Office Action Predictor
Last updated: April 16, 2026
Application No. 18/624,394

METHOD AND DEVICE FOR CORRECTING MAGNETIC RESONANCE IMAGE, STORAGE MEDIUM, AND TERMINAL

Non-Final OA §101§103§112
Filed
Apr 02, 2024
Examiner
MILLER, JOHN W
Art Unit
2422
Tech Center
2400 — Computer Networks
Assignee
Beijing Chao-Yang Hospital, Capital Medical University
OA Round
1 (Non-Final)
38%
Grant Probability
At Risk
1-2
OA Rounds
2y 8m
To Grant
41%
With Interview

Examiner Intelligence

Grants only 38% of cases
38%
Career Allow Rate
11 granted / 29 resolved
-20.1% vs TC avg
Minimal +3% lift
Without
With
+3.2%
Interview Lift
resolved cases with interview
Typical timeline
2y 8m
Avg Prosecution
7 currently pending
Career history
36
Total Applications
across all art units

Statute-Specific Performance

§101
2.5%
-37.5% vs TC avg
§103
56.3%
+16.3% vs TC avg
§102
22.2%
-17.8% vs TC avg
§112
14.6%
-25.4% vs TC avg
Black line = Tech Center average estimate • Based on career data from 29 resolved cases

Office Action

§101 §103 §112
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 § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the broadest reasonable interpretation of a ‘computer storage medium’ includes transitory, propagating signals (e.g., electrical/electromagnetic waves), which are considered non-statutory subject matter per In re Nuijten. To overcome this rejection, the claim can be amended to explicitly recite "non-transitory" computer storage media. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claims 1-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1 and 8 recite the acronym “ADC”. Because the claims do not define the acronym to mean “Apparent Diffusion Coefficient”, they are rendered indefinite. Appropriate correction is required. 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, 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. Claim(s) 1 and 8-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xie et al (US Pub. No. 2022/0230310 A1), hereinafter Xie. As to claim 1, Xie discloses a system for segmenting objects within medical images as follows: A method for correcting magnetic resonance image (see [0069-0072] which describes MRI as the imaging modality), comprising: acquiring an ADC image to be corrected, which is generated by scanning a target object using an echo planar imaging sequence (see [0071] which describes echo planar imaging for Multiple Sclerosis brain lesion segmentation) from a pulse sequence of a [portable mobile] magnetic resonance apparatus (as indicated above) and converting; inputting the ADC image to be corrected into a pre-constructed image correction model to correct a grayscale value of tissue position of the ADC image to be corrected (see Figure 4, a flowchart for volumetric segmentation (prior to block 430 where the derived image is input to a three-dimensional neural network model for volumetric segmentation) the derived image may be transmitted to a deep super resolution neural network for preprocessing in order to improve image spatial resolution (e.g., an enlargement and/or a refining of image details) of the portion of the image [0122]; because spatial resolution defines the smallest object that can be discerned in an image, it follows that this preprocessing results in the ‘correction’ of grayscale values in the image that define tissue, organs, lesions, etc.); wherein the pre-constructed image correction model is generated by function fitting based on magnetic resonance image sequences of different objects (the deep super resolution neural network is by definition a learning model; further, function fitting is the process of supervised learning in neural networks and Xie teaches the use of a convolutional neural network [0122] which can be used for supervised learning); and outputting a target image corresponding to the ADC image to be corrected (met by the output of the deep super resolution neural network). While Xie discloses a convolutional neural network, it does not explicitly disclose that the model is generated by ‘function fitting’ as part of supervised learning. However, this is not considered to be a patentable distinction in that it was notoriously well-known in the art of imaging to use CNNs trained through supervised learning. It would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the invention to implement the CNN in Xie trained through supervised learning in order to optimize the process of refining image details. Xie further fails to explicitly disclose a ‘portable mobile’ MR apparatus. However, this is not considered to be a patentable distinction in that such apparatuses were notoriously well-known in the art at the time of the invention. It would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the invention to implement Xie with imaging derived from a portable mobile MR apparatus in order to expand the applicability of the object segmentation method to patients requiring a mobile imaging system, such as those in ICUs and emergency departments. Claim 8 is met as discussed above for claim 1. Claim 9 is met as discussed above for claim 1. Further, note the non-transitory computer readable storage medium disclosed in [0150]. Claim 10 is met as discussed above for claim 1. Further, note the computing environment 100, Figure 1, and the non-transitory computer readable storage medium disclosed in [0150]. Allowable Subject Matter Claims 2-7 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. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN W MILLER whose telephone number is (571) 272-7353. The examiner can normally be reached Monday - Friday 7:30 AM - 4:00 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, Deborah Reynolds can be reached at is (571) 272-0734. 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. /JOHN W MILLER/Supervisory Patent Examiner, Art Unit 2422
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Prosecution Timeline

Apr 02, 2024
Application Filed
Jan 29, 2026
Non-Final Rejection — §101, §103, §112
Mar 24, 2026
Response Filed

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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
38%
Grant Probability
41%
With Interview (+3.2%)
2y 8m
Median Time to Grant
Low
PTA Risk
Based on 29 resolved cases by this examiner. Grant probability derived from career allow rate.

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