DETAILED ACTION
Status of the Claims
Original claims 1-8 filed April 1, 2025, are pending.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Objections
Claims 1 and 5 are objected to because of the following informalities:
In claim 1, line 8, the acronym “WSI” should be defined
In claim 5, line 30, “configuration module” should be “classification module”
In claim 5, line 2 on page 2/15, the acronym “WSI” should be defined
Appropriate correction is required.
Claim Interpretation
Examiner notes the presence of reference characters in the claim, such as “(500)” in claim 1. “Generally, the presence or absence of such reference characters does not affect the scope of a claim.” MPEP 608.01(m). None of the reference characters recited in the claim are interpreted as affecting the scope of a claim.
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) is/are:
the classification module of claim 1,
the training module of claim 4,
the classification module of claim 5, and
the training module of claim 8.
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.
Examiner notes that claim 5 is not a “single means” claim at least because it recites a combination of multiple functional limitations. See MPEP 2181, Subsection V.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 is directed to a “method for detecting at least one anomaly in a tissue sample” (reference characters omitted) and lists steps performed by a classification module. Claim 4 depends from claim 1 and recites “wherein a method for training the classification model comprises:” (ref. chars. omitted) followed by a series of steps, all performed by a training module rather than the classification module.
It is unclear whether any of the training method steps recited in claim 4 are required to occur as part of the method for detecting at least one anomaly recited in claim 1. I.e., while the claim defines a training method, it does not apparently require actually performing that training method. Note, for example, that the training method is presented within a “wherein” clause and such clauses may not require steps to be performed. See MPEP 2111.04, Subsection I.
This ambiguity makes the scope of the claim unclear and renders the claim indefinite.
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.
Claim(s) 1-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over ‘Kanse’ (“Cautious Artificial Intelligence Improves Outcomes and Trust by Flagging Outlier Cases,” 2022; The provided copy includes the paper itself, along with an appended “Data Supplement” provided with the paper and referenced in the paper’s text).
Regarding claims 1-4, Examiner notes that these claims recite methods that are substantially the same as the methods performed by the systems of claims 5-8, respectively. The systems of claims 5-8 are obvious over Kanse (see below). Accordingly, claims 1-4 are also rejected under 35 U.S.C. 103 as being unpatentable over Kanse for substantially the same reasons as claims 5-8, respectively.
Regarding claim 5, Kanse teaches a system (300) for detecting at least one anomaly in a tissue sample, the system (300) comprising:
a classification module (302) (see Note Regarding Hardware below), wherein the configuration module (302) is configured to:
extract one or more feature representations from the tissue sample (e.g., Page 4, ClassClust Method of Training DNNs, a DNN is trained to extract features from a tissue image; e.g., Pages 4-5, Image Classification and Outlier Identification, the trained DNN is applied to a test image, thereby extracting feature representations; Figure 1D illustrates feature representations 1, 2 and 3 extracted from different tissue samples; Table 1 summarizes different types of tissue samples used for feature extraction);
determine whether a given sample falls within a normal distribution or deviates as an anomaly using a distance measured between one or more test WSI features and each of normal class distributions using a classification model (e.g., Pages 4-5, Image Classification and Outlier Identification, ClassClust identifies whether a test sample is normal or an outlier/anomaly by determining whether a Mahalanobis distance measured between test WSI features [i.e., features of a test image] and each of normal [i.e., inlier] class distribution centroids falls within a distance threshold; This is illustrated at Fig. 1D, where test sample #1 is normal because it falls within a threshold distance of normal class A, test sample #2 is normal because it falls within a threshold distance of normal class B, and test sample #3 is an anomaly/outlier because it is over a threshold distance to all of the normal class distributions; Note that the “classification model” is the DNN); and
determine that there is at least one anomaly in the tissue sample, if the determined distance has a deviation from all normal class distributions greater than a pre-defined threshold (e.g., as explained above, in Fig. 1D test tissue sample #3 is determined to include at least one anomaly because the determined distance is greater than a pre-defined threshold from all normal class distributions A and B).
