DETAILED ACTION
This action is made in response to the amendments/remarks filed on February 2, 2026. This action is made final.
Claims 1-39 are pending. Claims 1, 5, 7, 15, 19, and 20 have been amended. Claims 36-39 are newly added. Claims 1, 15, and 19 are independent claims.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed February 2, 2026 have been fully considered but they are not persuasive.
As to the 101 rejection, Applicant argues the claim limitation wherein the feature set comprises “spatial relationship data representing relative positions and arrangements of the automatically classified cell types” constitutes a specific technical step to address the concrete problem of analyzing a complex tumor microenvironment for prognosis and is a specific improvement in the technological process of computerized histopathological analysis. However, the examiner respectfully disagrees.
MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. See, e.g., DDR Holdings, LLC. v. Hotels.com, L.P., 773 F.3d 1245, 1259 (Fed. Cir. 2014) (finding that claiming a website that retained the “look and feel” of a host webpage provided a technological solution to the problem of retention of website visitors by utilizing a website descriptor that emulated the “look and feel” of the host webpage, where the problem arose out of the internet and was thus a technical problem). Here, the Applicant’s argued problem is not a technological problem caused by computerized process histopathological analysis. The problem of histopathology analysis was not a problem cause by the computer, is it a problem that existed and/or exists regardless of whether a computer is involved in the process. At best, Applicant’s identified problem is a diagnosis problem. Because no technological problem is present, the claims do not provide a practical application.
As to the 103 rejection, Applicant argues Yip fails to teach “spatial relationship data representing relative positions and arrangements of the automatically classified cell types”. However, the examiner respectfully disagrees.
As a first matter, it is noted the claim limitation is subject to a 112b rejection for failing to particularly point out and distinctly claim the subject matter. As further stated below, it is unclear as to what two or more elements/objects/concepts are assessed in which to make a spatial relationship. Furthermore, Yip, teaches identifying various features in the histological samples such as visual characteristics, clustering, densities, spacing and distance, etc. (e.g., see [0187], [0188]). As such, Yip having taught identifying various ways in which cell/tissues are arranged in their space, Yip reads upon the claimed limitation.
Claim Rejections - 35 USC § 112
Claims 1-39 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.
As to independent claims 1, 15, and 19, the claims recite “spatial relationship data representing relative positions and arrangements of the automatically classified cell types”. However, it is unclear as to what elements/objects the spatial relationship data is being obtained. It is unclear if the spatial relationship represents the positions and arrangements of the classified cell types to each other or to something else. In other words, it is unclear what two or more elements/objects/concepts are identified in which a relationship and/or relative position can be assessed. For the purposes of compact prosecution, the claim will be interpreted in a manner as best understood by the examiner in light of the specification. Accordingly, where the prior art teaches any spatial relationship of any parts of the cell/tissue, then it meets the claimed limitation. Appropriate correction is required.
Dependent claims 2-14, 16-18, and 20-39 fail to resolve the 112 deficiency of their parent claims and are similarly rejected.
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 1-39 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-14, 24-26, and 36-39 recite a predicting an outcome related to cancer, which is within the statutory category of a process. Claims 15-18 and 28-31 recite a non-transitory computer readable memory performing instructions for predicting an outcome related to cancer, which is within the statutory class of a manufacture. Claims 19-23 and 32-35 recite a system for predicting an outcome related to cancer, which is within the statutory class of a machine.
Claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability. Alice Corp. v. CLS Bank Int'l, 573 U.S. ___ (2014). Claims 1-39, each considered as a whole and as an ordered combination, are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
MPEP 2106 Step 2A – Prong 1:
The bolded limitations of:
Claims 1 and 15 (claim 1 being representative)
obtaining a histological sample of a cancer tumor of a patient; determining a feature set for the histological sample by applying a deep learning module trained on a population of histological samples of cancer tumors, wherein the feature set comprises: (i) data representing automatically classified cell types within the histological sample, and (ii) spatial relationship data representing relative positions and arrangements of the automatically classified cell types; and generating an outcome set for the patient by applying a second model to the determined feature set.
