DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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
2. 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).
3. 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).
4. 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.
5. 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.
6. Claims 1,5,7,10,12-17 and 19-20 of the instant application is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2,4,10-11,20,28-29 and 33-34 of copending Application No. 18/711053, in view of Saltz et al. (WO 2019/108888). Although the conflicting claims are not identical, they are not patentably distinct from each other because they claim the same scope of the invention, but using different variations; please see below;
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
18/918401
18/711053 (US 2025/0029240)
1. A method for identifying at least one tertiary lymphoid structure (TLS) in an image of
tissue previously-obtained from a subject, the method comprising: using at least one computer hardware processor to perform: obtaining a TLS mask indicating, for each particular pixel of multiple pixels of the image, a respective numeric value indicative of a likelihood that the particular pixel is part of the at least one TLS; processing at least a portion of the image using a trained neural network model to obtain a tumor infiltrating lymphocyte (TIL) mask indicating, for each particular pixel of pixels of at least the portion of the image, a respective numeric value indicative of a likelihood that the particular pixel is part of a TIL, wherein the trained neural network model is trained to predict, for the particular pixel, the respective numeric value indicative of the likelihood that the particular pixel is part of the TIL; identifying boundaries of the at least one TLS in the image using the TLS mask and the TIL mask; and identifying one or more characteristics of the at least one TLS using the boundaries of the at least one TLS in the image.
1. A method for using a trained neural network model to identify at least one tertiary lymphoid structure (TLS) in an image of tissue obtained from a subject having, at risk of having, or suspected of having cancer, the method comprising: using at least one computer hardware processor to perform: obtaining a set of overlapping sub-images of the image of tissue; processing the set of overlapping sub-images using the trained neural network model to obtain a respective set of pixel-level sub-image masks, each of the set of pixel-level sub-image masks indicating, for each particular pixel of multiple individual pixels in a respective particular sub-image, a respective probability that the particular pixel is part of a tertiary lymphoid structure; determining a pixel-level mask for at least a portion of the image of the tissue covered by at least some of the sub-images in the set of overlapping sub-images, the determining comprising determining the pixel-level mask using at least some of the set of pixel-level sub-image masks corresponding to the at least some of the set of overlapping sub-images covering at least the portion of the image; identifying boundaries of at least one TLS in at least the portion of the image using the pixel-level mask; and identifying one or more features of the at least one TLS using the identified boundaries and at least the portion of the image.
5. The method of claim 1, wherein the trained neural network model is a TIL neural network model, and wherein obtaining the TLS mask comprises processing the image using a
TLS neural network model to obtain the TLS mask, the method further comprising:
obtaining a set of overlapping sub-images of the image of the tissue, wherein processing the image using the TLS neural network model to obtain the TLS mask comprises: processing the set of overlapping sub-images using the TLS neural network model to obtain a respective set of pixel-level sub-image masks, each pixel-level sub-image mask in the set of pixel-level sub-image masks indicating, for each particular pixel
of multiple individual pixels in a respective particular sub-image, a respective probability
that the particular pixel is part of a TLS; and
generating the TLS mask using the set of pixel-level sub-image masks corresponding to the set of overlapping sub-images of the image of the tissue.
1. … obtaining a set of overlapping sub-images of the image of tissue; processing the set of overlapping sub-images using the trained neural network model to obtain a respective set of pixel-level sub-image masks, each of the set of pixel-level sub-image masks indicating, for each particular pixel of multiple individual pixels in a respective particular sub-image, a respective probability that the particular pixel is part of a tertiary lymphoid structure; determining a pixel-level mask for at least a portion of the image of the tissue covered by at least some of the sub-images in the set of overlapping sub-images, the determining comprising determining the pixel-level mask using at least some of the set of pixel-level sub-image masks corresponding to the at least some of the set of overlapping sub-images covering at least the portion of the image; …
7. The method of claim 1, wherein identifying the one or more characteristics of the at least
one TLS comprises identifying at least one characteristic selected from the group consisting of: a number of TLSs in at least the portion of the image, the number of TLSs in at least the portion of the image normalized by an area of at least the portion of the image, a total area of TLSs in at
least the portion of the image, the total area of the TLSs in at least the portion of the image
normalized by the area of at least the portion of the image, median area of TLSs in at least the
portion of the image, and the median area of the TLSs in at least the portion of the image
normalized by the area of at least the portion of the image.
20. … wherein identifying the one or more features of the at least one TLS comprises identifying at least one feature selected from the group consisting of: a number of TLSs in at least the portion of the image, the number of TLSs in at least the portion of the image normalized by area of at least the portion of the image, a total area of TLSs in at least the portion of the image, the total area of TLSs in at least the portion of the image normalized by the area of at least the portion of the image, median area of TLSs in at least the portion of the image, the median area of TLSs in at least the portion of the image normalized by the area of at least the portion of the image.
