DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Subject Matter Eligibility Analysis Step 1:
Claims 1-7 recite method claims. Claims 8-20 are machine/system/product claims. Therefore, claims 1-20 are directed to one of the four statutory categories of patentable subject matter.
Regarding claim 1:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 1 recites the step:
implementing a data distribution sampling algorithm on the first set of training data to generate corresponding sampled data by partitioning the first set of training data into non- overlapping subsets with differentially private clustering; (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually write down non-overlapping subsets of data)
computing a plurality of cluster centers with differential privacy guarantees for each of said plurality of cluster centers; (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually determine the center of a cluster)
generating a graph that connects each cluster center with different weights between each cluster center; (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually write a graph representation that connects cluster centers)
and automatically generating, for a data point that receives a negative outcome from the trained model, a recourse path with privacy guarantees based on a plurality of set points from the graph that provides shortest path to output a positive outcome. (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually determine a path on the graph that is the shortest for the desired outcome)
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 1 recites the additional elements:
“by utilizing one or more processors along with allocated memory and a machine learning model,” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
“receiving a first set of training data that is usable for training a machine learning model;” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
“training the machine learning model by using the at least the first set training data;” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f) Examiner notes high level recitation of training a machine learning model with previously obtained data)
Therefore, claim 1 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 1 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“by utilizing one or more processors along with allocated memory and a machine learning model,” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
“receiving a first set of training data that is usable for training a machine learning model;” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
“training the machine learning model by using the at least the first set training data;” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f) Examiner notes high level recitation of training a machine learning model with previously obtained data)
Therefore, claim 1 is subject-matter ineligible.
Regarding claim 2:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 2 recites the step:
“The method according to claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – refers to the mental process continued from claim 1.)
“wherein the weights between each cluster center is defined by one or more of the following: distance thresholds between cluster centers, constraints specified for graph edges, and data density.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually define the weights as any combination of distance thresholds between cluster centers, constraints specified for graph edges, and data density)
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 2 does not further recite any additional elements. Therefore, claim 2 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 2 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 2 is subject-matter ineligible.
Regarding claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 3 recites the step:
“The method according to claim 2,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – refers to the mental process continued from claim 2.)
“wherein the plurality of cluster centers generated in the computing step represent a private version of the training data.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually determine cluster centers that represent a private version of the training data)
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 3 does not further recite any additional elements. Therefore, claim 3 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 3 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 3 is subject-matter ineligible.
Regarding claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 recites the step:
“The method according to claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – refers to the mental process continued from claim 1.)
“further comprising: validating the generated recourse path by utilizing existing and new metrics, wherein the metrics include distance between points in the recourse path, density of points in the recourse path, and path length.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually validate the path using the metrics disclosed)
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 4 does not further recite any additional elements. Therefore, claim 4 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 4 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 4 is subject-matter ineligible.
Regarding claim 5:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites the step:
“The method according to claim 1, in generating the graph, the method further comprising:” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – refers to the mental process continued from claim 1.)
“connecting each cluster center; updating the weights to every other cluster based on distance threshold parameter, constraints for features, and density of data; and generating the graph based on the updated weights.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually write down the updated weights based on the distance threshold parameter, constraints for features and density of data and draw out, on paper, a graph with cluster centers connected with consideration for the updated weights)
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 5 does not further recite any additional elements. Therefore, claim 5 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 5 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 5 is subject-matter ineligible.
Regarding claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 6 recites the step:
“The method according to claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – refers to the mental process continued from claim 1.)
“further comprising: implementing differentially private k-means or differentially private maximum mean discrepancy algorithm on the sampled data to compute the plurality of cluster centers.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually implement either algorithm on the data to compute cluster centers)
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 6 does not further recite any additional elements. Therefore, claim 6 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 6 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 6 is subject-matter ineligible.
Regarding claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 7 recites the step:
“The method according to claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – refers to the mental process continued from claim 1.)
