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 .
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Response to Amendment
The amendment filed on August 21, 2026 has been entered.
The amendment of claims1, 4, 6, 14, 17, 18, and 20 has been acknowledged.
In view of the amendment, the 35 U.S.C. 112(b) rejections have been withdrawn.
Response to Arguments
Applicant's arguments filed on August 21, 2026, with respect to the 35 U.S.C. 112(b) rejections, have been fully considered and are persuasive. The rejections have been withdrawn.
Applicant’s arguments with respect to the amended claims have been fully considered but are moot because the arguments rely on newly added and/or amended claim limitations (e.g., “form an enhanced first series of intravascular images”). The examiner has revised the rejections to match the new claim limitations.
Claim Rejections - 35 USC § 103
Claim(s) 1-8 and 14-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Olender et al. (US 2023/0076868 A1), in view of Tu et al. (“Fusion of 3D QCA and IVUS/OCT,” Int J Cardiovasc Imaging (2011) 27:197–207), hereinafter referred to as Olender and Tu, respectively.
Regarding claim 1, Olender teaches an apparatus for an intravascular ultrasound (IVUS) imaging system, comprising a memory and a processor coupled to the memory and configured to couple to an IVUS probe, the memory comprising instructions executable by the processor, which instructions when executed by the processor cause the processor (Olender Title: “Systems and methods …”) to:
receive a first series of intravascular images of a vessel of a patient, the first series of intravascular images of a first imaging modality (Olender ¶¶0060: “one or more classification maps 1402 each correspond to an acquired image of a physical segment acquired using a first imaging modality”; Olender Fig. 14: 1402);
generate, from the first series of intravascular images, image features of a second imaging modality (Olender ¶¶0060: “The classification map(s) 1402 are input to the trained generative network 1406”; Olender ¶¶0061: “The trained generative network 1406 (including generator 1408) generates one synthetic image 1410 associated with the second imaging modality based on each classification map 1402 (associated with the first imaging modality), i.e., the generative network 1406 converts the classification map 1402 from the first imaging modality to the second imaging modality. For example, the generator 1408 of generative network 1406 may receive a classification map 1402 associated with IVUS and generate a synthetic image 1410 associated with OCT. Each synthetic image 1410 generated for each classification map 1402 should be of the same modality (e.g., the second imaging modality) and should be the same modality (e.g., the second imaging modality) of any acquired image(s) 1404 to which the synthetic images will be fused using the post-processing module 1412. For example, the synthetic image(s) 1410 and the acquired images 1404 may be OCT images (e.g., intravascular OCT images)”; Olender Fig. 14: 1406, 1408);
enhance the first series of intravascular images with the image features of the second imaging modality (Olender ¶¶0060: “The acquired images corresponding to the classification map(s) 1402 are to be fused with one or more acquired images 1404 of the same physical segment that are acquired with a second imaging modality that is different than the first imaging modality”; Olender ¶¶0061: “post-processing module 1412 merges the plurality of synthetic images 1410 and the acquired images 1404 into a single fused image 1414”; Olender Fig. 14: 1412); and
generate a graphical user interface comprising an indication of the enhanced first series of intravascular images (Olender ¶¶0061: “the fused image 1414 may also be provided to and displayed on a display 1416”; Olender Fig. 14: 1416).
Olender forms an enhanced image based on the features of the second imaging modality (Olender ¶¶0060-¶¶0061 & Fig. 14 discussed above), but does not appear to explicitly teach that the enhanced image is a series of images.
Pertaining to the same field of endeavor, Tu teaches forming an enhanced first series of intravascular images (Tu Abstract: “The combination/fusion of quantitative coronary angiography (QCA) and intravascular ultrasound (IVUS)/optical coherence tomography (OCT) … the vessel of interest is reconstructed in 3D and registered with the corresponding IVUS/OCT pullback series by a distance mapping algorithm … the new XA-IVUS/OCT co-registration approach is a straightforward and reliable solution to combine X-ray angiography and IVUS/OCT imaging for the assessment of the extent of coronary artery disease … provides the interventional cardiologist with detailed information about vessel size and plaque size at every position along the vessel of interest, making this a suitable tool during the actual intervention”; Tu pg. 201 left column: “co-registration of XA images with IUS/OCT pullback series … indicating a baseline position in the vessel centerline that corresponds to the same axial position in IVUS/OCT”; Tu Fig. 3).
Olender and Tu are considered to be analogous art because they are directed to IVUS/OCT imaging of blood vessels. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the neural network systems and methods for utilizing synthetic medical images (as taught by Olender) to use generate an enhanced series of images (as taught by Tu) because the combination allows the images to be used during the actual intervention (Tu Abstract).
