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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 365(c) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. US 63346956, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. The named provisional application fails to provide explicit or implicit support for “wherein the deep learning network is trained on 4D flow training data from a 4D flow magnetic resonance imaging process” as claimed in independent Claims 1 & 11. Accordingly, the effective filing date of the claims is now that of PCT/US2023/066652, 5 May 2023.
Response to Amendment
The rejections under 35 U.S.C. 102(a)(1) & 35 U.S.C. 102 have been withdrawn in light of the amendment to the claims filed on 12 June 2026.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-12, & 14-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chitiboi et al. (US PGPUB 20220151567; hereinafter "Chitiboi").
With regards to Claim 1, Chitiboi discloses a computer-implemented method for non-invasive assessment of vascular 4D hemodynamics (predicting intracardiac blood flow based on a trained cycle gan; see Chitiboi FIG. 2 & ¶ [0025]), the method comprising:
receiving standard anatomic imaging data at a local network or cloud-based analysis platform (receive 4D MRI images, e.g. 4D flow MRI acquisition; see Chitiboi ¶ [0019-0020]);
identifying a vessel of interest from the received anatomic imaging data (digital image is often composed of digital representations of one or more objects (or shapes); extracting blood flow in the aorta using known intracardiac blood flow extraction techniques; see Chitiboi ¶ [0016 & 0020]; it should be appreciated that one of ordinary skill in the art would recognize that the shape of the aorta is utilized when calculating blood flow in the aorta because the cross-sectional area is required);
deriving hemodynamic features from the vessel of interest from the received anatomic imaging data using deep learning by inputting the received anatomic imaging data into a deep learning network (determine predicted myocardial flow data {i.e. hemodynamic feature} from cycle GAN; see Chitiboi ¶ [0023-0024]), wherein the deep learning network is trained on 4D flow training data from a 4D flow magnetic resonance imaging process (training the CycleGAN based on both generated synthetic intracardiac blood flow data and real intracardiac blood flow data; see Chitiboi ¶ [0024]; wherein the real intracardiac blood flow data is derived from 4D flow MRI acquisition methods; see Chitiboi ¶ [0020]); and
calculating 4D hemodynamic parameters and generating output data based on the 4D hemodynamic parameters that are calculated from the hemodynamic features derived from the vessel of interest by the deep learning network (the CycleGAN, trained via 4D flow MRI, used in step 106 to predict intracardiac blood flow data {i.e. predict 4D hemodynamic features} which may be depicted as dynamic flow visualizations, such as, e.g., velocity vectors, particle traces, stream lines, path lines, or streak lines {i.e. calculated 4D hemodynamic parameters}; see Chitiboi ¶ [0026 & 0072]; wherein the cited depiction require post-processing to convert the predicted flow data into said dynamic visualization).
Claim 11 recites similar limitations and are rejected under the same rationale as Claim 1 with the addition of the processor 604.
With regards to Claim 21, wherein identifying the vessel of interest comprises pre-processing the anatomic imaging data that is received (digital image is often composed of digital representations of one or more objects (or shapes); extracting blood flow in the aorta using known intracardiac blood flow extraction techniques; see Chitiboi ¶ [0016 & 0020]; it should be appreciated that identifying shapes amounts to pre-processing).
Claim 12 recites similar limitations and are rejected under the same rationale as Claim 2.
With regards to Claim 41, further comprising passing the received anatomic images to a deep learning network for performing 4D hemodynamic quantification and pre-processing of anatomic imaging data of the vessel of interest on the deep learning network (determine predicted myocardial flow data {i.e. hemodynamic quantification} from cycle GAN; see Chitiboi ¶ [0023-0024]; it is well known in the art that GANs, including CycleGAN, generally requires resizing and cropping1 in order to reduce GPU burden).
Claim 14 recites similar limitations and are rejected under the same rationale as Claim 4.
With regards to Claim 5. The method of claim 4, further comprising training the deep learning network using expert labeled datasets of previously obtained vascular imaging data (machine learning network is trained via supervised training {i.e. expert labelled dataset}; see Chitiboi ¶ [0034]).
Claim 15 recites similar limitations and are rejected under the same rationale as Claim 5.
With regards to Claim 61, further comprising deriving spatially and temporally resolved 3D blood flow velocities in the vessel of interest from the received anatomic imaging data using deep learning by inputting the received anatomic imaging data into a deep learning network (determine predicted myocardial flow data {i.e. flow velocities} from cycle GAN, and that the input medical imaging data may already include 3D vector data representing velocities; see Chitiboi ¶ [0019 & 0023-0024]; it should be appreciated that flow data requires velocity to calculate).
Claim 16 recites similar limitations and are rejected under the same rationale as Claim 6.
With regards to Claim 71, wherein calculating 4D hemodynamic parameters is performed on a deep learning network from anatomic imaging data (determine predicted myocardial flow data {i.e. flow velocities} from cycle GAN; see Chitiboi ¶ [0023-0024]).
Claim 17 recites similar limitations and are rejected under the same rationale as Claim 7.
With regards to Claim 87, wherein the deep learning network is trained using expert-analyzed 4D flow MRI data as ground truth data (machine learning network is trained via supervised training {i.e. expert analyzed dataset}; see Chitiboi ¶ [0034]; it should be appreciated that one of ordinary skill in the art would recognize that if the input medical imaging data is 4D MRI flow data, then the corresponding trained CycleGan is trained on corresponding labelled 4D MRI flow data {see footnote [1]}).