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 7 recites the additional elements:
“wherein the machine learning model includes one or more of the following models: decision tree, ensemble trees, logistic regression, neural network architectures, and predictive model.” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f) Examiner notes high level recitation of machine learning models)
Therefore, claim 7 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 7 do not provide significantly more than the abstract idea itself, taken alone and in combination because:
“wherein the machine learning model includes one or more of the following models: decision tree, ensemble trees, logistic regression, neural network architectures, and predictive model.” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f) Examiner notes high level recitation of machine learning models)
Therefore, claim 7 is subject-matter ineligible.
Claim 8 recites the same limitations as method claim 1, in the form of a system. Thus, claim 8 is also directed to performing a mental process without significantly more, therefore it is rejected under the same rationale.
Claim 9 recites the same limitations as method claim 2, in the form of a system. Thus, claim 9 is also directed to performing a mental process without significantly more, therefore it is rejected under the same rationale.
Claim 10 recites the same limitations as method claim 3, in the form of a system. Thus, claim 10 is also directed to performing a mental process without significantly more, therefore it is rejected under the same rationale.
Claim 11 recites the same limitations as method claim 4, in the form of a system. Thus, claim 11 is also directed to performing a mental process without significantly more, therefore it is rejected under the same rationale.
Claim 12 recites the same limitations as method claim 5, in the form of a system. Thus, claim 12 is also directed to performing a mental process without significantly more, therefore it is rejected under the same rationale.
Claim 13 recites the same limitations as method claim 6, in the form of a system. Thus, claim 13 is also directed to performing a mental process without significantly more, therefore it is rejected under the same rationale.
Claim 14 recites the same limitations as method claim 7, in the form of a system. Thus, claim 14 is also directed to performing a mental process without significantly more, therefore it is rejected under the same rationale.
Claim 15 recites the same limitations as method claim 1, in the form of a non-transitory computer readable medium. Thus, claim 15 is also directed to performing a mental process without significantly more, therefore it is rejected under the same rationale.
Claim 16 recites the same limitations as method claim 2, in the form of a non-transitory computer readable medium. Thus, claim 16 is also directed to performing a mental process without significantly more, therefore it is rejected under the same rationale.
Claim 17 recites the same limitations as method claim 3, in the form of a non-transitory computer readable medium. Thus, claim 17 is also directed to performing a mental process without significantly more, therefore it is rejected under the same rationale.
Claim 18 recites the same limitations as method claim 4, in the form of a non-transitory computer readable medium. Thus, claim 18 is also directed to performing a mental process without significantly more, therefore it is rejected under the same rationale.
Claim 19 recites the same limitations as method claim 5, in the form of a non-transitory computer readable medium. Thus, claim 19 is also directed to performing a mental process without significantly more, therefore it is rejected under the same rationale.
Claim 20 recites the same limitations as method claim 6, in the form of a non-transitory computer readable medium. Thus, claim 20 is also directed to performing a mental process without significantly more, therefore it is rejected under the same rationale.
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.
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) 1-2, 8-9, 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hamer et al. (https://arxiv.org/pdf/2306.15557v1) (hereafter referred to as Hamer) in view of Park et al. (US 20190347278 A1) (hereafter referred to as Park) further in view of Mouden et al. (https://www.researchgate.net/publication/333224539_An_application_of_spectral_clustering_approach_to_detect_communities_in_data_modeled_by_graphs) (hereafter referred to as Mouden) further in view of Gong et al. (US 2024/0169256 A1) (hereafter referred to as Gong).
Regarding claim 1, Hamer teaches:
“utilizing one or more processors along with allocated memory” (Hamer §F.6, “We used a distributed cluster for our empirical evaluation, including for base model training (i.e. logistic regression, random forest), for all recourse experiments, and post-processing of results to produce metrics. The cluster contains:
• compute nodes with 28 cores of Xeon E5-2680 v4 (2.40GHz, 128GB RAM, and 200GB local SSD disk memory),
• compute nodes with 28 cores of Xeon Gold 6240 CPU units (2.60GHz, 192GB RAM, and 240GBlocal SSD), and
• compute nodes with 56 cores of Xeon E5-2680 v4 units (2.40GHz, 264GB RAM, and 30TB local disk).”)
