DETAILED ACTION
This action is in response to the amendment filed 2/27/26. Claims 1-5, 8-13, 16-17, and 19-23 are pending and have been examined.
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 Interpretation
Claim 16 refers to, “a computer readable storage medium”. Paragraph [0069] of the instant Specification states, “A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire”. Accordingly, the computer readable storage media is not interpreted to include transitory signals per se.
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.
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.
Claim(s) 1-5 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Mandal et al. (A Topological Data Analysis Approach on Predicting Phenotypes from Gene Expression Data, 2020, AlCoB 2020, LNBI 12099, pp. 178-187) in view of Chazal et al. (Subsampling Methods for Persistent Homology, published 2014, arXiv:1406.1901v1), and further in view of Bubenik (STATISTICAL TOPOLOGICAL DATA ANALYSIS USING PERSISTENCE LANDSCAPES, published 1/23/2015, arXiv:1207.6437v4).
Regarding claim 1, Mandal teaches [a] computer-implemented method of training a neural network for disease detection in a sample, comprising:
creating a training set based on topological summaries: “we use the gene expressions of subjects with and without Parkinsons disease to generate topological summaries per subject. These summaries essentially act as unique fingerprints that describe the topology of the gene expression in a sample. We use these fingerprints to enhance the feature vector that is used for disease phenotype prediction, “ (Mandal, page 179, paragraph 5); “For each subject, we obtained a feature vector of 19,581 gene expression measurements (see Sect. 2.6) and a known class label 0 (161 control subjects) or 1 (264 affected subjects) according to the Parkinson’s disease phenotype. We split the data 80-20 into training and test sets, over 50 iterations, except for the computationally more intensive TDA-CNN where we considered 4 iterations after observing the results between iterations were nearly identical” (Mandal, page 184, paragraph 1).
… wherein the topological summaries are associated with different phenotypes: “The topological summaries of the weighted point clouds (persistence landscapes) are then used to construct a machine learning model to predict the phenotype (healthy or PD) for each sample” (Mandal, page 180, paragraph 2).
…creating the training set including at least:
receiving gene expression data associated with a plurality of subjects: “For each subject, we obtained a feature vector of 19,581 gene expression measurements (see Sect. 2.6) and a known class label 0 (161 control subjects) (plurality of subjects) or 1 (264 affected subjects) (plurality of subjects) according to the Parkinson’s disease phenotype” (Mandal, page 184, paragraph 1).
determining pair-wise similarities between genes in the gene expression data of all of the plurality of subjects: “We work under the hypothesis that the set X of all subjects’ samples (gene expression data of all the plurality of subjects), each encoded as a collection of gene expression values, can provide us with enough topological information to discern between healthy subjects and subjects with Parkinson’s disease. We denote by X a matrix of size nrows × ncols where each row corresponds to a subject and each column corresponds to a gene. Each entry Xi,j then corresponds to the j-th gene expression of the i-th subject.” (Mandal, page 179, paragraph 6); “Co-expression can be examined by computing pairwise correlations between gene expression measurements. Therefore, we construct a new matrix
X
-
from X, consisting of all pairwise distance correlations between genes” (Mandal, page 180, paragraph 2).
transforming the gene expression data into the topological summaries based on the pair-wise similarities: “Later, we show how to use the theory of persistent homology to determine the persistent topological landscapes present in the gene expression data of a sample, by first transforming it into a weighted point cloud. We do this transformation by utilizing the gene correlations across all available samples (matrix
X
-
). The topological summaries of the weighted point clouds (persistence landscapes) are then used to construct a machine learning model to predict the phenotype (healthy or PD) for each sample” (Mandal, page 180, paragraph 2).
the topological summaries including a persistence landscape for each degree of at least three homology degrees per subject of the plurality of subjects: “For each of the simplicial complexes we obtained persistence landscapes [5] for homology dimensions 0 and 1. Such landscapes are, for each homology degree, sequences {λk} of decreasing piecewise linear (PL) functions λk : R −→ R … After computation of all landscapes, for each subject we then obtained its average landscape” (Mandal, page 183, paragraph 3);
the transforming further including subsampling data points of the gene expression data by randomly selecting a subset of a preconfigured size, the sub-sampling preserving a topology associated with the topological summaries: “To mitigate the computational cost of our setup we used a subsampling approach, as studied in [11], so that instead of working with the entire set of genes at all times, for each subject we repeatedly subsampled smaller sets of
n
s
u
b
s
a
m
p
l
e
genes, obtaining several filtered simplicial complexes” (Mandal, page 183, paragraph 2).
training a neural network using the training set created based on the topological summaries: “Ultimately, we use the gene expressions of subjects with and without Parkinson’s disease to generate topological summaries per subject. These summaries essentially act as unique fingerprints that describe the topology of the gene expression in a sample. We use these fingerprints to enhance the feature vector that is used for disease phenotype prediction” (Mandal, page 179, paragraph 5); “For each subject, we obtained a feature vector of 19,581 gene expression measurements (see Sect. 2.6) and a known class label 0 (161 control subjects) or 1 (264 affected subjects) according to the Parkinson’s disease phenotype. We split the data 80-20 into training and test sets, over 50 iterations, except for the computationally more intensive TDA-CNN where we considered 4 iterations after observing the results between iterations were nearly identical” (Mandal, page 184, paragraph 1).