10. The method of claim 1, wherein the image of the tissue is a whole slide image (WSI), wherein the image is a three-channel image comprising at least 10,000 by 10,000 pixel values per channel, and wherein the trained neural network model comprises at least 10 million, at least 25 million, at least 50 million, or at least 100 million parameters.
2. The method of claim 1, wherein the image of the tissue is a whole slide image (WSI).
4. The method of claim 1, wherein the image is a three-channel image comprising at least 10,000 by 10,000 pixel values per channel.
10. The method of claim 1, wherein the trained neural network model comprises at least 10 million, at least 25 million, at least 50 million, or at least 100 million parameters.
12. The method of claim 1, wherein the trained neural network model is a TIL neural network model, and wherein obtaining the TLS mask comprises processing the image using a TLS neural network model.
1. … using a trained neural network model to identify at least one tertiary lymphoid structure (TLS) in an image of tissue obtained from a subject having,
13. The method of claim 12, wherein the TLS neural network model comprises at least 10
million, at least 25 million, at least 50 million, or at least 100 million parameters.
10. The method of claim 1, wherein the trained neural network model comprises at least 10 million, at least 25 million, at least 50 million, or at least 100 million parameters.
14. The method of claim 13, wherein the TLS neural network model comprises an encoder
sub-model, a decoder sub-model, and an auxiliary classifier sub-model.
11. The method of claim 1, wherein the trained neural network model comprises an encoder sub-model, a decoder sub-model, and an auxiliary classifier sub-model.
15. The method of claim 1, wherein the subject has, is suspected of having, or is at risk of having cancer, and wherein the cancer is lung adenocarcinoma, breast cancer, cervical squamous cell carcinoma, lung squamous cell carcinoma, head and neck squamous cell carcinoma, gastric adenocarcinoma, colorectal adenocarcinoma, liver adenocarcinoma, pancreatic adenocarcinoma, or melanoma.
28. The method of claim 1, wherein the cancer is lung adenocarcinoma, breast cancer, cervical squamous cell carcinoma, lung squamous cell carcinoma, head & neck squamous cell carcinoma, gastric adenocarcinoma, colorectal adenocarcinoma, liver adenocarcinoma, pancreatic adenocarcinoma, or melanoma.
16. The method of claim 1, further comprising:
determining, based on the one or more characteristics of the at least one TLS, to
administer an immunotherapy to the subject.
29. The method of claim 1, further comprising: determining, based on the one or more features of the at least one TLS, to administer an immunotherapy to the subject; and administering the immunotherapy to the subject.
17. The method of claim 16, further comprising:
administering the immunotherapy to the subject.
29. … administer an immunotherapy to the subject; and administering the immunotherapy to the subject.
19. A system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for identifying at least one tertiary lymphoid structure (TLS) in an image of tissue previously-obtained from a subject, the method comprising: obtaining a TLS mask indicating, for each particular pixel of multiple pixels of
the image, a respective numeric value indicative of a likelihood that the particular pixel is part of the at least one TLS; processing at least a portion of the image using a trained neural network model to obtain a tumor infiltrating lymphocyte (TIL) mask indicating, for each particular pixel of
pixels of at least the portion of the image, a respective numeric value indicative of a likelihood that the particular pixel is part of a TIL, wherein the trained neural network model is trained to predict, for the particular pixel, the respective numeric value indicative of the likelihood that the particular pixel is part of the TIL; identifying boundaries of the at least one TLS in the image using the TLS mask and the TIL mask; and identifying one or more characteristics of the at least one TLS using the boundaries of the at least one TLS in the image.
34. A system, comprising: at least one computer hardware processor; and at least one non-transitory computer readable storage medium storing processor executable instructions that, when executed by at least one processor, cause the at least one processor to perform the method for using a trained neural network model to identify at least one tertiary lymphoid structure (TLS) in an image of tissue obtained from a subject having, at risk of having, or suspected of having cancer, the method comprising: obtaining a set of overlapping sub-images of the image of tissue; processing the set of overlapping sub-images using the trained neural network model to obtain a respective set of pixel-level sub-image masks, each of the set of pixel-level sub-image masks indicating, for each particular pixel of multiple individual pixels in a respective particular sub-image, a respective probability that the particular pixel is part of a tertiary lymphoid structure; determining a pixel-level mask for at least a portion of the image of the tissue covered by at least some of the sub-images in the set of overlapping sub-images, the determining comprising determining the pixel-level mask using at least some of the set of pixel-level sub-image masks corresponding to the at least some of the set of overlapping sub-images covering at least the portion of the image; identifying boundaries of at least one TLS in at least the portion of the image using the pixel-level mask; and identifying one or more features of the at least one TLS using the identified boundaries and at least the portion of the image.
