Prosecution Insights
Last updated: April 19, 2026
Application No. 18/576,722

MULTI-SCALE 3D CONVOLUTIONAL CLASSIFICATION MODEL FOR CROSS-SECTIONAL VOLUMETRIC IMAGE RECOGNITION

Non-Final OA §101§112
Filed
Jan 04, 2024
Examiner
AUGUSTIN, MARCELLUS
Art Unit
2682
Tech Center
2600 — Communications
Assignee
The Education University of Hong Kong
OA Round
1 (Non-Final)
82%
Grant Probability
Favorable
1-2
OA Rounds
2y 8m
To Grant
98%
With Interview

Examiner Intelligence

Grants 82% — above average
82%
Career Allow Rate
684 granted / 838 resolved
+19.6% vs TC avg
Strong +16% interview lift
Without
With
+15.9%
Interview Lift
resolved cases with interview
Typical timeline
2y 8m
Avg Prosecution
31 currently pending
Career history
869
Total Applications
across all art units

Statute-Specific Performance

§101
11.0%
-29.0% vs TC avg
§103
50.7%
+10.7% vs TC avg
§102
18.5%
-21.5% vs TC avg
§112
12.0%
-28.0% vs TC avg
Black line = Tech Center average estimate • Based on career data from 838 resolved cases