by feeding the persistence landscape for each degree of the plurality of homology degrees into the neural network: “In the TDA-CNN approach, for a given resolution ry, we fed each subject’s vectorized persistence landscape as a tensor of shape (rx, ry, 2), one channel per homology degree, into a Convolutional Neural Network (CNN)” (Mandal, page 184, paragraph 3)
… the neural network being trained to predict a phenotype of a sample associated with a subject whose phenotype is unknown: “The topological summaries of the weighted point clouds (persistence landscapes) are then used to construct a machine learning model to predict the phenotype (healthy or PD) for each sample.” (Mandal, page 180, paragraph 3)
While Mandal fails to disclose the further limitations of the claim, Chazal discloses a method of subsampling data points of the gene expression data by randomly selecting a subset of a preconfigured size, the sub-sampling preserving a topology associated with the topological summaries:
“For any positive integer m, let
X
=
{
x
1
,
…
,
x
m
}
⊂
X
be a sample of m points from the measure
μ
∈
P
(
X
)
. The corresponding persistence landscape is
PNG
media_image1.png
321
879
media_image1.png
Greyscale
” Left: 3D shapes of the first experiment. Middle and Left: 500 random points from the magnetometer data of the second experiment.” (Chazal, page 7, Figure 3)
“In practice, each shape consists of a 3D point cloud embedded in the Euclidean space, with a number of vertices that ranges from 7K to 40K … For n = 100 times we subsample m = 300 points (preconfigured size) from each shape; then we select the closest subsample to the corresponding original point cloud and compute
4
×
n
persistence diagrams (dimension 1), one for each subsample” (Chazal, page 7, paragraph 3)
“We study the risk of two estimators and we prove that the subsampling approach carries stable topological (preserving a topology) information while achieving a great reduction in computational complexity” (Chazal, page 1, Abstract)
Mandal and Chazal relate to subsampling point clouds of data to reduce the cost of persistent homology analysis, and are analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Mandal to randomly subsample data such that subsamples maintain topological properties, as disclosed by Chazal. Chazal demonstrated that topological data analysis through these subsets accurately approximates persistent homology of the full set of data points, while being computationally faster and simple to execute. This is particularly useful when trying to perform topological data analysis is prohibitively expensive due to large data sets. See Chazal, page 1, Abstract & pages 7-9.
While Chazal fails to disclose the further limitations of the claim, Bubenik discloses a method, wherein the topological summaries including a persistence landscape for each degree of at least three homology degrees per subject of the plurality of subjects: “We sample 100 points from the uniform distribution on the unit cube [0, 1]^3, and calculate the persistence landscapes in degrees 0, 1 and 2 (homology degrees) of the corresponding Vietoris-Rips complex” (Bubenik, page 13, paragraph 4)
Bubenik relates to topological data analysis with machine learning and is analogous to the claimed invention. the combination of Mandal and Chazal teaches a method of training neural networks with persistence landscapes. Bubenik teaches a method of calculating persistence landscapes for homology degrees up to three. It would have been obvious to one of ordinary skill in the art to combine the combination of Mandal and Chazal with Bubenik by using Bubenik’s method to calculate persistence landscapes. This would achieve the predictable result of calculating persistence landscapes for a finite number of homology degrees, with Mandal and Chazal’s method of training neural networks and Bubenik’s method of calculating persistence landscapes performing the same together as they did separately. (MPEP 2143 I. (A) Combining prior art elements according to known methods to yield predictable results).
Regarding claim 2, the rejection of claim 1 in view of Mandal, Chazal, and Bubenik is incorporated. Mandal further teaches a method of receiving a new sample; creating a new topological summary associated with the new sample based on the pairwise similarities; and inputting the new topological summary to the neural network, the neural network predicting the new sample’s phenotype: “Later, we show how to use the theory of persistent homology to determine the persistent topological landscapes present in the gene expression data of a sample (new sample), by first transforming it into a weighted point cloud ... The topological summaries ( of the weighted point clouds (persistence landscapes) are then used to construct a machine learning model to predict the phenotype (healthy or PD) for each sample” (Mandal, page 180, paragraph 3); “In the TDA-CNN approach, for a given resolution
r
y
, we fed each subject’s vectorized persistence landscape ... into a Convolutional Neural Network (CNN)” (Mandal, page 184, paragraph 3).
Regarding claim 3, the rejection of claim 1 in view of Mandal, Chazal, and Bubenik is incorporated. Mandal further teaches a method, wherein the neural network includes a convolutional neural network: “In the TDA-CNN approach, for a given resolution
r
y
, we fed each subject’s vectorized persistence landscape as a tensor of shape (
r
x
,
r
y
, 2), one channel per homology degree, into a Convolutional Neural Network (CNN)” (Mandal, page 184, paragraph 3).
Regarding claim 4, the rejection of claim 1 in view of Mandal, Chazal, and Bubenik is incorporated. Mandal further teaches a method, wherein the pair-wise similarities include distance measures between pairs of genes in the gene expression data: “Co-expression can be examined by computing pairwise correlations between gene expression measurements. Therefore, we construct a new matrix
X
-
from X, consisting of all pairwise distance correlations between genes” (Mandal, page 180, paragraph 2).
Regarding claim 5, the rejection of claim 1 in view of Mandal, Chazal, and Bubenik is incorporated. Mandal further teaches a method, wherein the pair-wise similarities are used to create a point cloud, the point cloud used to transform the gene expression data into the topological summaries: “Later, we show how to use the theory of persistent homology to determine the persistent topological landscapes present in the gene expression data of a sample, by first transforming it into a weighted point cloud. We do this transformation by utilizing the gene correlations across all available samples (matrix
X
-
). The topological summaries of the weighted point clouds (persistence landscapes) are then used to construct a machine learning model to predict the phenotype (healthy or PD) for each sample” (Mandal, page 180, paragraph 2).
Regarding claim 8, the rejection of claim 1 in view of Mandal, Chazal, and Bubenik is incorporated. Mandal further teaches a method, wherein:
the topological summaries are converted to a tensor: “We then quantized the resulting landscapes (topological summaries) by sampling
r
x
values evenly in the interval [0,
t
m
a
x
], where
t
m
a
x
is a value estimated from the data that corresponds to the last time of the filtration where there were changes in the persistent homology of the complex being processed. This results, for each persistence landscape, in a 2D array of size
r
x
×
n
λ
(tensor)” (Mandal, page 183, paragraph 4). As stated in paragraph [0022] of the instant Specification, a persistence landscape is an instance or example of a topological summary.
the tensor is fed into the neural network for training the neural network: “For each subject, we obtained a feature vector of 19,581 gene expression measurements (see Sect. 2.6) and a known class label 0 (161 control subjects) or 1 (264 affected subjects) according to the Parkinson’s disease phenotype. We split the data 80-20 into training and test sets, over 50 iterations, except for the computationally more intensive TDA-CNN where we considered 4 iterations after observing the results between iterations were nearly identical” (Mandal, page 184, paragraph 1); “In the TDA-CNN approach, for a given resolution
r
y
, we fed each subject’s vectorized persistence landscape as a tensor of shape (
r
x
,
r
y
, 2), one channel per homology degree, into a Convolutional Neural Network (CNN)” (Mandal, page 184, paragraph 3).
