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
This action is in response to the amendment filed on 02/04/2026. Claims 4 and 32 have been amended, claims 13-15, 18-19, 22-31 and 33-45 have been cancelled, while claims 46-48 and have been added. Amendments have overcome the objection to claim 4, but fail to overcome the 103 rejections for claims 1-6, 9-12, 16 and 32. Claims 7-8, 17, 20-21 and 46-48 are objected to.
Response to Arguments
In response to applicant’s argument regarding Zhang failing to disclose mask image, argument is fully considered but is not persuasive. The office action does not rely on Zhang, but relies on Zhu who teaches the limitation [Zhu: 0006 “a set of mask images is received and a contrast image for the coronary region is received. The contrast image may be one of a sequence of contrast images”].
In response to applicant’s argument regarding Smirnov failing to disclose high frequency components, argument is fully considered but is not persuasive. Smirnov blends the high frequency component of both images. (Smirnov: Col. 23, Lines 37-51 “Blending circuit 516 blends pixel value 518 of the pixel of the high frequency component of downscaled warped image HF(N1), with pixel value 522 of a corresponding pixel of the high frequency component of downscaled image HF(N-1)2 using blend parameter 514 for the pixel (as defined by Equation 9) to generate a blended pixel value for a pixel of a high frequency component of downscaled fused image HF(N-1) passed onto upscaling/accumulator circuit 544. This process of determining blending parameter 514, upscaling by upscaling circuit 526 and per-pixel blending by blending circuit 516 is recursively repeated until a high frequency component of a first downscaled version of fused image HF(1),is generated at the output of blending circuit 516 and passed onto upscaling/accumulator circuit 544.”)(Smirnov: Col. 18, Lines 1-5 “circuit merges the high frequency component of the unscaled single color version (received from the SBS) and the enhanced version of the first downscaled version of the fused image (received from the LCE) to generate merged fused image data that is passed onto the sharpening circuit”). Claims 1-6, 9-12, 16 and 32 remain rejected in the application.
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.
Claims 1, 2, 3, 4, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (U.S. Patent Publication No. 2011/0033102), in view of Zhang et al. (U.S. Patent Publication No. 2008/0025588).
Regarding claim 1, Zhu discloses a method for medical image processing, comprising: obtaining a contrast image of an object and at least one mask image of the object (interpreted as acquiring a contrast enhanced X ray image and at least one pre contrast mask image of the same object, contrast image interpreted as image after contrast injection and mask image is interpreted as image acquired before contrast injection)[Zhu: 0006 “a set of mask images is received and a contrast image for the coronary region is received. The contrast image may be one of a sequence of contrast images”]; and generating a subtracted image by subtracting the plurality of target structures from the contrast image (interpreted as removing the (anatomical) components from the contrast image to create a subtracted image)[Zhu: 0005 “The predictions from multiple mask images are statistically fused to obtain the final estimation of the background layer of the contrast image, which can then be subtracted from the contrast image to generate the coronary vessel layer.”](teaches the same logic which is subtracting from the contrast image to generate a coronary vessel layer corresponding to subtract image), but fails to explicitly disclose extracting a plurality of target structures from the at least one mask image by using one or more preset processing algorithms, each of the plurality of target structures corresponding to a part of the contrast image.
However, Zhang discloses extracting a plurality of target structures from the at least one mask image by using one or more preset processing algorithms, each of the plurality of target structures corresponding to a part of the contrast image (interpreted as using an algorithm to remove target structures (anatomical components) from the contrast image)[Zhang: 0036 “Images 804 and 814 show background layers of images 802 and 812, respectively, including static objects such as bones. Images 806 and 816, show slow moving layers of images 802 and 812, respectively, including the diaphragm. Images 808 and 818 show fast moving layers of images 802 and 812, respectively, including the vessel tree.”] (teaches the explicit element of bones which corresponds to target structures, these are the target structures removed from the images).