as presently drafted, under the broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions). For example, but for the noted computer elements, the claim encompasses a person following rules or instructions to analyze histological samples and compare to a dataset to predict an outcome of a similar patient. The examiner further notes that “methods of organizing human activity” includes a person’s interaction with a computer (see October 2019 Update: Subject Matter Eligibility at Pg. 5). If the claim limitation, under its broadest reasonable interpretation, covers managing persona behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
MPEP 2106 Step 2A – Prong 2:
This judicial exception is not integrated into a practical application because there are no meaningful limitations that transform the exception into a patent eligible application. The additional elements merely amount to instructions to apply the exception using generic computer components (“processor”, "a non-transitory computer readable medium”, “a server”, “a network”—all recited at a high level of generality). Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claim further recites the additional elements of (1) a deep learning model that is trained on a population of histological samples of cancer tumors (2) a second model trained on feature data and outcome data (as per claim 19) and (3) use of the learning model to predict outcomes and (4) use of specific models (e.g., U-Net and multivariate as per claim 19)). When given the broadest reasonable interpretation in light of the nonexistent description of training in the disclosure, training of a deep learning model with the noted data amounts to a mathematical concept that creates data associations. As such, this training of the deep learning model is interpreted to be subsumed within the identified abstract idea and the use of the trained model provides nothing more than mere instructions to implement the abstract idea, supra. July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2, discussion of item (c) at Pgs. 7-9. Regarding (3) and (4), the use of the trained model provides nothing more than mere instructions to implement an abstract idea on a generic computer (“apply it”). See MPEP 2106.05(f). MPEP 2106.05(f); July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2, discussion of items (d) and (e) at Pgs. 8-9.
The claims only manipulate abstract data elements into another form. They do not set forth improvements to another technological field or the functioning of the computer itself and instead use computer elements as tools in a conventional way to improve the functioning of the abstract idea identified above. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. None of the additional elements recited "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Alice Corp., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
At the levels of abstraction described above, the claims do not readily lend themselves to a finding that they are directed to a nonabstract idea. Therefore, the analysis proceeds to step 2B. See BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1349 (Fed. Cir. 2016) ("The Enfish claims, understood in light of their specific limitations, were unambiguously directed to an improvement in computer capabilities. Here, in contrast, the claims and their specific limitations do not readily lend themselves to a step-one finding that they are directed to a nonabstract idea. We therefore defer our consideration of the specific claim limitations’ narrowing effect for step two.") (citations omitted).
MPEP 2106 Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons as presented in Step 2A Prong 2. Moreover, the additional elements recited are known and conventional generic computing elements (“processor”, "a non-transitory computer readable medium”, “a server”, “a network”-- see Specification Fig. 3, [0065]-[0069] describing the various components as general purpose, common, standard, known to one of ordinary skill, and at a high level of generality, and in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy the statutory disclosure requirements). Therefore, these additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept that amounts to significantly more. See MPEP 2106.05(f).
The Federal Circuit has recognized that "an invocation of already-available computers that are not themselves plausibly asserted to be an advance, for use in carrying out improved mathematical calculations, amounts to a recitation of what is 'well-understood, routine, [and] conventional.'" SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 1023 (Fed. Cir. 2018) (alteration in original) (citing Mayo v. Prometheus, 566 U.S. 66, 73 (2012)). Apart from the instructions to implement the abstract idea, they only serve to perform well-understood functions (e.g., receiving, translating, and displaying data—see Specification above as well as Alice Corp.; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016); and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015) covering the well-known nature of these computer functions).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements (1) a deep learning model that is trained on a population of histological samples of cancer tumors (2) a second model trained on feature data and outcome data (as per claim 19) and (3) use of the learning model to predict outcomes and (4) use of specific models (e.g., U-Net and multivariate as per claim 19)) were considered to be part of the abstract idea and “apply it,” respectively. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. Regarding (1) and (2), the training of the model is considered part of the abstract idea and thus cannot provide a practical application. Regarding (3) and (4), the use of the trained model represented saying “apply it.” Items (3) and (4) has been revaluated under the “significantly more” analysis and does not provide “significantly more” to the abstract idea. MPEP 2106.05(A) indicates also indicates that merely adding the words “apply it” or equivalent use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Dependent Claims
The limitations of dependent but for those addressed below merely set forth further refinements of the abstract idea without changing the analysis already presented. Claims 2-4, 13, and 24-27 (22, 28-35) merely recites the type of outcome, type of cancer, type of sample, cell type, and administering a treatment of a plurality of treatment types; claims 5, 7-8, 11, 12, 36-39 (17, 20, 21) merely recite the type of features to make the prediction, which covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions).
Claims 6, 9, 10 (18) include the additional element of a “U-Net model”, “multivariate model”, and “cox proportional hazards (CPH) models” that is analyzed in the same manner as modelds of the independent claims and which does not provide a practical application or amounts to significantly more for the same reasons detailed above.
