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 .
Election/Restrictions & Status of the Claims
Applicant’s election without traverse of (Species A) claims 1-2, 13-14, 16-17, 26, 28, and 30 in the reply filed on April 9, 2026, is acknowledged. Claims 12, 15, 18-25, 27, 29, 31-32, 34-41, 43, 45, 47, 49-52, 55-62, 64, 67-68, 72, 76-85, 87, 89, 91, 93-100, and 102-108 are cancelled. Claims 33, 42, 44, 46, 48, 53-54, 63, 65-66, 69-71, 73-75, 86, 88, 90, 92, and 101 are withdrawn as being directed to a non-elected invention.
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 1-2, 13-14, 16-17, 26, 28, and 30 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental processes and a mathematical concept. This judicial exception is not integrated into a practical application because the additional elements are generic data gathering, i.e., insignificant pre-solution activity. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because receiving a tumor image is well-understood, routine, and conventional in digital pathology.
Step 1 (Statutory Category): Claim 1 recites a method for determining a tumor immunophenotype and therefore falls within the statutory category of a process.
Step 2A, Prong One (Judicial Exception): The claim recites "calculating an epithelium-immune cell density for the image based on a number of immune cells detected within the one or more regions of the image depicting the tumor epithelium," which is a mathematical calculation; specifically, a computation of a ratio or count of detected cells within identified regions.
The claim further recites "determining a tumor immunophenotype of the image based on the epithelium-immune cell density and at least a first density threshold for classifying images into one of a set of tumor immunophenotypes," which is a mathematical comparison of a computed value against a threshold value to perform a classification, and also constitutes a mental process in that a pathologist could mentally compare a density value to a threshold and render a classification judgment.
The claim also recites "identifying one or more regions of the image depicting tumor epithelium," which is a mental process – an observation and evaluation that a human pathologist routinely performs when examining a stained tissue image. The specification confirms that pathologists have traditionally performed this type of analysis manually. See ¶¶3-4 (acknowledging that manually categorizing tumors based on the spatial distribution of immune effector cells is predictive for checkpoint inhibitor therapies). These limitations, considered together, constitute a single abstract idea encompassing both mathematical concepts and mental processes. MPEP 2106.04(a)(2)(I), (III).
Step 2A Prong Two (Practical Application): The claim does not integrate the judicial exception into a practical application. The additional element of "receiving an image of a tumor" is
insignificant extra-solution activity in the form of mere data gathering, recited at a high level
of generality i.e., insignificant pre-solution activity (MPEP 2106.05(g)). The claim does not recite any particular imaging apparatus, any specific treatment or prophylaxis for a patient based on the determined immunophenotype, any transformation of a particular article, or any improvement to the functioning of a computer or other technology. The claim terminates at the step of determining a tumor immunophenotype – a classification result without requiring any
real-world application of that result. The claim as a whole merely applies known analytical
techniques (density calculation and threshold-based classification) to a particular
technological environment (digital pathology images), which amounts to generally linking
the judicial exception to a field of use. See MPEP §2106.05(h). The claim does not reflect
the asserted improvement of standardizing the tumor immunophenotyping process
because it does not recite the specific technical components or steps that achieve such
improvement; rather, it recites the abstract analytical steps themselves at a high level of
generality.
Step 2B (Inventive Concept): The claim does not recite additional elements that amount to
significantly more than the judicial exception. The limitation of "receiving an image of a tumor" was identified as insignificant extra-solution activity at Step 2A, Prong Two and is reevaluated
here. This limitation amounts to well-understood , routine, and conventional activity. See, e.g., Chukka US 2016/0042511 ¶¶ 4, 27, 28 (routine imaging apparatus capture of digital whole-slide images).
Receiving digital pathology images for analysis is conventional data gathering in the field of computational pathology. The remaining limitations constitute the abstract idea itself and do not provide an inventive concept. Accordingly, claim 1 is not patent eligible.
Dependent Claims:
Claim 2 further recites mental-process steps (sliding-window scan and classify region as epithelium or stroma). Same analysis. Ineligible.
Claim 13 recites "using one or more machine learning models" at high level of generality. This is a mere instruction to apply the exception under MPEP 2106.05(f). Generic ML for a pathological counting task previously performed by hand is well-understood, routine, and conventional. See Yip US 2023/0230195 A1 ¶9 (H&E "is a long-standing method used by pathologists to analyze tissue morphological features"); Chukka ¶¶ 22, 23 (training a classifier on labeled ground truth slides). Same analysis. Ineligible.
