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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/19/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Furthermore, the attempt to incorporate subject matter into this application by reference to https://www.featuretools.com/ as found in para. [0044] of Applicant’s Specification is ineffective because it contains an embedded hyperlink and/or other form of browser-executable code. See MPEP § 608.01(p). The incorporation by reference will not be effective until correction is made to comply with 37 CFR 1.57(c), (d), or (e). If the incorporated material is relied upon to meet any outstanding objection, rejection, or other requirement imposed by the Office, the correction must be made within any time period set by the Office for responding to the objection, rejection, or other requirement for the incorporation to be effective. Compliance will not be held in abeyance with respect to responding to the objection, rejection, or other requirement for the incorporation to be effective. In no case may the correction be made later than the close of prosecution as defined in 37 CFR 1.114(b), or abandonment of the application, whichever occurs earlier.
Any correction inserting material by amendment that was previously incorporated by reference must be accompanied by a statement that the material being inserted is the material incorporated by reference and the amendment contains no new matter. 37 CFR 1.57(g).
Claim Objections
Claim 6 is objected to because of the following informalities: the claim contains the phrase “aka” which has not been defined in the Specification. For purposes of examination “aka” is being interpreted as: “also known as.” If this interpretation is not correct, Examiner requests that Applicant make the Appropriate correction.
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 13 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.Claim 13 recites the limitation "the information gain.” There is insufficient antecedent basis for this limitation in the claim.
Claim 14 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.Claim 14 recites the limitation "the most predictive.” There is insufficient antecedent basis for this limitation in the claim.
Claim 15 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.Claim 15 recites the limitation "the result of systematic bias.” There is insufficient antecedent basis for this limitation in the claim.
Claim 15 is also 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 “valuable phenomena” in claim 15 is a relative term which renders the claim indefinite. The term “valuable phenomena” 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. Because the term “valuable phenomena” is indefinite it makes the comparison of the result of systematic bias that should be learned by a machine learning algorithm indefinite.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Claim 1 partly recites the following limitations:
generating a feature matrix by applying transforms to the dataset; generating a Boolean feature matrix using the feature matrix; generating a Boolean feature adjacency matrix using the Boolean feature matrix.
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable
interpretation can be performed using mathematical relationships, mathematical formulas or
equations, and/or mathematical calculations and falls under the mathematical concepts grouping. Thus, the claim recites a mathematical concept under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A Prong
Two because the claim recites the following additional elements:
selecting a dataset;
and providing the Boolean feature adjacency matrix as input to a Boolean feature graph.
The additional claim elements of selecting a dataset; and providing the Boolean feature adjacency matrix as input to a Boolean feature graph amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation, inputting, and/or outputting of data.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above, the additional claim elements of selecting a dataset; and providing the Boolean feature adjacency matrix as input to a Boolean feature graph are well-understood, routine, conventional activity that court decisions, such as OIP Techs and Bancorp Services cited in MPEP section 2106.05(d)(II) have indicated that the mere receiving or transmitting data over a network and/or performing repetitive calculations are well-understood, routine, and conventional activities when claimed in a merely generic manner (as it is here).
Accordingly, claim 1 is not patent eligible.
Claim 2 partly recites the following limitations:
wherein the dataset comprises a target variable column and columns of semi-structured data.
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable
interpretation can be performed using concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
The judicial exception is not integrated into a practical application under Step 2A, Prong Two because there are no additional elements recited in the claim beyond the judicial exception. And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 2 is not patent eligible.
Claim 3 partly recites the following limitations:
wherein the transforms are functional transforms....
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable
interpretation can be performed using mathematical relationships, mathematical formulas or equations, and/or mathematical calculations and falls under the mathematical concepts grouping. Thus, the claim recites a mathematical concept under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A Prong
Two and significantly more under Step 2B because the additional claim elements of and are automatically applied to the dataset do not amount to a particular transformation since the mere manipulation of basic mathematical construct with respect to data have not been deemed as transformative especially when stated (as it is here) in a generic manner. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).