Claim(s) 9-13, 16-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mandal et al. (A Topological Data Analysis Approach on Predicting Phenotypes from Gene Expression Data, 2020, AlCoB 2020, LNBI 12099, pp. 178-187) in view of Chazal et al. (Subsampling Methods for Persistent Homology, published 2014, arXiv:1406.1901v1), and further in view of Bubenik (STATISTICAL TOPOLOGICAL DATA ANALYSIS USING PERSISTENCE LANDSCAPES, published 1/23/2015, arXiv:1207.6437v4), and Brandsma et al. (US 2023/0018537 A1), hereafter referred to as Brandsma.
Regarding claim 9, Mandal teaches a method, comprising:
create a training set based on topological summaries: “we use the gene expressions of subjects with and without Parkinsons disease to generate topological summaries per subject. These summaries essentially act as unique fingerprints that describe the topology of the gene expression in a sample. We use these fingerprints to enhance the feature vector that is used for disease phenotype prediction, “ (Mandal, page 179, paragraph 5); “For each subject, we obtained a feature vector of 19,581 gene expression measurements (see Sect. 2.6) and a known class label 0 (161 control subjects) or 1 (264 affected subjects) according to the Parkinson’s disease phenotype. We split the data 80-20 into training and test sets, over 50 iterations, except for the computationally more intensive TDA-CNN where we considered 4 iterations after observing the results between iterations were nearly identical” (Mandal, page 184, paragraph 1).
… wherein the topological summaries are associated with different phenotypes: “The topological summaries of the weighted point clouds (persistence landscapes) are then used to construct a machine learning model to predict the phenotype (healthy or PD) for each sample” (Mandal, page 180, paragraph 2).
the processor configured to create the training set at least by performing operations of:
receive gene expression data associated with a plurality of subjects: “For each subject, we obtained a feature vector of 19,581 gene expression measurements (see Sect. 2.6) and a known class label 0 (161 control subjects) (plurality of subjects) or 1 (264 affected subjects) (plurality of subjects) according to the Parkinson’s disease phenotype” (Mandal, page 184, paragraph 1).
determine pair-wise similarities between genes in the gene expression data of all of the plurality of subjects at the same time: “We work under the hypothesis that the set X of all subjects’ samples (gene expression data of all the plurality of subjects), each encoded as a collection of gene expression values, can provide us with enough topological information to discern between healthy subjects and subjects with Parkinson’s disease. We denote by X a matrix of size nrows × ncols where each row corresponds to a subject and each column corresponds to a gene. Each entry Xi,j then corresponds to the j-th gene expression of the i-th subject.” (Mandal, page 179, paragraph 6); “Co-expression can be examined by computing pairwise correlations between gene expression measurements. Therefore, we construct a new matrix
X
-
from X, consisting of all pairwise distance correlations between genes” (Mandal, page 180, paragraph 2).
transform the gene expression data into the topological summaries based on the pair-wise similarities: “Later, we show how to use the theory of persistent homology to determine the persistent topological landscapes present in the gene expression data of a sample, by first transforming it into a weighted point cloud. We do this transformation by utilizing the gene correlations across all available samples (matrix
X
-
). The topological summaries of the weighted point clouds (persistence landscapes) are then used to construct a machine learning model to predict the phenotype (healthy or PD) for each sample” (Mandal, page 180, paragraph 2).
the topological summaries including a persistence landscape for each degree of at least three homology degrees per subject of the plurality of subjects: “For each of the simplicial complexes we obtained persistence landscapes [5] for homology dimensions 0 and 1. Such landscapes are, for each homology degree, sequences {λk} of decreasing piecewise linear (PL) functions λk : R −→ R … After computation of all landscapes, for each subject we then obtained its average landscape” (Mandal, page 183, paragraph 3);
the transforming further including subsampling data points of the gene expression data by randomly selecting a subset of a preconfigured size, the sub-sampling preserving a topology associated with the topological summaries: “To mitigate the computational cost of our setup we used a subsampling approach, as studied in [11], so that instead of working with the entire set of genes at all times, for each subject we repeatedly subsampled smaller sets of
n
s
u
b
s
a
m
p
l
e
genes, obtaining several filtered simplicial complexes” (Mandal, page 183, paragraph 2).
train a neural network using the training set created based on the topological summaries: “Ultimately, we use the gene expressions of subjects with and without Parkinson’s disease to generate topological summaries per subject. These summaries essentially act as unique fingerprints that describe the topology of the gene expression in a sample. We use these fingerprints to enhance the feature vector that is used for disease phenotype prediction” (Mandal, page 179, paragraph 5); “For each subject, we obtained a feature vector of 19,581 gene expression measurements (see Sect. 2.6) and a known class label 0 (161 control subjects) or 1 (264 affected subjects) according to the Parkinson’s disease phenotype. We split the data 80-20 into training and test sets” (Mandal, page 184, paragraph 1).
by feeding the persistence landscape for each degree of the plurality of homology degrees into the neural network: “In the TDA-CNN approach, for a given resolution ry, we fed each subject’s vectorized persistence landscape as a tensor of shape (rx, ry, 2), one channel per homology degree, into a Convolutional Neural Network (CNN)” (Mandal, page 184, paragraph 3)
… the neural network being trained to predict a phenotype of a sample associated with a subject whose phenotype is unknown: “The topological summaries of the weighted point clouds (persistence landscapes) are then used to construct a machine learning model to predict the phenotype (healthy or PD) for each sample.” (Mandal, page 180, paragraph 3)
While Mandal fails to disclose the further limitations of the claim, Chazal discloses a method of subsampling data points of the gene expression data by randomly selecting a subset of a preconfigured size, the sub-sampling preserving a topology associated with the topological summaries:
“For any positive integer m, let
X
=
{
x
1
,
…
,
x
m
}
⊂
X
be a sample of m points from the measure
μ
∈
P
(
X
)
. The corresponding persistence landscape is
PNG
media_image1.png
321
879
media_image1.png
Greyscale
” Left: 3D shapes of the first experiment. Middle and Left: 500 random points from the magnetometer data of the second experiment.” (Chazal, page 7, Figure 3)
“In practice, each shape consists of a 3D point cloud embedded in the Euclidean space, with a number of vertices that ranges from 7K to 40K … For n = 100 times we subsample m = 300 points (preconfigured size) from each shape; then we select the closest subsample to the corresponding original point cloud and compute
4
×
n
persistence diagrams (dimension 1), one for each subsample” (Chazal, page 7, paragraph 3)
“We study the risk of two estimators and we prove that the subsampling approach carries stable topological (preserving a topology) information while achieving a great reduction in computational complexity” (Chazal, page 1, Abstract)
Mandal and Chazal relate to subsampling point clouds of data to reduce the cost of persistent homology analysis, and are analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Mandal to randomly subsample data such that subsamples maintain topological properties, as disclosed by Chazal. Chazal demonstrated that topological data analysis through these subsets accurately approximates persistent homology of the full set of data points, while being computationally faster and simple to execute. This is particularly useful when trying to perform topological data analysis is prohibitively expensive due to large data sets. See Chazal, page 1, Abstract & pages 7-9.
