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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 7/10/2026 has been entered.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-4 and 7-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a mental process. This judicial exception is not integrated into a practical application because the claimed. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claimed elements such as processors and computer storage media are generic computer elements.
In regards to claim 1, a method for training a segmentation model, comprising: using first training images to train a segmentation model (A person of ordinary skill can observe images and use those to train their own ability to segment images for details); using second training images to train an image generator (A person of ordinary skill could then observe a set of images and train themselves to create images using a pen and paper that are similar to the training images); inputting real images into the segmentation model to generate predicted annotation images (A person of ordinary skill in the art can look at real images and predict relevant annotations to those images); inputting the predicted annotation images into the image generator to generate fake images (A person of ordinary skill in the art can generate images based off of the predicted annotated images); and updating the segmentation model and the image generator according to a loss caused by differences between the real images and the fake images (A person of ordinary skill in the art can see the differences between the real images and their generated images and make corrections to their images based off of the differences).
In regards to claim 2, wherein the first training images are labeled images (A person of ordinary skill can use labeled images to train off of).
Further, wherein the first training images are labeled images (This is extra solution activity as it is merely selecting a particular data source to be manipulated).
In regards to claim 3, wherein the second training images are labeled images (A person of ordinary skill can use labeled images to train off of).
Further, wherein the second training images are labeled images (This is extra solution activity as it is merely selecting a particular data source to be manipulated).
In regards to claim 4, wherein the real images comprise labeled images and unlabeled images (A person can use images that are real with or without annotations).
Further, wherein the real images comprise labeled images and unlabeled images (This is extra solution activity as it is merely selecting a particular data source to be manipulated).
In regards to claim 7, it is similar to claim 1, and it is similarly rejected.
In regards to claim 8, it is similar to claim 2, and it is similarly rejected.
In regards to claim 9, it is similar to claim 3, and it is similarly rejected.
In regards to claim 10, it is similar to claim 4, and it is similarly rejected.
In regards to claim 11, further comprising: utilizing a discriminator implemented by the one or more processors to identify differences between the real images and the fake images (A person of ordinary skill in the art can look at a real image and a fake image and identify which is real and which is fake, the disclosure of a discriminator is simply tying the abstract idea to a particular field of technology).
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-4 and 6-11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Xia et al. (“Liver Semantic Segmentation Algorithm Based on Improved Depp Adversarial Networks in Combination of Weighted Loss Function on Abdominal CT Images”), hereinafter referred to as Xia.
In regards to claim 1, Xia discloses a method for training a segmentation model, comprising: using first training images to train a segmentation model, wherein the segmentation model is configured to generate predicted annotation images corresponding to respective input images (Page 5 section IV A and Page 4 Section B, discloses that the dataset is made up of data that includes labelled data that is used for training with the section on page 4 disclosing that the segmentation model creates a prediction mask); using second training images to train an image generator (Last paragraph of page 3 that goes into page4 and page 4’s first new paragraph, the Gan is trained following that algorithm which includes masks from the segmentation model); inputting real images, into the segmentation model to generate the predicted annotation images corresponding to the real images (Page 4 Section B, disclosing that the segmentation model creates a prediction mask that corresponds to the original or real image; see also Fig. 2); inputting the predicted annotation images into the image generator to generate fake images corresponding to the predicted annotation images (Last paragraph of page 3 that goes into page 4; page 4’s first new paragraph; and figure 2, the Gan is trained following that algorithm which includes masks from the segmentation model and these are fed into the GAN to create the reconstruction image); and computing, by one or more processors, a loss based on differences between the real images and the fake images (Figure 2 and weighted loss section on pages 4-5, discloses a loss function that includes the adversarial loss and content-based loss between the original image I and the reconstructed image IR, as shown in figure 2), and updating parameters of the segmentation model and the image generator based on the computed loss (Figure 2 and weighted loss section/training process on pages 4-5, Xia discloses the use of the loss function for training both the segmentation model and the GAN).
In regards to claim 2, Xia discloses wherein the first training images are labeled images (Page 5 section IV A, discloses that the dataset is made up of data that includes labelled data that is used for training).
In regards to claim 3, Xia discloses wherein the second training images are labeled images (Page 5 section IV A, discloses that the dataset is made up of data that includes labelled data that is used for training).
In regards to claim 4, Xia discloses wherein the real images comprise labeled images and unlabeled images (Page 5 section IV A, discloses that the dataset is made up of data that includes some labelled data which means that some of the data is unlabeled that is used for training).
In regards to claim 6, Xia discloses wherein the image generator is based on a Generative Adversarial Network (GAN) model with pixel to pixel correspondence (Page 3 section III, The paper focuses on a Pix2Pix network for the GAN which is a network with pixel to pixel correspondence).
In regards to claim 7, it is similar to claim 1, and it is similarly rejected.
In regards to claim 8, it is similar to claim 2, and it is similarly rejected.
In regards to claim 9, it is similar to claim 3, and it is similarly rejected.
In regards to claim 10, it is similar to claim 4, and it is similarly rejected.
In regards to claim 11, Xia discloses further comprising: utilizing a discriminator implemented by the one or more processors to identify differences between the real images and the fake images (Abstract and Figure 2, Discloses the use of a discriminator that identifies the differences between the images).
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Xia et al. (“Liver Semantic Segmentation Algorithm Based on Improved Depp Adversarial Networks in Combination of Weighted Loss Function on Abdominal CT Images”), hereinafter referred to as Xia, in view of Rajapakse et al. (US 20250022137 A1), hereinafter referred to as Rajapakse.
In regards to claim 5, Xia does not explicitly disclose wherein the segmentation model is based on a Visual Geometry Group (VGG) U-net model.
However, Rajapakse discloses wherein the segmentation model is based on a Visual Geometry Group (VGG) U-net model (Paragraph 105, Describes that the two segmentation models, the U-Nets, use images input for training purposes).
It would be prima facie obvious to combine the teachings of these two arts. Xia discloses a discriminator which is a type of segmentation model. Rajapakse is a related art in medical imaging that discloses the usage of this specific segmentation model for training purposes. As such, one could simply substitute the two to yield predictable results. Further it would be a predictable increase in accuracy as the related works section of Page 3 notes that “The deep module of the structure is generally improved by the VGG-16 or ResNet-1 architecture.” The fact that the author notes that in general the system is improved by using a VGG system would make it prima facie obvious to combine.
Response to Amendment
The amendments entered 7/10/2026 have been considered in full. The amendments overcome all 112(a) and 112(b) related rejections. The amendments made, however, are reverting the claims mostly back to what they were in the non-final rejection. As such, the 35 U.S.C. 101 rejections are reintroduced.
Response to Arguments
Applicant’s arguments with respect to claims 1 and 7 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Madani et al. (US-20220076075-A1) covers a similar system that does not fully use a segmentation model in the process.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CONOR AIDAN O'MALLEY whose telephone number is (571)272-0226. The examiner can normally be reached Monday - Friday 9:00 am. - 5:00 pm. EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Moyer can be reached at 5722729523. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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CONOR AIDAN. O'MALLEY
Examiner
Art Unit 2675
/CONOR A O'MALLEY/ Examiner, Art Unit 2675
/SEAN M CONNER/ Primary Examiner, Art Unit 2663
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