Regarding claim 2, Olender, in view of Tu, teaches the apparatus of claim 1, the instructions when executed by the processor further cause the processor to cause the graphical user interface to be displayed on a display coupled to the computing device (Olender Fig. 14: 1416 & ¶¶0061 and Tu Fig. 3 discussed above).
Regarding claim 3, Olender, in view of Tu, teaches the apparatus of claim 1, the instructions when executed by the processor further cause the processor to:
generate, via a machine learning (ML) model, a second series of intravascular images of the vessel of the patient, the second series of intravascular images of the second imaging modality (Olender Fig. 14: 1406, 1408 & ¶¶0060-¶¶0061 discussed above; Olender ¶¶0051: “The generated images and labels may then be fed as an input into a computational or numerical model (e.g., a machine learning model) to train, test, validate, and/or otherwise optimize the model or image-processing method/technique”); and
generate, via the ML model, the image features of the second imaging modality from the first series of intravascular images (Olender ¶¶0061: “A fused image 1414 may facilitate the interpretation of imaging data through consolidation of disparate datasets into a single coherent dataset … the fused image is generated using information such as the tissue characterization confidence of corresponding classification map(s) at each point and the selected classes with the highest degree of confidence are all from the same classification map”).
Regarding claim 4, Olender, in view of Tu, teaches the apparatus of claim 3, the instructions when executed by the processor further cause the processor to:
translate, via the ML model, the first series of intravascular images to the second imaging modality to form a series of translated intravascular images, wherein the series of translated intravascular image features correspond to image features of the second imaging modality (The term “translate” is not specifically defined or described in the claims or the description. Using the broadest reasonable interpretation, the term “translate” has been interpreted as convert, correlate, match, register, etc. (e.g., inter-, cross-modality registration/ matching/ correspondence of image features). Olender Fig. 14: 1406, 1408, 1410 & ¶¶0060-¶¶0061 discussed above); and
extract, via the ML model, features from the series of translated images (Olender Fig. 14: 1410 & ¶¶0061 discussed above).
Regarding claim 5, Olender, in view of Tu, teaches the apparatus of claim 4, the instructions when executed by the processor further cause the processor to generate, via the ML model, a series of hybrid intravascular images comprising the first series of intravascular images and the image features of the second imaging modality (Olender Fig. 14: 1414 & ¶¶0060-¶¶0061 discussed above).
Regarding claim 6, Olender, in view of Tu, teaches the apparatus of claim 5, wherein the ML model comprises a medical image generative network and auxiliary task networks, wherein the auxiliary task networks are arranged to preserve the geometry of extracted features (Olender ¶¶0061: “Before fusing the synthetic image(s) 1410 and the acquired images 1404, the post-processing module 1412 may be configured to align the synthetic image(s) 1410 and the acquired images 1404 using known methods for aligning images”; also see Olender ¶¶0038: the vessel inner & outer wall geometry and edges are determined).
Regarding claim 7, Olender, in view of Tu, teaches the apparatus of claim 6, wherein the ML model is trained using a plurality of series of intravascular images of the first modality paired or unpaired with a respective series of a plurality of series of intravascular images of the second modality (Olender ¶¶0060 discussed above).
Regarding claim 8, Olender, in view of Tu, teaches the apparatus of claim 7, wherein the medical image generative model is trained with non-adversarial loss from the auxiliary task network (Olender ¶¶0033: “the generative network 104 is trained using a conditional generative adversarial network (cGAN)”; Olender ¶¶0034: “calculating generator loss, and the number of training epochs … the adversarial aspect of the network may not be strictly necessary because loss functions directly penalizing deviation between generated and real images (e.g., L1 distance) may be sufficient to train a generator to produce relatively convincing, though sometimes somewhat blurry images”).
Regarding claim 14, Olender, in view of Tu, teaches at least one non-transitory machine readable storage device, comprising a plurality of instructions that in response to being executed by a processor of an intravascular ultrasound (IVUS) imaging system cause the processor to perform the processes described in claim 1 (Olender Fig. 16). Therefore, claim 14 is rejected using the same rationale as claim 1 discussed above.
Claim 15 is rejected using the same rationale as applied to claim 2 discussed above.
Claim 16 is rejected using the same rationale as applied to claim 3 discussed above.
Claim 17 is rejected using the same rationale as applied to claim 4 discussed above.
Regarding claim 18, Olender, in view of Tu, teaches a method for a computing device, comprising the processes described in claim 1 (Olender Title: “Systems and methods …”). Therefore, claim 18 is rejected using the same rationale as applied to claim 1 discussed above.
Claim 19 is rejected using the same rationale as applied to claim 2 discussed above.
Claim 20 is rejected using the same rationale as applied to claims 3-4 discussed above.