While Chazal fails to disclose the further limitations of the claim, Bubenik discloses a method, wherein the topological summaries including a persistence landscape for each degree of at least three homology degrees per subject of the plurality of subjects: “We sample 100 points from the uniform distribution on the unit cube [0, 1]^3, and calculate the persistence landscapes in degrees 0, 1 and 2 (homology degrees) of the corresponding Vietoris-Rips complex” (Bubenik, page 13, paragraph 4)
Bubenik relates to topological data analysis with machine learning and is analogous to the claimed invention. the combination of Mandal and Chazal teaches a method of training neural networks with persistence landscapes. Bubenik teaches a method of calculating persistence landscapes for homology degrees up to three. It would have been obvious to one of ordinary skill in the art to combine the combination of Mandal and Chazal with Bubenik by using Bubenik’s method to calculate persistence landscapes. This would achieve the predictable result of calculating persistence landscapes for a finite number of homology degrees, with Mandal and Chazal’s method of training neural networks and Bubenik’s method of calculating persistence landscapes performing the same together as they did separately. (MPEP 2143 I. (A) Combining prior art elements according to known methods to yield predictable results).
While Bubenik fails to disclose the further limitations of the claim, Brandsma teaches [a] system comprising: a processor; and a memory device coupled with the processor; the processor configured to at least: [execute operations]: “In embodiments, there are provided systems for predicting severe disease in an individual with sepsis or at risk of developing sepsis, comprising: one or more processors; a memory; a communication platform” (Brandsma, [0019]).
Brandsma relates to neural networks and topological data analysis for disease phenotype prediction and is analogous to the claimed invention. The combination of Mandal, Chazal, and Bubenik teaches a computational method of predicting disease phenotypes. The claimed invention improves upon this method by executing its method on computer hardware. Brandsma teaches computer hardware capable of running neural network methods for predicting phenotypes, applicable to the combination of Mandal, Chazal, and Bubenik. A person of ordinary skill in the art would have recognized that running the combination’s method on Brandsma’s hardware would lead to the predictable result of the computational method being executed as described, and would improve the known device by allowing the method to produce concrete results on a computer (MPEP 2143 I. (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results).
Regarding claim 10, the rejection of claim 9 in view of Mandal, Chazal, Bubenik, and Brandsma is incorporated. Mandal further teaches instructions to receive a new sample; create a new topological summary associated with the new sample based on the pair-wise similarities; and input the new topological summary to the neural network, the neural network predicting a new sample’s phenotype: “Later, we show how to use the theory of persistent homology to determine the persistent topological landscapes present in the gene expression data of a sample (new sample), by first transforming it into a weighted point cloud ... The topological summaries ( of the weighted point clouds (persistence landscapes) are then used to construct a machine learning model to predict the phenotype (healthy or PD) for each sample” (Mandal, page 180, paragraph 3); “In the TDA-CNN approach, for a given resolution
r
y
, we fed each subject’s vectorized persistence landscape ... into a Convolutional Neural Network (CNN)” (Mandal, page 184, paragraph 3).
Regarding claim 11, the rejection of claim 9 in view of Mandal, Chazal, Bubenik, and Brandsma is incorporated. Mandal further teaches a method, wherein the neural network includes a convolutional neural network: “In the TDA-CNN approach, for a given resolution
r
y
, we fed each subject’s vectorized persistence landscape as a tensor of shape (
r
x
,
r
y
, 2), one channel per homology degree, into a Convolutional Neural Network (CNN)” (Mandal, page 184, paragraph 3).
Regarding claim 12, the rejection of claim 9 in view of Mandal, Chazal, Bubenik, and Brandsma is incorporated. Mandal further teaches a method, wherein the pair-wise similarities include distance measures between pairs of genes in the gene expression data: “Co-expression can be examined by computing pairwise correlations between gene expression measurements. Therefore, we construct a new matrix
X
-
from X, consisting of all pairwise distance correlations between genes” (Mandal, page 180, paragraph 2).
Regarding claim 13, the rejection of claim 9 in view of Mandal, Chazal, Bubenik, and Brandsma is incorporated. Mandal further teaches a method, wherein the pair-wise similarities are used to create a point cloud, the point cloud used to transform the gene expression data into the topological summaries: “Later, we show how to use the theory of persistent homology to determine the persistent topological landscapes present in the gene expression data of a sample, by first transforming it into a weighted point cloud. We do this transformation by utilizing the gene correlations across all available samples (matrix
X
-
). The topological summaries of the weighted point clouds (persistence landscapes) are then used to construct a machine learning model to predict the phenotype (healthy or PD) for each sample” (Mandal, page 180, paragraph 2).
Regarding claim 16, Mandal teaches a method, comprising:
create a training set based on topological summaries: “we use the gene expressions of subjects with and without Parkinsons disease to generate topological summaries per subject. These summaries essentially act as unique fingerprints that describe the topology of the gene expression in a sample. We use these fingerprints to enhance the feature vector that is used for disease phenotype prediction, “ (Mandal, page 179, paragraph 5); “For each subject, we obtained a feature vector of 19,581 gene expression measurements (see Sect. 2.6) and a known class label 0 (161 control subjects) or 1 (264 affected subjects) according to the Parkinson’s disease phenotype. We split the data 80-20 into training and test sets, over 50 iterations, except for the computationally more intensive TDA-CNN where we considered 4 iterations after observing the results between iterations were nearly identical” (Mandal, page 184, paragraph 1).
