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 .
Claim Status
Claims 1-20 are pending.
Priority
This application is a 371 of PCT/CN2020/137029, filed 12/17/2020, and claims foreign priority to application no.202010644331.4, filed 07/07/2020 in CN. The instant application has the effective filing date of 07 July 2020.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/04/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Drawings
The drawings, submitted on 09/04/2023, are accepted by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) include: region determining module in claim 8, tumor cell identification module in claim 8, and content evaluation module in claim 8.
The specification states the memory of the computer device [0102], which includes a processor [0101, figure 9] may store various program modules constituting the tumor cell content evaluation system, the region determining module 802, the tumor cell identification module 804, and the content evaluation module 806 [00102].
Therefore broadest reasonable interpretation of the components is software programs(MPEP 2181 II(A).
Such claim limitation(s) further include: binarization processing unit in claim 9, with structure in [0037]; region determining unit in claim 9, with structure in [0083]; area determinining unit in claim 10, with structure in [0085]; content evaluation unit in claim 10, with structure in [0080]; diameter obtaining module in claim 11, with structure in [0088]; cell quantity calculation module in claim 11, with structure in [0090]; first quantity calculation module in claim 11, with structure in [0091]; second quantity determining module in claim 12, with structure in [0092]; training sample obtaining module in claim 13, with structure in [0094]; classifier training module in claim 13, with structure in [0095]; test sample obtaining module in claim 14, with structure in [0097]; classification test module in claim 14, with structure in [0098]; verification module in claim 14, with structure in [0099].
The specification states the above modules and units are located within the previously evaluated, tumor cell identification module, and/or content evaluation module, and therefore have access to a processor and an structure in the form of an algorithm in their highlighted specification passages.
Therefore, broadest reasonable interpretation of the components are algorithms for performing computer implemented functions (MPEP 2181 II(B)).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 11 is rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, because the claim purports to invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, but fails to recite a combination of elements as required by that statutory provision and thus cannot rely on the specification to provide the structure, material or acts to support the claimed function.
As such, the claim recites a function that has no limits and covers every conceivable means for achieving the stated function, while the specification discloses at most only those means known to the inventor. Accordingly, the disclosure is not commensurate with the scope of the claim.
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.
Claim limitation “area calculation module” in claim 11 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 1-20 are rejected under U.S.C 101 because the claimed invention is directed to abstract ideas without significantly more, as detailed in the analysis below.
Eligibility Step 1: Subject matter eligibility evaluation in accordance with MPEP § 2106:
Claims 1-7 are directed to a statutory category (method).
Claim 8-14 are directed to a statutory category (system).
Claim 15-20 are directed to a statutory category (apparatus).
Therefore, in accordance with MPEP § 2106.03 all claims have patent eligible subject matter.
[Eligibility Step 1: YES]
Eligibility Step 2A: This step determines whether a claim is directed to a judicial exception in accordance with MPEP § 2106.
Eligibility Step 2A -- Prong One: Limitations are analyzed to determine if the claims recite any concepts that could equate to a judicial exception (i.e. abstract idea, law of nature, or natural phenomenon). Possible judicial exceptions are explored below.
