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.
Claim 7 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.
The term “near” in claim 7 is a relative term which renders the claim indefinite. The term “near” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For examination purpose, the term has been interpreted as within a certain distance from cell surface.
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) 1-2, 4, 6-14, 16 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Leyh et al (Front Cell Neurosci. Jun 29, 2021), hereinafter Leyh in view of Ding et al (IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 14, No. 6, 2017), hereinafter Ding.
-Regarding claim 1, Leyh discloses a method of analyzing a biological sample of nerve cells including microglial cells, the method comprising (Abstract; FIGS. 1-8
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): segmenting, using a machine learning model (FIG. 2(D); Page 2, 2nd Col., 3rd paragraph, “morphological evaluation using machine learning”), a plurality of microglial cells in image data into soma and processes (FIG. 2(A), soma and process detection), the image data obtained from the biological sample (FIG. 1; FIG. 2(A), image acquisition); for each microglial cell of the plurality of microglial cells (FIG. 2(B), 2(C); Page 4, 2nd Col., Sec. Cell Classification): measuring, from the image data (FIGS. 1-2; Page 3., Sec. Image Acquisition and Processing), values of a set of features of the soma, the processes, and the cell body (FIG. 2(D) (feature map of each convolution layer); FIG. 3; Page 6, 1st Col., last paragraph; Page 8, Sec. Quantification of Morphological Parameters, “cell area, cell perimeter …soma area … were measured for each … cell) ), thereby measuring a plurality of feature values for the plurality of microglial cells (FIG. 2(D); FIGS. 3- 5); and clustering the plurality of feature values for the plurality of microglial cells into a plurality of clusters (FIG. 4; Page 6, 2nd Col., “NC classification was performed for all already CNN classified cells by calculating the distance of their parameter combinations to all four class centroids and assigning the class of the nearest centroid”), each cluster including a subset of the plurality of microglial cells (FIG. 4 (D)(I)(N)), wherein each cluster corresponds to a different state of microglial cells (FIG. 4 (E)(J)(O)).
Leyh does not disclose a feature vector for a set of feature values of microglial cells. However, a person of ordinary skills in the art would understand that a vector is just a representation of a set of values. The feature map in each convolution layers in Leyh’s FIG.2(D) and triangular matrix can be considered as a row of feature vectors. Leyh’s eighteen morphological parameters can also be considered as a feature vector.
In the same field of endeavor, Ding teaches a method for microglia segmentation, feature extraction, and classification (Ding: Abstract; FIGS. 1-10; Table 1). Ding further teaches a vector of values of a set of features of the soma, the processes, and the cell body, thereby measuring a plurality of vectors of feature values for the plurality of microglial cells (Ding: Table 1; Page 1372, Sec. 3.4, “a non-linear Support Vector Machine (SVM) classifier with Gaussian Radial Basis Function (RBF) ” (inputs to SVM are feature vectors); Page 1371, Sec. 3.3, “eight set of features”; Page 1373, 2nd Col., “monofractal dimension features”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Leyh with the teaching of Ding by using a vector of feature values that can be used various machine learning models such as SVM, etc., in order to more accurate performing microglia segmentation and classification.
-Regarding claim 2, Leyh in view of Ding teaches the method of claim 1. the combination further teaches for each cluster of the plurality of clusters: comparing a plurality of representative values of a plurality of representative features for the cluster with a plurality of reference values of the plurality of representative features for one or more reference cells, each having a same known state (Leyh: FIGS. 3, 8; Page 10, 2nd Col., 2nd paragraph, “compared to the ischemic-affected corresponding area”; Page 2, 2nd Col., 3rd paragraph), and determining a state of the microglial cells in the cluster based on the comparing of the plurality of representative values with the plurality of reference values; comparing one or more amounts of microglial cells in one or more states to one or more reference amounts; and determining a classification of the biological sample based on the comparing (Leyh: Abstract, “classification of microglial morphological phenotypes using machine learning … characterization of microglial changes in healthy and disease mouse models”; FIGS. 2-4, 6-8; Page 7, 2nd Col., last paragraph; Page 2, 2nd Col., 3rd paragraph; Page 8, 1st Col., “whereas the amount of ramified microglia was lower compared to the control hippocampal area”; See also Ding: Abstract; FIGS. 7, 10; Page 1374, 1st Col., 2nd paragraph).