… wherein the topological summaries are associated with different phenotypes: “The topological summaries of the weighted point clouds (persistence landscapes) are then used to construct a machine learning model to predict the phenotype (healthy or PD) for each sample” (Mandal, page 180, paragraph 2).
the processor configured to create the training set at least by performing operations of:
receive gene expression data associated with a plurality of subjects: “For each subject, we obtained a feature vector of 19,581 gene expression measurements (see Sect. 2.6) and a known class label 0 (161 control subjects) (plurality of subjects) or 1 (264 affected subjects) (plurality of subjects) according to the Parkinson’s disease phenotype” (Mandal, page 184, paragraph 1).
determine pair-wise similarities between genes in the gene expression data of all of the plurality of subjects at the same time: “We work under the hypothesis that the set X of all subjects’ samples (gene expression data of all the plurality of subjects), each encoded as a collection of gene expression values, can provide us with enough topological information to discern between healthy subjects and subjects with Parkinson’s disease. We denote by X a matrix of size nrows × ncols where each row corresponds to a subject and each column corresponds to a gene. Each entry Xi,j then corresponds to the j-th gene expression of the i-th subject.” (Mandal, page 179, paragraph 6); “Co-expression can be examined by computing pairwise correlations between gene expression measurements. Therefore, we construct a new matrix
X
-
from X, consisting of all pairwise distance correlations between genes” (Mandal, page 180, paragraph 2).
transform the gene expression data into topological summaries based on the pair-wise similarities: “Later, we show how to use the theory of persistent homology to determine the persistent topological landscapes present in the gene expression data of a sample, by first transforming it into a weighted point cloud. We do this transformation by utilizing the gene correlations across all available samples (matrix
X
-
). The topological summaries of the weighted point clouds (persistence landscapes) are then used to construct a machine learning model to predict the phenotype (healthy or PD) for each sample” (Mandal, page 180, paragraph 2).
the topological summaries including a persistence landscape for each degree of at least three homology degrees per subject of the plurality of subjects: “For each of the simplicial complexes we obtained persistence landscapes [5] for homology dimensions 0 and 1. Such landscapes are, for each homology degree, sequences {λk} of decreasing piecewise linear (PL) functions λk : R −→ R … After computation of all landscapes, for each subject we then obtained its average landscape” (Mandal, page 183, paragraph 3);
transforming further including resampling data of the gene expression data to replace data points used in the topological summaries: “To mitigate the computational cost of our setup we used a subsampling approach, as studied in [11], so that instead of working with the entire set of genes at all times, for each subject we repeatedly subsampled smaller sets of 𝑛𝑠𝑢𝑏𝑠𝑎𝑚𝑝𝑙𝑒 genes, obtaining several filtered simplicial complexes” (Mandal, page 183, paragraph 2). Subsampling is a type of resampling.
train a neural network using a training set created based on the topological summaries: “Ultimately, we use the gene expressions of subjects with and without Parkinson’s disease to generate topological summaries per subject. These summaries essentially act as unique fingerprints that describe the topology of the gene expression in a sample. We use these fingerprints to enhance the feature vector that is used for disease phenotype prediction” (Mandal, page 179, paragraph 5); “For each subject, we obtained a feature vector of 19,581 gene expression measurements (see Sect. 2.6) and a known class label 0 (161 control subjects) or 1 (264 affected subjects) according to the Parkinson’s disease phenotype. We split the data 80-20 into training and test sets” (Mandal, page 184, paragraph 1).
by feeding the persistence landscape for each degree of the plurality of homology degrees into the neural network: “In the TDA-CNN approach, for a given resolution ry, we fed each subject’s vectorized persistence landscape as a tensor of shape (rx, ry, 2), one channel per homology degree, into a Convolutional Neural Network (CNN)” (Mandal, page 184, paragraph 3)
While Mandal fails to disclose the further limitations of the claim, Chazal discloses a method of subsampling data points of the gene expression data by randomly selecting a subset of a preconfigured size, the sub-sampling preserving a topology associated with the topological summaries:
“For any positive integer m, let
X
=
{
x
1
,
…
,
x
m
}
⊂
X
be a sample of m points from the measure
μ
∈
P
(
X
)
. The corresponding persistence landscape is
PNG
media_image1.png
321
879
media_image1.png
Greyscale
” Left: 3D shapes of the first experiment. Middle and Left: 500 random points from the magnetometer data of the second experiment.” (Chazal, page 7, Figure 3)
“In practice, each shape consists of a 3D point cloud embedded in the Euclidean space, with a number of vertices that ranges from 7K to 40K … For n = 100 times we subsample m = 300 points (preconfigured size) from each shape; then we select the closest subsample to the corresponding original point cloud and compute
4
×
n
persistence diagrams (dimension 1), one for each subsample” (Chazal, page 7, paragraph 3)
“We study the risk of two estimators and we prove that the subsampling approach carries stable topological (preserving a topology) information while achieving a great reduction in computational complexity” (Chazal, page 1, Abstract)
Mandal and Chazal relate to subsampling point clouds of data to reduce the cost of persistent homology analysis, and are analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Mandal to randomly subsample data such that subsamples maintain topological properties, as disclosed by Chazal. Chazal demonstrated that topological data analysis through these subsets accurately approximates persistent homology of the full set of data points, while being computationally faster and simple to execute. This is particularly useful when trying to perform topological data analysis is prohibitively expensive due to large data sets. See Chazal, page 1, Abstract & pages 7-9.
