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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-8, 10-15, 17-22 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12115015. Although the claims at issue are not identical, they are not patentably distinct from each other because the inventions (claim 1 for example) are both directed to obtaining PET/CT/MRI images, preprocessing the data to generate first and second subsets of standardized images, generating a first two-dimensional segmentation mask, generating a second two-dimensional segmentation mask, and generating a final masked image based on the first and second two-dimensional segmentation masks. While claim 1 of the instant invention is broader in scope, a three-dimensional segmentation mask is set forth in claim 4. Furthermore, details of a residual block and skip connection are set forth in claim 2. Furthermore, the instant invention refers to extracting features from the first and second two-dimensional masks for generating the final masked image, while the patent more broadly refers to combining information from the first and second two-dimensional masks. It would be within the level of one of ordinary skill in the art to derive the information from the first and second two-dimensional masks by performing extracting, as extracting is a known image analysis technique for deriving information from an image.
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.
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(s) 1, 7-8, 14-15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guo et al. (“Deep Learning-Based Image Segmentation on Multimodal Medical D1 Imaging”, IEEE Transactions on Radiation and Plasma Medical Sciences, IEEE, Vol. 3, no. 2, March 1 2019, pages 162-169; hereinafter Guo) in view of Groth et al. (US 2021/0004960; hereinafter Groth).
Guo shows a method and system comprising: obtaining a plurality of positron emission tomography (PET) scans and a plurality of computerized tomography (CT) or magnetic resonance imaging (MRI) scans for a subject (page 164, column 1); preprocessing the PET scans and the CT or MRI scans to generate a first subset of standardized images for a first plane or region of the subject and a second subset of standardized images for a second plane or region of the subject, wherein the first subset of standardized images and the second subset of standardized images incorporate information from the PET scans and the CT or MRI scans (page 164, column 1); generating a first two-dimensional segmentation mask (patches; page 164), using a first two-dimensional segmentation model implemented as part of a convolutional neural network architecture that takes as input the first subset of standardized images, wherein the first two-dimensional segmentation model uses a first residual block comprising a first layer that: (i) feeds directly into a subsequent layer (page 164, column 2; Figure 2); generating a second two-dimensional segmentation mask (patches; page 164), using a second two- dimensional segmentation model implemented as part of the convolutional neural network architecture that takes as input the second subset of standardized images, wherein the second two-dimensional segmentation model uses a second residual block comprising a second layer that: (i) feeds directly into a subsequent layer (page 164, column 2; Figure 2); extracting, using a feature extractor, features from the first two-dimensional segmentation mask and the second two-dimensional segmentation mask (page 163, column 2); and generating a final imaged mask by combining information from the first two-dimensional segmentation mask and the second two-dimensional segmentation mask (final output; page 164, column 2; Figure 2; see also pages 167-168, which describe benefits of multimodal fusion network as compared to single modality network).
Guo also shows preprocessing comprises co-registering the PET scans and the CT or MRI scans to generate the first subset of standardized images and the second subset of standardized images, wherein each standardized image includes information from the PET scans and CT or MRI scans (page 164, column 1).
Guo fails to show the first two-dimensional segmentation model is trained for processing images from a first plane or region and the second two-dimensional segmentation model is trained for processing images from a second plane or region.
Groth discloses systems and methods for display of medical image data. Groth teaches the first two-dimensional segmentation model is trained for processing images from a first plane or region and the second two-dimensional segmentation model is trained for processing images from a second plane or region (training for different types of anatomical structures, where different types of anatomical structures are located in different planes or regions of a patient; [0128]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Guo to train different models for processing images of different regions or planes as taught by Groth, in order to efficiently process a volume of images of a patient containing different anatomical features.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guo et al. (“Deep Learning-Based Image Segmentation on Multimodal Medical D1 Imaging”, IEEE Transactions on Radiation and Plasma Medical Sciences, IEEE, Vol. 3, no. 2, March 1 2019, pages 162-169; hereinafter Guo) in view of Groth et al. (US 2021/0004960; hereinafter Groth) as applied to claim 1 above, and further in view of Sjolund et al. (US 2019/0332900; hereinafter Sjolund).
Guo fails to show (ii) uses a skip connection to feed directly into a layer that is multiple layers away from the first layer; and (ii) uses a skip connection to feed directly into a layer that is multiple layers away from the second layer.
Sjolund discloses a modality-agnostic method for medical image representation. Sjolund teaches use of a skip connection in a neural network ([0140]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Guo and Groth to utilize skip connections as taught by Sjolund, as Sjolund teaches for example that the use of a skip connection will enhance gradient flow in the network. A skip connection is a known type of connection used across the artificial intelligence/neural network arts, and may be readily employed by one of ordinary skill in the art as desired in any type of neural network.