-Regarding claim 4, Leyh in view of Ding teaches the method of claim 2. The combination further teaches wherein the one or more amounts are proportions of the microglial cells having the one or more states (Leyh: FIGS. 2, 7-8; Page 8, 1st Col.).
-Regarding claim 6, Leyh in view of Ding teaches the method of claim 2. The combination further teaches wherein: the biological sample is a first biological sample, the first biological sample is obtained from a subject having a genetic perturbation, the reference amounts are from a second biological sample obtained from a control subject without the genetic perturbation, and the classification of the biological sample is a level of an effect of the genetic perturbation (Leyh: Abstract, “classification of microglial morphological phenotypes using machine learning … characterization of microglial changes in healthy and disease mouse models”; FIGS. 2, 4, 6-8; Page 7, 2nd Col., last paragraph; Page 2, 2nd Col., 3rd paragraph; Page 8, 1st Col.; Page 2, 1st Col., 1st paragraph, “respond to tissue damage, infection, or homeostatic perturbations”).
-Regarding claim 7, Leyh in view of Ding teaches the method of claim 2. The combination further teaches wherein the set of features comprises proximity to a plaque; intensity of a marker of microglia activation; percentage of overlap with a marker of cell division; volume; surface area; a moment of inertia; unitless combinations of volume, surface area, and/or moment of inertia; skeletal parameters; fractal parameters; an intensity and variation of fluorescent counterstains within, at, or near each cell surface, a number of other segmented objects contained within the microglial surface; a Boolean combination of their volumes, the distances between microglia, and to other segmented objects; or network parameters calculated from the induced graph of microglial nearest neighbors at different neighborhood sizes (Leyh: FIG. 3; Page 2, 2nd Col., 2nd paragraph, “convex hull area, soma perimeter, process length, number of processes, process branching process volume, circularity, solidity, fractal dimension and, lacunarity”).
-Regarding claim 8, Leyh in view of Ding teaches the method of claim 1. The combination further teaches wherein the set of features is predetermined (Leyh: FIG.3; See also Ding: Table 1).
-Regarding claim 9, Leyh in view of Ding teaches the method of claim 2. The combination further teaches wherein the known state of the reference cells is responsive, homeostatic, dysfunctional, activated, not activated, quiescent, amoeboid, undergoing cell division, rod-like, ramified, hypertrophic, dystrophic, or an Alzheimer-specific state (Leyh: FIG. 2; See also Ding).
-Regarding claim 10, Leyh in view of Ding teaches the method of claim 2. The combination further teaches wherein the plurality of representative values comprises a plurality of statistical values (Leyh: Page 7, 1st Col., Sec. Statistical Analysis; See also Ding: Page 1369, 1st Col, 3rd paragraph; Table 1).
-Regarding claim 11, Leyh in view of Ding teaches the method of claim 2. The combination further teaches wherein the plurality of reference values comprises a plurality of statistical values (Leyh: Page 7, 1st Col., Sec. Statistical Analysis; See also Ding: Page 1369, 1st Col, 3rd paragraph; Table 1).
-Regarding claim 12, Leyh in view of Ding teaches the method of claim 2. The combination further teaches comprising receiving, by a computer system, the image data (Leyh: FIGS. 1-2).