While Chazal fails to disclose the further limitations of the claim, Bubenik discloses a method, wherein the topological summaries including a persistence landscape for each degree of at least three homology degrees per subject of the plurality of subjects: “We sample 100 points from the uniform distribution on the unit cube [0, 1]^3, and calculate the persistence landscapes in degrees 0, 1 and 2 (homology degrees) of the corresponding Vietoris-Rips complex” (Bubenik, page 13, paragraph 4)
Bubenik relates to topological data analysis with machine learning and is analogous to the claimed invention. the combination of Mandal and Chazal teaches a method of training neural networks with persistence landscapes. Bubenik teaches a method of calculating persistence landscapes for homology degrees up to three. It would have been obvious to one of ordinary skill in the art to combine the combination of Mandal and Chazal with Bubenik by using Bubenik’s method to calculate persistence landscapes. This would achieve the predictable result of calculating persistence landscapes for a finite number of homology degrees, with Mandal and Chazal’s method of training neural networks and Bubenik’s method of calculating persistence landscapes performing the same together as they did separately. (MPEP 2143 I. (A) Combining prior art elements according to known methods to yield predictable results).
While Bubenik fails to disclose the further limitations of the claim, Brandsma teaches [a] computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable by a device to: [execute operations]: “In embodiments, there is provided a non-transitory computer-readable medium having information recorded (instructions) thereon for generating a model for predicting severe disease in an individual with sepsis or at risk of developing sepsis, wherein the information, when read by a computer, causes the computer to perform operations” (Brandsma, [0020]).
Brandsma relates to neural networks and topological data analysis for disease phenotype prediction and is analogous to the claimed invention. The combination of Mandal and Chazal teaches a computational method of predicting disease phenotypes. The claimed invention improves upon this method by executing its method on computer hardware. Brandsma teaches computer hardware capable of running neural network methods for predicting phenotypes, applicable to the combination of Mandal and Chazal. A person of ordinary skill in the art would have recognized that running the combination’s method on Brandsma’s hardware would lead to the predictable result of the computational method being executed as described, and would improve the known device by allowing the method to produce concrete results on a computer (MPEP 2143 I. (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results).
Regarding claim 17, the rejection of claim 16 in view of Mandal, Chazal, Bubenik, and Brandsma is incorporated. Mandal further teaches a method to receive a new sample; create a new topological summary associated with the new sample based on the pair-wise similarities; and input the new topological summary to the neural network, the neural network predicting a new sample’s phenotype: “Later, we show how to use the theory of persistent homology to determine the persistent topological landscapes present in the gene expression data of a sample (new sample), by first transforming it into a weighted point cloud ... The topological summaries ( of the weighted point clouds (persistence landscapes) are then used to construct a machine learning model to predict the phenotype (healthy or PD) for each sample” (Mandal, page 180, paragraph 3); “In the TDA-CNN approach, for a given resolution
r
y
, we fed each subject’s vectorized persistence landscape ... into a Convolutional Neural Network (CNN)” (Mandal, page 184, paragraph 3).
Regarding claim 19, the rejection of claim 16 in view of Mandal, Chazal, Bubenik, and Brandsma is incorporated. Mandal further teaches a method, wherein the pair-wise similarities include distance measures between pairs of genes in the gene expression data: “Co-expression can be examined by computing pairwise correlations between gene expression measurements. Therefore, we construct a new matrix
X
-
from X, consisting of all pairwise distance correlations between genes” (Mandal, page 180, paragraph 2).
Regarding claim 20, the rejection of claim 16 in view of Mandal, Chazal, Bubenik, and Brandsma is incorporated. Mandal further teaches a method, wherein the pair-wise similarities are used to create a point cloud, the point cloud used to transform the gene expression data into the topological summaries: “Later, we show how to use the theory of persistent homology to determine the persistent topological landscapes present in the gene expression data of a sample, by first transforming it into a weighted point cloud. We do this transformation by utilizing the gene correlations across all available samples (matrix
X
-
). The topological summaries of the weighted point clouds (persistence landscapes) are then used to construct a machine learning model to predict the phenotype (healthy or PD) for each sample” (Mandal, page 180, paragraph 2).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Mandal et al. (A Topological Data Analysis Approach on Predicting Phenotypes from Gene Expression Data, 2020, AlCoB 2020, LNBI 12099, pp. 178-187) in view of Chazal et al. (Subsampling Methods for Persistent Homology, published 2014, arXiv:1406.1901v1), and further in view of Bubenik (STATISTICAL TOPOLOGICAL DATA ANALYSIS USING PERSISTENCE LANDSCAPES, published 1/23/2015, arXiv:1207.6437v4), and Ferry et al. (RECONSTRUCTING FUNCTIONS FROM RANDOM SAMPLES, 2014, Journal of Computational Dynamics, American Institute of Mathematical Sciences Volume 1, Number 2, December 2014).
Regarding claim 21, the rejection of claim 1 in view of Mandal, Chazal, and Bubenik is incorporated. Mandal further discloses a method, wherein the transforming further includes resampling data of the gene expression data to replace the data points used in the topological summaries, wherein the data is resampled by enveloping each point by a sphere of a given radius and sampling points from a union of spheres, the resampling of the data preserving a topology associated with the topological summaries: “To mitigate the computational cost of our setup we used a subsampling approach, as studied in [11], so that instead of working with the entire set of genes at all times, for each subject we repeatedly subsampled smaller sets of 𝑛𝑠𝑢𝑏𝑠𝑎𝑚𝑝𝑙𝑒 genes, obtaining several filtered simplicial complexes” (Mandal, page 183, paragraph 2). Subsampling is a type of resampling.
While Mandal, Chazal, and Bubenik fail to disclose the further limitations of the claim, Ferry discloses a method, wherein the transforming further includes resampling data of the gene expression data to replace the data points used in the topological summaries, wherein the data is resampled by enveloping each point by a sphere of a given radius and sampling points from a union of spheres, the resampling of the data preserving a topology associated with the topological summaries:
“let U(X) denote the union of n-dimensional open ∈-balls (spheres) centered at the points in X” (Ferry, page 2, paragraph 3)
“Thus, the union of balls (union of spheres) of a suitably chosen radius around a sufficiently large point sample success to recover the homotopy type of that manifold with high confidence. From a computational perspective, recall that the nerve of a cover [12, 15] is the abstract simplicial complex where each d-dimensional simplex corresponds to an intersection of d + 1 sets of that cover. If we let N(X) denote the nerve corresponding to the cover of U(X) by its constituent open balls, then one obtains an isomorphism
H
*
(
χ
)
≃
H
*
∆
(
N
X
)
between the singular homology of 𝜒 and the simplicial homology of N(X)” (Ferry, page 2, paragraph 4). The homotopy type of the nerve and the original data manifold is the same, indicating they can be simply transformed into one another, preserving the topology of the original manifold. This is reinforced by the homology of the nerve and the original manifold being isomorphic.
