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 .
Information Disclosure Statement
The information disclosure statements (IDS) filed on 1/527/2025, 5/6/2026, and 7/8/2026 were considered and placed on the file of record by the examiner.
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 1-4, 8, 9, and 11-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
In Alice Corporation Pty. Ltd. v. CLS Bank International, et al. {“Alice Corp.’’), the Supreme Court made clear that it applies the framework set forth in Mayo Collaborative Services v. Prometheus Laboratories, Inc., 566 U.S._(2012) {Mayo), to analyze claims directed towards laws of nature and abstract idea. Alice Corp. also establishes that the same analysis applies for all categories of claims (e.g., product and process claims). The basic inquiries to determine subject matter eligibility remain the same as explained in MPEP 2106(1). First, determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. Next, determine if the claim is directed towards a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). The two-part test provided in Alice Corp. to determine whether a claim directed towards an abstract idea is statutory under § 101 requires an evaluation to determined 1) whether the claims is directed to an abstract idea and 2) if an abstract idea is present in the claim, whether the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas reference in Alice Corp. include:
- Fundamental economic principles
- Certain methods of organizing human activities
- An idea of itself
- Mathematical relationships/formulas
In accordance with judicial precedent, the 2019 Revised Patent Subject Matter Eligibility Guidance sets forth a procedure to determine whether a claim is ‘‘directed to’’ a judicial exception. Under the procedure, if a claim recites a judicial exception (a law of nature, a natural phenomenon, or an abstract idea), it must then be analyzed to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not ‘‘directed to’’ a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.
Step 1 - Statutory Category
The claims 1-22 recite a process of receiving pathology image slides from a tissue and providing a score, therefore it recites at least one of the enumerated categories, a process, eligible subject matter in 35 USC 101. Accordingly, claims 1-22 satisfy Step 1.
Step 2A(i) -Focus of the Claim
As a result, the claims 1-22 will be reviewed under Step 2A(i) to determine whether the claim is directed to one of the judicially recognized exceptions (i.e., a law of nature, a natural phenomenon, or an abstract idea). Alice, 573 U.S. at 217. As part of this inquiry, we must "look at the 'focus of the claimed advance over the prior art' to determine if the claim's 'character as a whole' is directed to excluded subject matter." Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1257 (Fed. Cir. 2016) (citations omitted). The claims recite providing scores for pathology image slides from a tissue, thus the organization of human activity.
The court have ruled that receiving and authenticating identity data to permit access was abstract since the functions were claimed generically rather than offering a "'concrete, specific solution" See Prisnz Technologies LLC v. T-Afobile USA, 696 F. App'x 1014 (Fed. Cir.2017). Abstract ideas include the concepts of collecting data, recognizing certain data within the collected data set, storing the data in memory, and notifying the user of the results. Content Extraction & Transmission LLC v. Wells Fargo Bank, N.A. Ass 'n, 776 F.3d 1343, 1347 (Fed. Cir. 2014); see also Smart Sys. Innovations, LLC v. Chicago Transit Auth., 873 F.3d 1364, 1372 (Fed. Cir. 2017) (concluding "claims directed to the collection, storage, and recognition of data are directed to an abstract idea"). Moreover, the reviewing court has concluded that acts of parsing, comparing, storing, and editing data are abstract ideas. Berkheimer v. HP Inc., 890 F.3d 1369, 1370 (Fed. Cir. 2018). In addition, the collection of information and analysis of information ( e.g., recognizing certain data within the dataset, such as rules) are also abstract ideas. Elec. Power Grp., LLC v. Alstom SA., 830 F.3d 1350, 1353 (Fed. Cir. 2016). Similarly, "collecting, displaying, and manipulating data" is an abstract idea. Intellectual Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1340 (Fed. Cir. 2017); see also SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1167 (Fed. Cir. 2018) ("[M]erely presenting the results of abstract processes of collecting and analyzing information ... is abstract as an ancillary part of such collection and analysis").
The process of receiving pathology image slides from a tissue and providing a score is a method of organizing human activity, as considered under MPEP § 2106.04(a)(2)(II), Certain Methods of Organizing Human Activity. Therefore, claims 1-22 recite an abstract idea.
Step 2A(ii) -Practical Application
Limitations that are indicative of integration into a practical application when recited in a claim with a judicial exception include:
Improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a);
Applying or using a judicial exception to affect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018.