Claim(s) 9-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Olender et al. (US 2023/0076868 A1), in view of Zhou et al. (US 2019/0205606 A1), hereinafter referred to as Olender and Zhou, respectively.
Regarding claim 9, Olender, in view of Tu, teaches the apparatus of claim 3, wherein the ML model comprises a convolutional neural network (CNN) based encoder network and a first decoder network and a second decoder network (Olender ¶¶0037: “a trained classification model based on deep learning or other artificial intelligence methods (e.g., a neural network, a convolutional neural network (CNN)”).
However, Olender does not appear to explicitly teach that the ML model comprises a CNN based encoder network and a first and second decoder network.
Pertaining to the same field of endeavor, Zhou teaches teach that the ML model comprises a CNN based encoder network and a first and second decoder network (Zhou ¶¶0073: “A DNN, such as a convolutional neural network (CNN), is trained to estimate action-values for the various actions that best match the rewards over the set of training samples”; Zhou Fig. 10 & ¶¶0090: “a deep image-to-image network (DI2IN) is trained based on the multi-scale ground truths generated for the training images … a common encoder 1004 is shared across all output scales, while multiple decoders 1006a, 1006b, and 1006c are used”).
Olender, in view of Tu, and Zhou are considered to be analogous art because they are directed to machine learning models for medical image processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the neural network systems and methods for utilizing synthetic medical images (as taught by Olender, in view of Tu) to use encoder-decoder CNNs (as taught by Zhou) because the combination enables the estimation of the multi-scale ground truth probabilities (Zhou ¶¶0090).
Regarding claim 10, Olender, in view of Tu and Zhou, teaches the apparatus of claim 9, wherein the CNN based encoder network is arranged to translate a series of intravascular images of the first imaging modality into a series of intravascular images of the second imaging modality and translate a series of intravascular images of the second imaging modality into a series of intravascular images of the first imaging modality (Olender Fig. 14 & ¶¶0060-¶¶0061 discussed above).
Regarding claim 11, Olender, in view of Tu and Zhou, teaches the apparatus of claim 10, wherein the first decoder network is arranged to extract features from the series of intravascular images translated from the first imaging modality (Olender Fig. 14 , ¶¶0038, ¶¶0060-¶¶0061; Zhou Fig. 10 discussed above; also see Zhou ¶¶0076: “the trained DNN may be trained by training a convolutional neural network to extract an image feature vector with a predefined dimension”; Zhou Fig. 19 & ¶¶0140: “feature-level supervision transfer learning … domain A includes a large database of medical images for training and domain B includes smaller-sized database of medical images for training than domain A. CNN-A is a CNN trained from training medical images in domain A … The CNN-A network includes two parts: the convolutional layers that encode the input image into features, and the fully connected (FC) layers that convert the features for the final outcome (e.g., classification results, segment results, etc.). The goal of the feature-level supervision transfer learning is to train a second CNN, potentially of a smaller size than CNN-A, to perform a medical image processing task (e.g., classification, segmentation, etc.) for domain B, which possesses a small-size database of medical images”).
Regarding claim 12, Olender, in view of Tu and Zhou, teaches the apparatus of claim 11, wherein the second decoder network is arranged to extract features form the series of intravascular images translated from the second imaging modality (Olender Fig. 14, ¶¶0038, ¶¶0060-¶¶0061 & Zhou Figs. 10, 19, ¶¶0076, ¶¶0140 discussed above).
Regarding claim 13, Olender, in view of Tu and Zhou, teaches the apparatus of claim 9, wherein the ML model is trained with a plurality of series of intravascular images of the first modality paired or unpaired with a respective series of a plurality of series of intravascular images of the second modality annotated with ground truth masks (Olender Fig. 14, ¶¶0038, ¶¶0060-¶¶0061 & Zhou Figs. 10, 19, ¶¶0076, ¶¶0140 discussed above; also see Zhou ¶¶0044: “The master segmentation artificial agent 102 can be trained based on training data including medical images and known ground truth segmentation results for given segmentation tasks”; Zhou ¶¶0057: “training images and corresponding ground truth segmentations (segmentation masks) are obtained or generated”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the neural network systems and methods for utilizing synthetic medical images (as taught by Olender, in view of Tu) to use ground truth masks (as taught by Zhou) because the combination optimizes the neural network training and performance (Zhou ¶¶0058).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/903,046 and claims 1-20 of 19/338,753 (reference applications). Although the claims at issue are not identical, they are not patentably distinct from each other because they are directed to aligning medical image features (e.g., blood vessel features) from different imaging modalities (e.g., IVUS, OCT) using machine learning models.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOO J SHIN whose telephone number is (571)272-9753. The examiner can normally be reached M-F; 10-6.
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/Soo Shin/Primary Examiner, Art Unit 2667 571-272-9753
soo.shin@uspto.gov