Note Regarding Hardware. Examiner notes that the classification module invokes 35 U.S.C. 112(f) – see Claim Interpretation above. The corresponding structure, material, acts, and equivalents thereof includes a computer and/or a processor within a computer that is configured to perform the module’s function – see, e.g., pars. [0037] and [0041] of the published specification.
As shown above, Kanse teaches the functions of the claimed module. Kanse also at least implies the use of a computer and processor. For example, Kanse teaches training and inference using deep neural networks (DNNs), which are typically performed using computer processors. Nevertheless, Kanse does not explicitly teach implementing its processing functions using a computer processor [i.e., a classification module and/or training module].
However, Examiner takes Official Notice that it is old and well-known within the art of image analysis to implement processing functions, such as classification and training functions, using computer processors. Such computer implementation advantageously allows processing functions to be performed quickly and efficiently.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to implement the processing functions of Kanse, such as classification and training, using one or more computer processors with the reasonable expectation that this would result in a system that could perform the processing functions quickly and efficiently.
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Kanse to obtain the invention as specified in claim 5.
Regarding claim 6, Kanse teaches the system, as claimed in claim 5, and Kanse further teaches that the distance is one of a Euclidean distance, and a Mahalanobis distance (e.g., Pages 4-5, Image Classification and Outlier Identification; Fig. 1D and caption).
Regarding claim 7, Kanse teaches the system, as claimed in claim 5, wherein the classification module (302) is configured to determine that there is no anomaly in the tissue sample, if the determined distance has a deviation from all normal class distributions which is not greater than a pre-defined threshold (e.g., Pages 4-5, Image Classification and Outlier Identification, and Fig. 1D with caption, test tissue samples #1 and #2 are determined to belong to classes A and B, respectively, and thus contain no anomaly/outlier because they have distances from those normal classes that deviate from them by less than a pre-defined distance threshold).
Regarding claim 8, Kanse teaches the system, as claimed in claim 5, wherein the system (300) further comprises a training module (301) (see Note Regarding Hardware given above in the rejection of claim 5; The same analysis/reasoning applies to the training module), wherein the training module (301) is configured to:
learn one or more feature representations for different normal classes of a plurality of tissue samples (e.g., Page 4, ClassClust Method of Training DNNs, a DNN is trained to learn clustered feature representations for different normal classes of tissue samples; Table 1 summarizes the different datasets of tissue samples used, with classes having asterisks being left out as abnormal/outliers and classes without asterisks being the normal/in-distribution classes for which clustered feature representations are learned), wherein the one or more feature representations capture one or more distinct structural and morphological characteristics of each subtype of histopathology related tissue (e.g., Fig. 3, lower, the heatmaps demonstrate that the feature representations of ClassClust capture distinct structural and morphological characteristics of each subtype of histopathology related tissue included in-distribution within the dataset; I.e., the features focus attention on the structure/shape of a lesion of interest for in-distribution subtypes of histopathology related tissue; Additional heatmaps are shown in the Data Supplement; Note that the heatmaps are color-coded; As another example, Fig. 2C illustrates that ClassCust achieves good classification accuracy, which indicates that it has been able to capture distinct characteristics, including structural and morphological characteristics, that distinguish between teach subtype of histopathology related tissue);
define an in-distribution space using the one or more learned feature representations (e.g., Pages 4-5, Image Classification and Outlier Identification, and Fig. 1D with caption, the spaces within the dashed rings [i.e., distance thresholds] around each class within the feature space); and
constrain the in-distribution space using contrastive learning (e.g., Page 4, ClassClust Method of Training DNNs, contrastive learning is used to ensure that “in that feature space, similar samples move closer to each other in some sense, whereas dissimilar samples get pushed apart” – i.e., the in-distribution space is constrained using contrastive learning).
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
‘Negishi’ (US 2025/0173866 A1)
Detects abnormal tissue images based on distance to a normal feature distribution – e.g., Figs. 10-13
May evaluate multiple types of tissue together – [0152] et seq.
Uses contrastive learning – [0151]
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/GEOFFREY E SUMMERS/Examiner, Art Unit 2669
