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) are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered 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 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.
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 1, 3, 4, 6-16 are rejected under 35 U.S.C. 103 as being unpatentable over US 2020/0184643 to Coudray et al, hereinafter referred to as “Coudray” in view of US 2020/0105413 to Vladimirova et al, hereinafter referred to as “Vladimirova.”
As per claim 1, Coudray discloses an information processing system in Figure 5 comprising: an acquisition unit configured to acquire a pathologic tissue image of a patient having a target disease (lung cancer [0006]); a division unit configured to divide the pathologic tissue image of the patient into a plurality of region images (“tiling” [0008]); a feature prediction unit configured to input each of the plurality of region images (tiles in Coudray) to each of a plurality of feature prediction models constructed one-to-one for types of histopathological features (see Fig 5, “lung cancer classification”), to acquire prediction information on presence or absence of the histopathological features (See again Fig 5, “LUSC/Normal/LUAD features predicted); a sorting unit configured to sort a plurality of region images of which respective combinations of presence or absence of the histopathological features based on the acquisition match a previously set combination of presence or absence of the histopathological features at time of sorting (See again Fig 5 “Filter out LUSC/Normal tiles”); a gene mutation prediction unit configured to input each of the plurality of region images selected by the sorting to each of gene mutation prediction models constructed one-to-one for types of gene mutations, the gene mutation prediction models each having a combination of presence or absence of the histopathological features, to acquire prediction information on presence or absence of the gene mutations (see [0017]); and a prediction result output unit configured to output (a display [0063]), with the prediction information on presence or absence of the gene mutations acquired for each region image, a prediction result of presence or absence of at least one gene mutation in the patient (the display displays the prediction).
Coudray fails to disclose the one-to-one prediction models for both the feature prediction and gene mutation prediction models.
On the other hand, Vladimirova, in the same field of endeavor as Coudray, discloses an image analysis method for ovarian cancer that employs multiple stages of different machine learning models, with each machine learning model tailored to recognize a specific pathological feature (see [0043]-[0045] “first stage model 452”, “second stage model 454” and “third stage model 456”).
Therefore, it would have been obvious before the effective filing date of the claimed invention to have provided Coudray with multiple machine models tailored to recognize specific pathological features in tissue images as taught by Vladimirova as doing this would have increased the accuracy of the various predictions (recognitions) since each machine model would be specifically trained to recognize unique features in the image.
As per claims 3 and 6, Vladimirova at [0043]-[0046] discloses the use of multiple machine learning models trained to recognize different histopathological features of the biopsy image. The use of individual machine learning models tailored for specific prediction of various gene mutations would have been obvious to one of ordinary skill in the art as tailoring and training a machine model to the specific cancer being diagnosed (e.g. lung, ovarian, colon, breast, etc.) would increase the accuracy of the prediction for that particular form of cancer.
As per claims 4, 8, 10-16, the specific mention of colorectal cancer and the various gene mutations in a tumor would have been obvious in light of the teachings in Coudray of lung cancer, skin cancer (Coudray [0033]) as well as Vladimirova disclosing ovarian cancer and “other disease areas and for other clinical hypotheses.” (Vladimirova [0003]).
Claims 7 and 9 are rejected, mutatis mutandis, for reasoning similar to claim 1 above. As per the computer readable medium storing a program, Coudray teaches this at [0062].
Claims 2 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Coudray in view of Vladimirova as applied to claims 1, 3, 4, 6-16 above, and further in view of the article “H&E-stained Whole Slide Image Deep Learning Predicts SPOP Mutation State in Prostate Cancer” to Schaumberg et al, hereinafter referred to as “Schaumberg.”
As per claims 2 and 5, Coudray discloses in Figure 5 the sorting of segmented images (tiles) into different lung cancer classifications and subsequently filters out normal cell tiles. Coudray in view of Vladimirova fail to disclose the acquisition unit further acquires a primary lesion site of the target disease, and the sorting unit sorts the plurality of region images, with the acquired primary lesion site of the target disease together with combinations of presence or absence of the histopathological features based on the acquisition.
Schaumberg, in the same field of endeavor as Coudray and Vladimirova (image analysis of slides containing biological tissue), discloses on page 6, under the “Independent evaluation” section that the patches (tiles) of sub-images containing localized lesions are sorted and weighted with respect to the predicted mutation.
Therefore, it would have been obvious before the effective filing date of the claimed invention to have provided the classification of histopathological features provided by the combination of Coudray and Schaumberg with the additional ability to also recognize (classify) localized lesion sites in the segmented tiles. The rationale being that doing so would augment the identification and accuracy of the mutations if present in the tissue sample imaged since lesion sites are well known to contain possible gene mutations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. WO 2018/207261A1 is exemplary of the state of the art in pathological tissue analysis that employs image recognition in tissue samples in order to obtain cell classifications via machine learning (neural networks).
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DAVID OMETZ
Primary Examiner
Art Unit 2672
/DAVID OMETZ/Primary Examiner, Art Unit 2672