Office Action

§101 §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 . Filed IDS of 1/04/2023 has been entered and considered. Claims 1-14 are currently pending. Please refer to the action below. Examiner Notes The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. However, the claimed subject matter, not the specification, is the measure of the invention. Claim Rejections - 35 USC § 112 The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. Claims 1, 9, and 14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 (being the representative claim) recites similar limitations to the respective independent claims 9 and 14, therefore discussion is omitted for brevity. Claim 1 recites “concatenating convolutional output channels from the two branches to fuse global and local features, on which two fully-connected layers are stacked as a classifier to recognize cross-sectional volumetric images accurately and quickly” lack written description. The specification does not sufficiently identify how the function is performed or result is achieved as defined in MPEP 2163(I)(A). The specification makes reference to utilizing a multiscale three-dimensional classification network with either an efficient channel attention or a local cross channel interaction in addition to fusing local and global features for some types of implied accuracy measures. The claimed limitations appear to lack critical or essential elements to the practice of the invention. Furthermore, it has been stated that simply restating the function recited in the claim is not necessarily sufficient. Examiner suggests the Applicant to make amendments to the claims to resolve the deficiencies, provided the amendments would be supported by the application as filed MPEP 2163.04, 2164.04. Dependent claims 2-8, and 10-13 are further rejected since they depend from the rejected base claims and as they fail to solve the above problems. 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, 9, and 14 are further 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 pre-AIA the applicant regards as the invention. Claim 1 (being the representative claim) recites similar limitations to the respective independent claims 9 and 14, therefore discussion is omitted for brevity. The claim requires “feeding the rescaled plurality of cross-sectional images into a second branch for reducing resolution, then performing a plurality of convolutions on the reduced resolution plurality of cross-sectional images to learn features for distinguishing phases thereof; and concatenating convolutional output channels from the two branches to fuse global and local features, on which two fully-connected layers are stacked as a classifier to recognize cross-sectional volumetric images accurately and quickly”. In relying on the claim languages, and that of the disclosure, the limitations of “classifier to recognize cross-sectional volumetric images accurately and quickly” as cited above are ambiguous and unclear in the terms of how the design itself ensures ensure the classifier recognizes cross-sectional volumetric images accurately and quickly without explicitly citing the details of how the accurate and quick recognition are achieved. The specification on page 6 of 22 makes reference to utilizing a multiscale three-dimensional classification network with either an efficient channel attention or a local cross channel interaction in addition to fusing local and global features for some types of implied accuracy measures. Therefore, it is unclear and ambiguous how the specified classifier realizes said accurate and quick volumetric cross-sectional images recognition. The claims as currently understood are rejected as being indefinite and unclear for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Applicant needs to positively recite the necessary elements, and novel subject matter to more effectively claim the subject matter which applicant regards as his invention. Dependent claims 2-8, and 10-13 are further rejected since they depend from the rejected base claims and as they fail to solve the above problems. Examiner respectfully advises applicant to review all pending claims and to positively and particularly point out the claimed subject matter which the applicant regards as the invention, in order to expedite the precaution of the application and shorten the time of examining process. Accordingly, the claimed subject matter of this application as currently claimed is unpatentable under the provisions of 35 U.S.C. 112, second paragraph. Therefore, the above claim is rejected under USC 112 second, as best understood by examiner as indicated in this office action above. 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. Claims 1-14 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) 1, recite(s) mental processes and software processes directed to a classification system, a memory, and a processor. Independent claim 1 includes limitations that recite an abstract idea. Claim 1 recites: A three dimensional classification system for recognizing cross-sectional images automatically, comprising: a memory that stores computer executable components; and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: rescaling a plurality of cross-sectional images, and feeding the rescaled plurality of cross-sectional images into two branches; feeding the rescaled plurality of cross-sectional images into a first branch for performing a plurality of convolutions on the rescaled plurality of cross-sectional images directly to learn features for distinguishing phases thereof; feeding the rescaled plurality of cross-sectional images into a second branch for reducing resolution, then performing a plurality of convolutions on the reduced resolution plurality of cross-sectional images to learn features for distinguishing phases thereof; and concatenating convolutional output channels from the two branches to fuse global and local features, on which two fully-connected layers are stacked as a classifier to recognize cross-sectional volumetric images accurately and quickly. This judicial exception is not integrated into a practical application because the claims merely recite mental steps that can be performed by a person and/or software steps that can be performed by component or units of a software. That is, other than reciting “learn features for distinguishing phases thereof and concatenating convolutional output channels from the two branches to fuse global and local features, on which two fully-connected layers are stacked as a classifier to recognize cross-sectional volumetric images accurately and quickly” nothing in the claim element precludes the steps from practically being performed in the mind and/or purely by software. The additional elements of “learn features for distinguishing phases thereof and concatenating convolutional output channels from the two branches to fuse global and local features, on which two fully-connected layers are stacked as a classifier to recognize cross-sectional volumetric images accurately and quickly” does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Hence, claim 1 is not subject matter eligible. Claim 1 recites similar limitations to those discussed above with regards to independent claims 9, and 14, and therefore discussion is omitted for brevity. Hence, independent claims 1, 9, and 14 are not subject matter eligible. The dependent claims 2-8, and 10-13 do not recite any further limitations that cause the claim(s) to be subject matter eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Based on broadest reasonable interpretation of the claims, all of the steps recited in the independent claims 1, 9, and 14, and corresponding dependent claims 2-8, and 10-13 further correspond to concepts performed by at least software components which may be further performed in the human mind. Additionally, a person can mentally performed in the human mind and/or software the reduced rescale MR images resolution so as to be fed in parallel or to at least a first and second convolution branch independently and/or simultaneously for performing a plurality of convolutions on the rescaled plurality of cross-sectional images directly to learn features for distinguishing types of contrast phases and concatenating convolutional output channels from the two branches to fuse global and local features, on which two fully-connected layers are stacked as a classifier to recognize said cross-sectional volumetric images. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. Concepts performed in the human mind have been identified in the 2019 PEG as an exemplar in the “Mental Process” grouping of abstract ideas. For the reasons above, the claims do not amount to significantly more than an abstract idea. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept and therefore, the claims are not patent-eligible. Furthermore, these additional generic hardware elements perform no more than their basic computer function. Generic computer‐implementation of a method is not a meaningful limitation that alone can amount to significantly more than an abstract idea. Moreover, when viewed as a whole with such additional element considered as an ordered combination, claims modified by adding generic hardware elements are nothing more than a purely conventional computerized implementation of an idea in the general field of computer processing and do not provide significantly more than an abstract idea. Consequently, the identified additional generic hardware elements taken into consideration individually and in combination fail to amount to significantly more than the abstract idea above. The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure. (US 9767557.uses using a trained recurrent neural network (RNN) for classifying cross-sectional images…..as US20090129535 teaches the displayed cross-section images from processing of resolution reduction processing). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARCELLUS AUGUSTIN whose telephone number is (571)270-3384. The examiner can normally be reached 9 AM- 5 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, BENNY TIEU can be reached at 571-272-7490. 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. /MARCELLUS J AUGUSTIN/Primary Examiner, Art Unit 2682 12/11/2025
Read full office action

Prosecution Timeline

Jan 04, 2024
Application Filed
Dec 22, 2025
Non-Final Rejection — §101, §112 (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
82%
Grant Probability
98%
With Interview (+15.9%)
2y 8m
Median Time to Grant
Low
PTA Risk
Based on 838 resolved cases by this examiner. Grant probability derived from career allow rate.

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