Recitations of Judicial Exceptions:
Claims 1, 8, and 15: identifying, by using a deep learning-based pathology image classifier, a tumor cell region corresponding to the effective pathological region; (mental process)
determining tumor cell content of the digital pathology slide image based on the tumor cell region according to a preset evaluation rule. (mental process)
Claim 2, 9, and 16: wherein the determining an effective pathological region based on the digital pathology slide image comprises: performing binarization processing on the digital pathology slide image to obtain a grayscale image; (mathematical concept)
extracting, from the grayscale image, a region whose grayscale value is less than a preset grayscale threshold as the effective pathological region. (mental process)
Claim 3, 10, and 17: wherein the determining tumor cell content of the digital pathology slide image based on the tumor cell region according to a preset evaluation rule comprises: determining a first area of the tumor cell region, and determining a second area of the effective pathological region; and calculating a tumor proportion and a tumor-stroma ratio of the digital pathology slide image based on the first area and the second area. (mental process, mathematical concept)
Claim 4, 11, and 18: wherein after the determining tumor cell content of the digital pathology slide image based on the tumor cell region according to a preset evaluation rule, the method further comprises: calculating a mean value of the plurality of test diameters, to obtain an average diameter of a single tumor cell; (mental process, mathematical concept)
determining an average area of the single tumor cell based on the average diameter of the single tumor cell; (mental process, mathematical concept)
calculating a quantity of tumor cells per unit area based on the average area of the single tumor cell; (mental process, mathematical concept)
calculating a first quantity of tumor cells based on the first area and the quantity of tumor cells per unit area. (mental process, mathematical concept)
Claim 5, 12, and 19: wherein after the determining tumor cell content of the digital pathology slide image based on the tumor cell region according to a preset evaluation rule, the method further comprises: performing cell segmentation on the tumor cell region by using a cell segmentation algorithm, to determine a second quantity of tumor cells. (mental process, mathematical concept)
Claim 6, 13, and 20: training the preset classifier, to obtain the pathology image classifier for which training is completed. (mental process)
Claim 7 and 14: obtaining an error between the verification cell type and the test cell type, and when the error is less than a preset error, determining that training for the preset classifier is completed; or obtaining a quantity of training times corresponding to the preset classifier, and when the quantity of training times reaches a maximum preset quantity, determining that training for the preset classifier is completed. (mental process)
Step 2A – Prong One Analysis:
Analysis techniques such as identifying regions of data by viewing data and thresholding other data points, and calculations that can be done requiring nothing more than the human mind and pen/paper, read on observations, evaluations, judgments, and opinions, and fall under the mental process grouping of abstract ideas.
Analysis techniques such as binarization, using algorithms, and calculating averages, areas, and diameters recite mathematical calculations, equations, and/or relationships that fall under the mathematical concept grouping of abstract ideas.
Therefore, the claims appear to recite judicial exceptions.
[Eligibility Step 2A – Prong One: YES]
Eligibility Step 2A – Prong Two: 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. If the claim contains no additional claim elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)). Additional elements are recited, categorized, and analyzed below.
Data Gathering/Outputting Elements:
Claims 1, 8, and 15: obtaining a digital pathology slide image, and determining an effective pathological region based on the digital pathology slide image;
Claims 4, 11, 18: obtaining a plurality of test diameters corresponding to a plurality of tumor cells
Claims 6, 13, and 20: obtaining a training sample set, wherein the training sample set comprises a training pathological region and a corresponding training cell type; using the training pathological region as an input of a preset classifier; using the training cell type as an expected output
Claims 7 and 14: obtaining a test sample set, wherein the test sample set comprises a test effective region and a corresponding test cell type; inputting the test effective region to the preset classifier, to obtain an output verification cell type
Computer Components Elements:
Claim 5: computer device, comprising a memory, processor, and computer readable instructions stored in the memory capable of running on the processor wherein the processor executes the computer-readable instructions
Step 2A – Prong Two Analysis:
The data gathering elements merely obtain data necessary to complete the judicial exceptions. Such elements are classified as insignificant extra-solution activity and thus do not integrate the judicial exceptions into practical application per MPEP 2016.05(g).
Generic computer components and implementations provide mere instructions to implement the abstract ideas onto a technological environment per Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
As such, the additional elements, when viewed separately and in the context of a whole claimed invention, do not integrate the judicial exceptions into practical application.
[Eligibility Step 2A – Prong Two: NO]
Eligibility Step 2B: Claim elements are probed for inventive concept equating to significantly more than the judicial exception (MPEP 2106.04(II)).
Step 2B Analysis:
The data gathering elements are found to be well-understood, routine, and conventional per Abels et al. (Journal of Pathology; Vol. 249: 3; 2019) which reviews best practices of computational pathology for the gathering of training and testing sets of pathological classifiers; and Aeffner et al. (J Pathol Inform; Vol. 10:9; 2019) for the gathering of tumor cell diameters.
The computer components are further found to be well-understood, routine, and conventional per Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 for storing and retrieving information in memory and Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (MPEP 2106.05 (a)).
As such, the additional elements are further found to lack inventive concept.