Accordingly, claim 3 is not patent eligible.
Claim 4 partly recites the following limitations:
wherein generating the feature matrix comprises....
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable
interpretation can be performed using concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A Prong
Two and significantly more under Step 2B because the additional claim elements of applying automated feature engineering to the dataset do not amount to a particular transformation since the mere manipulation of basic mathematical construct with respect to data have not been deemed as transformative especially when stated (as it is here) in a generic manner. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).
Accordingly, claim 4 is not patent eligible.
Claim 5 partly recites the following limitations:
wherein the feature matrix comprises features....
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable
interpretation can be performed using concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A Prong
Two because the claim recites the following additional elements:
received from an automated feature engineering algorithm
The additional claim elements of received from an automated feature engineering algorithm amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation, inputting, and/or outputting of data.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above, the additional claim elements of received from an automated feature engineering algorithm are well-understood, routine, conventional activity that court decisions, such as OIP Techs and Bancorp Services cited in MPEP section 2106.05(d)(II) have indicated that the mere receiving or transmitting data over a network and/or performing repetitive calculations are well-understood, routine, and conventional activities when claimed in a merely generic manner (as it is here).
Accordingly, claim 5 is not patent eligible.
Claim 6 partly recites the following limitations:
wherein the features are of type string, numeric, list (aka array), and/or dictionary (aka map).
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable
interpretation can be performed using concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
The judicial exception is not integrated into a practical application under Step 2A, Prong Two because there are no additional elements recited in the claim beyond the judicial exception. And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 6 is not patent eligible.
Claim 7 partly recites the following limitations:
wherein generating the Boolean feature matrix comprises applying simple Boolean expressions to the feature matrix.
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable interpretation can be performed using mathematical relationships, mathematical formulas or equations, and/or mathematical calculations and falls under the mathematical concepts grouping. Thus, the claim recites a mathematical concept under Step 2A, Prong One.
The judicial exception is not integrated into a practical application under Step 2A, Prong Two because there are no additional elements recited in the claim beyond the judicial exception. And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 7 is not patent eligible.
Claim 8 partly recites the following limitations:
wherein generating the Boolean feature matrix comprises applying Boolean feature selection to the feature matrix.
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable interpretation can be performed using mathematical relationships, mathematical formulas or equations, and/or mathematical calculations and falls under the mathematical concepts grouping. Thus, the claim recites a mathematical concept under Step 2A, Prong One.
The judicial exception is not integrated into a practical application under Step 2A, Prong Two because there are no additional elements recited in the claim beyond the judicial exception. And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 8 is not patent eligible.
Claim 9 partly recites the following limitations:
wherein generating the Boolean feature matrix comprises...and exhaustively applying Boolean expressions.
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable interpretation can be performed using mathematical relationships, mathematical formulas or equations, and/or mathematical calculations and falls under the mathematical concepts grouping. Thus, the claim recites a mathematical concept under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A Prong
Two and significantly more under Step 2B because the additional claim elements of iterating through automatically generated features do not amount to a particular transformation since the mere manipulation of basic mathematical construct with respect to data have not been deemed as transformative especially when stated (as it is here) in a generic manner. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).
Accordingly, claim 9 is not patent eligible.
Claim 10 partly recites the following limitations:
wherein generating the Boolean feature adjacency matrix comprises calculating the similarity between the features in the Boolean feature matrix.
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable interpretation can be performed using mathematical relationships, mathematical formulas or equations, and/or mathematical calculations and falls under the mathematical concepts grouping. Thus, the claim recites a mathematical concept under Step 2A, Prong One.
The judicial exception is not integrated into a practical application under Step 2A, Prong Two because there are no additional elements recited in the claim beyond the judicial exception. And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 10 is not patent eligible.
Claim 11 partly recites the following limitations:
wherein generating the Boolean feature adjacency matrix comprises performing a feature similarity calculation on the Boolean feature matrix.