Zhu and Zhang are both considered to be analogous to the claimed invention because they are in the same field of medical imaging. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Zhu to incorporate Zhang’s explicit teachings of extracting anatomical structures like bones. The motivation for such a combination would provide the benefit of eliminating multiple anatomical structure types.
Regarding claim 2, Zhu discloses the method of claim 1, wherein the extracting a plurality of target structures from the at least one mask image by using one or more preset processing algorithms includes: extracting a plurality of candidate target structures (interpreted from the specification as preliminary structures extracted from mask images) from the at least one mask image by processing the at least one mask image with the one or more preset processing algorithms (interpreted as running defined algorithms to extract components from the mask image)[Zhu: 0025 “background motion is estimated between each of the mask images and the back ground region of the contrast image. As described above, the background region S2 of the contrast image is obtained by excluding the detected vessel regions S2 from the original contrast image. A dense motion field is calculated between each mask image and the background region of the contrast image resulting in multiple motion field estimates. The covariances of the displacement vectors of the motion field estimates are also calculated. According to an advantageous embodiment, the Lucas-Kanade-Fusion (LKF) algorithm is used to estimate a dense motion field V(x) between each mask image and the background region of the contrast image.”](teaches the same logic, which is using a defined algorithm to extract background motion (corresponding to the candidate target structures) from the mask images); and determining the plurality of target structures based on the plurality of candidate target structures [Zhu: 0006 “Multiple background layer predictions are generated by generating a background layer prediction from each mask image based on the calculated motion field and covariances.”](teaches that the motion field (corresponding to candidate target structures) are used to generate multiple background predictions, one per mask (corresponding to target structures) and since its one per mask, it determines the amount of target structures based on amount of candidate target structures).
Regarding claim 3, Zhu discloses the method of claim 2, wherein the extracting a plurality of candidate target structures from the at least one mask image by processing the at least one mask image with the one or more preset processing algorithms includes: processing each of the at least one mask image with a different preset processing algorithm from the one or more preset processing algorithms to extract the plurality of candidate target structures (interpreted as the ability to complete previous steps but with a different defined algorithm than the one previously used) [Zhu: 0021 “The PBT algorithm learns a binary decision tree, where each node of the tree is a binary classifier by itself and is learned using the Adaboost algorithm.”][Zhu: 0022 “the objects may be detected automatically using learning-based detection methods.”][Zhu: 0025 “The LKF algorithm combines the Lucas-Kanade algorithm, which iteratively estimates incremental motion”](Zhu teaches multiple different algorithms, and the LKF algorithm that is used for the mask images is a combination of 2 different algorithms, so it is inherent that that the process can be completed with different algorithms. Furthermore, using a different algorithm that is different from the first that was used is a broad and an overly obvious limitation).
Regarding claim 4, Zhu discloses the method of claim 2, wherein the determining the plurality of target structures based on the plurality of candidate target structures includes:
determining, based on the contrast image, a plurality of structure templates (interpreted as regions derived from the contrast image like vessel regions and the background region) corresponding to the plurality of target structures by using the one or more preset processing algorithms [Zhu: 0021 “vessel regions in the contrast image are detected using learning-based vessel segment detection”](teaches that from the contrast image and using a specified algorithm, Zhu determines vessel regions corresponding to structure templates); and determining, based on similarities between the plurality of candidate target structures and the plurality of structure templates, the plurality of target structures, each of the plurality of target structures corresponding to one of the plurality of structure templates (interpreted as use the amount of candidate target structures and the amount of structure templates to determine the amount of target structures and each of the target structures is the same amount to the structure templates)[Zhu: 0025 “background motion is estimated between each of the mask images and the background region of the contrast image”][Zhu: 0026 “at step 110, a background layer prediction is generated for each mask image based on the motion estimate and covariance calculated for each mask image. This results in multiple predictions for the background layer of the contrast image, each background layer prediction corresponding to one of the mask images.”](teaches using the contrast derived background region template when generating per mask background predictions from the candidate motion fields, yielding a background layer for each one of the mask images meaning the amount of mask images determines the amount of background layer prediction).