Claim 14 recites that the machine learning models of claim 13 "comprise a computer vision model trained to recognize immune cells." This is generic computer vision invoked at high level of generality. It is a mere instruction to apply the abstract idea on a conventional ML tool. MPEP 2106.05(f). Under Step 2B, a computer vision model trained to recognize lymphocytes
(i.e., immune cells) is well-understood, routine, and conventional in digital pathology. See Yip
US 2023/0230195 A1 ¶120 ("the model 316 may be configured to perform lymphocyte identification and segmentation using a three-class FCN model"); Chukka US 2016/0042511 A1 ¶23 (ground-truth training data identifies "a lymphocyte" as one of the object types); Chukka ¶46 (classifier outputs include "a lymphocyte"). Ineligible.
Claim 16 recites that the threshold is determined "based on a ranking of a plurality of images of tumors ... each image of the plurality of images is associated with a patient ... participating in a first clinical trial; and each image of the plurality of images includes a label." Ranking labeled data and selecting a cutoff is a mathematical/mental concept. Same analysis. Ineligible.
Claim 17 recites the same identifying/calculating steps as claim 1 applied to the labeled training images. Same analysis. Ineligible.
Claim 26 recites "training a classifier" at high level of generality. Same MPEP 2106.05(f) defect as claim 13. Ineligible.
Claim 28 recites "training one or more machine learning models to detect biological objects" using "training data comprising a plurality of images and labels indicating a type of biological object." Supervised training of an ML model on labeled image data is generic ML invoked at high level of generality, disclosed in MPEP 2106.05(f) "apply it" defect. Under Step 2B, supervised pixel-level training of cell-segmentation models on labeled pathology images is well-understood, routine, and conventional. See Yip ¶124 ("training data includes digital slide images where every pixel has been labeled as either the interior of a cell, the outer edge of a cell, or the background"); Chukka ¶22 (training "an object classifier. .. using a plurality of 'ground truth' sample slides or training images"). Ineligible.
Claim 30 recites that the image "comprises a digital pathology image captured using a digital pathology imaging system." Recitation of a generic "digital pathology imaging system" does not invoke a particular machine under MPEP 2106.05(b). The recitation refers to the class of standard whole-slide scanners, not any specific machine with meaningful limits on the claim. Image capture by such a system is also insignificant pre-solution activity under MPEP 2106.05(g). Under Step 2B, digital whole-slide image acquisition is well-understood, routine, and conventional. See Yip ¶84 ("any suitable optical histopathology slide scanner including 20x and 40x resolution magnification scanners"); Chukka ¶4 (routine "digital whole-slide image" acquisition); Chukka ¶28 (standard imaging apparatus components such as a camera and CCD sensor). Ineligible.
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, 2, 13, 14, 16, 17, 26, 28, and 30 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4, 9, 15, and 16 of copending Application No. 19/391 ,554 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the reference '554 claims recite a tile-based variation of the same epithelium immune density to immunophenotype processing recited in the instant claims.
Instant claim
'554 claim(s)
What the '554 claim provides
1
1 + 4
image ➔ tile-based epithelium-immune cell density ➔ density threshold ➔ immunophenotype
2
1
tile-based epithelium vs. stroma classification
13
1 + 9
per tile immune cell counting via color deconvolution computer vision pipeline
14
1 + 16
trained computer vision model recognizing pathology image
features
16
1 + 4
density threshold (clinical trial cohort language is routine training label source)
17
1 + 4
same density threshold derivation as claim 16
26
1 + 4
first and second density thresholds (stroma-immune +
epithelium-immune per '554 claim 4)
28
1 + 16
trained computer vision model on pathology images
30
1 + 15
digital pathology image
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim Rejections - 35 USC § 102
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)(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.
Claim(s) 1, 2, 13, 14, 26, 28, and 30 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Yip et al., US 2023/0230195 A1 (hereinafter “Yip”).
Claim 1.
