sequence, and wherein each of the angiographic images in the second sequence of angiographic images is temporally contiguous with at least one other angiographic image in the second sub-sequence.
However, Butler further teaches wherein each of the angiographic images in the first sequence of angiographic images is temporally contiguous with at least one other angiographic image in the first sequence, and wherein each of the angiographic images in the second sequence of angiographic images is temporally contiguous with at least one other angiographic image in the second sequence; and wherein each of the angiographic images in the first sequence of angiographic images is temporally contiguous with at least one other angiographic image in the first sequence, and wherein each of the angiographic images in the second sequence of angiographic images is temporally contiguous with at least one other angiographic image in the second sub-sequence ([0070] states that “Time stamps T1, T3, T5, T7 and T9 are stored or otherwise correlated with the ultrasound images 1-5 in the image set 366, while time stamps T1, T4, T7, T10 and T13 are stored or otherwise correlated with IVUS images 1-5 in image set 370. The time stamps 374, 376 and 378 are stored in the corresponding memories 380, 382 and 384 with associated images 356, 358 and 360, respectively, in a one-to-one relation…The fluoroscopy images 356 may be loaded directly into the memory 380 by way of example…The interpolator module 386 repeats this process to generate a number of ultrasound images equal in number to the number of fluoroscopy images 356”. This shows that the images are obtained serially so that the images are temporally contiguous with one another).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Zaharchuk, wherein the plurality of angiographic images in the second sequence of angiographic images are fluoroscopically obtained; and wherein the plurality of angiographic images in the second sequence of angiographic images are based on one or more images that are fluoroscopically obtained, as taught by Butler, to provide improved monitoring of the region of interest during diagnostic or interventional procedures ([0002]-[0004]).
Claims 13-14, 18-19, 29, and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Zaharchuk in view of Butler, Lenga, and Bagherzadeh, as applied to claim 21 above, and further in view of Erkamp, et al, US 20240029257 A1.
Regarding claims 13 and 14, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claim 21 above.
Zaharchuk in view of Butler, Lenga, and Bagherzadeh fails to teach wherein the angiographic images in the second sub-sequence are angiographic images of non-human vascular structures; wherein the angiographic images in the second sub-sequence are angiographic images of artificial vascular structures.
However, within the same field of endeavor, Erkamp teaches a computer-implemented method of locating a vascular constriction in a temporal sequence of angiographic images, includes identifying (S130), from a temporal sequence of differential images, temporal sequences of a subset of sub-regions (120.sub.i,j) of the vasculature wherein contrast agent enters the sub-region, and the contrast agent subsequently leaves the sub-region; and inputting (S140) the identified temporal sequences of the subset into a neural network (130) trained to classify, from temporal sequences of angiographic images of the vasculature, a sub-region (120.sub.i,j) of the vasculature as including a vascular constriction (140) (see abstract) wherein the angiographic images in the second sub-sequence are angiographic images of non-human vascular structures; wherein the angiographic images in the second sub-sequence are angiographic images of artificial vascular structures (FIG. 1 illustrates a temporal sequence of angiographic images 110 according to [0027]. The limitation is interpreted as an intended use of the machine learning model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Zaharchuk, as modified by Butler, Lenga, and Bagherzadeh, wherein the angiographic images in the second sub-sequence are angiographic images of non-human vascular structures; wherein the angiographic images in the second sub-sequence are angiographic images of artificial vascular structures, as taught by Erkamp, to allow more accurate diagnoses ([0003]-[0004]).
Regarding claim 18, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claim 17 above.
Zaharchuk in view of Butler, Lenga, and Bagherzadeh fails to teach wherein the randomly generated noise is added only to pixels of the second angiographic image that are determined to correspond to vessels.
However, Erkamp further teaches wherein the randomly generated noise is added only to pixels of the second angiographic image that are determined to correspond to vessels ([0065] states that “the images may be further processed by low-pass filtering in order to reduce the motion from e.g. cardiac and respiratory sources, patient movement, and noise.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Zaharchuk, as modified by Butler, Lenga and Bagherzadeh wherein the angiographic images in the second sub-sequence are angiographic images of non-human vascular structures; wherein the angiographic images in the second sub-sequence are angiographic images of artificial vascular structures, as taught by Erkamp, to allow more accurate diagnoses ([0003]-[0004]).
Regarding claims 19, 29, and 39, Zaharchuk in view of Butler, Lenga, and Bagherzadeh teaches all the limitations of claims 1, 21, and 31, respectively above.
Zaharchuk in view of Butler, Lenga, and Bagherzadeh does not teach wherein the third angiographic image is a segmented angiographic image, and wherein the output obtained from the machine learning model is an angiographic image that is a segmented version of the first target angiographic image.
However, Erkamp further teaches wherein the third angiographic image is a segmented angiographic image, and wherein the output obtained from the machine learning model is an angiographic image that is a segmented version of the first target angiographic image ([0043] states “The temporal sequence for the sub-region 120.sub.i,j illustrated in FIG. 5 would therefore form part of the subset that are identified in operation S130 and inputted into the neural network in operation S140. The entering, and the leaving of the contrast agent from the sub-region, and also the sub-regions themselves, may be detected by applying a threshold to the contrast agent detected in a branches of the segmented vasculature. By contrast, a temporal sequence (not illustrated) for a sub-region of the vasculature in which the contrast agent does not enter the sub-region, would not form part of the subset that are identified in operation S130, and would therefore not be inputted into the neural network in operation S140”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Zaharchuk, as modified by Butler, Butler, Bagherzadeh, wherein the third angiographic image is a segmented angiographic image, and wherein the output obtained from the machine learning model is an angiographic image that is a segmented version of the first target angiographic image, as taught by Erkamp, to allow more accurate diagnoses ([0003]-[0004]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Farouk A Bruce whose telephone number is (408)918-7603. The examiner can normally be reached Mon-Fri 8-5pm PST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christopher Koharski can be reached at (571) 272-7230. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/FAROUK A BRUCE/ Examiner, Art Unit 3797
Prosecution Insights