20. At least one non-transitory computer-readable storage medium storing processor-
executable instructions that, when executed by at least one computer hardware processor, cause
the at least one computer hardware processor to perform a method for identifying at least one
tertiary lymphoid structure (TLS) in an image of tissue previously-obtained from a subject, the
method comprising: obtaining a TLS mask indicating, for each particular pixel of multiple pixels of the image, a respective numeric value indicative of a likelihood that the particular pixel is part of the at least one TLS; processing at least a portion of the image using a trained neural network model to obtain a tumor infiltrating lymphocyte (TIL) mask indicating, for each particular pixel of pixels of at least the portion of the image, a respective numeric value indicative of a likelihood that the particular pixel is part of a TIL, wherein the trained neural network model is trained to predict, for the particular pixel, the respective numeric value indicative of the likelihood that the particular pixel is part of the TIL; identifying boundaries of the at least one TLS in the image using the TLS mask and the TIL mask; and identifying one or more characteristics of the at least one TLS using the boundaries of the at least one TLS in the image.
33. At least one non-transitory computer readable storage medium storing processor executable instructions that, when executed by at least one processor, cause the at least one processor to perform the method for using a trained neural network model to identify at least one tertiary lymphoid structure (TLS) in an image of tissue obtained from a subject having, at risk of having, or suspected of having cancer, the method comprising: obtaining a set of overlapping sub-images of the image of tissue; processing the set of overlapping sub-images using the trained neural network model to obtain a respective set of pixel-level sub-image masks, each of the set of pixel-level sub-image masks indicating, for each particular pixel of multiple individual pixels in a respective particular sub-image, a respective probability that the particular pixel is part of a tertiary lymphoid structure; determining a pixel-level mask for at least a portion of the image of the tissue covered by at least some of the sub-images in the set of overlapping sub-images, the determining comprising determining the pixel-level mask using at least some of the set of pixel-level sub-image masks corresponding to the at least some of the set of overlapping sub-images covering at least the portion of the image; identifying boundaries of at least one TLS in at least the portion of the image using the pixel-level mask; and identifying one or more features of the at least one TLS using the identified boundaries and at least the portion of the image.
The above co-pending application is directed to the same invention, and the only difference is,
neural network model to obtain a tumor infiltrating lymphocyte (TIL) mask.
However, obtaining/analysis of the tissue image or region of interest to obtain a tumor infiltrating lymphocyte (TIL), is well known and used in the conventional prior art of the record, see entire disclosure of Saltz et al. (WO 2019/108888). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to implement such known teaching.
Claim Rejections - 35 USC § 103
7. 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.
8. 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.
9. Claims 1 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over LI XIAO (WO 2021/236547, provided in the IDS) in view of Saltz et al. (WO 2019/108888).
Regarding claim 1, Li teaches a method for identifying at least one tertiary lymphoid structure (TLS) in an image of tissue previously-obtained from a subject (e.g., processing of digital pathology images, figs. 1-3A, paragraph 0027, etc.), the method comprising; using at least one computer hardware processor to perform; obtaining a TLS mask indicating, for each particular pixel of multiple pixels of
the image, a respective numeric value indicative of a likelihood that the particular pixel is part of the at least one TLS (e.g., figs. 1-3a, paragraphs 0036-0037), processing at least a portion of the image using a trained neural network model to obtain (e.g., paragraph 0037, processing of the pathology image), for each particular pixel of pixels of at least the portion of the image, a respective numeric value indicative of a likelihood that the particular pixel is part of (e.g., figs. 1-3a, paragraphs 0036-0037), wherein the trained neural network model is trained to predict, for the particular pixel, the respective numeric value indicative of the likelihood that the particular pixel is part (e.g., abstract, paragraphs 0006,0023,0027-0028, etc.), identifying boundaries of the at least one TLS in the image using the TLS mask and the TIL mask (e.g., paragraph 0074), and identifying one or more characteristics of the at least one TLS using the boundaries of the at least one TLS in the image (e.g., paragraph 0047-0048).
Li is silent to explicitly teach, tumor infiltrating lymphocyte (TIL) mask.
Saltz, in the same field of endeavor, and throughout the disclosure teaches TIL information associated with tumoral tissue image data during pathology analysis/processing of the tissue image, by region mask segmentation, and further teaches neural network trained using pixel patches in tissue images, etc.
In view of the above, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the teaching of Li, in accordance with the
teaching of Saltz, in order to clinically processing, analyzing, and analyzing tumor-infiltrating
lymphocytes (TILs) based on prediction and generate improved diagnostic classifications and/or assessments associated with a range of detected cancer cell types, as suggested by the reference.
Regarding claims 19-20, the limitations claimed are substantially similar to claim 1 above, and has been addressed in the above claim 1.
Allowable Subject Matter
10. Claims 2-4,6,8,9,11 and 18 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.
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should be directed to Behrooz Senfi, whose telephone number is (571)272-7339. The examiner can
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/BEHROOZ M SENFI/Primary Examiner, Art Unit 2482
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