Prosecution Insights
Last updated: April 19, 2026
Application No. 18/628,342

DEEP LEARNING BASED ACCELERATED MRI RECONSTRUCTION USING MIXED CNN AND VISION TRANSFORMER

Non-Final OA §102
Filed
Apr 05, 2024
Examiner
PARK, SOO JIN
Art Unit
2675
Tech Center
2600 — Communications
Assignee
The Chinese University of Hong Kong
OA Round
1 (Non-Final)
82%
Grant Probability
Favorable
1-2
OA Rounds
2y 8m
To Grant
99%
With Interview

Examiner Intelligence

Grants 82% — above average
82%
Career Allow Rate
589 granted / 720 resolved
+19.8% vs TC avg
Strong +17% interview lift
Without
With
+17.3%
Interview Lift
resolved cases with interview
Typical timeline
2y 8m
Avg Prosecution
15 currently pending
Career history
735
Total Applications
across all art units

Statute-Specific Performance

§101
9.0%
-31.0% vs TC avg
§103
37.3%
-2.7% vs TC avg
§102
26.3%
-13.7% vs TC avg
§112
19.3%
-20.7% vs TC avg
Black line = Tech Center average estimate • Based on career data from 720 resolved cases

Office Action

§102
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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SJ PARK whose telephone number is (571)270-3569. The examiner can normally be reached M-F 8:00 AM - 5:00 PM. 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, ANDREW MOYER can be reached at 571-272-9523. 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. /SJ Park/Primary Examiner, Art Unit 2675
Read full office action

Prosecution Timeline

Apr 05, 2024
Application Filed
Feb 12, 2026
Non-Final Rejection — §102 (current)

Precedent Cases

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
82%
Grant Probability
99%
With Interview (+17.3%)
2y 8m
Median Time to Grant
Low
PTA Risk
Based on 720 resolved cases by this examiner. Grant probability derived from career allow rate.

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