“and a machine learning model,” (Hamer §2, ¶2, “Our setting centers on binary classification using a model of interest f : Rm 7→ ±1 trained on the dataset X,”)
“training the machine learning model by using the at least the first set training data;” (Hamer §2, ¶2, “Our setting centers on binary classification using a model of interest f : Rm 7→ ±1 trained on the dataset X,”)
“automatically generating, for a data point that receives a negative outcome from the trained model, a recourse path with privacy guarantees based on a plurality of set points from the graph that provides shortest path to output a positive outcome.” (Hamer §4, ¶3, “Given a negatively classified PoI
x
→
, all three methods (StEP, DiCE, and FACE) result in a sequence of points
(
x
→
0
,
x
→
1
, …,
x
→
l
) where
x
→
1
is the PoI after following the first direction recommendation by the recourse method,
x
→
2
is the PoI after following the second direction, and so on. We refer to this sequence of points as a recourse path. Each of the ℓ directions can be referred to as a “step”.” Hamer §3.3, ¶1 “However, we can show that the StEP distance computation itself is privacy-preserving” Hamer §3, ¶2, “We repeat the process until the stakeholder’s situation produces a positively labeled point, i.e. a counterfactual.” Examiner notes that the recourse path calculated by StEP is the shortest path according to StEP.)
Hamer does not distinctly disclose:
“receiving a first set of training data that is usable for training a machine learning model;”
“implementing a data distribution sampling algorithm on the first set of training data to generate corresponding sampled data by partitioning the first set of training data into non- overlapping subsets with differentially private clustering;”
“computing a plurality of cluster centers with differential privacy guarantees for each of said plurality of cluster centers;”
“generating a graph that connects each cluster center with different weights between each cluster center;”
However, Park teaches:
“implementing a data distribution sampling algorithm on the first set of training data to generate corresponding sampled data by partitioning the first set of training data into non- overlapping subsets with differentially private clustering;” (Park ¶35, “To address this issue, an embodiment of the present invention uses a differentially private k-means clustering algorithm using a quad-tree for expressing the data distribution while reducing the number of buckets. Hereinafter, an embodiment of the present invention is described in detail with reference to the drawings.”
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500
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Examiner notes the subsets are non overlapping as shown in Fig. 3.)
“computing a plurality of cluster centers with differential privacy guarantees for each of said plurality of cluster centers;” (Park ¶79 “When the clustering begins, the center coordinates of the clusters should be determined.”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating recourse paths of Hamer with the differentially private clustering of Park in order to prevent information from being exposed (Park ¶3, sentence 1).
Hamer as modified by Park does not distinctly disclose:
“receiving a first set of training data that is usable for training a machine learning model;”
“generating a graph that connects each cluster center with different weights between each cluster center;”
However, Mouden teaches:
“generating a graph that connects each cluster center with different weights between each cluster center;” (Mouden §3.2, ¶1, “After defining the individuals of our problem, we build a graph by calculating the similarities between each pair of individuals; those similarities will present the edges between the vertices of the graph where each vertex represents an individual. We have used the Gaussian similarity based on the Euclidian distance between each two vertices and a positive parameter σ to control the size of the neighborhood,” Examiner notes the individuals referred to are the cluster centers previously calculated.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating recourse paths of Hamer with the graph generation of Mouden in order to represent similarities between the points on the graph (Mouden §2.2 lines 7-8).
Hamer as modified does not distinctly disclose:
“receiving a first set of training data that is usable for training a machine learning model;”
However, Gong teaches:
“receiving a first set of training data that is usable for training a machine learning model;” (Gong ¶47, “Model building 290 receives training data 287 and private key 292 and builds a machine learning model. For example, training data 287 may include data x representing the processed and encrypted representation of message content as well as label y representing the associated labels (e.g., abusive or non-abusive).”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for generating recourse paths of Hamer with the training data of Gong in order to train the model weights of the model in Hamer. (Gong ¶47)
Regarding claim 2, Hamer as modified in claim 1 teaches all the limitations of claim 1.