Claims 14 (16, 23) further refine the abstract idea described in the independent claim and merely recites a graphical user interface for displaying. These additional elements are considered to “apply it” under both the practical application and significantly more analysis, as detailed in the analysis above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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) 1-9 and 11-39 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yip et al. (USPPN: 2021/0233238; hereinafter Yip) in further view of Hassan-Shafique et al. (USPPN: 2021/0118136; hereinafter Hassan).
As to claim 1, Yip teaches A method performed by at least one processor for predicting outcomes related to a cancer (e.g., see Title, [0096] teaching a method for determining image-derived information correlated to the existence of cancer, likelihood of survival, likelihood of progression, etc.), the method comprising:
obtaining a histological sample of a cancer tumor of a patient (e.g., see [0108], [0115] wherein histopathology images of a patient are collected);
determining a feature set for the histological sample by applying a deep learning module trained on a population of histological samples of cancer tumors (e.g., see [0093], [0115], [0123], [0124], [0134], [0145], [0158] wherein the sample is provided to a machine learning model that has been trained from a plurality of different sources for the same type of disease to identify regions of interest in the sample), wherein the feature set comprises:
data representing automatically classified cell types within the histological sample (e.g., see [0090], [0093] wherein one or more features are identified for different cell types), and
spatial relationship data representing relative positions and arrangements of the automatically classified cell types (See 112 rejection. e.g., see [0166], [0355] wherein the received sample image can be further classified as nucleus pixels and cytoplasm pixels wherein the nucleus and cytoplasm are identified based on their position to one another. Additionally, each cell may be classified by their cell type including the location of each cell and pixel that contains a cell outer edge); and
generating an outcome set for the patient by applying a second model to the determined feature set (e.g., see [0123], [0260], [0404] wherein a report is generated based on the determined features, predicting patient survival, response to treatment, therapy recommendations, etc.).
While Yip teaches generating recommending therapies, survival reports, etc., using a therapy decision system (i.e., model), for the purposes of compact prosecution and in the same field of endeavor of patient diagnostics, Hassan teaches the use of a machine learning model for generating the outcome.
In the same field of endeavor of patient diagnostics, Hassan teaches generating an outcome set for the patient by applying a second model to the determined feature set (e.g., see Abstract, [0084], [0097]-[0100] wherein machine learning models, using identified features, generate relevant metadata, including treatment plans and associated statistics and metrics about mortality, morbidity, etc.). Accordingly, it would have been obvious to modify Yip in view of Hassan with a reasonable expectation of success. One would have been motivated to make the modification in order to improve the time-consuming task of analyzing oncology data to provide personalized therapeutic plans for treating patients (e.g., see [0002] of Hassan).
As to claim 2, the rejection of claim 1 is incorporated. Yip further teaches wherein the outcome set comprises at least one of a risk category, or risk-score for at least one of recurrence free survival, progression free survival, event free survival, overall survival, response to therapy, or disease-free survival (e.g., see [0096], [0260] wherein the system predicts various outcomes including, likelihood of a type of cancer, positive response to therapy, survival and/or progression-free survival, likelihood of regression/progression, etc.).
As to claim 3, the rejection of claim 1 is incorporated. Yip further teaches wherein the cancer is at least one of bladder cancer, non-muscle invasive bladder cancer, muscle invasive bladder cancer, urothelial carcinoma of the bladder, squamous cell carcinoma of the bladder, adenocarcinoma of the bladder, and small cell carcinoma of the bladder (e.g., see [0102] wherein the system is used to detect various types of cancer, including bladder cancer).
As to claim 4, the rejection of claim 1 is incorporated. Yip further teaches further comprising: providing a set of recommended therapies responsive to the determined feature set for the histological sample (e.g., see [0404] wherein a list of recommended therapies can be provided).
As to claim 5, the rejection of claim 1 is incorporated. Yip further teaches wherein the feature set for the histological sample comprises at least one of morphology data, tissue region data, colocalization data, and hotspot data (e.g., see ]0094], [0128], [0166] wherein identified features include morphological features, inflamed/hot spots, cell position, and/or characteristics of regions etc.).
As to claim 6, the rejection of claim 1 is incorporated. Yip further teaches wherein the deep learning module comprises a U- Net model, wherein the U-Net model comprises a fully convolutional neural network having an encoder and decoder (e.g., see [0376] teaching the use of UNet models, which are known image segmentation convolutional encoder-decoder architectures).
As to claim 7, the rejection of claim 1 is incorporated. Yip further teaches further comprising: training the deep learning module on the population of histological samples of cancer tumors to determine nuclei location and shape data (e.g., see [0093], [0115], [0123], [0124], [0134]. [0145], [0158] wherein the sample is provided to a machine learning model that has been trained from a plurality of different sources for the same type of disease to identify regions of interest in the sample, including determination of the tumor characteristics such as nucleus location and shape, see [0166], [0187]).
