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 .
Information Disclosure Statement
1. The information disclosure statement (IDS) submitted on 07/10/2025 has been considered by the examiner.
Claim Rejections - 35 USC § 102
2. 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.
3. 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.
4. Claim(s) 1-5 and 11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Guotao, CN111462020A (Publication Date – July 28, 2020) (as cited by applicant).
Regarding claim 1, Guotao discloses “A method for performing motion artifact correction in medical images, the method comprising: receiving, with an electronic processor, a medical image associated with a patient, the medical image including at least one motion artifact (paras 0143-144 – receiving reconstructed image of the heart; para 0010 – obtaining reconstructed image of the heart);
applying to the medical image, with the electronic processor, a model developed using machine learning for correcting motion artifacts, the model including at least one of a spatial transformer network and an attention mechanism network (para 0011 – input the reconstructed heart image into the preset deep learning network; para 0013 – the deep learning network includes a non-rigid space transformation network or a rigid space transformation network; para 0145 – the image processing unit 520 inputs the heart reconstruction image to the preset deep learning network, to obtain the heart motion artifact correction image output by the deep learning network; para 0147 – the deep learning network includes a non-rigid space transformation network or a rigid space transformation network the non-rigid space transformation network or the rigid space transformation network is used to perform spatial transformation on the reconstruction heart image); and
generating, with the electronic processor, a new version of the medical image as an output by applying the model to the medical image, wherein the new version of the medical image at least partially corrects the at least one motion artifact (para 0145 – the image processing unit 520 inputs the heart reconstruction image to the preset deep learning network, to obtain the heart motion artifact correction image output by the deep learning network; para 0088 – the deep learning network can output the corrected images for cardiac motion artifacts – where the output corrected image is considered a new version of medical image).
Regarding claim 2, Guotao discloses “The method of claim 1, wherein receiving the medical image includes receiving a computed tomography (CT) medical image (paras 0064-0065, 0130-0131, 0140).
Regarding claim 3, Guotao discloses “The method of claim 2, wherein the CT medical image is a cardiac CT image.” (paras 0130-0131, 0140).
Regarding claim 4, Guotao discloses “The method of claim 1, wherein receiving the medical image includes receiving a medical image associated with a first motion artifact characteristic, wherein the new version of the medial image is associated with a second motion artifact characteristic different than the first motion artifact characteristic.” (para 0092 - The motion characteristics of the image are transformed – which being that before transformation the motion characteristics of the received image is different than the output image generated after transformation).
Regarding claim 5, Guotao discloses “The method of claim 4, wherein the first motion artifact characteristic and the second motion artifact characteristic are associated with delineation of a feature depicted in the medical image, wherein the second motion artifact characteristic is associated with an improved delineation of the feature in comparison to the first motion artifact characteristic” (para 0092 - The motion characteristics of the image are transformed – which being that before transformation the motion characteristics of the received image is different than the output image generated after transformation; paras 0110-0121, 0131 – cardiac coronary artery area segmentation).
Regarding claim 11, Guotao discloses “The method of claim 1, further comprising transmitting the new version of the medical image to a remote device for display” (para 0088 – the deep learning network can output the corrected images for cardiac motion artifacts; para 0070 – the processing engine 140 may process data and/or information obtained from the scanner 110 and store in the memory/storage 150; and the storage 150 may be implemented on a cloud platform; para 0070 – the data stored on memory 150 can be accessed through the network 120; para 0069 – the processing engine can include server groups that can be distributed, and may be implemented on cloud – in distributed platform the other remote device has access to the processed data).
Claim Rejections - 35 USC § 103
5. 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.
6. 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.
7. Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Guotao, CN111462020A (Publication Date – July 28, 2020), and further in view of Edic et al., U.S. Patent No. 11,918,393 B2.
Regarding claim 7, claim 7 recites “The method of claim 1, wherein applying the model includes applying a model that was developed using machine learning using pseudo single-source CT images”. Guotao as cited teaches training a deep learning network with target CT samples (paras 0130-0133), but does not specifically teach using pseudo single-source CT images for training purposes. However, Examiner here asserts that pseudo images are nothing but synthetic images that are well known to be used; and further cites Edic for evidentiary teachings. Edic teaches using pseudo single-source CT images for training purposes (col. 67, lines 2-21 – “The deep learning network may be trained with training data that includes: a dense view projection dataset and/or one or more training images generated from the dense view projection dataset, and a pseudo sparse view projection dataset generated from the dense view projection dataset and/or one more training images generated from the pseudo sparse view projection dataset. The pseudo sparse view projection dataset may be generated by discarding a plurality of views of the dense view projection dataset so that a total number of views, a spacing of views, a view angle range, and a field of view of the pseudo sparse view dataset matches the total number of views, the spacing of views, the view angle range, and the field of view of the sparse view projection dataset. In this way, the training data that is used to train the deep learning network may match the projection data obtained by the stationary CT system at least in terms of the number and spacing of views, which may enable the trained network to more accurately aid in high-quality image reconstruction using sparse view datasets.”). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to use pseudo single-source CT images for training purposes as taught by Edic in the invention of Guotao. A person having ordinary skill in the art would have been motivated before the effective filing date of the claimed invention to use pseudo single-source CT images for training purposes as taught by Edic in the invention of Guotao, helping overcome data scarcity and to more accurately aid in high-quality image reconstruction.
8. Claims 6, 8-10, and 12-19 are objected to as being dependent upon a rejected base claim 1, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
9. Claims 20-22 are allowed.
The following is an examiner’s statement of reasons of allowance:
None of the closest prior art(s) of record teach hybrid CNN comprising at least one attention mechanism network connected with at least one spatial transformer network as recited in claim 20. Therefore, claim 20 is allowed. All other claims depending on claim 20 are allowed at least by dependency on claim 20.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
10. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Krebs et al., U.S. Patent No. 2020/0311940, discloses a motion model trained using an unsupervised learning model to learn motion matrix in the medical images (para 0023).
Quan et al., U.S. Patent Publication No. 2021/0158492 A1, discloses motion correction in medical imaging.
NG et al, U.S. Patent Publication No. 2022/0005190, discloses method of identifying artifacts in medical images using machine learning models.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Manav Seth whose telephone number is (571) 272-7456. The examiner can normally be reached on Monday to Friday from 8:30 am to 5:00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Sumati Lefkowitz, can be reached on (571) 272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Manav Seth/
Primary Examiner, Art Unit 2672
August 30, 2026
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