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
Applicant’s election without traverse of sub-combination II consisting of claims 11-16 in the reply filed on 10/23/2025 is acknowledged. Therefore, Claims 1-10 and 17-20 corresponding to non-elected sub-combination I are withdrawn from further consideration.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 03/13/2024 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) has/have been considered by the examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claims 11 and 16 are objected to because of the following informalities:
Acronym “HER2” in line 1 of claim 11 should be spelled out “Human growth factor receptor 2 (HER2)”
Acronym “ASCO/CAP” in line 4 of claim 16 should be spelled out “American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP)”.
Appropriate correction is required.
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)(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.
Claim(s) 11, 13-14, and 16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Masmoudi (Masmoudi H, Hewitt SM, Petrick N, Myers KJ, Gavrielides MA. Automated quantitative assessment of HER-2/neu immunohistochemical expression in breast cancer. IEEE transactions on medical imaging. 2009 Jan 19;28(6):916-25.).
Regarding claim 11, Masmoudi discloses A method for training a predictive HER2 tissue scoring model, comprising: (Masmoudi Abstract; an automated HER2 tissue scoring method is disclosed.) receiving a first training data set including a first plurality of images comprising stained tissue samples; training a first machine learning model to classify segments of an input image as nuclei or membrane based on the first training data set, wherein the first plurality of images in the first training data set are labeled to identify membrane and nuclei in the plurality of images; and (Masmoudi Section III. Methods – A. Color Pixel Classifier – found on p. 918-919; ¶1 explains that pixels are classified as nuclei, membrane, or background. ¶2 discloses that a linear regression classifier identifies membrane pixels. It is trained using labeled pixels from stained slides. ¶6 discloses nuclei pixel classification. It is trained using labeled pixels from stained slides. Full classification is considered to be the first machine learning model.) training a second machine learning model to generate a predictive HER2 score for the input image based on a second training data set, wherein the second training data set comprises a second plurality of images labeled to identify membrane and/or nuclei in the plurality of images, and wherein the second plurality of images are classified in a plurality of categories corresponding to a plurality of features associated with membrane and nuclei in the second plurality of images. (Masmoudi Section III. Methods – E. Slide Classification – found on p. 920-921; extracted features (mean membrane completeness and mean membrane intensity, which utilize classified pixels – see Section III. Methods- B. Epithelial Nuclei Segmentation, C. Membrane Modeling Using adaptive Ellipse-fitting and D. Membrane Feature Extraction – found on p. 919-920) are used to classify each slide with a 1+, 2+ or 3+ HER2 classification score.)
Regarding claim 13, Masmoudi discloses the claim limitations with regards to claim 11, as described above. Masmoudi further discloses wherein the plurality of features associated with membrane and nuclei in the second plurality of images comprises features corresponding to one or more of the following: an intensity of membrane stain, a completeness of membrane stain, an underlying color of nuclei, an underlying color of membrane, a ratio of membrane and nuclei, a membrane stain deviation, a nuclei stain deviation, an area of completely stained membrane, and a stained membrane cell percentage. (Masmoudi III. Methods; features include color (see A. Color Pixel Classifier ¶2 and ¶6 p. 919), membrane completeness, membrane staining intensity (see D. Membrane Feature Extraction ¶1), percentage of membrane pixels (stained membrane cell percentage see De. Membrane Feature Extraction ¶2).)
Regarding claim 14, Masmoudi discloses the claim limitations with regards to claim 11, as described above. Masmoudi further discloses wherein the second machine learning model comprises one or more of a random forest machine learning model, a support vector machine, a decision tree, a convolutional neural network, or any other machine learning model capable of learning and solving classification problems. (Masmoudi Section III. Methods – E. Slide Classification – p. 920-921; a classifier that utilizes minimum cluster distance is disclosed.)
Regarding claim 16, Masmoudi discloses the claim limitations with regards to claim 11, as described above. Masmoudi further discloses wherein training the second machine learning model to generate a predictive HER2 score for the input image based on the second training data set further comprises training the second machine learning model based on the plurality of features relevant to ASCO/CAP guidelines for HER2 scoring of IHC stained breast cancer tissue cells. (Masmoudi Section I. Introduction ¶3-4 – found on p. 916-917; CAP/ASCO HER2 evaluation recommendations are discussed. ¶7 the disclosed methods uses staining intensity, as recommended by CAP/ASCO, to provide HER2 scoring.)
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.
Claim(s) 12 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Masmoudi (Masmoudi H, Hewitt SM, Petrick N, Myers KJ, Gavrielides MA. Automated quantitative assessment of HER-2/neu immunohistochemical expression in breast cancer. IEEE transactions on medical imaging. 2009 Jan 19;28(6):916-25.).) in view of Schmidt (Pub. No. WO2022054009A2).
Regarding claim 12, Masmoudi discloses the claim limitations with regards to claim 11, as described above.
Masmoudi does not explicitly disclose wherein the first machine learning model comprises a deep-learning neural network machine learning model.
However, Schmidt discloses wherein the first machine learning model comprises a deep-learning neural network machine learning model. (Schmidt ¶153 and ¶155; discloses a CNN U-Net to perform segmentation of nuclei and membranes)
It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify training method of Masmoudi with the teachings of Schmidt by utilizing a u-net CNN to perform segmentation of nuclei and membrane in order to utilize a method that can do pixel level segmentation for both classes in one step rather than multiple linear classification steps.
Regarding claim 15, Masmoudi discloses the claim limitations with regards to claim 11, as described above.
Masmoudi does not explicitly disclose wherein training the first machine learning model to classify segments of the input image as nuclei or membrane comprises training the first machine learning model to classify both nuclei and membrane segments utilizing shared training weights.
However, Schmidt discloses wherein training the first machine learning model to classify segments of the input image as nuclei or membrane comprises training the first machine learning model to classify both nuclei and membrane segments utilizing shared training weights. (Schmidt ¶153 and ¶155; discloses a CNN U-Net to perform segmentation of nuclei and membranes. Training weights are for both nuclei and membranes i.e. shared.)
It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify training method of Masmoudi with the teachings of Schmidt by utilizing a u-net CNN to perform segmentation of nuclei and membrane in order to utilize a method that can do pixel level segmentation for both classes in one step rather than multiple linear classification steps.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MEREDITH TAYLOR whose telephone number is (571)270-5805. The examiner can normally be reached M-Th 7:30-5. Examiner’s email is Meredith.taylor@uspto.gov.
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/MEREDITH TAYLOR/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671