-Regarding claim 13, Leyh in view of Ding teaches the method of claim 12. The combination further teaches comprising obtaining the image data by performing immunofluorescence microscopy of the biological sample (Leyh: Page 3, 1st Col., Sec. Staining, 1st paragraph, “microscopetic”, 2nd Col, 2nd paragraph, “immunofluorescence intensities”; Page 6, 1st Col., 2nd paragraph; Note: it is a common practice to use immunofluorescence microscopy of the biological sample to classify patterns in activity of human body such as brain. See Balaji et al ( US 20240282460 A1).
-Regarding claim 14, Leyh in view of Ding teaches the method of claim 1. The combination further teaches wherein the image data is immunofluorescence microscopy image data, and the immunofluorescence microscopy image data is obtained without using two stains to differentiate between the soma and the processes (Leyh: Page 3, 1st Col., Sec. Staining, 1st paragraph, “microscopetic”, 2nd Col, 2nd paragraph, “immunofluorescence intensities”; Page 6, 1st Col., 2nd paragraph).
-Regarding claim 16, Leyh in view of Ding teaches the method of claim 1. The combination further teaches wherein the machine learning model comprises a convolutional neural network (Leyh: FIG. 2).
-Regarding claim 20 Leyh in view of Ding teaches the method of claim 2. The combination further teaches wherein the plurality of representative features is the same as the set of features (Leyh: FIG. 3; See also Ding: Table 1).
Claim(s) 3 and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Leyh et al (Front Cell Neurosci. Jun 29, 2021), hereinafter Leyh in view of Ding et al (IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 14, No. 6, 2017), hereinafter Ding, and further in view of Rosi et al (US 20200140533 A1), hereinafter Rosi.
-Regarding claim 3, Leyh in view of Ding teaches the method of claim 2. the combination further teaches wherein: the biological sample is a first biological sample, the first biological sample is obtained from a subject for a disease (Leyh: Page 6, 1st Col., last paragraph – 2nd Col., 1st paragraph), the one or more reference amounts are from a second biological sample obtained from a control subject not undergoing the treatment for the disease, and the classification of the biological sample is a level of effectiveness of the treatment (Leyh: Abstract; FIG. 8; Page 10, 2nd Col., 2nd paragraph, “Ischemic-affected regions displayed more activated microglial cells, which showed smaller cell and soma area, cell perimeter, convex hull area, skeleton length, and branching index in the control area compared to the ischemic-affected corresponding area”; See also Ding: Abstract; FIG. 10; Page 1374, 1st Col., 2nd paragraph).
Leyh in view of Ding does not teach the first biological sample is obtained from a subject undergoing a treatment.
However, Rosi is an analogous art pertinent to the problem to be solved in this application and teaches a method of treating a brain injury (Rosi: Abstract; FIGS. 1A-6B; [0038], enhanced microglial cell”; [0040]). Rosi further teaches the first biological sample is obtained from a subject undergoing a treatment (Rosi: [0247], “A “biological sample” encompasses a variety of sample types obtained from an individual and can be used in a diagnostic or monitoring assay … such as by treatment with …”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Leyh in view of Ding with the teaching of Rosi by using a first biological sample that is obtained from a subject undergoing a treatment.in order to provide a real word application for inspecting treatment of a brain injury.
-Regarding claim 5, Leyh in view of Ding, and further in view of Rosi teaches the method of claim 5. The modification further teaches wherein the classification is that the treatment is effective, the method further comprising continuing treatment of the subject (Rosi: [0040], “a progressive increase in synaptic engulfment activity by microglial cells … provide therapeutic options for treatment of TBI-induced cognitive deficits in the aging brain”; [0247]; [0263], “underlying pathology of a condition to stabilize or improve a subject's condition or to reduce the likelihood that the subject's condition will worsen as much as if the subject did not receive the treatment”).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Leyh et al (Front Cell Neurosci. Jun 29, 2021), hereinafter Leyh in view of Ding et al (IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 14, No. 6, 2017), hereinafter Ding, and further in view of Hu et al (Front Neuroanat, Jan 2021 ), hereinafter Hu.