Claim(s) 3, 10, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guo et al. (“Deep Learning-Based Image Segmentation on Multimodal Medical D1 Imaging”, IEEE Transactions on Radiation and Plasma Medical Sciences, IEEE, Vol. 3, no. 2, March 1 2019, pages 162-169; hereinafter Guo) in view of Groth et al. (US 2021/0004960; hereinafter Groth) as applied to claims 1, 8, and 15 above, and further in view of Renisch et al. (US 2012/0123253; hereinafter Renisch).
Guo fails to show determining, using the final imaged mask, a total metabolic tumor burden (TMTV), and providing the TMTV.
Renisch discloses a method for anatomy modeling for tumor region of interest definition. Renisch teaches determining, using the final imaged mask, a total metabolic tumor burden (TMTV), and providing the TMTV ([0032], [0034]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Guo and Groth to determine a total metabolic tumor burden as taught by Renisch, as such a variable is known in the art for characterizing a tumor in a patient, which will yield a more accurate diagnosis.
Claim(s) 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guo et al. (“Deep Learning-Based Image Segmentation on Multimodal Medical D1 Imaging”, IEEE Transactions on Radiation and Plasma Medical Sciences, IEEE, Vol. 3, no. 2, March 1 2019, pages 162-169; hereinafter Guo) in view of Groth et al. (US 2021/0004960; hereinafter Groth) as applied to claim 1 above, and further in view of Stamatoyannopoulos et al. (US 2020/0167914; hereinafter Stamatoyannopoulos).
Guo fails to show wherein extracting the features comprises performing a dimensionality reduction process on the first two-dimensional segmentation mask and the second two-dimensional segmentation mask to generate the features having one dimension; wherein the features comprise at least one of: a texture feature extracted using a Gray level co-occurrence matrix, a shape feature extracted using a region property function, and a prognostic signature extracted using k-means clustering.
Stamatoyannopoulos discloses methods of analyzing images using machine learning. Stamatoyannopoulos teaches wherein extracting the features comprises performing a dimensionality reduction process on the first two-dimensional segmentation mask and the second two-dimensional segmentation mask to generate the features having one dimension (in preferred embodiments, the cell characterization data set is of reduced dimensionality compared to the multi-dimensional feature data set, thereby facilitating handling and comparison of image data compared to other types of experimental data, [0078]; [0082], [0088], [0094], [0096], [0115], [0145], [0154]-[0158]); wherein the features comprise at least one of: a texture feature extracted using a Gray level co-occurrence matrix ([0077]), a shape feature extracted using a region property function ([0076]-[0077]), and a prognostic signature extracted using k-means clustering ([0089]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Guo and Groth to reduce the dimensionality of the data set as taught by Stamatoyannopoulos, as Stamatoyannopoulos teaches that reducing the dimensionality facilitates handling and comparison of image data compared to other types of experimental data ([0041], [0078]). Furthermore, it would be within the level of one of ordinary skill in the art to employ known image processing techniques including the use of texture features, shape features, and prognostic signatures as taught by Stamatoyannopoulos, as Stamatoyannopoulos describes that a variety of known image processing techniques known in the art may be used for image pre-processing to identify objects within the image ([0077]), and any of a variety of machine learning algorithms may be utilized in implementing the image processing and analysis methods ([0089]).
Allowable Subject Matter
Claims 4-6, 11-13, and 18-19 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, and overcoming the double patenting rejection by amendment or terminal disclaimer.
Response to Arguments
Applicant's arguments filed 5/7/26 have been fully considered but they are not persuasive.
In response to applicant’s arguments that Guo fails to teach extracting, using a feature extractor, features from the first two-dimensional segmentation mask and the second two-dimensional segmentation mask, examiner respectfully disagrees. Applicant argues that Guo teaches extraction directly from the raw multi modal output images, however, Guo teaches obtaining image patches from the multi modal set, where the image patches are considered to be a segmentation of the raw image (page 164, paragraph bridging column 1-2). The image patches are then utilized in the CNN for forming an improved multimodality image combining the segmented patches.
Applicant’s arguments with respect to claim(s) 21-22 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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN CWERN whose telephone number is (571)270-1560. The examiner can normally be reached Monday - Friday, 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, Christopher Koharski can be reached at (571) 272-7230. 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.
/JONATHAN CWERN/Primary Examiner, Art Unit 3797
Prosecution Insights