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 .
Claim Rejections - 35 USC § 102
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.
Claims 1, 8, and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sandino et al. (USPAPN 2022/0375141).
Regarding claim 1, Sandino discloses:
providing a zero-filled image to a dual-branch image reconstruction network having a transformer branch and a convolutional neural network (CNN) branch, the zero-filled image having been generated using zero-filled k-space data (see para [28], [30], and fig 1C-2A, image space data, generated via zero-filing k-space imaging data, is provided to a network of two branches Lb and Rb, each branch including a CNN);
generating, using the CNN branch and based on the zero-filled image, a set of CNN output features (see para [30] and fig 2B, the CNN included in the Lb branch inherently includes a convolutional layer and pooling layer that generate Lb features from the image space data);
partitioning the zero-filled image to form a partitioned image (see para [30] and fig 2B, partitioning the image space data into blocks of image space data 206);
generating, using the transformer branch and based on the partitioned image, a set of transformer output features (see para [30] and fig 2A, the CNN included in the Rb branch inherently includes a convolutional layer and pooling layer that generate Rb features from the blocks of image space data);
fusing the set of transformer output features with the set of CNN features to form a fused output; and generating a reconstructed image from the fused output (see para [30] and fig 2A, combining the results of the two branches Lb and Rb to form a reconstructed output image).
Regarding claims 8 and 15, Sandino discloses everything claimed as applied above (see rejection of claim 1).
Allowable Subject Matter
Claims 2-7, 9-14, and 16-20 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. The prior art of record does not disclose the subject matter recited in any of these claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Each of Zhou et al. (USPAPN 2024/0290011), Kamilov et al. (USPAPN 2023/0122658), Zhu et al. (USPAPN 2022/0299588), and Sandino et al. (USPN 10,712,416) discloses generating a reconstructed MRI using k-space zero-filling employing algorithms with multiple branches.
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/SJ Park/Primary Examiner, Art Unit 2675