“Nerves. Let U be a topological space equipped with a finite cover U (union of spheres) consisting of subsets (spheres) of M. The nerve of U is the simplicial complex N(U) with vertex set U where each subcollection
σ
⊂
U
constitutes a simplex if and only if the intersection
∩
u
∈
σ
is a non-empty subset of U” (Ferry, page 4, paragraph 3). The simplexes of the nerve are built only for intersections of spheres. In other words, intersections within the union of spheres are sampl[ed] to construct the simplicial complex of the nerve.
Ferry relates to topological data analysis and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the existing combination to transform the data into a simplicial complex, as disclosed by Ferry. Geometric properties are a useful way to provide insight to large high-dimensional datasets, but such data is typically finite and noisy. Topological invariants like homology and homotopy groups – two things measured by Ferry’s method – are useful ways to capture the underlying structure of this data. Additionally, Ferry’s method is resistant to noisy sample data. Ferry, page 1, Abstract and page 1, paragraph 1.
Claims 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Mandal et al. (A Topological Data Analysis Approach on Predicting Phenotypes from Gene Expression Data, 2020, AlCoB 2020, LNBI 12099, pp. 178-187) in view of Chazal et al. (Subsampling Methods for Persistent Homology, published 2014, arXiv:1406.1901v1), and further in view of Bubenik (STATISTICAL TOPOLOGICAL DATA ANALYSIS USING PERSISTENCE LANDSCAPES, published 1/23/2015, arXiv:1207.6437v4), and Brandsma et al. (US 2023/0018537 A1), hereafter referred to as Brandsma, and Ferry et al. (RECONSTRUCTING FUNCTIONS FROM RANDOM SAMPLES, 2014, Journal of Computational Dynamics, American Institute of Mathematical Sciences Volume 1, Number 2, December 2014).
Regarding claim 22, the rejection of claim 9 in view of Mandal, Chazal, Bubenik, and Brandsma is incorporated. Mandal further discloses a method, wherein the transforming further includes resampling data of the gene expression data to replace the data points used in the topological summaries, wherein the data is resampled by enveloping each point by a sphere of a given radius and sampling points from a union of spheres, the resampling of the data: “To mitigate the computational cost of our setup we used a subsampling approach, as studied in [11], so that instead of working with the entire set of genes at all times, for each subject we repeatedly subsampled smaller sets of 𝑛𝑠𝑢𝑏𝑠𝑎𝑚𝑝𝑙𝑒 genes, obtaining several filtered simplicial complexes” (Mandal, page 183, paragraph 2). Subsampling is a type of resampling.
While Mandal, Chazal, Bubenik, and Brandsma fail to disclose the further limitations of the claim, Ferry discloses a method, wherein the transforming further includes resampling data of the gene expression data to replace the data points used in the topological summaries, wherein the data is resampled by enveloping each point by a sphere of a given radius and sampling points from a union of spheres, the resampling of the data:
“let U(X) denote the union of n-dimensional open ∈-balls (spheres) centered at the points in X” (Ferry, page 2, paragraph 3)
“Thus, the union of balls (union of spheres) of a suitably chosen radius around a sufficiently large point sample success to recover the homotopy type of that manifold with high confidence. From a computational perspective, recall that the nerve of a cover [12, 15] is the abstract simplicial complex where each d-dimensional simplex corresponds to an intersection of d + 1 sets of that cover. If we let N(X) denote the nerve corresponding to the cover of U(X) by its constituent open balls, then one obtains an isomorphism
H
*
(
χ
)
≃
H
*
∆
(
N
X
)
between the singular homology of 𝜒 and the simplicial homology of N(X)” (Ferry, page 2, paragraph 4). The homotopy type of the nerve and the original data manifold is the same, indicating they can be simply transformed into one another, preserving the topology of the original manifold. This is reinforced by the homology of the nerve and the original manifold being isomorphic.
“Nerves. Let U be a topological space equipped with a finite cover U (union of spheres) consisting of subsets (spheres) of M. The nerve of U is the simplicial complex N(U) with vertex set U where each subcollection
σ
⊂
U
constitutes a simplex if and only if the intersection
∩
u
∈
σ
is a non-empty subset of U” (Ferry, page 4, paragraph 3). The simplexes of the nerve are built only for intersections of spheres. In other words, intersections within the union of spheres are sampl[ed] to construct the simplicial complex of the nerve.
Ferry relates to topological data analysis and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the existing combination to transform the data into a simplicial complex, as disclosed by Ferry. Geometric properties are a useful way to provide insight to large high-dimensional datasets, but such data is typically finite and noisy. Topological invariants like homology and homotopy groups – two things measured by Ferry’s method – are useful ways to capture the underlying structure of this data. Additionally, Ferry’s method is resistant to noisy sample data. Ferry, page 1, Abstract and page 1, paragraph 1.
Regarding claim 23, the rejection of claim 16 in view of Mandal, Chazal, Bubenik, and Brandsma is incorporated. Mandal further discloses a method, wherein the transforming further includes resampling data of the gene expression data to replace the data points used in the topological summaries, wherein the data is resampled wherein the data is resampled by enveloping each point by a sphere of a given radius and sampling points from a union of spheres, the resampling of the data: “To mitigate the computational cost of our setup we used a subsampling approach, as studied in [11], so that instead of working with the entire set of genes at all times, for each subject we repeatedly subsampled smaller sets of 𝑛𝑠𝑢𝑏𝑠𝑎𝑚𝑝𝑙𝑒 genes, obtaining several filtered simplicial complexes” (Mandal, page 183, paragraph 2). Subsampling is a type of resampling.