Regarding claim 10, Zhu discloses the method of claim 1, but fails to explicitly disclose wherein the plurality of target structures include at least one movement structure and at least one non-movement structure; or the plurality of target structures include a plurality of movement structures of different movement types.
However, Zhang discloses wherein the plurality of target structures include at least one movement structure and at least one non-movement structure; or the plurality of target structures include a plurality of movement structures of different movement types [Zhang: 0020 “A fluoroscopic image 100 can be separated into three layers based on the relative motion of the layers (fast, slow, static). Since the motion of the heart 102 is faster than that of the lungs 104, the heart 102 and coronary tree can be separated from the rest of the image 100 by the fast movement layer. Similarly, the lungs 104 can be separated from the rest of the image 100 by the slow movement layer. Static objects such as the spine 106, as well as background, can be separated”](teaches static (non-movement like the spine) background and moving layers and further teaches fast vs slow structures like the heart compared to the lungs movement).
Zhu and Zhang are both considered to be analogous to the claimed invention because they are in the same field of medical imaging. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Zhu to incorporate Zhang’s explicit teachings of utilizing different structures with different types of movements. The motivation for such a combination would provide the benefit of cleaner vessel visualization.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (U.S. Patent Publication No. 2011/0033102), in view of Zhang et al. (U.S. Patent Publication No. 2008/0025588), in further view of Kakrania et al. (U.S. Patent Publication No. 2020/0020106).
Regarding claim 5, Zhu and Zhang disclose the method of claim 4, wherein the determining, based on the plurality of candidate target structures and the plurality of structure templates, the plurality of target structures, each of the plurality of target structures corresponding to one of the plurality of structure templates includes: (interpreted as use the amount of candidate target structures and the amount of structure templates to determine the amount of target structures and each of the target structures is the same amount to the structure templates)[Zhu: 0025 “background motion is estimated between each of the mask images and the background region of the contrast image”][Zhu: 0026 “at step 110, a background layer prediction is generated for each mask image based on the motion estimate and covariance calculated for each mask image. This results in multiple predictions for the background layer of the contrast image, each background layer prediction corresponding to one of the mask images.”](teaches using the contrast derived background region template when generating per mask background predictions from the candidate motion fields, yielding a background layer for each one of the mask images meaning the amount of mask images determines the amount of background layer prediction), generating a combined candidate target structure by combining the at least two candidate target structures (interpreted as fuse the selected candidates into one combined candidate for that template) [Zhu: 0006 “The multiple background layer estimates are combined using statistical fusion to generate a final estimated background layer.”](teaches the same logic of combining multiple candidates via statistical fusion) and designating the combined candidate target structure as a target structure corresponding to the structure template (interpreted as treat the fused result as the templates target structure)[Zhu: 0006 “The final estimated background layer is Subtracted from the contrast image to extract a coronary vessel layer for the contrast image.”](teaches that the fused background is the designated target structure for the background template and is used downstream for subtraction), but fail to explicitly disclose for each of the plurality of structure templates: selecting, from the plurality of candidate target structures, at least two candidate target structures corresponding to a structure template.
However, Kakrania discloses includes: for each of the plurality of structure templates: selecting, from the plurality of candidate target structures at least two candidate target structures corresponding to a structure template (interpreted as the reference pattern from the specification) (Kakrania: 802-806; Fig. 8 “SELECT TEMPLATE IMAGE ( S ) MOST SIMILAR TO TARGET IMAGE”, “PERFORM WARPING ON RETRIEVED REGISTRATIONS AND ONLINE REGISTRATIONS TO DERIVE CANDIDATE SEGMENTATIONS”, “PERFORM LABEL FUSION TO FORM INITIAL SEGMENTATION”)(teaches selecting template image which corresponds to selecting structure template).