Yip discloses a method for determining a tumor immunophenotype, comprising:
receiving an image of a tumor (YIP: "Hematoxylin and eosin (H&E) staining is a long-standing method used by pathologists to analyze tissue morphological features for malignancy diagnosis. H&E slides, for example, can illustrate visual characteristics of tissue structures such as cell nuclei and cytoplasm, to inform identification of cancer tumors" (¶ 9); "the
imaging-based biomarker prediction system 102 is communicatively coupled to receive
medical images, for example of histopathology slides such as digital H&E stained slide
images"(¶ 84). This teaches receiving a digital H&E image of a tumor.);
identifying one or more regions of the image depicting tumor epithelium (YIP: "the tissue classification model 320 may classify tissue using tissue classifications, such as Tumor-IHC positive, TumorIHC negative, Necrosis, Stroma, Epithelium, or Blood"(¶ 137). This teaches identifying tile regions as the "Epithelium" tissue class.);
calculating an epithelium-immune cell density for the image based on a number of immune cells detected within the one or more regions of the image depicting the tumor epithelium (YIP: "Information regarding TIL density, location, organization, and composition provide valuable insight as to prognosis and potential treatment options. In various aspects, the disclosure provides a method of predicting TIL density in a sample, a method of distinguishing subpopulations of TILs in a sample ... , a method of distinguishing stromal versus intratumoral TILs, and the like" (¶ 67); "the tissue classification model 320 is trained to identify a percentage TILs within a tile image, the cell segmenter 316 determines the cell boundary, and the biomarker classification model 322 classifies the tile image based on the percentage of TILs within a cell interior" (¶ 130); and "the system 300 then takes this new list of locations of
confirmed lymphocytes from the module 304 and compares to a tissue segmenter module
318 list of tissue, e.g., tumor and non-tumor tissue locations determined from tissue classification model 320 and determines whether the lymphocyte is in tumor or non-tumor
region" (¶ 121). This teaches TIL density quantified per region, distinguishing
intratumoral (i.e., within the epithelium compartment) from stromal TILs.); and
determining a tumor immunophenotype of the image based on the epithelium-immune cell density and at least a first density threshold for classifying images into one of a set of tumor immunophenotypes (YIP: "tissue classes or cell types that are positive (contain a target
molecule of an IHC stain, especially in a quantity larger than a certain threshold) or
negative for an IHC stain target molecule (do not contain that molecule or contain a
quantity of that molecule lower than a certain threshold)" (¶ 128); and "the immune
state of a tumor (for example, inflamed/'hot' vs. non-inflamed/'cold' vs immune excluded)"
(¶ 97). This teaches assigning a categorical tumor immunophenotype (inflamed/non-inflamed/immune excluded) based on whether the per-tile TIL density exceeds a
threshold.).
Claim 2.
The method of claim 1, wherein identifying the one or more regions comprises: scanning the image using a sliding window; and for each portion of the image included within the sliding window: classifying the portion as at least one of a region depicting tumor epithelium or a region depicting tumor stroma (YIP: "image data is to be analyzed on a tile-basis, in some examples, image pre-processing includes receiving an initial histopathology image, at a first image resolution, downsampling that image to a second image resolution, and then performing a normalization on the downsampled histopathology image" (¶ 87); and "the deep learning framework module 306 is configured to classify tissue using a tiling analysis. For example, in the pipeline 315, the tissue detection process sends histopathology images ... to the image tiling process that selects and applies a tiling mask to the received images to
parse the images into small sub-images" (¶ 115). The tile classifier outputs "Tumor-IHC
positive, Tumor-IHC negative, Necrosis, Stroma, Epithelium, or Blood" (¶ 137).).
Claim 13.
The method of claim 1, wherein calculating the epithelium-immune cell density comprises: determining the number of immune cells detected within each of the one or more regions of the image using one or more machine learning models (YIP: "the tissue classification model 320 is trained to identify a percentage TILs within a tile image, the cell segmenter 316 determines the cell boundary, and the biomarker classification model 322 classifies the tile image based on the percentage of TILs within a cell interior" (¶ 130); the framework is "a deep learning framework" (¶ 53). This teaches using trained ML models for the TIL-counting step.).
Claim 14.
The method of claim 13, wherein the one or more machine learning models comprise a computer vision model trained to recognize immune cells (YIP: "the cell segmentation model 316 of the module 304 may be configured as a three-class semantic segmentation FCN model developed by modifying a UNet classifier"(¶ 120); "For both TILs biomarkers, the model 316 may be configured to perform lymphocyte identification and segmentation using a three-class FCN model" (¶ 120). This teaches a computer vision model (UNet/FCN) trained to recognize lymphocytes, i.e., immune cells.).
Claim 26.
The method of claim 1, further comprising: training a classifier to determine a tumor immunophenotype of an image input to the classifier based on a calculated epithelium-immune cell density of the image input to the classifier, the first density threshold, and a second density threshold (YIP: tissue classes are characterized as "positive (contain a target molecule of an IHC stain, especially in a quantity larger than a certain threshold) or negative for an IHC stain target molecule (do not contain that molecule or contain a quantity of that molecule lower than a certain threshold)"(¶ 128), disclosing two thresholds, upper and lower. Further: "the biomarker classification model 322 may be trained ... using histopathology images and associated ... scores"(¶ 131). This teaches training a classifier applying multiple thresholds to per-tile density.).
Claim 28.