Hamer as modified further discloses:
“wherein the weights between each cluster center is defined by one or more of the following: distance thresholds between cluster centers, constraints specified for graph edges, and data density.” (Mouden §3.2, ¶1, “After defining the individuals of our problem, we build a graph by calculating the similarities between each pair of individuals; those similarities will present the edges between the vertices of the graph where each vertex represents an individual. We have used the Gaussian similarity based on the Euclidian distance between each two vertices and a positive parameter σ to control the size of the neighborhood,” Examiner notes the edges are determined by distance and limited by the positive parameter which controls neighborhood size can be considered a threshold for the distance.)
Regarding claim 3, Hamer as modified in claim 2 teaches all the limitations of claim 2.
Hamer as modified further discloses:
“wherein the plurality of cluster centers generated in the computing step represent a private version of the training data.” (Gong ¶47, “Model building 290 receives training data 287 and private key 292 and builds a machine learning model. For example, training data 287 may include data x representing the processed and encrypted representation of message content as well as label y representing the associated labels (e.g., abusive or non-abusive).” Examiner notes the encrypted representation of messages is a private version of the messages dataset.)
Regarding claim 5, Hamer as modified in claim 1, teaches all the limitations of claim 1.
Hamer as modified further teaches:
“connecting each cluster center;” (Mouden §3.2, ¶1, “by calculating the similarities between each pair of individuals; those similarities will present the edges between the vertices of the graph where each vertex represents an individual.” Examiner notes the individuals referred to are the cluster centers previously calculated.)
“updating the weights to every other cluster based on distance threshold parameter, constraints for features, and density of data;” (Mouden §3.2, ¶1, “We have used the Gaussian similarity based on the Euclidian distance between each two vertices and a positive parameter σ to control the size of the neighborhood,” Examiner notes that the distance threshold is the limiter of the neighborhood size and notes the density of the data determines the cluster centers which impact the Euclidian distances of the location of the cluster centers on the graph. Examiner also notes that similarities are based on features of the data.)
“and generating the graph based on the updated weights.” (Mouden §3.2, ¶1, “After defining the individuals of our problem, we build a graph”)
Regarding claim 6, Hamer as modified in claim 1, teaches all the limitations of claim 1.
Hamer as modified further teaches:
“implementing differentially private k-means or differentially private maximum mean discrepancy algorithm on the sampled data to compute the plurality of cluster centers.” (Park ¶35, “To address this issue, an embodiment of the present invention uses a differentially private k-means clustering algorithm using a quad-tree for expressing the data distribution while reducing the number of buckets. Hereinafter, an embodiment of the present invention is described in detail with reference to the drawings.”
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460
500
media_image1.png
Greyscale
Examiner notes the subsets are non overlapping as shown in Fig. 3. Park ¶79 “When the clustering begins, the center coordinates of the clusters should be determined.”)
Regarding claim 7, Hamer as modified in claim 1, teaches all the limitations of claim 1.
Hamer as modified further teaches:
“wherein the machine learning model includes one or more of the following models: decision tree, ensemble trees, logistic regression, neural network architectures, and predictive model.” (Hamer “Table 3: Comparative analysis results using the random forest model. All metrics are averaged and presented along with standard error bounds where applicable. Note that we scale StEP’s directions to have magnitude 1; therefore, the number of steps is always equal to the path length.” Examiner notes the random forest model is a type of ensemble trees.)
