Prosecution Insights
Last updated: August 17, 2026
Application No. 18/927,288

SYSTEM FOR PREDICTING THE ACCURACY OF SEGMENTATION LABELS USING MULTIPLE CONVOLUTIONAL NEURAL NETWORKS FROM MAGNETIC RESONANCE IMAGES

Non-Final OA §102§103
Filed
Oct 25, 2024
Examiner
SHUI, MING
Art Unit
2663
Tech Center
2600 — Communications
Assignee
Springbok Inc.
OA Round
1 (Non-Final)
58%
Grant Probability
Moderate
1-2
OA Rounds
1y 7m
Est. Remaining
99%
With Interview

Examiner Intelligence

Grants 58% of resolved cases
58%
Career Allowance Rate
193 granted / 334 resolved
-4.2% vs TC avg
Strong +50% interview lift
Without
With
+50.5%
Interview Lift
resolved cases with interview
Typical timeline
3y 5m
Avg Prosecution
22 currently pending
Career history
353
Total Applications
across all art units

Statute-Specific Performance

§101
31.8%
-8.2% vs TC avg
§103
30.9%
-9.1% vs TC avg
§102
15.5%
-24.5% vs TC avg
§112
17.2%
-22.8% vs TC avg
Black line = Tech Center average estimate • Based on career data from 334 resolved cases

Office Action

§102 §103
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 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. DETAILED ACTION 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. (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claims 1-3, 5-11, 13-16 are rejected under 35 USC 102 as being anticipated by US 2021/0264589, Jacob. 1. A system for segmenting one or more regions of interest (ROI) in medical image data, comprising: a memory configured to store instructions and a plurality of medical images of a subject, wherein the medical images comprise a plurality of pixels or voxels; (Jacob ¶39 memory) a processor configured to: access the memory; (Jacob ¶39 processor) segment the plurality of medical images by inputting the plurality of medical images into each of a plurality of trained convolutional neural networks (CNNs) to identify a group of the plurality of pixels or voxels belonging to one or more ROI; (Jacob ¶19-24 plurality of CNN to identify a ROI) calculate a plurality of variables from each of the segmented plurality of medical images on a pixel-by-pixel basis, a voxel-by-voxel basis, or a ROI-by-ROI basis; (Jacob ¶24 variables median/mode, etc.) generate a segmentation accuracy score from the plurality of calculated variables; (Jacob ¶28 segmentation uncertainty measurement) output a label for the one or more ROI; and (Jacob ¶25 segmentation mask) a display configured to display at least one of the segmentation accuracy score or the label. (Jacob ¶28 displaying the segmentation mask/uncertainty measurement) 2. The system of claim 1, wherein generating the segmentation accuracy score includes averaging the plurality of variables. (Jacob ¶24 average) 3. The system of claim 2, wherein each of the plurality of trained CNNs vary in at least one of a network structure, one or more network parameters during training, a training set, or one or more parameters during a deployment. (Jacob ¶21 different machine learning networks) 5. The system of claim 1, wherein the plurality of variables includes at least one of a median, a standard deviation, a label volume variation, a label dice, or a label probability between each of the plurality of trained CNNs. (Jacob ¶24 label) 6. The system of claim 1, wherein each of the plurality of trained CNNs are trained using labeled medical images. (Jacob ¶18 medical images, ¶22 training images) 7. The system of claim 1, wherein the one or more ROI includes one or more muscles. (Jacob ¶25 heart) 8. The system of claim 1, wherein the plurality of medical images includes magnetic resonance (MR) images. (Jacob ¶18 MRI) Claims 9-11, 13-16 are rejected under a similar rationale. Claim Rejections - 35 USC § 103 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 of this title, 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. Claims 4 and 12 are rejected under 35 USC 103 as being unpatentable over Jacob in view of Disparity Sliding Window: Object Proposals From Disparity Images, Julian Müller Jacob does not disclose 4. The system of claim 3, wherein the trained CNNs use a sliding window approach to segment the plurality of medical images during a deployment. Müller discloses the common and well-known use of sliding window approaches for object recognition tasks. Thus it would have been obvious to modify the system of Jacob to utilize a well-known technique of sliding windows to guarantee and investigation of the entire input image for the object and localization of the object of interest as taught by Müller. Claim 12 is rejected under a similar rationale. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ming Shui whose telephone number is (303)297-4247. The examiner can normally be reached on 7-5 Pacific Time, M-Th. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Greg Morse can be reached on 571-272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Ming Shui/ Primary Examiner, Art Unit 2663
Read full office action

Prosecution Timeline

Oct 25, 2024
Application Filed
Jul 02, 2026
Non-Final Rejection mailed — §102, §103 (current)

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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
58%
Grant Probability
99%
With Interview (+50.5%)
3y 5m (~1y 7m remaining)
Median Time to Grant
Low
PTA Risk
Based on 334 resolved cases by this examiner. Grant probability derived from career allowance rate.

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