[Eligibility Step 2B: NO]
Therefore, claims 1-20 are directed to judicial exceptions without significantly more and are rejected under 35 U.S.C 101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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, 6, 8, 13, 15, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Saltz et al. (2020/0388029).
Saltz et al. describes systems and methods to quantify tumor-infiltrating lymphocytes (TILs) for clinical pathology analysis.
Claims 1, 8, and 15 are directed to tumor cell content evaluation methods, systems and computing devices that performs the steps of: obtaining a digital pathology slide image; determining an effective pathological region based on the slide image; identifying, using a deep learning-based pathology image classifier, a tumor cell region corresponding to the effective pathological region; and determining tumor cell content of the digital pathology slide image based on the tumor cell region according to a preset evaluation rule.
Saltz et al. teaches a system and method that include receiving digitized diagnostic and stained whole-slide image data related to tissue of a particular type of tumoral data [0024]; defining regions of interest that represents a portion of, or a full image of the whole-slide image data [0024]; and determining the density of tumor-infiltrating lymphocytes of bounded segmented data portions for respective classification of the regions of interest [0024] by determining whether an assigned classification label is above a pre-determined threshold probability value of lymphocyte infiltrated [0024]; and identifying tumor-infiltrating lymphocytes (TILs) from standard pathology cancer images by a deep-learning-derived “computational stain” [0086], which uses a convolutional neural network trained to classify patches of images [0088].
Saltz et al. teaches the computing system may include at least one processor and system memory [0388]; and a device may further include on-board data storage, such as memory coupled to the processor; the memory may store software that can be accessed and executed by the processor [0392].
Claims 6, 13, and 20 are directed to obtaining a training sample set, wherein the training sample set includes a training pathological region and a corresponding training cell type; using the training pathological region as an input of a preset classifier; using the training cell type as an expected output; and and training the preset classifier, to obtain the pathology image classifier for which training is completed.
Saltz et al. teaches during the creation of a labeled training set, the system will undergo certain processes to create a dataset of digitized whole slide images that have been found to have adequate amounts and variety of TILs [0210], in which the example process begins with specimens identified from a number of patients who have a confirmed diagnosis of a new cancer type [0210]; the lymphocyte CNN is trained with 50×50 μm2 patches from WSIs [0107]; and the lymphocyte CNN categorizes tiny patches of an input image into those with lymphocyte infiltration and those without [0099].
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.
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 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Saltz et al. (2020/0388029) as applied to claims 1, 6, 8, 13, 15, and 20 above, in view of Qaiser et al. (Medical Image Analysis; Vol. 55: 1-14; 2019), and in further view of Janowczyk et al. (J Pathol Inform 7:29; 2016).
Saltz et al. teaches a method, system, and device that determines tumor cell content of the digital pathology slide image according to an evaluation rule, as described above.
Claims 2, 9 and 16 are directed to wherein the determining an effective pathological region based on the digital pathology slide image comprises: performing binarization processing on the digital pathology slide image to obtain a grayscale image; and extracting, from the grayscale image, a region whose grayscale value is less than a preset grayscale threshold as the effective pathological region.
Qaiser et al. describes fast and accurate segmentation of histology images.
Qaiser et al. teaches the WSIs were digitally scanned at a pixel resolution of 0.275 µm/pixel (40 ×) using an Omnyx VL120 scanner (page 8, column 1); and for generating the tumor probability map of a WSI, we first split the given WSI into patches and then applied our methods to each patch (page 8, column 1), in which to explain how we generate the filtration of a given greyscale image, we suppose for definiteness that the intensity of each pixel is an integer in the range [0, 255](page 4, column 1); and select a sequence of integers, where these integers are various threshold levels, at which the image is binarized (page 4, column 1).
Qaiser et al. does not teach extracting, from the grayscale image, a region whose grayscale value is less than a preset grayscale threshold as the effective pathological region (claims 2, 9, and 16).
Janowczyk et al. describes deep learning for digital pathology image analysis.
Janowczyk et al. teaches aiming to reduce the presence of uninteresting training examples in the dataset, so that learning time can be dedicated to more complex edge cases; and epithelium segmentation can have areas of fat or the white background of the stage of the microscope removed by applying a threshold at conservative level of 0.8 to the grayscale image, thus removing those pixels from the patch selection pool (page 9, column 2).