Zhu, Zhang, and Kakrania are considered to be analogous to the claimed invention because they are in the same field of medical imaging. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Zhu and Zhang to incorporate Kakrania’s teachings of selecting structure templates. The motivation for such a combination would provide the benefit of eliminating multiple anatomical structure types.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (U.S. Patent Publication No. 2011/0033102), in view of Zhang et al. (U.S. Patent Publication No. 2008/0025588), in view of Kakrania et al. (U.S. Patent Publication No. 2020/0020106), in further view of Smirnov et al. (U.S. Patent No. 11,798,146).
Regarding claim 6, Zhu, Zhang, and Kakrania disclose the method of claim 5, but fail to explicitly disclose wherein the generating a combined target structure by combining the at least two candidate target structures includes: obtaining one or more high-frequency components and one or more low-frequency components of each of the at least two candidate target structures and generating the combined candidate target structure by fusing a low-frequency component of one of the at least two candidate target structures and the high-frequency components of the at least two candidate target structures.
However, Smirnov discloses wherein the generating a combined target structure by combining the at least two candidate target structures includes: obtaining one or more high-frequency components and one or more low-frequency components of each of the at least two candidate target structures (interpreted as decompose each selected candidate structure into frequency bands, high frequency corresponds to edges/details and low frequency corresponds to coarse/base content) (Smirnov: Col. 17, Lines 52-58 “The SBS circuit performs sub-band splitting of processed unscaled single color version 446 to generate a high frequency component of the unscaled single color version passed onto the SBM circuit. The SBS circuit also performs sub-band splitting of processed first downscaled version 448 to generate a low frequency component of first downscaled version passed onto The LTM circuit.”)(Smirnov: Col. 20, Lines 19-31 “receives low frequency components of the downscaled multi-color warped images LF(1)1, LF(2)1,.., LF(N)1 as part of warped image pyramid 436 (obtained by warping each stage of the second image pyramid 426), where N represents levels of downsampling performed on the stage of the warped image pyramid 430, e.g., for an image pyramid having seven stages 0 through 6, stage 0 would correspond to the unscaled single-color image of the pyramid, and N=6 represents 6 levels of downscaling. Multi-scale image fusion circuit 502 further receives low frequency components of the downscaled multi-color images LF(1)2, LF(2)2,..., LF(N)2 as part of the second image pyramid 428.”)(Smirnov: Col. 18, Lines 1-5 “high frequency component of the unscaled single color version (received from the SBS) and the enhanced version of the first downscaled version of the fused image (received from the LCE) to generate merged fused image data that is passed onto the sharpening circuit”)(teaches obtaining high and low frequency components for each of two inputs (warped image pyramid and the other image pyramid)); and generating the combined candidate target structure by fusing a low-frequency component of one of the at least two candidate target structures and the high-frequency components of the at least two candidate target structures (interpreted as build one combined result by taking low frequency from one selected candidate and high frequency from both candidates, fused) (Smirnov: Col. 23, Lines 37-51 “Blending circuit 516 blends pixel value 518 of the pixel of the high frequency component of downscaled warped image HF(N1), with pixel value 522 of a corresponding pixel of the high frequency component of downscaled image HF(N-1)2 using blend parameter 514 for the pixel (as defined by Equation 9) to generate a blended pixel value for a pixel of a high frequency component of downscaled fused image HF(N-1) passed onto upscaling/accumulator circuit 544. This process of determining blending parameter 514, upscaling by upscaling circuit 526 and per-pixel blending by blending circuit 516 is recursively repeated until a high frequency component of a first downscaled version of fused image HF(1),is generated at the output of blending circuit 516 and passed onto upscaling/accumulator circuit 544.”)(Smirnov: Col. 18, Lines 1-5 “circuit merges the high frequency component of the unscaled single color version (received from the SBS) and the enhanced version of the first downscaled version of the fused image (received from the LCE) to generate merged fused image data that is passed onto the sharpening circuit”)(teaches fusing high frequency from both inputs and combining with a low frequency component used as the base in reconstruction).