Limitations that are not indicative of integration into a practical application when recited in a claim with a judicial exception include:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f);
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g); and
Generally linking the use of the judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).
In this instance, this judicial exception is not integrated into a practical application because the claims merely detect objects in images and determining a score. The claims do not provide an improvement to the functionality of a computer or image analysis technical field; the claims are not implemented with or used with a particular machine; the claims do not transform an article to a different state or thing when viewing images and determining scores; and the claims do not provide a meaningful way of analyzing image regions in the image analysis technical environment.
Step 2B - Inventive Concept
As set forth under MPEP § 2106.05( d), only if a claim: (1) recites a judicial exception; and (2) does not integrate that exception into a practical application, do we then look under Step 2B to determine; (3) whether the claim adds a specific limitation beyond the judicial exception that is not "well-understood, routine, conventional activity" (WURC) in the field; or (4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
Having determined claims 1-4, 8, 9, and 11-22 are directed to an abstract idea that is not integrated into a practical application, we now evaluate whether the additional elements, whether examined alone or as an ordered combination, add a specific limitation that is not well-understood, routine, or conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the abstract idea. See generally Revised Guidance.
It is possible that a claim that does not ‘‘integrate’’ a recited judicial exception is nonetheless patent eligible. For example, the claim may recite additional elements that render the claim patent eligible even though a judicial exception is recited in a separate claim element. Along these lines, the Federal Circuit has held claims eligible at the second step of the Alice/Mayo test because the additional elements recited in the claims provided ‘‘significantly more’’ than the recited judicial exception (e.g., because the additional elements were unconventional in combination).
Limitations reference in Alice Corp. that may be enough to quality as “significantly more” when recited in a claim with an abstract idea include, as nonexclusive examples:
- Improvements to another technology or technical field
- Improvements to the functioning of the computer itself
- Meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment
Examples that are not enough to quality as “significantly more” when recited in a claim with an abstract idea include, as non-limiting or non-exclusive example:
- Adding the words “apply it” (or an equivalent) with an abstract idea, or mere instructions to implement an abstract idea on a computer
- Requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry
The additional elements recited in claims 1-4, 8, 9, and 11-22 are well-understood, routine, and conventional steps in image analysis. The claims 1-4, 8, 9, and 11-22 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims are directed to viewing an image and determining a score.
Additionally, as noted in MPEP § 2106.05(d)(II), the courts have previously recognized that using computer processors and memories to collect data and keep records, perform repetitive calculations, and/or receive/send data are well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP § 2106.05(d)(II)(i)-(iv)). See also Berkheimer, 881 F.3d at 1366 (acts of parsing, comparing, storing, and editing data are abstract ideas); SAP Am., Inc. v. Investpic, LLC, 890 F.3d 1016, 1021 (Fed. Cir. 2018) ("[M]erely presenting the results of abstract processes of collecting and analyzing information ... is abstract as an ancillary part of such collection and analysis"); Intellectual Ventures I, 850 F .3d at 1340 ("[C]ollecting, displaying, and manipulating data" is an abstract idea); Smart Sys. Innovations, 873 F .3d at 1372 (concluding "claims directed to the collection, storage, and recognition of data are directed to an abstract idea.").
In claim 9, a machine learning system is trained to correlate features to a score. According to the MPEP Section 2106, simply mentioning "machine learning" in a patent claim is generally not enough to guarantee patentable subject matter; the claims must clearly define a specific, practical application of machine learning that goes beyond a generic or abstract idea, often requiring detailed description of the training process, data inputs, and specific outputs to avoid rejection under Section 101 for being too abstract. The machine learning language in the claims does not go beyond the abstract idea of identifying features images.
In claims 1-4, 8, 9, and 11-22, the steps of receiving images of pathology slides is not an improvement to a fundamental practice and/or method of organizing human activity. The claims do not include additional elements that are sufficient to amount to significantly more than generalized steps well-known and routine in the art such as image feature detection. Therefore, claims 1-4, 8, 9, and 11-22 are directed to patent-ineligible abstract idea that is not integrated into a practical application, with steps that do not add significantly more to the abstract idea. Claims 1-4, 8, 9, and 11-22 are ineligible. Claims 5-7, 10 are eligible.
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.
Claims 1, 3, 4, 6, 7, 9, 10, 12-19, 22 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Nie et al. (US 2021/0216746).