Janowczyk et al. teaches the biological motivation for epithelium segmentation is that epithelium regions contribute to the identification of tumor infiltrating lymphocytes (TILs) (page 4, table 1).
Therefore Qaiser et al. teaches a method of binarizing a digital pathology slide to obtain a grayscale image. It would be obvious to one of ordinary skill in the art to combine the method of Qaiser et al. to the applicable method of Saltz et al., with each element merely performing the same function as they do separately and the results of the combination being predictable. Furthermore, Janowczyk et al. provides sufficient motivation for one of ordinary skill in the art to extract an area of interest for tumor-infiltrating lymphocyte study by applying a grayscale value thresholding technique. As such, it would be further obvious to one of ordinary skill in the art to apply the technique of Janowczyk et al. to the method of Saltz et al. in view of Qaiser et al. with a reasonable expectation of success.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Saltz et al. (2020/0388029) as applied to claims 1, 6, 8, 13, 15, and 20 previously, and in view of Martin et al. (Virchows Archiv; Vol. 477: 185-193; 2020).
Saltz et al. teaches a method, system, and device that determines tumor cell content of the digital pathology slide image according to an evaluation rule, as described above.
Claims 3, 10, and 17 are directed to wherein the determining tumor cell content of the digital pathology slide image based on the tumor cell region according to a preset evaluation rule includes: determining a first area of the tumor cell region, and determining a second area of the effective pathological region; and calculating a tumor proportion and a tumor-stroma ratio of the digital pathology slide image based on the first area and second area.
Saltz et al. does not teach the evaluation rules according to claims 3, 10, and 17.
Martin et al. describes results from a semiautomatic image analysis approach that quantify tumor proportion in colon cancer.
Martin et al. teaches digital images were captured of the immunohistochemical stained slide against a cytokeratin that highlighted the tumor tissue (page 2, column 2); the Tumor Proportion (TP) and Stroma proportion (SP) were assessed in a field of 3.58 mm2, but, differing from the recommendations from van Pelts et al., we used a rectangular selection, where the side lengths were 2.18 mm and 1.64 mm; and field size was 3.58 mm2 (page 2, column 2), where we selected only regions in which tumor cells were present at all four borders of the image field (page 2, column 2); defined tumor proportion as the following: sum of all tumor areas/3.58 mm2 (page 3, column 1); and defined stroma proportion as the following: (3.58 mm2- sum of all tumor areas)/3.58 mm2 (page 3, column 1).
Martin et al. further teaches the tumor stroma ratio (TSR) is a promising prognostic biomarker in colon cancer, which could provide additional risk stratification for therapy adaption (page 1, column 1); and the objective of this study was the investigation of the prognostic significance of stroma/tumor proportion at different tumor sites of colon adenocarcinomas of no special type with a simple semiautomatic approach with the open-source program ImageJ (page 2, column 1).
As Saltz et al. further teaches colon adenocarcinoma [0349] is one of the various TCGA tumor types used in the exemplary evaluation [0345], Martin et al. provides sufficient motivation for one of ordinary skill in the art to apply the tumor stroma ratio calculation technique to the method of Saltz et al., in order to provide additional risk stratification for therapy adaption, particularly applicable to such tumor types with a reasonable expectation of success and each element merely performing the same function as they do separately.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Saltz et al. (2020/0388029) in view of Martin et al. as applied to claims 3, 10, and 17 above, and in further view of Prall et al. (Human Pathology; vol. 35; 7; 2004) and Mahmoud et al. (J Clin Oncol; Vol. 29; 2011).
Saltz et al., in view of Martin et al. teach a method, system, and device that determines tumor cell content of the digital pathology slide image according to an evaluation rule that accounts for tumor-stroma ratio, as described above.
Claims 4, 11, and 18 are directed to the method further including obtaining a plurality of test diameters corresponding to a plurality of tumor cells, and calculating a mean value of the plurality of test diameters, to obtain an average diameter of a single tumor cell; determining an average area of the single tumor cell based on the average diameter of the single tumor cell; calculating a quantity of tumor cells per unit area based on the average area of the single tumor cell; and calculating a first quantity of tumor cells based on the first area and the quantity of tumor cells per unit area.