As to claim 8, the rejection of claim 1 is incorporated. Yip further teaches wherein determining a feature set for the histological sample further comprises:
determining locations of tissue within the histological sample (e.g., see [0112], [0187] wherein regions of interest of the samples are determined);
detecting positions of nuclei and cells of interest within the determined locations of tissue (e.g., see [0166], [0187] wherein position and shape of the nuclei and cells are determined);
determining at least one of morphologic, geometric, and textural features for each of the detected nuclei and cells of interest (e.g., see [0128], [0166] wherein identified features include morphological features, inflamed/hot spots, cell position, etc.); and
determining a spatial location feature for each of the detected nuclei and cells of interest (e.g., see [0166] wherein the cells and space between nucleus and border are determined).
As to claim 9, the rejection of claim 1 is incorporated. Yip-Hassan further teaches wherein the second model comprises a multivariate model (e.g., see Fig. 29, [0124] of Yip and [0097], [0099] of Hassan wherein the outcome is generated from an analysis using more than one variable (i.e., multivariate)).
As to claim 11, the rejection of claim 1 is incorporated. Yip further teaches further comprising: training the second model on non-histological data comprising at least one of medical images, clinical variables, genomics, and medical text (e.g., see [0124] wherein the learning model is trained using data from a plurality of sources, including molecular data, demographic data, etc.).
As to claim 12, the rejection of claim 1 is incorporated. Yip further teaches further comprising: training the second model to determine a signature, wherein the signature comprises the combination of histological features and weights (e.g., see [0163], [0314] wherein the training data may contain weight values for different features).
As to claim 13, the rejection of claim 1 is incorporated. Yip further teaches further comprising: administering to the patient a particular treatment type, responsive to the outcome set corresponding to the for a particular treatment type (e.g., see [0249], [0391] wherein the outcome is use to provide targeted treatment to the patient).
As to claim 14, the rejection of claim 1 is incorporated. Yip-Hassan further teaches further comprising: displaying, on a graphical user interface, at least a portion of the outcome set (e.g., see [0404] wherein the recommendation is provided to a user on a graphical user interface. See also ]0107] of Hassan teaching presenting the personalized therapeutic plan on a display).
As to claim 24, the rejection of claim 1 is incorporated. Yip further teaches wherein the histological sample comprises a whole slide image and/or virtual microscopy image (e.g., see Title, [0004], [0010] wherein the sample is a slide image or virtual microscope slide image).
As to claim 25, the rejection of claim 1 is incorporated. Yip further teaches further comprising: determining a cell type for the detected cells of interest, wherein the cell type comprises a tumor cell, immune cell, or stromal cell (e.g., see [0006] wherein the cell types can be immune cells, tumor cells, or stroma cells).
As to claim 26, the rejection of claim 25 is incorporated. Yip further teaches wherein the cell type comprises at least one of neutrophil, lymphocyte, eosinophil, tumor/neoplastic, macrophage, mitosis, plasma, endothelial, apoptosis or stromal (e.g., see [0090] wherein the cell type can include, tumor, stroma, normal, lymphocyte, fat, muscle, immune, etc.).
As to claim 27, the rejection of claim 13 is incorporated. Yip further teaches wherein administering to the patient the particular treatment type, responsive to the outcome set for a particular treatment type comprises determining at least one of a risk category, or risk-score for at least one of recurrence free survival, progression free survival, event free survival, overall survival, response to therapy, or disease-free survival corresponding to a particular treatment type (e.g., see [0096], [0260] wherein the system predicts various outcomes including, likelihood of a type of cancer, positive response to therapy, survival and/or progression-free survival, likelihood of regression/progression, etc.).
As to claims 15-18 and 28-31, the claims are directed to the non-transitory computer-readable medium implementing the method of claims 1, 14, 8, 9, and 24-27 and are similarly rejected.