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable interpretation can be performed using mathematical relationships, mathematical formulas or equations, and/or mathematical calculations and falls under the mathematical concepts grouping. Thus, the claim recites a mathematical concept under Step 2A, Prong One.
The judicial exception is not integrated into a practical application under Step 2A, Prong Two because there are no additional elements recited in the claim beyond the judicial exception. And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 11 is not patent eligible.
Claim 12 partly recites the following limitations:
wherein the Boolean feature graph comprises nodes and edges, wherein each node represents a Boolean feature and each edge represents the similarity between the two nodes.
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable interpretation can be performed using concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
The judicial exception is not integrated into a practical application under Step 2A, Prong Two because there are no additional elements recited in the claim beyond the judicial exception. And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 12 is not patent eligible.
Claim 13 partly recites the following limitations:
wherein the Boolean feature graph further comprises the information gain of the Boolean feature node.
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable interpretation can be performed using mathematical relationships, mathematical formulas or equations, and/or mathematical calculations and falls under the mathematical concepts grouping. Thus, the claim recites a mathematical concept under Step 2A, Prong One.
The judicial exception is not integrated into a practical application under Step 2A, Prong Two because there are no additional elements recited in the claim beyond the judicial exception. And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 13 is not patent eligible.
Claim 14 partly recites the following limitations:
wherein the Boolean feature graph is limited to show only the most predictive and/or correlated features, by setting thresholds of information gain and/or similarity score.
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable interpretation can be performed using mathematical relationships, mathematical formulas or equations, and/or mathematical calculations and falls under the mathematical concepts grouping. Thus, the claim recites a mathematical concept under Step 2A, Prong One.
The judicial exception is not integrated into a practical application under Step 2A, Prong Two because there are no additional elements recited in the claim beyond the judicial exception. And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 14 is not patent eligible.
Claim 15 partly recites the following limitations:
investigating clusters of features to discern if they are valuable phenomena or the result of systematic bias that should be learned by a machine learning algorithm.
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable interpretation can be performed using concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
The judicial exception is not integrated into a practical application under Step 2A, Prong Two because there are no additional elements recited in the claim beyond the judicial exception. And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 15 is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Yan, Hui, et al. "Correntropy based feature selection using binary projection." Pattern Recognition 44.12 (2011)(“Yan”) in view of Das AK et al., An information-theoretic graph-based approach for feature selection. Sādhanā. 2020 Dec;45(“Das”).
Regarding claim 1, Yan teaches a method comprising:
selecting a dataset(Yan, pg., 2837, “To evaluate the performance of our algorithm, we conduct a series of experiments on...real-world datasets: Dermatology[selecting a dataset].”);
generating a feature matrix by applying transforms to the dataset(Yan, pg., 2837, see also table 1, “ All missing values embodied in some data sets have been discarded during the preprocessing phases[generating a feature matrix by applying transforms to the dataset].”);
generating a Boolean feature matrix using the feature matrix(Yan, pg., 2836, see also Proposition 2, and Algorithm 1, “[L]earn a transformation matrix
W
∈
R
p
×
d
(p<d) such that W is optimal according to our objective function... the feature selection algorithms require W being a 0-1 binary matrix, i.e.,
W
i
,
k
i
=
1
(i=1,...,p) and the rest entries should be equal to zero[generating a Boolean feature matrix].” & Yan, pg., 2837, see also fig. 1, “In Fig. 1(a), we set the number of selected features as 10 and use the first 20 samples per class for training on Dermatology[using the feature matrix]...[t]he linear programming problem in [equation] (7) can be rapidly processed...the training time for each iteration is about 0.5 s...and the error reaches
W
t
+
1
-
W
t
2
2
<
e
-
6
in about three iterations.”);
[generating a Boolean feature adjacency matrix] using the Boolean feature matrix(Yan, pg., 2836, see also Proposition 2, and Algorithm 1, “[L]earn a transformation matrix
W
∈
R
p
×
d
(p<d) such that W is optimal according to our objective function... the feature selection algorithms require W being a 0-1 binary matrix, i.e.,
W
i
,
k
i
=
1
(i=1,...,p) and the rest entries should be equal to zero... [f]or test, we transform the unlabeled d-dimensional sample x by applying y=Wx and classify this unlabeled sample in p-dimensional space[using the Boolean feature matrix].”).1
While Yan does teach using the Boolean feature matrix, Yan does not teach: generating a Boolean feature adjacency matrix; and providing the Boolean feature adjacency matrix as input to a Boolean feature graph.
