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 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.
Claims 1-9 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.
Claim 1 recites the limitations “extracting influencing features of the nuclei in the corresponding area through the mask image,” “removing redundant features from the influencing features through feature selection,” and “using the UMAP feature dimensionality reduction method to select the two most important features from the influencing features after feature selection for clustering the nuclei.” However, the specification does not clearly define what makes a feature “influencing” such as whether “influencing” refers to biological influence, statistical influence, predictive importance, or something else. Therefore, the term “influencing features” is subjective and lacks objective boundaries, as the claims and specification fail to provide criteria by which one of ordinary skill in the art could reasonably determine what constitutes an “influencing” feature versus a non-influencing feature. As claim 9 contains identical subject matter, it is also rejected. Accordingly, as claims 2-8 depend from claim 1, they are also 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more. The method of claim 1 is directed to a process, which
is one of the statutory categories of invention, and passes Step 1: Statutory Category- MPEP § 2106.03.
However, the following limitations of Claim 1 recite steps that can be performed in the human mind or
with pen and paper, therefore failing Step 2A Prong One. These limitations constitute mental processes
because they describe acts of observation, evaluation, and judgement that can practically be performed in
the human mind, or by a human using pen and paper as a physical aid.
reading pathological tissue images;
calculating all closed contours present in the pathological tissue images based on a contour tracing method;
evaluating overlapping closed contours based on the gradient features of the input pathological tissue image to obtain the most prominent local contours;
removing redundant features from the influencing features through feature selection;
Claim 1 fails Step 2A Prong Two because the additional elements beyond the judicial exception
do not integrate the judicial exception into a practical application. The claim does not recite a specific
asserted improvement to the functioning of a computer or any other technology or technical field (MPEP § 2106.05(a)), and, instead, applies the abstract idea on a computer (MPEP § 2106.05(f)) for implementation in a particular field of use, specifically pathology (MPEP § 2106.05(h)). Furthermore, the additional elements of the claim do not effect a transformation/reduction of a particular article to a different state or thing, and, instead, use mere data manipulation, such as dividing the nucleus segmented image into individual nucleus images according to the corresponding mask image (MPEP § 2106.05(c)). Claim 1 also fails Step 2B, as these additional elements are well-understood, routine, and conventional (WURC), adding nothing significantly more than the abstract idea itself (MPEP § 2106.07(a)((III)). The UMAP feature dimensionality reduction method is WURC (see pg. 2 of Dorrity et. al, “Dimensionality reduction by UMAP to visualize physical and genetic interactions”). As claim 9 contains this identical ineligible subject matter, it is also rejected.
Claims 2-8 recite steps that can be performed in the human mind or with pen and paper, therefore failing Step 2A Prong One. These limitations constitute mental processes because they describe acts of observation, evaluation, and judgement that can practically be performed in the human mind, or by
a human using pen and paper as a physical aid. Additionally, claims 2-5 and 7-8 recite Mathematical Concepts, which are defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations. The claim must recite (i.e. set forth or describe) a mathematical concept rather than include limitations that are merely based on math. These claims fail Step 2A Prong Two and Step 2B because there are no additional elements beyond the judicial exception, meaning that they are rejected.
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.
Claims 1, 3, 4, 7, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Wienert et. al (“Detection and Segmentation of Cell Nuclei in Virtual Microscopy Images: A Minimum-Model Approach”) in view of Ji et. al (“Nuclear shape, architecture and orientation features from H&E images are able to predict recurrence in node-negative gastric adenocarcinoma”), further in view of Dorrity et. al (“Dimensionality reduction by UMAP to visualize physical and genetic interactions”).
Regarding Claim 1, Wienert teaches a method for the segmentation and clustering of nuclei based on single-cell pathological images, characterized in that, comprising (Fig. 4 (shown below)):
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reading pathological tissue images (Fig. 1 (shown below));
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calculating all closed contours present in the pathological tissue images based on a contour tracing method;
Results: “First, all possible closed contours present in the grayscale-transformed image are computed regardless of size, shape or whether they belong to hills or valleys in the intensity landscape.”
