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 .
DETAILED ACTION
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 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.
Election/Restrictions
Applicant’s election without traverse of Invention II, claims 8-14, in the reply filed on Apr. 13, 2026 is acknowledged.
Claims 1-15 remain pending in the current application, claims 17 and 15 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention.
The requirement for the restriction of Inventions I-III is still deemed proper and is therefore made FINAL.
Claims 8-14 have been considered on the merits.
Status of the Claims
Claims 1-15 are currently pending.
Claims 17 and 15 have been withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected Invention, there being no allowable generic or linking claim.
Claims 8-14 have been considered on the merits.
Drawings
The disclosure is objected to because of the following informalities:
The drawings are objected to because of the following informalities: illegible text in Figures 10B.
The drawings are further objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference characters not mentioned in the description: No. 19 for Fig. 3, Nos. S310 and S320 for Fig. 11, and No. 500 for Fig. 13.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities: the use of trademarks.
The use of the terms Diff Quik® in paras. 70, 74-75, 111, Table 3; and 3D Histech® in Table 3, which are a trade names or a marks used in commerce, have been noted in this application. The terms should be accompanied by the generic terminology; furthermore the terms should be capitalized wherever they appear or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the terms.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 9 and 10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 9 contains the trademark/trade name Diff Quik®. Where a trademark or trade name is used in a claim as a limitation to identify or describe a particular material or product, the claim does not comply with the requirements of 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph. See Ex parte Simpson, 218 USPQ 1020 (Bd. App. 1982). The claim scope is uncertain since the trademark or trade name cannot be used properly to identify any particular material or product. A trademark or trade name is used to identify a source of goods, and not the goods themselves. Thus, a trademark or trade name does not identify or describe the goods associated with the trademark or trade name. In the present case, the trademark/trade name is used to identify/describe a Romanowsky stain and, accordingly, the identification/description is indefinite.
Since claim 10 depends from indefinite claim 9 and does not clarify the above points of confusion, claim 10 must also be rejected under 35 U.S.C. § 112, second paragraph.
Appropriate correction is required.
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.
Claims 8-14 are rejected under 35 U.S.C. 103 as being unpatentable over Matlock et al. (US 2020/0388033 A1) .
The claims are directed to an artificial intelligence (AI) powered detection method of cancer in a digital cytology specimen image. This is accomplished by performing a preprocessing step where the image of the cytology slide is divided into tile images depending on how the slide is stained. Additionally, a classification step is performed where the AI scans the image and determines whether cancer or a cancer type is present using a prediction model that has undergone an annotation-based learning or a machine learning model that was trained on datasets of cytology specimen images stained by a particular method and annotated for cancer, cancer type, or healthy tissue.
With respect to claim 8, Matlock teaches a method of analyzing digital images of tissue samples and slides to determine whether cancer is present and the type of cancer present in a stained slide of target tissue (a specimen cytology supporting method) (abstract, 0015, 0049-0051, 0084, 0117, and Fig. 28).
With respect to the pre-processing step of claim 8, Matlock teaches a pre-processing step where the digital image of a stained slide of target tissue is separated into a plurality of tile images containing a different portion of the image (0051). Additionally, Matlock teaches before analysis of tissue biopsy images, high resolution images are broken into smaller sub-images (tile images) (Fig. 15B).
With respect to the classification step of claim 8, Matlock teaches the method where consensus molecular subtype (CMS, a set of classification subtypes of cancer such as colorectal cancer) is detected and the system is configured to classify cells as corresponding to cancer specific classification by training a classification model that has undergone annotation-based learning using slide images (specimen cytology slide images) (0018-0020, 0093, 0104, 0125, and 0174-0177).
Matlock does not teach the method where the slide images or sub-images are divided according to the cell staining method as recited in the pre-processing step of claim 8. Similarly, Matlock does not teach the method using the specimen cytology slide image divided according to the cell staining method as recited in the classification step of claim 8. However, Matlock teaches an image discriminator may analyze the image data to determine the slide staining used to generate the digital image such as hematoxylin-eosin (H&E), IHC (immunohistochemistry), etc. (0141). Matlock teaches the classification model uses immunofluorescence or immunohistochemistry images to train labelled histological stained images (0044). Matlock teaches other histological stains may be used for training including methylene blue, Giemsa, and periodic acid-Schiff reaction (0054). Accordingly, at the effective time of filing of the claimed invention, one of ordinary skill in the art would have been motivated to further divide the cytology specimen images of Matlock based on the type of staining for the benefit of analyzing the specimen cytology slide based on the type of stain used. It would have been obvious to one of ordinary skill in the art to make such a modification, since Matlock teaches using different stains and using an image discriminator to determine the slide staining and different histological stains are known in the art. Furthermore, one of ordinary skill in the art would have had a reasonable expectation of success in making such a modification Matlock to divide the images based on staining, since Matlock teaches multiple stains, using an image discriminator to determine the slide staining, and the dividing of the images into tile images.
With respect to claim 9, Matlock teaches the method where the cell staining method is hematoxylin-eosin (H&E) staining and Giemsa staining (0108).