However, Das teaches:
generating a Boolean feature adjacency matrix(Das, pg., 4, “[A]n adjacency
matrix is created with value of all cells greater than
α
0
replaced by 1 and those that are less than
α
0
replaced by zero[generating a Boolean feature adjacency matrix].”);
and providing the Boolean feature adjacency matrix as input to a Boolean feature graph(Das, pg., 4, see also Algorithm of 3.2, “[A]n adjacency matrix is created with value of all cells greater than
α
0
replaced by 1 and those that are less than
α
0
replaced by zero... [u]sing this matrix, the FIM [i.e., feature information map] is redrawn. Out of the []green[] nodes, the ones that get connected are marked []blue[]. These nodes have good amount of information contribution as well as potential redundancy. This is the stage 2 of FIM[and providing the Boolean feature adjacency matrix as input to a Boolean feature graph].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Yan with the teachings of Das the motivation to do so would be to implement a graph theoretic approach to feature selection for added visualization of the feature selection process(Das, pgs., 1-2, “Ideally, for feature selection, a complete search strategy should be used for selecting the most optimal feature subset. However, considering the computation cost of such a strategy, an approximate search technique is used in most of the cases. Graph-based approach can be adopted for modelling the relationship between the data set features... [h]owever, the extent of relevance or redundancy of the features is not represented in the feature graph. They are critical aspects, which can help the users to take decision on whether to select or reject a specific feature. Graphs give a visualization of the relationship between objects, and help the users in making useful inferences.”).
Regarding claim 2, Yan in view of Das teaches the method of claim 1, wherein the dataset comprises a target variable column and columns of semi-structured data(Yan, pg., 2837, “To evaluate the performance of our algorithm, we conduct a series of experiments on...real-world datasets: Dermatology....” & Yan, As table 1 details:
PNG
media_image1.png
245
423
media_image1.png
Greyscale
The Dermatology dataset’s target variable column consists of 6 classes and consists of 33 columns of semi-structured data[a target variable column and columns of semi-structured data]).
Regarding claim 3, Yan in view of Das teaches the method of claim 1, wherein the transforms are functional transforms and are automatically applied to the dataset(Das, pgs., 2-4, see also Algorithm of 3.2, “MI of a feature F with respect to the class variable C, i.e.
I
(
F
;
C
)
, is measured by the difference in entropy of the class variable, i.e.
H
(
C
)
, and the conditional entropy of the class variable given the value of the feature variable, i.e.
H
(
C
|
F
)
[ wherein the transforms are functional transforms]...[i]n the proposed method, MI between the feature variables and the class variable is calculated...[t]his is the stage 1 of FIM... 8 standard data sets, details provided in table 1...have been used[and are automatically applied to the dataset].” ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Yan with the above teachings of Das for the same rationale stated at Claim 1.
Regarding claim 4, Yan in view of Das teaches the method of claim 1, wherein generating the feature matrix comprises applying automated feature engineering to the dataset(Das, pgs., 2-4, see also Algorithm of 3.2, “In the proposed method, MI between the feature variables and the class variable is calculated... [t]his is the stage 1 of FIM... [i]n the next step... [a] similarity matrix is then created in a way such that each cell in the matrix holds a value equal to the correlation between the features represented by the respective row and column for that cell...[t]his is the stage 2 of FIM...between 8 standard data sets, details provided in table 1...have been used[applying automated feature engineering to the dataset].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Yan with the above teachings of Das for the same rationale stated at Claim 1.