Zhu, Zhang, Kakrania, and Smirnov are considered to be analogous to the claimed invention because they are in the same field of image processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Zhu, Zhang, and Kakrania to incorporate Smirnov’s teachings of decomposing candidates into low and high frequency components. The motivation for such a combination would provide the benefit of predictable technical improvement, preserving edges while stabilizing background and noise.
Claim 16 is a method claim corresponding to claim 6 without any additional limitations. Thus, claim 16 is rejected for the same reasons as claim 6 above.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (U.S. Patent Publication No. 2011/0033102), in view of Zhang et al. (U.S. Patent Publication No. 2008/0025588), in view of Kakrania et al. (U.S. Patent Publication No. 2020/0020106), in further view of Tico et al. (U.S. Patent Publication No. 2014/0363087).
Regarding claim 9, Zhu, Zhang, and Kakrania disclose the method of claim 5, but fail to explicitly disclose wherein the generating a combined candidate target structure by combining the at least two candidate target structures includes: determining at least two similarities, each of the at least two similarities being a similarity between the structure template and one of the at least two candidate target structures; determining, based on the at least two similarities, at least two weights corresponding to the at least two candidate target structures; and determining the combined candidate target structure corresponding to the structure template by combining the plurality of corresponding candidate target structures based on the plurality of weights.
However, Tico discloses wherein the generating a combined candidate target structure by combining the at least two candidate target structures includes: determining at least two similarities, each of the at least two similarities being a similarity between the structure template and one of the at least two candidate target structures (interpreted as compute at least 2 similarity values, each compare a structure template to a candidate target structure) [Tico: 0028 “This may be done by comparing each pixel in a non-reference image with the corresponding pixel in the reference image (block 220) to determine the similarity between the two pixels.”](teaches computing similarities between images at the pixel level, here, the structure template corresponds to reference image); determining, based on the at least two similarities, at least two weights corresponding to the at least two candidate target structures (interpreted as converting those similarities into weights, one per candidate)[Tico: 0029 “the operation may calculate a weight function for each non-reference pixel (block 225). The weight function, in one embodiment, may have a value between 0 and 1.”][Tico: 0030 “the weight may be calculated by comparing each non-reference pixel to its corresponding pixel in the reference image. In an alternative embodiment, the weight can be calculated based on the pixel similarity value and the expected noise content at the specific exposure parameters.”](teaches deriving weights from similarity to the reference); and determining the combined candidate target structure corresponding to the structure template by combining the plurality of corresponding candidate target structures based on the plurality of weights (interpreted as forming the combined candidate by weight fusion)[Tico: 0007 “selecting a first pixel from the first image and determining a non-binary weight value for the first pixels corresponding pixel in the second image. The first pixel may then be combined with its corresponding pixel from the second image using the non-binary weight value to obtain a first fused pixel.”][Tico: 0040 “The calculated weight value may then be used to determine a value for a corresponding pixel in the output image (block 635).”][Tico: 0036 “Each pixel (x, y) in the non-reference images may then be compared to the corresponding pixel in the reference image (block 620) to calculate the weight function for each non-reference pixel (block 625). Once the weight is calculated, it can be compared to a pre-determined threshold to determine if it is likely that the pixel is a ghost (block 630).”](teaches combining candidates based on weight).
Zhu, Zhang, Kakrania, and Tico are considered to be analogous to the claimed invention because they are in the same field of image processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Zhu, Zhang, and Kakrania to incorporate Tico’s teachings of computing template to candidate similarities, derive weights from those similarities, and fuse the candidates using those weights. The motivation for such a combination would provide the benefit of predictable artifact reduction and improved subtraction quality.
Claims 11, 12, and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (U.S. Patent Publication No. 2011/0033102), in view of Zhang et al. (U.S. Patent Publication No. 2008/0025588), in further view of Kargar et al. (U.S. Patent Publication No. 2011/0026790).