With respect to claim 10, Matlock teaches the method where the machine learning is tailored toward to diagnostic classification of cancer and can be configured to specifically identify the type of cancer by utilizing histopathology slide images of healthy and cancerous classes (0013-0015 and 0084). Matlock teaches the method where the prediction model has undergone annotation-based learning for each biomarker (cell staining) method of the specimen and for each type of cancer undergone annotation-based learning for each cell staining method of the specimen and for each type of cancer (0019-0020, 0089-0091, 0093, and 0095). Therefore, Matlock teaches the limitations of where there are a plurality of prediction models that have undergone annotation-based learning for each cell staining method of the specimen and for each type of cancer, and where the classification step classifies a class of at least one of whether there is cancer or the type of cancer according to each cell staining method in any specimen cytology slide image using each prediction model that has undergone the annotation-based learning for each cell staining method of the specimen and for each type of cancer of claim 10.
With respect to claim 11, Matlock teaches wherein the specimen cytology slide image is obtained by applying Z-stacking or focus stacking to an original slide image obtained by spearing and capturing or scanning on a slide of the specimen (0109). Although, Matlock is silent with respect to the composition of the slide and does not teach that the slide is glass, it would have been obvious to one of ordinary skill in the art to use a common slide type for a cytology specimen slide such as glass.
With respect to claim 12, Matlock teaches the method where the specimen cytology slide image is obtained by synthesizing images focused at different phases from the original slide image into one image through secondary post-processing, using Z-stacking or focus stacking (0109).
With respect to claim 13, Matlock teaches before analysis of the digital images, are separated into a plurality of tile images and where windows are used to divide the images (0022-0023, 0358, 0368 and Fig. 15A-B). Accordingly, Matlock teaches the limitation of where the pre-processing step generates the plurality of tile images based on a sliding window algorithm.
With respect to claim 14, Matlock teaches that the slides can be annotated to mark tissues or regions of interest such as by boxing the different regions (0190, 0343, 0358, Fig. 4). Accordingly, Matlock teaches the method where the prediction model that has undergone the annotation-based learning undergoes learning by adding one or more of a partial annotation indicating a cancer area in a bounding box annotation indicating the cancer area in a box form.
Matlock does not teach the method where the specimen cytology slide image is divided according to the cell staining method or the plurality of tile images used for learning image as recited in claim 14. However, Matlock teaches an image discriminator may analyze the image data to determine the slide staining used to generate the digital image such as hematoxylin-eosin (H&E), IHC (immunohistochemistry), etc. (0141). Matlock teaches the classification model uses immunofluorescence or immunohistochemistry images to train labelled histological stained images (0044). Matlock teaches other histological stains may be used for training including methylene blue, Giemsa, and periodic acid-Schiff reaction (0054). Accordingly, at the effective time of filing of the claimed invention, one of ordinary skill in the art would have been motivated to further divide the cytology specimen images of Matlock based on the type of staining for the benefit of analyzing the specimen cytology slide based on the type of stain used. It would have been obvious to one of ordinary skill in the art to make such a modification, since Matlock teaches using different stains and using an image discriminator to determine the slide staining and different histological stains are known in the art. Furthermore, one of ordinary skill in the art would have had a reasonable expectation of success in making such a modification Matlock to divide the images bases on staining, since Matlock teaches multiple stains, using an image discriminator to determine the slide staining, and the dividing of the images into tile images.
Therefore, the invention as a whole was prima facie obvious to one of ordinary skill in the art at the effective time of filing of the invention, especially in the absence of evidence to the contrary.
Conclusion
No claims are allowed.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nie et al. (US 2021/0216746 A1)
Nie teaches a method of analyzing cytology images to determine whether cancer is present and the type of cancer present in the specimen cytology slide image (0005-0007).
Nie teaches the method where the image is a multiplex image which is a digital image obtained from different fluorescent stains for different biological structure which would comprises of a plurality of images (0058).
Nie teaches a convolutional neural network is trained using a dataset comprising a plurality of training images where the plurality of training images are derived from a biological specimen stained with a primary stain or stained for the presence of one or more biomarkers and teaches there is at least one class label (0011-0014).
Rothrock et al. (US 2023/0360414 A1)
Rothrock teaches a method of analyzing cytology images to determine whether cancer is present and the type of cancer present in the specimen cytology slide image (abstract, 0033-0034, 0076, and 0127).
Rothrock teaches the analyzing method (specimen cytology supporting method) involving a cell staining of specimens (a cell staining method) (0049) and a plurality of images (0007).
Rothrock teaches the method where a machine learning model is used to output a task-specific prediction and training the machine learning model to recognize tumor cells and diagnosis tumors (0012 and 0033-0035).
Stanitsas et al. (US 2018/0165809 A1)
Stanitsas teaches a method of analyzing images of tissue samples and slides to determine whether cancer is present and the type of cancer present in the specimen cytology slide image (a specimen cytology supporting method) (abstract, 0003, 0044-0046 and 0050).
Stanitsas a pre-processing step where regions suspected to be cancerous and regions are identified as benign or healthy by a pathologist (0051).
Stanitsas teaches before analysis of tissue biopsy images, high resolution images are broken into smaller sub-images (0056).
Stanitsas teaches using labeled images from various classes sufficient from machine learning to learn models to classify the corresponding tissue regions and extract semantic information and which can be tailored toward to diagnostic classification of cancer and can be configured to specifically identify the type of cancer (0047-0049).
Examiner Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMILY ANN CORDAS whose telephone number is (571)272-2905. The examiner can normally be reached on M-F 9:00-5:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Paras can be reached on 571-272-4517. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EMILY A CORDAS/Primary Examiner, Art Unit 1632
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