While Mandal, Chazal, Bubenik, and Brandsma fail to disclose the further limitations of the claim, Ferry discloses a method, wherein the transforming further includes resampling data of the gene expression data to replace the data points used in the topological summaries, wherein the data is resampled wherein the data is resampled by enveloping each point by a sphere of a given radius and sampling points from a union of spheres, the resampling of the data:
“let U(X) denote the union of n-dimensional open ∈-balls (spheres) centered at the points in X” (Ferry, page 2, paragraph 3)
“Thus, the union of balls (union of spheres) of a suitably chosen radius around a sufficiently large point sample success to recover the homotopy type of that manifold with high confidence. From a computational perspective, recall that the nerve of a cover [12, 15] is the abstract simplicial complex where each d-dimensional simplex corresponds to an intersection of d + 1 sets of that cover. If we let N(X) denote the nerve corresponding to the cover of U(X) by its constituent open balls, then one obtains an isomorphism
H
*
(
χ
)
≃
H
*
∆
(
N
X
)
between the singular homology of 𝜒 and the simplicial homology of N(X)” (Ferry, page 2, paragraph 4). The homotopy type of the nerve and the original data manifold is the same, indicating they can be simply transformed into one another, preserving the topology of the original manifold. This is reinforced by the homology of the nerve and the original manifold being isomorphic.
“Nerves. Let U be a topological space equipped with a finite cover U (union of spheres) consisting of subsets (spheres) of M. The nerve of U is the simplicial complex N(U) with vertex set U where each subcollection
σ
⊂
U
constitutes a simplex if and only if the intersection
∩
u
∈
σ
is a non-empty subset of U” (Ferry, page 4, paragraph 3). The simplexes of the nerve are built only for intersections of spheres. In other words, intersections within the union of spheres are sampl[ed] to construct the simplicial complex of the nerve.
Ferry relates to topological data analysis and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the existing combination to transform the data into a simplicial complex, as disclosed by Ferry. Geometric properties are a useful way to provide insight to large high-dimensional datasets, but such data is typically finite and noisy. Topological invariants like homology and homotopy groups – two things measured by Ferry’s method – are useful ways to capture the underlying structure of this data. Additionally, Ferry’s method is resistant to noisy sample data. Ferry, page 1, Abstract and page 1, paragraph 1.
Response to Arguments
The following responses address arguments and remarks made in the instant remarks dated 2/27/2026.
112 Rejections
In light of the instant amendments, rejections under 35 U.S.C. 112 have been withdrawn.
101 Rejections
On pages 8-9 of the instant remarks, the Applicant argues that recited judicial exceptions are practically integrated through improvements to technology:
“Amended claim 1 also recites features that integrate a practical application of improving
machine learning technology. The consideration of whether the claim as a whole includes an improvement to a computer or to a technological field requires an evaluation of the specification
and the claim to ensure that a technical explanation of the asserted improvement is present in the
specification, and that the claim reflects the asserted improvement. See MPEP 2106.04(d)(l).
The specification in paragraph [0018] refers to "a system and/or method" that
"characterize subjects with topological signatures based on their biomarker measurements." The
specification in paragraph [0016] provides, " ... topological summaries of the biological data,"
"used to train one or more machine learning models to predict or classify potential disease in a
given sample." Paragraphs [0024] and [0026] refer to 'training data that includes biomarkers of
population of subjects" "used to predict the phenotype, or characterizing, of a new sample," such
"trained model" allowing for "understanding of disease mechanism and advance individualized
medicine, improving phenotype prediction and/or explaining characteristics of a phenotype."
Paragraph [0027] refers to "topological summaries allow for using features that are relevant for a
particular prediction, allowing a machine (machine learning model) to decide whether a sample
is healthy or not and/or why the machine made that prediction," e.g., where "patient data is large
dimensional," and allowing for "discerning what is relevant to the disease and what is not
relevant to the disease."
Claim 1 by way of example recites features that incorporate a practical application to a
technology of providing a pipeline that automates generating of topological summaries and
training a neural network that predicts that a given sample presents certain phenotype, e.g., is
diseased or healthy”
The Applicant’s arguments above have been fully considered in conjunction with the instant amendments, and are persuasive. The recited judicial exceptions of the claims are found to be practically integrated through improvement to an existing technology / technical field. Previous rejections under 35 U.S.C. 101 have been withdrawn.
103 Rejections
On page 11 of the instant remarks, the Applicant argues that the cited references do not disclose the amended claims:
“The cited references do not appear to disclose or suggest in particularity, the amended
features of claim 1, "the transforming further including subsampling data points of the gene
expression data by randomly selecting a subset of a preconfigured size, the sub-sampling
preserving a topology associated with the topological summaries." For instance, the cited
references do not appear to disclose or suggest a particular manner of sub-sampling recited in
amended claim 1.
The same reasons apply to independent claims 9 and 16, and the pending dependent
claims at least by virtue of their dependencies. For those reasons, it is respectfully requested that
the rejection of the claims under this section be withdrawn.
Dependent claims 21-23 are added to recite a resampling feature previously recited in
respective independent claims. For at least the above reasons, the new claims are also believed
to be unobvious over the cited references.”
Regarding the Applicant’s arguments above, the Examiner agrees. However, upon further search and consideration, the claimed invention is found to be obvious over Mandal in view of Chazal, Bubenik, Brandsma, and Ferry.
Regarding "the transforming further including subsampling data points of the gene expression data by randomly selecting a subset of a preconfigured size, the sub-sampling preserving a topology associated with the topological summaries", Mandal discloses a method of subsampling gene expression data (Mandal, page 183, paragraph 2). While Mandal fails to disclose the further limitations of the claim, Chazal discloses a method of randomly subsampling data points in a manner that preserves topological information (Chazal, page 1, Abstract; page 7, Figure 3; page 7, paragraph 3). Such a combination would have been obvious to one of ordinary skill in the art, as Chazal’s method explicitly improves performance for large datasets and makes feasible persistent topology in topological data analysis (the method used by Mandal) (Chazal, page 1 & pages 7-9).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure
(Wu et al., 2008, Network‐based global inference of human disease genes, Molecular Systems Biology (2008) 4: 189) teaches a method of using topological data analysis to build a predictive landscape for genotype-phenotype relationships for diseases
(Platt et al., 2016, Characterizing redescriptions using persistent homology to isolate genetic pathways contributing to pathogenesis, Platt et al. BMC Systems Biology 2016, 10(Suppl 1):10) teaches a method of using persistent homology to identify genetic pathways for diseases
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Aaron P Gormley whose telephone number is (571)272-1372. The examiner can normally be reached Monday - Friday 12:00 PM - 8:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle T Bechtold can be reached on (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AG/Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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