Methods: “This contour detection is based on a conventional contour tracing approach for binary images, which we extended for the use with grayscale images.”
evaluating overlapping closed contours based on the gradient features of the input pathological tissue image to obtain the most prominent local contours;
Results: “This step yields multiple, often overlapping contours, which are evaluated in a second module based on gradient features of the input image. We introduce a ‘‘contour value’’ as a measure to rank and select those contours that best represent the image objects.”
optimizing the closed areas of the most prominent contours and segmenting the pathological tissue images based on the optimized contours to obtain nucleus segmented images;
Results: “Subsequently, this segmentation is improved using a novel contour optimization method and an optional cluster separation step. Finally, cell nuclei are detected by assessing the (nucleus-specific) Hematoxylin within each contour area (for details see Methods section).”
dividing the nucleus segmented image into individual nucleus images according to the corresponding mask image;
Results: “A non-overlapping segmentation is generated in the third step: The enclosed areas of the ranked contours are labeled in a two-dimensional map.”
Explanation: Labeling each nucleus corresponds to segmentation into individual objects.
Wienert fails to teach the method comprises extracting influencing features of the nuclei in the corresponding area through the mask image, removing redundant features from the influencing features through feature selection, and using the UMAP feature dimensionality reduction method to select the two most important features from the influencing features after feature selection for clustering the nuclei.
However, Ji teaches the following limitations:
extracting influencing features of the nuclei in the corresponding area through the mask image;
Materials and methods: “Three different types of quantitative histomorphometric cellular features, covering local architectural features, shape/texture features, local Cell Orientation Graphs features, were extracted from local cluster regions [6, 8] in this study…Finally, a total of 189 features were yielded for each TMA core (Table 1) in our study.”
removing redundant features from the influencing features through feature selection;
Materials and methods: “Three different feature selection schemes, including the minimum redundancy maximum relevance (MRMR), Wilcoxon rank sum test (WRST), and random forest (RF), were employed to identify the most outstanding pathological morphometric features in the training groups… In this paper we limited the number of candidate features to 5 aimed to avoid curse of dimensionality or over fitting challenges using box and whisker plots.”
Ji fails to teach that the method comprises using the UMAP feature dimensionality reduction method to select the two most important features from the influencing features after feature selection for clustering the nuclei.
However, Dorrity teaches this limitation, stating that “To project single-gene deletion strains into two dimensions we performed dimensionality reduction with the UMAP algorithm using the wrapper function in Monocle 3 (v2.99.3) to project single gene deletion strains into two dimensions and subsequently used Louvain clustering on strains in 2D UMAP space using default parameters…” (Methods).
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to apply Ji’s feature extraction and Dorrity’s UMAP dimensionality reduction method into Wienert’s clustering and segmentation method. Ji teaches that extracting large numbers of quantitative nuclear features and applying statistical feature selection improves downstream classification and analysis of pathological images, explicitly saying that feature extraction and screening is necessary to “to avoid curse of dimensionality or over fitting challenges.” Meanwhile, Dorrity further explains that when datasets contain large numbers of features, dimensionality reduction techniques such as UMAP are necessary to preserve relationships while enabling effective clustering, stating that such techniques are imperative as datasets grow in size and complexity. Accordingly, a person of ordinary skill in the art would have been motivated to apply Ji’s feature extraction and selection techniques to the segmented nuclei produced by Weinert in order to obtain quantitative representations of nuclei and further apply Dorrity’s UMAP dimensionality reduction prior to clustering in order to handle high-dimensional feature vectors and improve clustering quality.
Regarding Claim 3, Wienert in view of Ji and Dorrity teaches the method of claim 1, and Wienert further teaches that, the evaluating overlapping closed contours based on the gradient features of the input pathological tissue image to obtain the most prominent local contours comprises:
determining the value of each contour based on the following three criteria: (1) identifying the object within the same local area that is the most prominent and has the highest average gradient; (2) comparing the gradient fit between contour pixels and the maximum local gradient variation; (3) utilizing the Sobel operator and its 3x3 convolution kernel for calculation;
Methods: “An object is considered more important if it has a higher mean gradient along the objects contour (Equation 3), which largely corresponds to the (trivial) concept that an image region is more likely to correspond to an actual object if it is silhouetted sharply against the background (or other objects)…Additionally, multiple contours from the primary segmentation may describe the same object and hence the decision has to be made, which of several alternative contours best represents a certain object: Contours are visually regarded as fitting best if they show a high concordance between contour pixels and the maximum local gradient. This ‘‘gradient fit’’ is defined as the relative number of contour pixels that represent a local maximum in a 3x3 neighborhood in the gradient image.... and is computed by using the Sobel operator S with its 3x3 convolution kernels Gx and Gy (Equation 1) and the 2-D image function I.”
and obtaining the most prominent local contours involves labeling graded contour lines, with the labeling process conducted in a sorted order, starting from the most valuable contour and preventing the overwriting of already assigned labels to obtain the most prominent local contours.