Regarding claim 1, Nie teaches a method of determining a raw score of a pathology slide from a tissue sample, the method comprising:
receiving, by a regression system, a plurality of first slide features corresponding to the pathology slide (see para. 0002, 0033, 0059, Nie discusses performing image analysis on pathology slides);
calculating, by the regression system, one or more second slide features corresponding to the pathology slide based on the plurality of first slide features; (see para. 0007, Nie discusses detecting cells in the sample image using a trained object detection engine, wherein the trained object detection engine comprises a convolutional neural network adapted to detect cellular features within the sample image; and extracting cellular features from one or more layers of the convolutional neural network); and
determining, by the regression system, the raw score based on one or more features of an accumulated feature set comprising the plurality of first slide features and the one or more second slide features (see para. 0072, Nie discusses a scoring module to score the detected and/or classified cells).
Regarding claim 3, Nie teaches wherein the plurality of first slide features comprise at least one of: an area of a tumor region of the pathology slide; a number of stained immune cells of the pathology slide; a number of unstained immune cells of the pathology slide; a number of stained tumor cells of the pathology slide; a number of unstained tumor cells of the pathology slide; a number of other cells of the pathology slide; and a total number of cells of the pathology slide (see para. 0018, Nie discusses detecting cells in the sample image using a trained object detection engine).
Regarding claim 4, Nie teaches wherein the calculating the one or more second slide features comprises: calculating at least one of a field of view (FOV) area score, a cell area score, and a cell count score (see para. 0171, Nie discusses calculating number of identified cells output corresponds to the total number of biomarker-positive tumor cells detected in a region, as evidenced by the number of tumor cells counted).
Regarding claim 6, Nie teaches wherein the cell area score is expressed as: (average size of IC+ cells) x (number of IC+ cells) cell area score = Area of Tumor, where average size of IC+ cells represents an average size of stained immune cells, number of IC+ cells represents a number of stained immune cells of the pathology slide, and Area of Tumor represents an area of a tumor region corresponding to the pathology slide (see para. 0171, Nie discusses each detected cellular blob, average blob intensity, color and geometric features, such as area and shape of the detected cellular blob may be computed, and the cellular blobs are classified into tumor cells and cells of non-tumor cells. The number of identified cells output corresponds to the total number of biomarker-positive tumor cells detected in a region, as evidenced by the number of tumor cells counted).
Regarding claim 7, Nie teaches wherein the cell count score is expressed as: number of IC+ cells cell count score = total number of cells where number of IC+ cells represents a number of stained immune cells of the pathology slide, and total number of cells represents a total number of cells of the pathology slide (see para. 0171, Nie discusses each detected cellular blob, average blob intensity, color and geometric features, such as area and shape of the detected cellular blob may be computed, and the cellular blobs are classified into tumor cells and cells of non-tumor cells. The number of identified cells output corresponds to the total number of biomarker-positive tumor cells detected in a region, as evidenced by the number of tumor cells counted).
Regarding claim 9, Nie teaches wherein the regression system comprises a trained machine learning model configured to correlate the one or more features of the accumulated feature set to the raw score (see para. 0168, Nie discusses training and implementing a trained supervised classifier; the classifier may be a Support Vector Machine SVM).
Regarding claim 10, Nie teaches wherein the trained machine learning model comprises one of a K-nearest neighbors (KNN) model, a support vector machine (SVM) model, a random forest (RF) model, and a multilayer perceptron (MLP) model (see para. 0168, Nie discusses training and implementing a Support Vector Machine SVM classifier).
Regarding claim 12, Nie teaches further comprising: receiving, by a classifier, an image of the pathology slide; classifying, by the classifier, each cell of a plurality of cells captured in the image by identifying each cell of the plurality of cells and assigning a cell type from among a plurality of cell types to each one of the plurality of cells; and generating, by the classifier, the plurality of first slide features based on the classification of each cell (see figure 2C, figure 3A, figure 5, para. 0034, Nie discusses detecting and/or classifying cells in a test image; see para. 0090, 0099, Nie discusses finding and classifying objects in an image using a region-based convolutional neural networks (RCNN)).
Regarding claim 13, Nie teaches wherein the classifier comprises a convolutional neural network (see para. 0090, Nie discusses finding and classifying objects in an image using a region-based convolutional neural networks (RCNN)).