Regarding claim 8, Hamer as modified in claim 1 teaches a system for generating realistic multi-step recourse paths with privacy guarantees comprising a processor and a memory operatively connected to the processor via a communication interface, the memory storing computer readable instructions (Gong ¶81, “For example, a computer system or other data processing system, such as the computing system 100, can carry out the computer-implemented method 400 in response to its processor executing a computer program (e.g., a sequence of instructions) contained in a memory or other non-transitory machine-readable storage medium. Such a computer program can be stored in a computer readable storage medium, such as, but not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, each coupled to a computer system bus.”) to perform the method of claim 1 (see rejection of claim 1) and is therefore rejected under the same analysis.
Regarding claim 9, claim 9 recites substantially similar limitations to claim 2, and is therefore rejected under the same analysis.
Regarding claim 10, claim 10 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 12, claim 12 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 13, claim 13 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis.
Regarding claim 14, claim 14 recites substantially similar limitations to claim 7, and is therefore rejected under the same analysis.
Regarding claim 15, Hamer as modified in claim 1 teaches a non-transitory computer readable medium configured to store instructions for generating realistic multi-step recourse paths with privacy guarantees, the instructions, when executed, cause a processor (Gong ¶81, “For example, a computer system or other data processing system, such as the computing system 100, can carry out the computer-implemented method 400 in response to its processor executing a computer program (e.g., a sequence of instructions) contained in a memory or other non-transitory machine-readable storage medium. Such a computer program can be stored in a computer readable storage medium, such as, but not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, each coupled to a computer system bus.”) to perform the method of claim 1 (see rejection of claim 1) and is therefore rejected under the same analysis.
Regarding claim 16, claim 16 recites substantially similar limitations to claim 2, and is therefore rejected under the same analysis.
Regarding claim 17, claim 17 recites substantially similar limitations to claim 3, and is therefore rejected under the same analysis.
Regarding claim 19, claim 19 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 20, claim 20 recites substantially similar limitations to claim 6, and is therefore rejected under the same analysis.
Claim(s) 4, 11, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hamer et al. (https://arxiv.org/pdf/2306.15557v1) (hereafter referred to as Hamer) in view of Park et al. (US 20190347278 A1) (hereafter referred to as Park) further in view of Mouden et al. (https://www.researchgate.net/publication/333224539_An_application_of_spectral_clustering_approach_to_detect_communities_in_data_modeled_by_graphs) (hereafter referred to as Mouden) further in view of Gong et al. (US 2024/0169256 A1) (hereafter referred to as Gong) further in view of Verma et al. (https://arxiv.org/pdf/2010.10596v1) (hereafter referred to as Verma).
Regarding claim 4, Hamer as modified in claim 1, teaches all the limitations of claim 1.
Hamer as modified does not distinctly disclose:
“ validating the generated recourse path by utilizing existing and new metrics,”
“wherein the metrics include distance between points in the recourse path, density of points in the recourse path, and path length.”
However, Verma teaches:
“ validating the generated recourse path by utilizing existing and new metrics,” (Verma §3.2(1) “A counterfactual that indeed is classified in the desired class is a valid counterfactual. As illustrated in fig. 1, the points shown in red and green are valid counterfactuals, as they are in the positive class region. The distance to the red counterfactual is smaller than the distance to the green counterfactual.” Examiner notes the counterfactual explanations are defined as recourses (see Verma Page 2, ¶1))
“wherein the metrics include distance between points in the recourse path, density of points in the recourse path, and path length.” (Verma §3.2(1) “A counterfactual that indeed is classified in the desired class is a valid counterfactual. As illustrated in fig. 1, the points shown in red and green are valid counterfactuals, as they are in the positive class region. The distance to the red counterfactual is smaller than the distance to the green counterfactual.” Examiner notes that reference considers distance while validating the recourse path which is affected by both the distance between points and the density of the points.)
Regarding claim 11, claim 11 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Regarding claim 18, claim 18 recites substantially similar limitations to claim 4, and is therefore rejected under the same analysis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zhang et al. (CN 108549904 A) also discloses the use of a private K-means clustering method.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Peter T Annis whose telephone number is (571)270-1059. The examiner can normally be reached M-F, 7:30am to 5pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PETER THOMAS ANNIS/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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