-Regarding claim 15, , Leyh in view of Ding teaches the method of claim 1.
Leyh in view of Ding teaches a first region labeled as a soma and one or more second regions labeled as processes (Leyh: FIG. 1; Page 6, 1st Col., 2nd paragraph, “soma white, processes gray”) and detection of soma and process (Leyh: FIG. 2).
Leyh in view of Ding teaches does not teach optimizing parameters of the machine learning model based on outputs of the machine learning model matching or not matching the first region and the one or more second regions when the plurality of training images is input into the machine learning model, wherein an output of the model specifies a region corresponding to a soma or a process.
However, Hu is an analogous art pertinent to the problem to be solved in this application and teaches a method for soma segmentation (Hu: Abstract; FIGS. 1-9). Hu further teaches labeling soma region and other regions (Hu: FIGS. 1, 3, 9) and optimizing parameters or training of the machine learning model based on outputs of the machine learning model matching or not matching the first region and the one or more second regions when the plurality of training images is input into the machine learning model, wherein an output of the model specifies a region corresponding to a soma or a process (Hu: FIGS. 2, 4; Page 3, Sec. Pre-processing; Page 5, 1st Col., 3rd paragraph; equations (1)-(4)).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Leyh in view of Ding teaches with the teaching of Hu by performing soma and process detection using neural networks in order to achieve accurate segmentation.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Leyh et al (Front Cell Neurosci. Jun 29, 2021), hereinafter Leyh in view of Ding et al (IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 14, No. 6, 2017), hereinafter Ding, and further in view of Lipsky et al (US 20210104321 A1), hereinafter Lipsky.
-Regarding claim 17, Leyh in view of Ding teaches the method of claim 1.
Leyh in view of Ding does not teach wherein clustering comprises using principal component analysis, UMAP, K-means clustering, hierarchical clustering, or HDBSCAN.
However, Lipsky is an analogous art pertinent to the problem to be solved in this application and teaches a method for disease prediction using machine learning (Lipsky: Abstract; FIGS. 1-154B). Lipsky further teaches wherein clustering comprises using principal component analysis, UMAP, K-means clustering, hierarchical clustering, or HDBSCAN (Lipsky: [0412], “clustering using a k-means algorithm”; [1065]; [1040], “principal component analysis (PCA) was performed”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Leyh in view of Ding with the teaching of Lipsky by using various clustering algorithms in order to group unlabeled data into clusters based on their similarities.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Leyh et al (Front Cell Neurosci. Jun 29, 2021), hereinafter Leyh in view of Ding et al (IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 14, No. 6, 2017), hereinafter Ding, and further in view of Heindl et al (Frontiers in Cellular Neuroscience, Vol. 12, Article 106, 2018), hereinafter Heindl.
-Regarding claim 18, Leyh in view of Ding teaches the method of claim 1.
Leyh in view of Ding does not teach wherein the image data is from one or more three-dimensional images.
However, Heindl is an analogous art pertinent to the problem to be solved in this application and teaches a method for determining tissue characteristics (Heindl: Abstract; FIGS. 1-6). Heindl further teaches wherein the image data is from one or more three-dimensional images (Heindl: FIG. 4(F); Table 1; FIG. 2; Page 4, 2nd Col., Sec. Image Segmentation, “3D masks”; Page 9, 2nd Col., 1st paragraph; Page 10, 1st Col., 2nd paragraph).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Leyh in view of Ding with the teaching of Heindl by using the image data which is from one or more three-dimensional images in order to more accurate describing three-dimensional microglial morphology.
Allowable Subject Matter
Claim 19 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Heindl et al (Automated Morphological Analysis of Microglia After Stroke, Frontiers in Cellular Neuroscience, Vol. 12, Article 106, 2018), hereinafter Heindl teaches a method for analysis of microglia morphology.
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/XIAO LIU/Primary Examiner, Art Unit 2664