Regarding claim 11, Zhu and Zhang disclose the method of claim 1, but fail to explicitly disclose further comprising: determining a heartbeat status of the object corresponding to each of a plurality of mask images and a heartbeat status of the object corresponding to the contrast image; and selecting, from the plurality of mask images, the at least one mask image corresponding to heartbeat status that is the same as or substantially similar to the heartbeat status of the object corresponding to the contrast image.
However, Kargar discloses further comprising: determining a heartbeat status of the object corresponding to each of a plurality of mask images and a heartbeat status of the object corresponding to the contrast image (interpreted as determine the cardiac phase of each mask image)(Kargar: Abstract “an image data Subtraction system receives an electrical signal representing a heart cycle electrical waveform during multiple heart cycles in the absence of a contrast agent. The system acquires data representing a second image set comprising a multiple temporally sequential individual contrast enhanced images of vessels of the portion of patient anatomy during the multiple heart cycles in the presence of a contrast agent. An image data processor automatically uses the electrical signal to identify temporally corresponding pairs of images comprising a mask image and a contrast enhanced image acquired Substantially at a same point within a heart cycle.”)[Kargar: 0015 “System 10 analyzes an acquired ECG signal by identifying PQRST waves for each heart cycle. ECG data for one or more typical heart cycles is acquired for corresponding acquired image frames. FIG. 4 heart cycle (PQRST wave) 403 (see also FIG. 2) is divided into n segments (segments n1, m2, n3. na . . . nX) and a corresponding image frame of each segment of the heart cycle is identified and used as a mask frame”](teaches deriving heart cycle phase from an ECG (PQRST segmentation), labels frames by segment, and pairs mask and contrast frames at the same point in the cycle); and selecting, from the plurality of mask images, the at least one mask image corresponding to heartbeat status that is the same as or substantially similar to the heartbeat status of the object corresponding to the contrast image (interpreted as select at least one mask frame whose cardiac phase matches or is substantially similar to the contrast images phase)(Kargar: Abstract “identify temporally corresponding pairs of images comprising a mask image and a contrast enhanced image acquired Substantially at a same point within a heart cycle”)[Kargar: 0014 “image data processor 29 identifies and selects candidate mask images. In step 312 following injection of a contrast agent in Step 309, imaging system 25 acquires data representing a second image set comprising multiple temporally sequential individual contrast enhanced images of vessels of the portion of patient anatomy during the at least one heart cycle in the presence of a contrast agent. Image data processor 29 in step 314, identifies and selects candidate contrast enhanced images tempo rally corresponding to the selected mask images to provide temporally corresponding pairs of images comprising a mask image and a contrast enhanced image acquired Substantially at a same point within a heart cycle”](teaches selecting mask/contrast frames that correspond to the same point in the heart cycle).
Zhu, Zhang, and Kargar are considered to be analogous to the claimed invention because they are in the same field of medical imaging. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Zhu and Zhang to incorporate Kargar’s teachings of determining heartbeat status for mask and contrast images and selecting contrast images with similar cardiac phase as the contrast image. The motivation for such a combination would provide the benefit of reducing motion-induced misregistration and subtraction artifacts, yielding clearer vessel depiction.
Regarding claim 12, Zhu and Zhang disclose the method of claim 11, but fails to explicitly disclose wherein the selecting, from the plurality of mask images, the at least one mask image corresponding to heartbeat status that is the same as or substantially similar to the heartbeat status of the object corresponding to the contrast image includes: determining image acquisition frequencies of the plurality of mask images and the contrast image and a cardiac cycle of the object; and selecting, from the plurality of mask images, based on the image acquisition frequencies of the plurality of mask images and the contrast image and a cardiac cycle of the object, the at least one mask image corresponding to heartbeat status that is the same as or substantially similar to the heartbeat status of the object corresponding to the contrast image.