Methods: “To obtain the locally most prominent contours (as defined by the contour value defined in Eq. 4) the labeling process is performed in sorted order, beginning with the top most valued contour and blocking overwriting of already assigned labels.”
Regarding Claim 4, Wienert in view of Ji and Dorrity teaches the method of claim 1, and Wienert further teaches that, the step of performing contour optimization comprises:
testing the compactness of object pixels based on the distance value d;
Methods: “A distance value d is defined for testing the compactness of object pixels (3 in our examples).”
establishing a loop to process pixels with a specific distance value dt, from dt=d−1 to 1;
Methods: “A loop is set up to process pixels with a specific distance value dt starting with dt = d-1 down to 1.”
each cycle requiring scanning the entire distance map, if a pixel pi with a distance value of di=dt does not have a neighbor with a distance value of dt+1, then the distance value di=dt of pixel pi is decreased by 1;
Methods: “The whole distance map is scanned in each cycle. The distance value of pixels pi with di = dt is decremented by 1 if they do not have a paraxial neighbor with a distance value of dt + 1.”
separating objects at concave boundary points by removing object pixels around the cut line between two concavities to obtain the nucleus segmented image (also see Fig. 4d-e (shown above)).
Methods: “To handle cases of cells forming clusters our approach is based on separating objects at concave borders by removing object pixels (labels) around a cutting line between two concavities (Fig. 4d).”
Regarding Claim 7, Wienert in view of Ji and Dorrity teaches the method of claim 1, and Ji further teaches that Wilcoxon rank sum test is used to remove redundant features from influencing features through feature screening (see Materials and methods (shown above)).
Regarding Claim 9, Wienert in view of Ji and Dorrity teaches all of the limitations of claim 1 above because claim 9 recites a system that performs substantially the same steps as claim 1.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Wienert et. al in view of Ji et. al and Dorrity et. al, further in view of Suzuki and Abe (“Topological Structural Analysis of Digitized Binary Images by Border Following”).
Regarding Claim 2, Wienert in view of Ji and Dorrity teaches the method of claim 1, and Wienert further teaches that, the calculating all closed contours present in the pathological tissue images based on a contour tracing method comprises:
converting the original color image of the pathological tissue image into a grayscale image, calculating the average of the three RGB channels of the image, and converting the pathological tissue image into an image function;
Methods: “The grayscale image is generated by computing the mean of the red, green and blue channel of the corresponding pixel in the original color image.”
scanning the grayscale image line by line and storing all local minimum values and local maximum values and the corresponding maximum gradient between them, and determining a starting pixel and corresponding intensity range of the contour detection based on the local minimum value, the maximum value and the maximum gradient;
Methods: “In our approach each image row is regarded as a one dimensional image function I(x) and the contour start pixels are defined as the positions at which the gradient between each pair of neighboring local minima/maxima or maxima/minima is maximal (Fig. 2). All pixels with grayscale values between the intensity at the contour start pixel and at the corresponding local minimum or the corresponding local maximum, respectively, are initially interpreted as object-pixels (Fig. 3). As a consequence each contour tracing is performed within a locally-adapted (‘‘individual’’) intensity range and objects may represent either ‘‘hills’’ or ‘‘valleys’’ in the intensity landscape. The detection of the contour start pixels and the determination of the corresponding intensity range is performed by scanning the image row-wise from left to right and storing all local minima and maxima with the corresponding maximum gradients in between (visual tests showed that the overall result is invariant with respect to the scan direction).”
using an 8-connected neighborhoods to follow the contours of the tracking object clockwise;
Methods: “Subsequently, the contour tracing follows the (potential) object contours clockwise using an 8-connected neighborhood.”
stopping tracking when the contour tracking returns to a seed position, and the contour pixel that continues to be tracked is the same as a second contour pixel;
Methods: “A contour is considered valid if and only if it reaches back to its start pixel.”
Methods: “Otherwise, the contour tracing is terminated if a maximum contour length (225 pixels in our examples) is exceeded.”
when the contour returns to its starting pixel, marking the starting pixel as a valid pixel; if the maximum contour length is exceeded, contour tracing being terminated.