As to claim 19, Yip teaches A system for predicting outcomes related to a cancer (e.g., see Title, Fig. 1, [0096] teaching a system for determining image-derived information correlated to the existence of cancer, likelihood of survival, likelihood of progression, etc.), the system comprising:
at least one server communicatively coupled to a user device by a network, wherein the at least one server further comprises a non-transitory memory storing computer-readable instructions and at least one processor (e.g., see Fig. 1); the execution of the computer-readable instructions causing the at least one server to:
train a deep learning module on a population of histological samples of cancer tumors, wherein the deep learning model comprises a U-net model (e.g., see [0093], [0115], [0123], [0124], [0134]. [0145], [0158] wherein the sample is provided to a machine learning model that has been trained from a plurality of different sources for the same type of disease to identify regions of interest in the sample, wherein the model is a unet model, see [0376]);
train a second model on feature set data and outcomes data, wherein the second model comprises a multivariate model (e.g., see [0404] wherein a therapy decision system utilizes a plurality of variables (i.e., multivariate) to determine appropriate recommendations based on features);
obtain a histological sample of a cancer tumor of a patient (e.g., see [0108], [0115] wherein histopathology images of a patient are collected);
determine a feature set for the histological sample by applying the trained deep learning module (e.g., see [0093], [0115], [0123], [0124], [0134]. [0145], [0158] wherein the sample is provided to a machine learning model that has been trained from a plurality of different sources for the same type of disease to identify regions of interest in the sample), wherein the feature set comprises:
data representing automatically classified cell types within the histological sample (e.g., see [0090], [0093] wherein one or more features are identified for different cell types), and
spatial relationship data representing relative positions and arrangements of the automatically classified cell types (See 112 rejection. e.g., see [0166], [0355] wherein the received sample image can be further classified as nucleus pixels and cytoplasm pixels wherein the nucleus and cytoplasm are identified based on their position to one another. Additionally, each cell may be classified by their cell type including the location of each cell and pixel that contains a cell outer edge);
generate an outcome set for the patient by applying a second model to the determined feature set (e.g., see [0123], [0260], [0404] wherein a report is generated based on the determined features, predicting patient survival, response to treatment, therapy recommendations, etc.).
While Yip teaches generating recommending therapies, survival reports, etc., using a therapy decision system (i.e., model), for the purposes of compact prosecution and in the same field of endeavor of patient diagnostics, Hassan teaches the use of a machine learning model for generating the outcome.
In the same field of endeavor of patient diagnostics, Hassan teaches train a second model (e.g., see Abstract, [0084], [0097]-[0100] wherein machine learning models, using identified features, generate relevant metadata, including treatment plans and associated statistics and metrics about mortality, morbidity, etc.). Accordingly, it would have been obvious to modify Yip in view of Hassan with a reasonable expectation of success. One would have been motivated to make the modification in order to improve the time-consuming task of analyzing oncology data to provide personalized therapeutic plans for treating patients (e.g., see [0002] of Hassan).
As to claims 20-23, and 32-35, the claims are directed to the system implementing the method of claims 5, 8, 2, 14, and 24-27 and are similarly rejected.
As to claim 36, the rejection of claim 1 is incorporated. Yip further teaches wherein the feature set comprises data representing measurements of cell and cell nucleus shapes and sizes (e.g., see [0101], [0166], [0283] teaching measurement of the ratio of the size of the nucleus to cytoplasm and measure of size and smoothness of the image object).
As to claim 37, the rejection of claim 1 is incorporated. Yip further teaches wherein the feature set comprises morphologic, geometric, and textural features for detected nuclei and cells of interest (e.g., see [0095], [0155], [0188] wherein the features include morphological features, spacing of cells, geometric elements, shape, density, etc.).
As to claim 36, the rejection of claim 5 is incorporated. Yip further teaches wherein the tissue region data classifies tissue regions according to predominant cell types in the respective regions (e.g., see [0094], [0132], [0145], [0174], [0175] wherein classification can be based on tissue region indicating tumor/no tumor (i.e., predominant cell type)).
As to claim 39, the rejection of claim 1 is incorporated. Yip further teaches wherein the feature set comprises data representing automatically classified tissue types within the histological sample, and spatial relationship data representing relative positions and arrangements of the automatically classified tissue types (e.g., see [0089], [0168] wherein classifier can include tissue type classification).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yip and Hassan, as applied above, and in further view of Shanmugam et al. (USPPN: 2020/0355688; hereinafter Shanmugam).
As to claim 10, the rejection of claim 9 is incorporated. While Yip and Hassan teach the use of multiple variable for making a prediction (i.e., multivariate), Yip-Hasan fail to teach wherein the multivariate model comprises a Cox proportional hazards (CPH) model.
However, in the same field of endeavor of patient diagnostics, Shanmugam teaches wherein the multivariate model comprises a Cox proportional hazards (CPH) model (e.g., see [0014] teaching the use of cox proportional hazard models). Accordingly, it would have been obvious to modify Yip-Hassan in view of Shanmugam with a reasonable expectation of success. One would have been motivated to make the modification as a simple substitution of one known model for another to yield the predictable results of generating a model of the hazard rate for a given event. See KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007); and MPEP 2143.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/STELLA HIGGS/Primary Examiner, Art Unit 3681
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