The method of claim 1, further comprising: training one or more machine learning models to detect biological objects, wherein the one or more machine learning models are trained using training data comprising a plurality of images and labels indicating a type of biological object depicted within each of the plurality of images, wherein the epithelium-immune cell density is calculated based on the one or more machine learning models (YIP: "training data includes digital slide images where every pixel has been labeled as either the interior of a cell, the outer edge of a cell, or the background which is exterior to every cell" (¶ 124); "the deep learning framework 150 includes a histopathology image based classifier training module 160 that can access received and stored data from the external systems"(¶ 93); and Yip's claim 17 recites "receive a plurality of digital images of Hematoxylin and Eosin-stained training slides of training tissue samples corresponding to the respective different biomarker ... and generate one of the trained biomarker classification models, based on the plurality of digital images." This teaches supervised training using labeled images.).
Claim 30.
The method of claim 1, wherein the image comprises a digital pathology image captured using a digital pathology imaging system (YIP: "the imaging-based biomarker prediction system 102 is communicatively coupled to receive medical images ... from a variety of different sources. These sources may include a physician clinical records system 106 and a histopathology imaging system 108. Any number of medical image data sources could be accessible using the system 100. The histopathology images may be images captured by any dedicated digital medical image scanners, including any suitable optical histopathology slide scanner including 20x and 40x resolution magnification scanners" (¶ 84). This teaches a digital pathology imaging system.).
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.
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.
In the alternative to the §102 rejections set forth in ¶ 8 above, claims 1, 2, 13, 14, 26, 28, and 30 are rejected under 35 U.S.C. 103 as obvious over "Yip" (US 2023/0230195 A1).
Yip discloses, in a single reference, all of the limitations of claim 1, with the limitation mappings stated in ¶ 8 above. To the extent applicant argues that any individual limitation is disclosed in a different embodiment of Yip than another, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the disclosures of Yip ¶ 130 (per-tile percentage-TILs computation), Yip ¶ 137 (tile classification including "Epithelium"), Yip ¶ 121 (lymphocyte-in-tumor-or-non-tumor assignment), Yip ¶ 128 (threshold-based positive/negative classification), and Yip ¶ 97 (immune-state output as "inflamed/'hot' vs. non-inflamed/'cold' vs immune excluded"). The motivation for this combination is set forth in Yip, each of these disclosures is part of the same imaging-based biomarker prediction system 102 (Yip Fig. 1; ¶ 81), and Yip teaches that the system is configured to generate biomarker output including the immune state of a tumor using its per-tile TIL biomarker pipeline. Combining these disclosures yields the elected-species method with predictable results per MPEP 2143(A) (combining prior art elements according to known methods to yield predictable results).
Claims 16 and 17 are rejected under 35 U.S.C. 103 as obvious over Yip.
Claims 16 and 17.
Yip discloses the method of claim 1 ( as set forth above) but does not literally recite that the
threshold is derived from images of patients "participating in a
teaches deriving thresholds from a ranked, labeled training cohort of patient images (YIP claim 16: "receive a molecular training dataset for a plurality of training tissue samples, the
molecular training dataset comprising RNA transcriptome counts from sequencing of a
substantially similar sample associated with each training tissue sample; identify one or
more molecular data subsets in the molecular training dataset, each corresponding to a
different respective biomarker, by processing the molecular training dataset using a
clustering algorithm."), and further teaches that for each molecular data subset, the system
receives a plurality of H&E training slides corresponding to that biomarker and trains a
biomarker classification model on those labeled images (YIP claim 17). Further, Yip teaches
that training data may include images and patient information from "external systems, such as
the physician clinical records system 106, the histopathology imaging system 108, the genomic
sequencing system 112," which includes data from prospective patient studies (YIP:¶ 92).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to apply Yip's threshold-derivation method to a clinical trial cohort. The motivation for this combination is to ground the immunophenotype-classification thresholds in clinically validated outcome data. A POSITA implementing Yi p's biomarker classifier would routinely use available clinical trial cohorts (the most rigorously labeled patient datasets available in digital pathology) as training data, per MPEP 2143(D) (applying a known
technique to a known method ready for improvement to yield predictable results).
Conclusion
The prior art made of record but not relied, yet considered pertinent to the applicant’s disclosure, is listed on the PTO-892 form.
Chukka et al., US 2016/0042511 A1. Cumulative tile-based H&E pipeline.
Saltz et al., US 2020/0388029 A1. Cumulative spatial distribution of TIL teaching.
Tempus Labs, US 2021/0166380 A1, US 2021/0166381 A1 , WO 2020/198380 A1. Same assignee as Yip. Cumulative Tempus pipeline disclosures.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ross Varndell whose telephone number is (571)270-1922. The examiner can normally be reached M-F, 9-5 EST.
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, O’Neal Mistry can be reached at (313)446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Ross Varndell/Primary Examiner, Art Unit 2674
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