Claim 18 recites similar limitations and are rejected under the same rationale as Claim 8.
With regards to Claim 91, further comprising displaying the output data that is generated on a device selected from the group consisting of an image viewer (FIG. 3 shows an exemplary user interface 300 {i.e. image viewer & GUI} depicting predicted myocardium strain data of the heart and predicted intracardiac blood flow data of the heart overlaid on a 2D image data that represents a cross-section of the heart; see Chitiboi ¶ [0026]), a picture archiving and communication system (loading a previously acquired input medical imaging data from a storage or memory of a computer system or receiving the input medical imaging data from a remote computer system {i.e. PACS}; see Chitiboi ¶ [0019]), and a graphical user interface (FIG. 3 shows an exemplary user interface 300 {i.e. image viewer & GUI} depicting predicted myocardium strain data of the heart and predicted intracardiac blood flow data of the heart overlaid on a 2D image data that represents a cross-section of the heart; see Chitiboi ¶ [0026]).
Claim 19 recites similar limitations and are rejected under the same rationale as Claim 9.
With regards to Claim 109, wherein the graphical user interface is configured to facilitate at least one of quantitative interrogation (user interface 300 may include a custom lens tool to enable a user to interactively {i.e. quantitative interrogation} inspect predicted myocardium strain data 302 and predicted intracardiac blood flow data 304 in user selected segments of the myocardium; see Chitiboi ¶ [0026]), (claimed in the alternative).
Claim 20 recites similar limitations and are rejected under the same rationale as Claim 10.
Claim Rejections - 35 USC § 103
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3 & 13 are rejected under 35 U.S.C. 103 as being unpatentable over Chitiboi.
With regards to Claim 31, while Chitiboi discloses extracting flow data from the aorta and of a trained machine learning based segmentation network (see Chitiboi ¶ [0020 & 0032]), it appears that Chitiboi may be silent to wherein identifying the vessel of interest comprises performing 3D segmentation of the vessel of interest. However, as detailed above, Chitiboi discloses that of the digital representation of one or more objects or shapes (see Chitiboi ¶ [0016]). One of ordinary skill in the art would readily recognize that object or shape recognition in 4D MRI medical images notoriously relies on a segmentation step2 to isolate the object prior to classification. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chitiboi to provide at least 3D segmentation of the vessel of interest. Doing so would amount to combining prior art elements according to known methods to yield predictable results, i.e. machine learning based segmentation network and extracting flow data from the aorta.
Claim 13 recites similar limitations and are rejected under the same rationale as Claim 3.
Response to Arguments
Applicant's arguments filed 12 June 2026 have been fully considered but they are not persuasive.
In particular, Applicant contends that Chitiboi does not anticipate the newly amended independent Claim 1 because “Chitiboi is not understood to disclose "the deep learning network is trained on 4D flow training data from a 4D flow magnetic resonance imaging process.” Applicant goes on to describe written description support for such a limitation without explicitly arguing why Chitiboi cannot teach of such a limitation. Regardless, the Office respectfully disagrees. As detailed above in the rejection under 35 U.S.C. 102(a)(1), Chitiboi discloses that training the CycleGAN based on both generated synthetic intracardiac blood flow data and real intracardiac blood flow data in which “Discriminator Df 206 aims to distinguish between synthesized intracardiac blood flow data 210 (generated by Generator Gf 204) and real intracardiac blood flow data (not shown)” (see Chitiboi ¶ [0024]). Chitiboi goes on to disclose that extracted intracardiac blood flow data of the heart is determined from the input medical imaging data and that said intracardiac blood flow data is derived from 4D flow MRI acquisition methods (see Chitiboi ¶ [0020]). One of ordinary skill in the art would recognize that the input medical imaging data from a 4D flow MRI acquisition yields the real intracardiac blood flow data used during the training of the CycleGAN. For at least this reason, Applicant’s arguments are not persuasive.
With regards to independent Claim 11, applicant relies on the same rationale to argue that said independent claims distinguish from the cited prior art. Accordingly, said argument is not persuasive for at least the same reasons as Claim 1 as detailed above.
With regards to dependent claims, Applicant relies on the virtue of their dependency upon abovementioned independent claims to argue novelty. Accordingly, said argument is not persuasive for at least the same reasons as Claims 1 and 11 as detailed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ferdian et al. (“4DFlowNet: Super-Resolution 4D Flow MRI Using Deep Learning and Computational Fluid Dynamics4D Flow MRI Using Deep Learning and Computational Fluid Dynamics, (3 May 2020), Front. Phys., 03 May 2020.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHISH S. JASANI whose telephone number is (571)272-6402. The examiner can normally be reached M-F 8:00 am - 4:00 pm (CST).
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, Keith M. Raymond can be reached on (571) 270-1790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASHISH S. JASANI/Examiner, Art Unit 3798
/KEITH RAYMOND/ Supervisory Patent Examiner, Art Unit 3798
1 https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/a44f3f3a711578d1486f136596e0ecff8b4a56a8/docs/tips.md (19 December 2020) - see preprocessing section.
2 https://en.wikipedia.org/w/index.php?title=Object_detection&oldid=1068810325 (30 January 2022)
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