Regarding claim 14, Nie teaches a method of determining a raw score of a pathology slide from a tissue sample, the method comprising:
receiving, by a cell-based scoring system comprising a processing circuit and a memory, an image of the pathology slide (see figure 2C, figure 3A, figure 5, para. 0034, Nie discusses detecting and/or classifying cells in a test image);
classifying, by the cell-based scoring system, each cell of a plurality of cells captured in the image by providing the image to a classifier of the cell-based scoring system, the classifier being configured to identify each cell of the plurality of cells and to assign a cell type from among a plurality of cell types to each one of the plurality of cells (see figure 2C, figure 3A, figure 5, para. 0034, 0178, Nie discusses detecting and/or classifying cells in a test image);
generating, by the cell-based scoring system, a plurality of first slide features based on the classification of each cell (see figure 2C, figure 3A, figure 5, para. 0034, 0178, Nie discusses detecting and/or classifying cells in a test image); and
determining, by the cell-based scoring system, the raw score based on one or more features of an accumulated feature set comprising the plurality of first slide features (see figure 2C, figure 3A, figure 5, para. 0072, Nie discusses scoring module to score the detected and/or classified cells; see para. 0151-0152, Nie discusses acquiring images of slides comprising multiple tumor cells that are stained).
Regarding claim 15, Nie teaches wherein the generating the plurality of first slide features comprises: counting a number of cells assigned to each cell type of the plurality of cells; and generating the plurality of first slide features based on the number of cells assigned to each cell type (see para. 0171, Nie discusses number of identified cells output corresponds to the total number of biomarker-positive tumor cells detected in a region, as evidenced by the number of tumor cells counted; see para. 0151-0152, Nie discusses acquiring images of slides comprising multiple tumor cells that are stained).
Regarding claim 16, Nie teaches further comprising: receiving, by the cell-based scoring system, an area of a tumor region corresponding to the image, wherein the accumulated feature set further comprises the area of the tumor region (see para. 0087, Nie discusses portion of a tissue identified as belonging to a certain tissue type or belonging to a suspected tumor region).
Regarding claim 17, Nie teaches wherein the plurality of first slide features comprise at least one of: a number of stained immune cells of the pathology slide; a number of unstained immune cells of the pathology slide; a number of stained tumor cells of the pathology slide; a number of unstained tumor cells of the pathology slide; a number of other cells of the pathology slide; and a total number of cells of the pathology slide (see para. 0151-0152, Nie discusses acquiring images of slides comprising multiple tumor cells that are stained; see para. 0171, Nie discusses number of identified cells output corresponds to the total number of biomarker-positive tumor cells detected in a region, as evidenced by the number of tumor cells counted).
Regarding claim 18, Nie teaches further comprising: calculating, by the cell-based scoring system, one or more second slide features corresponding to the pathology slide based on the plurality of first slide features, wherein the accumulated feature set further comprises the one or more second slide features (see para. 0071, Nie discusses extracted cellular features, a clustering module subsequently be used to generate homogeneous clusters of cells).
Regarding claim 19, Nie teaches wherein the calculating the one or more second slide features comprises: calculating at least one of a field of view (FOV) area score, a cell area score, and a cell count score (see para. 0072, Nie discusses scoring module to score detected and classified cells; see para. 0171, Nie discusses calculating number of identified cells output corresponds to the total number of biomarker-positive tumor cells detected in a region, as evidenced by the number of tumor cells counted).
Regarding claim 22, Nie teaches a cell-based scoring system for determining a raw score of a pathology slide from a tissue sample, the cell-based scoring system comprising: a classifier comprising a convolutional neural network (see para. 0090, Nie discusses finding and classifying objects in an image using a region-based convolutional neural networks (RCNN)) configured to: receive an image of the pathology slide (see figure 2C, figure 3A, figure 5, para. 0034, Nie discusses detecting and/or classifying cells in a test image);
classify each cell of a plurality of cells captured in the image by identifying each cell of the plurality of cells and assigning a cell type from among a plurality of cell types to each one of the plurality of cells (see figure 2C, figure 3A, figure 5, para. 0034, Nie discusses detecting and/or classifying cells in a test image);
generate a plurality of first slide features based on the classification of each cell (see figure 2C, figure 3A, figure 5, para. 0072, Nie discusses scoring module to score the detected and/or classified cells).
a cell-based feature generator configured to calculate one or more second slide features corresponding to the pathology slide based on the plurality of first slide feature (see figure 2C, figure 3A, figure 5, para. 0072, Nie discusses scoring module to score the detected and/or classified cells; see para. 0151, Nie discusses pathology slides comprising multiple tumor cells that have been stained); and
a regressor configured to determine the raw score based on one or more features of an accumulated feature set comprising the plurality of first slide features and the one or more second slide features (see figure 2C, figure 3A, figure 5, para. 0072, Nie discusses scoring module to score the detected and/or classified cells; see para. 0172-0173, Nie discusses scoring slides).