Prall et al. teaches each tissue disk in the multitissue block represents a circle of 0.6-mm diameter that corresponds to a total area of 0.283 mm2 (page 3, column 1); and for each case, the number of CD8+ tumor-infiltrating lymphocytes per square millimeter of tumor cell area was calculated (page 3, column 1).
Therefore Prall et al. teaches obtaining multiple test diameters of tumor cell clusters; using the diameters to calculate the area of a single cluster; and calculating a quantity of tumor cells per unit area based off the calculation.
Prall et al. does not explicitly teach calculating the mean diameter per singular tumor cells.
Mahmoud et al. describes how tumor-Infiltrating CD8+ Lymphocytes predict clinical outcomes in breast cancer.
Mahmoud et al. teaches CD8+ T cells were counted in three locations in each tumor: intratumoral compartment (within the tumor cell nests), within the distant stroma, defined as one tumor cell diameter away from the tumor, within the adjacent stroma, defined as CD8 cells within one tumor cell diameter of the tumor; Fig 1E (page 2, column 2); and the total number of CD8+ T cells was determined by combining the counts for the three compartments (page 2, column 2).
Mahmoud et al. further teaches distant stromal CD8 T-cell positivity conferred an independent good prognostic value in the same mode (page 4, column 2); total CD8+ count also had a good prognostic effect on Breast cancer specific survival (BCSS) in HER2-negative patients (page 4, column 2); and the results provide evidence of the prognostic importance of the cytotoxic T-cell population in breast cancer (page 6, column 2).
Therefore Mahmoud et al. provides motivation for one of ordinary skill in the art to obtain diameters for individual tumor cells in order to further quantify the number of cell positively associated with good prognostic values for breast cancer. It would represent mere change in proportion/scale and routine optimization to calculate the mean diameter and mean area derived from the mean diameter. As such, it would still be obvious to one of ordinary skill in the art to combine the techniques of Mahmoud et al. of obtaining the diameters of singular tumor cells in addition to clusters, as in Prall et al. with a reasonable expectation of success and improvement to the digital pathological system leading to improved prognoses.
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Saltz et al. (2020/0388029) as applied to claims 1, 6, 8, 13, 15, and 20 previously, and in view of Yin et al. (IEEE Tran Med Imag; Vol. 37: 1; 2018).
Saltz et al. teaches a method, system, and device that determines tumor cell content of the digital pathology slide image according to an evaluation rule, as described previously.
Claim 5, 12, and 19 are directed to wherein after the determining tumor cell content of the digital pathology slide image based on the tumor cell region according to a preset evaluation rule, the method further comprises: performing cell segmentation on the tumor cell region by using a cell segmentation algorithm, to determine a second quantity of tumor cells.
Saltz et al. further teaches nucleus/cell detection and segmentation are common methodologies in tissue image analysis [0008]; over the past decade, researchers have developed a variety of nucleus segmentation methods; achieving accurate and robust segmentation results is desirable in cancer diagnostics because of image noise, such as image acquisition artifacts, differences in staining, and variability in nuclear morphology within and across tissue specimens; it is not uncommon that a segmentation pipeline optimized for a tissue type will produce bad segmentations in images from other tissue types, and even in different regions of the same image; therefore, implementation of accurate segmentation methods is desirable in cancer diagnostics [0008].
Saltz et al. does not explicitly teach using a cell segmentation algorithm to determine a second quantity of tumor cells.
Yin et al. describes tumor cell load and heterogeneity estimation from diffusion-weighted MRI calibrated with histological data.
Yin et al. teaches implementing a seed based automatic and robust cell segmentation algorithm (page 5, column 2), that performs high detection accuracy (page 7, column 1) and enables the automated quantification of cellularity in large histological samples with high accuracy (page 4, column 2).