Methods: “A contour is considered valid if and only if it reaches back to its start pixel.”
Methods: “Otherwise, the contour tracing is terminated if a maximum contour length (225 pixels in our examples) is exceeded.”
Wienert does not teach that the method comprises testing a paraxial neighborhood clockwise from the starting pixel on the basis of the existing contour pixels, if there is a pixel of the current object in the neighborhood, testing an counterclockwise neighborhood, and if the contour also belongs to the current object, then the contour continuing to using the pixel.
However, Suzuki and Abe teach a border following (contour tracing) method, stating that “Starting from (iz, jz), look around clockwise the pixels in the neighborhood of (i, j) and find a nonzero pixel… Starting from the next element of the pixel (i2, j,) in the counterclockwise order, examine counterclockwise the pixels in the neighborhood of the current pixel (i3, j,) to find a nonzero pixel and let the first one be (i4, j,)… If the pixel (i3, j, + 1) is a 0-pixel examined in the substep (3.3) then fi3,j3 [Wingdings font/0xDF] - NBD…(b) If the pixel (i3, j, + 1) is not a 0-pixel examined in the substep (3.3) and fi3,j3 = 1, then fi,,j, [Wingdings font/0xDF] NBD…(c) Otherwise, do not change fi3,j3. (3.5) If (i4, j,) = (i, j) and (i3, j,) = (iI, j,) (coming back to the starting point), then go to (4)” (APPENDIX I: THE FORMAL DESCRIPTION OF ALGORITHM 1). This teaches clockwise neighborhood testing from a starting pixel, reversal (counterclockwise) of search direction when continuing traversal, and conditioning of continuation on whether the pixel belongs to the border of the object.
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to apply Suzuki and Abe’s border following method into Wienert, Ji, and Dorrity’s segmentation and clustering system. Suzuki and Abe describe their algorithm as providing a reliable and topologically correct method for contour tracing, stating that their method is designed to ensure accurate border following, correct closure of contours, and robustness of contour extraction. Accordingly, a person of ordinary skill in the art would have been motivated to adopt Suzuki and Abe’s border following logic into Wienert’s contour tracing process to improve the correctness and robustness of the contour extraction, since Wienert already relies on neighborhood-based contour following and incorporates conventional contour tracing approaches.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Wienert et. al in view of Ji et. al and Dorrity et. al, further in view of Abdolhoseini et. al (“Segmentation of Heavily Clustered Nuclei from Histopathological Images”).
Regarding Claim 5, Wienert in view of Ji and Dorrity teaches the method of claim 1, and Wienert further teaches that the step of dividing the cell nucleus segmentation image into individual cell nucleus small images comprises:
identifying and labeling the connected domains within the mask image;
Methods: “To generate a non-overlapping segmentation, initially, a two-dimensional map of the same size as the corresponding image is used to label the area within each contour with a unique identifier.”
Wienert fails to teach that the method comprises drawing the minimum bounding rectangles of the connected domains and dividing them through the coordinates of these rectangles.
However, Abdolhoseini describes isolating each segmented object using its bounding box, and cropping both the binary object and original image using that bounding box, stating that “Each object of B which is bigger than ωmin in size is cropped and processed individually…They are cropped with boxes limited to the objects’ most outer pixels in each direction, called its ‘bounding box’…The original image is also cropped with the same size bounding box as for the object being processed…Then the distance map is calculated for the complement of the binary object inside the bounding box” (Method).
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to apply Abdolhoseini’s bounding boxes into Wienert, Ji, and Dorrity’s segmentation and clustering system. Abdolhoseini teaches that cropping by bounding boxes allows each nucleus to be processed individually, analyzed more efficiently, and improves accuracy of downstream operations such as distance mapping and segmentation refinement. Thus, Abdolhoseini teaches that bounding box-based division is a computationally efficient and accuracy-improving preprocessing step when handling segmented nuclei. Accordingly, a person of ordinary skill in the art would have been motivated to adopt Abdolhoseini’s bounding box cropping to improve computational efficiency and enable more precise per-nucleus processing, which are explicit goals in image segmentation pipelines.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Wienert et. al in view of Ji et. al and Dorrity et. al, further in view of Sriramakrishnan et. al (“An Medical Image File Formats and Digital Image Conversion”).