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.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Nie et al. (US 2021/0216746) in view of Chukka et al. (US 2016/0042511).
Regarding claim 2, Nie does not expressly disclose wherein the pathology slide is stained with an immunohistochemical assay. However, Chukka teaches wherein the pathology slide is stained with an immunohistochemical assay (see para. 0004, Chukka discusses automatically interpreting and scoring tissue specimen slides stained with an immunohistochemical (IHC) assay).
Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Nie with Chukka to derive at the invention of claim 2. The result would have been expected, routine, and predictable in order to perform pathology slide classification.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Nie in this manner in order to improve pathology slide classification by analyzing and scoring tissue specimen slides stained with an immunohistochemical assay. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Nie, while the teaching of Chukka continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of staining a specimen slide to perform pathology slide classification. The Nie and Chukka systems perform pathology slide analysis, therefore a person having ordinary skill in the art would have reasonable expectation of success in the combination yielding predictable results. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Claims 5, 8, 11, 20, 21 are rejected under 35 U.S.C. 103 as being unpatentable over Nie et al. (US 2021/0216746) in view of Barnes et al. (US 2017/0270666).
Regarding claim 5, Nie does not expressly disclose wherein the FOV area score is expressed as: (average size of IC+ cells) x (number of IC+ cells) FOV area score = . . .Area occupied by all cells m the slide where average size of IC+ cells represents an average size of stained immune cells, and number of IC+ cells represents a number of stained immune cells of the pathology slide.
However, Barnes teaches FOV area score calculation, expressed as: (average size of IC+ cells) x (number of IC+ cells) FOV area score = . . .Area occupied by all cells m the slide where average size of IC+ cells represents an average size of stained immune cells, and number of IC+ cells represents a number of stained immune cells of the pathology slides (see para. 0059-0065).
Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Nie with Barnes to derive at the invention of claim 5. The result would have been expected, routine, and predictable in order to perform pathology slide classification.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Nie in this manner in order to improve pathology slide classification by analyzing and scoring tissue specimen slides to calculate a FOV score. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Nie, while the teaching of Barnes continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of calculating the number of stained cells to perform pathology slide classification. The Nie and Barnes systems perform pathology slide analysis, therefore a person having ordinary skill in the art would have reasonable expectation of success in the combination yielding predictable results. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 8, Nie does not expressly disclose wherein the determining the raw score comprises: providing the one or more features of the accumulated feature set to a trained regression model configured to correlate raw score values to values of the one or more features; and estimating, by the trained regression model, the raw score corresponding to the one or more features.
However, Barnes teaches wherein the determining the raw score comprises: providing the one or more features of the accumulated feature set to a trained regression model configured to correlate raw score values to values of the one or more features; and estimating, by the trained regression model, the raw score corresponding to the one or more features (see para. 0008, Barnes discusses risk stratification system using a regression model trained using training histopathological (H&E and IHC) tissue slides from several patients along with survival outcome data for said patients).
Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Nie with Barnes to derive at the invention of claim 8. The result would have been expected, routine, and predictable in order to perform pathology slide classification.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Nie in this manner in order to improve pathology slide classification by analyzing and scoring tissue specimen slides using a trained regression model. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Nie, while the teaching of Barnes continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of using a trained regression model to perform pathology slide classification. The Nie and Barnes systems perform pathology slide analysis, therefore a person having ordinary skill in the art would have reasonable expectation of success in the combination yielding predictable results. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 11, Nie does not expressly disclose further comprising: comparing the raw score with a threshold to determine efficacy of a treatment on a patient associated with the tissue sample. However, Barnes teaches further comprising: comparing the raw score with a threshold to determine efficacy of a treatment on a patient associated with the tissue sample (see claim 1, Barnes discusses calculating a slide score for each one of the images, entering the slide scores into a statistical model that models a cancer reoccurrence risk, calculating a risk stratification score by the statistical model using the slide scores, generating a signal by thresholding the risk stratification score, the signal being indicative of a requirement for administration of a drug, such as adjuvant chemotherapy, to the patient for the avoidance of cancer reoccurrence).
Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Nie with Barnes to derive at the invention of claim 11. The result would have been expected, routine, and predictable in order to perform pathology slide classification.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Nie in this manner in order to improve pathology slide classification by analyzing and scoring tissue specimen slides using a threshold to determine risk scores. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Nie, while the teaching of Barnes continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of using a threshold to perform pathology slide classification. The Nie and Barnes systems perform pathology slide analysis, therefore a person having ordinary skill in the art would have reasonable expectation of success in the combination yielding predictable results. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 20, Nie does not expressly disclose wherein the determining the raw score comprises: providing the one or more features of the accumulated feature set to a trained regression model configured to correlate raw score values to values of the one or more features; and estimating, by the trained regression model, the raw score corresponding to the one or more features.
However, Barnes teaches wherein the determining the raw score comprises: providing the one or more features of the accumulated feature set to a trained regression model configured to correlate raw score values to values of the one or more features; and estimating, by the trained regression model, the raw score corresponding to the one or more features (see para. 0008, Barnes discusses risk stratification system using a regression model trained using training histopathological (H&E and IHC) tissue slides from several patients along with survival outcome data for said patients).
Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Nie with Barnes to derive at the invention of claim 20. The result would have been expected, routine, and predictable in order to perform pathology slide classification.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Nie in this manner in order to improve pathology slide classification by analyzing and scoring tissue specimen slides using a trained regression model. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Nie, while the teaching of Barnes continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of using a trained regression model to perform pathology slide classification. The Nie and Barnes systems perform pathology slide analysis, therefore a person having ordinary skill in the art would have reasonable expectation of success in the combination yielding predictable results. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding claim 21, Nie does not expressly disclose further comprising: comparing the raw score with a threshold to determine efficacy of a treatment on a patient associated with the tissue sample.
However, Barnes teaches further comprising: comparing the raw score with a threshold to determine efficacy of a treatment on a patient associated with the tissue sample (see claim 1, Barnes discusses calculating a slide score for each one of the images, entering the slide scores into a statistical model that models a cancer reoccurrence risk, calculating a risk stratification score by the statistical model using the slide scores, generating a signal by thresholding the risk stratification score, the signal being indicative of a requirement for administration of a drug, such as adjuvant chemotherapy, to the patient for the avoidance of cancer reoccurrence).
Motivation to combine may be gleaned from the prior art considered. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the invention of Nie with Barnes to derive at the invention of claim 21. The result would have been expected, routine, and predictable in order to perform pathology slide classification.
The determination of obviousness is predicated upon the following: One skilled in the art would have been motivated to modify Nie in this manner in order to improve pathology slide classification by analyzing and scoring tissue specimen slides using a threshold to determine risk scores. Furthermore, the prior art collectively includes each element claimed (though not all in the same reference), and one of ordinary skill in the art could have combined the elements in this manner explained using known engineering design, interface and/or programming techniques, without changing a fundamental operating principle of Nie, while the teaching of Barnes continues to perform the same function as originally taught prior to being combined, in order to produce the repeatable and predictable result of using a threshold to perform pathology slide classification. The Nie and Barnes systems perform pathology slide analysis, therefore a person having ordinary skill in the art would have reasonable expectation of success in the combination yielding predictable results. It is for at least the aforementioned reasons that the examiner has reached a conclusion of obviousness with respect to the claim in question.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kapil et al. (US 2019/0392580) discusses generating a score of a histopathological diagnosis of cancer.
Shanmugam et al. (US 20200355688) discusses a characteristic of a particular cell or tissue type or state; characteristic of a particular pathological condition or state; or indicative of the severity of a pathological condition, the likelihood of progression or regression of the pathological condition, and/or the likelihood that the pathological condition will respond to a particular treatment.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNY A CESE whose telephone number is (571) 270-1896. The examiner can normally be reached on Monday – Friday, 9am – 4pm.
If attempts to reach the primary examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached on (571) 272-3838. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/Kenny A Cese/
Primary Examiner, Art Unit 2663