Regarding claim 5, Yan in view of Das teaches the method of claim 1, wherein the feature matrix comprises features received from an automated feature engineering algorithm(Das, pgs., 2-4, see also Algorithm of 3.2, “In the proposed method, MI between the feature variables and the class variable is calculated... [t]his is the stage 1 of FIM... [i]n the next step... [a] similarity matrix is then created in a way such that each cell in the matrix holds a value equal to the correlation between the features represented by the respective row and column for that cell...[t]his is the stage 2 of FIM[features received from an automated feature engineering algorithm].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Yan with the above teachings of Das for the same rationale stated at Claim 1.
Regarding claim 6, Yan in view of Das teaches the method of claim 5, wherein the features are of type string, numeric, list (aka array), and/or dictionary (aka map)(Yan, pg., 2837, see also table 1, “The Feret face dataset contains images from 200 individuals, each providing seven different images. All images on Feret are grayscaled and normalized to a resolution of 40
×
40 pixels[wherein the features are of type numeric, list (aka array)].”).2
Regarding claim 7, Yan in view of Das teaches the method of claim 1, wherein generating the Boolean feature matrix comprises applying simple Boolean expressions to the feature matrix(Das, pgs., 2-4, see also Algorithm of 3.2, “From the similarity matrix, an adjacency matrix is created with value of all cells greater than
α
0
replaced by 1 and those that are less than
α
0
replaced by 0[applying simple Boolean expressions to the feature matrix].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Yan with the above teachings of Das for the same rationale stated at Claim 1.
Regarding claim 8, Yan in view of Das teaches the method of claim 1, wherein generating the Boolean feature matrix comprises applying Boolean feature selection to the feature matrix(Das, pgs., 2-4, see also Algorithm of 3.2, “From the similarity matrix, an adjacency matrix is created with value of all cells greater than
α
0
replaced by 1 and those that are less than
α
0
replaced by 0[applying Boolean feature selection to the feature matrix].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Yan with the above teachings of Das for the same rationale stated at Claim 1.
Regarding claim 9, Yan in view of Das teaches the method of claim 1, wherein generating the Boolean feature matrix comprises iterating through automatically generated features and exhaustively applying Boolean expressions(Das, pgs., 2-4, As steps 1-18 of the algorithm from section 3.2 details:
PNG
media_image2.png
541
484
media_image2.png
Greyscale
In step 8 a subset of Features is selected i.e., F’ and then in step 9 a similarity matrix is computed for the selected subset of Features in the Dataset i.e.
M
c
o
r
r
←
c
o
r
r
e
l
a
t
i
o
n
(
D
N
F
'
)
. In steps 10-12 a double For loop iterates through the similarity matrix and applies a Boolean expression in which 1 is output if the value of the similarity matrix is greater than
α
0
and
i
≠
j
, else 0 is outputted i.e., If
M
c
o
r
r
i
,
j
>
α
0
and
i
≠
j
[iterating through automatically generated features and exhaustively applying Boolean expressions].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Yan with the above teachings of Das for the same rationale stated at Claim 1.
Regarding claim 10, Yan in view of Das teaches the method of claim 1, wherein generating the Boolean feature adjacency matrix comprises calculating the similarity between the features in the Boolean feature matrix(Yan, pgs., 2835-2836, “The sample estimator of correntropy for X and Y is given by
V
^
X
;
Y
...[i]ntuitively, correntropy is directly related to the similarity between set X and set Y... [o]ur criterion is defined to encode the correntropy between feature set Y and the class label set C as follows:
W
*
=
a
r
g
m
a
x
W
J
W
≔
V
^
W
X
;
C
...s.t.
W
i
,
j
∈
{
0
,
1
}
for all i and j[calculating the similarity between the features in the Boolean feature matrix]”).
Regarding claim 11, Yan in view of Das teaches the method of claim 1, wherein generating the Boolean feature adjacency matrix comprises performing a feature similarity calculation on the Boolean feature matrix(Yan, pgs., 2835-2836, “The sample estimator of correntropy for X and Y is given by
V
^