Regarding Claim 6, Wienert in view of Ji and Dorrity teaches the method of claim 1, and Ji further teaches that the step of extracting the influence features of the cell nucleus comprises:
determining the image type and feature type used to extract features (Materials and methods (shown above));
extracting the influencing features of the cell nucleus according to the image type and feature type (Materials and methods (shown above)).
Ji fails to teach that the method comprises converting the file format of the segmented small image of a single cell nucleus to nii format.
However, Sriramakrishnan teaches this, stating that “NIFITI allows for storing the header and data as a single file with “.nii” extension” (B. NIFTI). Sriramakrishnan further teaches that format conversion is a routine and important step in medical image workflows, stating that “Medical image format conversion is a critical task for researchers…” (Introduction). Lastly, Sriramakrishnan clarifies that NIFITI is not restricted to a specific modality, stating that “NIFTI was created for handling Neuro-imaging but can be used for other fields as well” (B. NIFTI).
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to apply Sriramakrishnan’s nii format into Wienert, Ji, and Dorrity’s segmentation and clustering system because nii is a standardized medical imaging format that facilitates interoperability, data storage, and compatibility with downstream analysis pipelines. Sriramakrishnan identifies motivations for adopting the nii format that include standardized storage of image data, simplified handling using s single-file format, and broad applicability across medical image processing workflows. Accordingly, a person of ordinary skill in the art would have been motivated to convert segmented nucleus images produced by Ji’s feature extraction pipeline into NIFTI format to benefit from the standardized, interoperable file structure taught by Sriramakrishnan, especially where the pipeline involves downstream computational analysis.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Wienert et. al in view of Ji et. al and Dorrity et. al, further in view of Gonzalez (“Clustering to Minimize the Maximum Intercluster Distance”).
Regarding Claim 8, Wienert in view of Ji and Dorrity teaches the method of claim 1, and while Dorrity teaches the UMAP feature dimensionality reduction method to select the two most important features among the influencing features, he fails to teach the rest of the limitations.
However, Gonzalez teaches them, as shown below:
selecting any one of the cell nuclei as the first clustering center Z1;
Approximation algorithm for the k-tMM problem: “In the initialization phase all the elements are assigned to set B,, the first cluster. One of these elements is labeled the head of the cluster, (head,). The selection of this element is arbitrary.”
selecting the cell nucleus farthest from Z1 as the second clustering center Z2;
Approximation algorithm for the k-tMM problem: “The construction of the new cluster is accomplished by first finding a node, vi, in one of the first j clusters (B,, . . . , Bj) whose distance to the head of the cluster it belongs is maximal. Such a node will be moved to cluster Bj+, and called the head of the cluster.”
calculating the distance between each sample and all known cluster centers one by one, and
select the minimum distance among them;
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Explanation: This shows computing distances to multiple centers and choosing the smaller distance.
(4) selecting a maximum distance among all minimum distances, if the maximum value reaches more than the preset score ratio of ‖Z1-Z2‖, then the cell nucleus that produces the maximum distance is defined as a new clustering center and returns previous step; otherwise, the calculation step of cluster center ends (Algorithm APPROX (shown above));
Explanation: This is maximin center selection.
repeating steps (3) and (4) until no new clustering center appears;
Approximation algorithm for the k-tMM problem: “Our algorithm consists of an initialization phase and k - 1 ‘expanding’ phases.”
Explanation: Gonzalez describes iterative expanding phases.
(6) dividing the cell nuclei into the categories represented by the corresponding cluster centers according to the nearest distance to complete the clustering of cell nuclei.
Approximation algorithm for the k-tMM problem: “Such a node will be moved to cluster Bj+, and called the head of the cluster. A node in any of the previously defined clusters will be moved to cluster Bj+, if its distance to V, is not larger than the distance to the head of cluster it belongs to.”
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to apply Gonzalez’s clustering algorithm into Wienert, Ji, and Dorrity’s segmentation and clustering system. A person of ordinary skill in the art would have been motivated to adopt Gonzalez’s clustering algorithm because Gonzalex teaches that distance-based iterative cluster center selection improves clustering optimality, yielding more accuratce, more stable, and more interpretable nuclei clustering, which is a recognized goal in computational pathology and bioimage analysis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chang (US 20190042826 A1) teaches an automated image analysis pipeline for histopathology images that performs nuclei segmentation, generates per-nucleus mask images, extracts multiple quantitative features, and applies downstream unsupervised analysis such as clustering and spatial organization assessment.
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/WILLIAM ADU-JAMFI/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677