Yin et al. teaches the automated segmentation is in-line with the aims of digital pathology (page 3, column 1); the routine analysis of tissue samples performed by pathologists is tedious, subjective, and time consuming; tissue slides are digitized for quantitative analysis based on image processing techniques and automated analysis (page 3, column 1), in which the latter increase speed and reproducibility of cancer evaluation and staging (page 3, column 1).
Therefore Saltz et al. provides sufficient motivation for one of ordinary skill in the art to apply an accurate cell segmentation technique to the current method; and Yin et al. provides a cell segmentation algorithm, as an accurate method of cell segmentation in the field of digital pathology and tumor cell quantification. As such, it would be obvious to one of ordinary skill in the art to apply the method of Yin et al. to the method of Saltz et al. with a reasonable expectation of success and improvement to the system.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Saltz et al. (2020/0388029) as applied to claims 1, 6, 8, 13, 15, and 20 previously, and in view of Hagele et al. (Scientific Reports; Vol. 10: 6423; 2020).
Saltz et al. teaches a method, system, and device that determines tumor cell content of the digital pathology slide image according to an evaluation rule and training a classifier to predict patches of images, as described previously.
Claims 7 and 14 are directed to wherein before the using the training pathological region as an input of a preset classifier, using the training cell type as an expected output, and training the preset classifier, to obtain the pathology image classifier for which training is completed, the method further includes: obtaining a test sample set, wherein the test sample set comprises a test effective region and a corresponding test cell type; inputting the test effective region to the preset classifier, to obtain an output verification cell type;
Saltz et al. teaches the CNN model is applied to patches in the test set; and for each test patch, the lymphocyte CNN produces a probability of the patch being a lymphocyte-infiltrated patch [0126].
Claims 7 and 14 are further directed to obtaining an error between the verification cell type and the test cell type, and when the error is less than a preset error, determining that training for the preset classifier is completed; or obtaining a quantity of training times corresponding to the preset classifier, and when the quantity of training times reaches a maximum preset quantity, determining that training for the preset classifier is completed.
Saltz et al. teaches the label of the patch is decided by simple thresholding as shown for example, in FIG. 3C and FIGS. 5A-5B workflows [0126]; if the probability value is above a predefined threshold, the patch is classified as lymphocyte-infiltrated, as shown for example in FIG. 1C and FIG. 3D workflows [0126]; and the CAE is trained in an unsupervised fashion, to minimize the pixel-wise image patch reconstruction error [0115], in which the lymphocyte CNN is built based on the trained CAE [0115].
Saltz et al. does not teach obtaining an error less than a preset error or obtaining quantity of training times corresponding to the preset classifier, and when the quantity of training times reaches a maximum preset quantity, determining that training for the preset classifier is completed.
Hagele et al. describes resolving challenges in deep learning-based analyses of histopathological images using explanation methods.
Hagele et al. teaches demonstrating the following analyses using the GoogLeNet architecture, which is well established for generic computer vision tasks and has recently been proven to also work well in digital pathology; in particular, finetuning a pretrained GoogLeNet convolutional neural network (page 5, column 1) for each tumour entity (page 5, column 1); and performing a 3-fold cross validation to determine the epoch for early stopping, where the maximal number of epochs was set to 50, which was chosen as the lowest validation error averaged over all folds (page 5, column 1). Hagele et al. teaches this enabled us to make best use of the limited available data by using the full training set to train the model (page 5, column 1).
Hagele et al. further teaches another example for a potential issue is the co-occurrence of tumour and TILs (page 9, column 1); and if most of the samples indeed display this interaction, the classifier may learn to associate TILs with positive class labels instead of detecting cancer cells (page 9, column 1).
Therefore Saltz et al. teaches a method of training a convolutional neural network to find positively labeled patches of input containing tumor-infiltrating lymphocytes that minimizes a reconstruction error. Hagele et al. teaches a convolutional neural network that can be used to associate tumor-infiltrating lymphocytes with positive class labels; and uses a preset number of epochs as a stopping criterion, that must be obtained prior to use. As such, the stopping criteria technique of Hagele et al. can be applicable method of Saltz et al. to yield predictable results and an improved system that is enabled to make best use of the limited available data.
Conclusion
No claims are currently allowed.
Correspondence
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/M.K.T./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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