However, Kargar discloses wherein the selecting, from the plurality of mask images, the at least one mask image corresponding to heartbeat status that is the same as or substantially similar to the heartbeat status of the object corresponding to the contrast image includes: determining image acquisition frequencies of the plurality of mask images and the contrast image and a cardiac cycle of the object (interpreted as determine when images were acquired for both mask and contrast sets)[Kargar: 0006 “FIG. 2 shows an electrical signal representing a heart cycle electrical waveform indicating temporally sequential image acquisition points in the cycle”][Kargar: 0015 “System 10 analyzes an acquired ECG signal by identifying PQRST waves for each heart cycle. ECG data for one or more typical heart cycles is acquired for corresponding acquired image frames. FIG. 4 heart cycle (PQRST wave) 403 (see also FIG. 2) is divided into n segments (segments n1, m2, n3. na . . . nX) and a corresponding image frame of each segment of the heart cycle is identified and used as a mask frame (mask images n1n, n2m, n3m, nam... nxm 405) for use in performing coronary vessel DSA imaging. In one embodiment, for example, mask”](teaches ECG analysis yields the cardiac cycle segmentation and establishing the acquisition timing/intervals for mask and contrast frames); and selecting, from the plurality of mask images, based on the image acquisition frequencies of the plurality of mask images and the contrast image and a cardiac cycle of the object, the at least one mask image corresponding to heartbeat status that is the same as or substantially similar to the heartbeat status of the object corresponding to the contrast image (interpreted as using the timing info and cardiac cycle to pick the mask frames whose heartbeat status matches or is very close to the contrast frames status) [Kargar: 0014 “image data processor 29 identifies and selects candidate mask images. In step 312 following injection of a contrast agent in Step 309, imaging system 25 acquires data representing a second image set comprising multiple temporally sequential individual contrast enhanced images of vessels of the portion of patient anatomy during the at least one heart cycle in the presence of a contrast agent. Image data processor 29 in step 314, identifies and selects candidate contrast enhanced images tempo rally corresponding to the selected mask images to provide temporally corresponding pairs of images comprising a mask image and a contrast enhanced image acquired Substantially at a same point within a heart cycle.”][Kargar: 0016 “System 10 analyzes the acquired ECG data obtained during the image acquisition time period and identifies PQRST waveform portions and identifies and marks (with tags) corresponding acquired image frames n1 I, n2I, n3I, naI . . . nXI 505 so that mask images and corresponding contrast agent images are identified for the same heart cycle segment. In one embodiment, system 10 synchronizes acquisition of images over the n segments in response to ECG signal data so the mask images and corresponding contrast agent images are acquired at the same corresponding points within a heart cycle.”](teaches selecting mask/contrast frames that correspond to the same point in the cardiac cycle, this selection is based on the known acquisition timing/intervals and the cardiac cycle segmentation derived from ECG).
Zhu, Zhang, and Kargar are considered to be analogous to the claimed invention because they are in the same field of medical imaging. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Zhu and Zhang to incorporate Kargar’s teachings of using image acquisition frequency and cardiac cycle information to select mask images. The motivation for such a combination would provide the benefit of reduced phase mismatch and motion artifacts, thereby improving subtraction fidelity.
Claim 32 is a method claim corresponding to claim 11 with generally the same functionality/logic and limitations (including the limitations of claim 1 which claim 11 depends on). Therefore, claim 32 is rejected for the same reasons as claim 11 above.
Allowable Subject Matter
Claims 7-8, 17, 20-21, and 46-48 are 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.
Conclusion
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 AHMED TAHA whose telephone number is (571)272-6805. The examiner can normally be reached 8:30 am - 5 pm, Mon - Fri. 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, XIAO WU can be reached at (571)272-7761. 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.
/AHMED TAHA/Examiner, Art Unit 2613
/XIAO M WU/Supervisory Patent Examiner, Art Unit 2613
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