DETAILED ACTION
This action is responsive to claims filed on 16 June 2023.
Claims 1-11 are pending for examination.
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 Objections
Claim 4 and analogous claim 9 is objected to because of the following informalities: “the second neighbor training data” in lines 32-33 should be “the pieces of second neighbor training data”. Appropriate correction is required.
Claim 4 and analogous claim 9 is objected to because of the following informalities: “the third neighbor training data” in lines 36 should be “the pieces of third neighbor training data”. Appropriate correction is required.
Claim 4 and analogous claim 9 is objected to because of the following informalities: “the fourth neighbor training data” in lines 39-40 should be “the pieces of fourth neighbor training data”. Appropriate correction is required.
Claim 7 is objected to because of the following informalities: “The computer-implemented training data generation method according to claim 1” should be “The computer-implemented training data generation method according to claim 6”. Appropriate correction is required.
Claim 8 is objected to because of the following informalities: “The computer-implemented training data generation method according to claim 2” should be “The computer-implemented training data generation method according to claim 7”. Appropriate correction is required.
Claim 9 is objected to because of the following informalities: “The computer-implemented training data generation method according to claim 1” should be “The computer-implemented training data generation method according to claim 6”. Appropriate correction is required.
Claim 10 is objected to because of the following informalities: “The computer-implemented training data generation method according to claim 1” should be “The computer-implemented training data generation method according to claim 6”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4, 9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 and analogous claim 9 recites the limitation "the specific criterion" in lines 6-7. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the specific criterion" has been construed to be “a specific criterion”.
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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, abstract idea, without significantly more.
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory
category. MPEP 2106.03:
According to the first part of the Alice analysis, in the instant case, the claims were determined
to be directed to one of the four statutory categories: an article of manufacture, a method/process (Claims 6-10), a machine/system/product (Claims 1-5, 11), and a composition of matter. Based on the claims being determined to be within of the four categories (i.e., process, machine, manufacture, or composition of matter), (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea).
Step 2A Prong One: This part of the eligibility analysis evaluates whether the claim(s) recites a
judicial exception.
Regarding independent claims 1, 6, 11, the claims recite a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG) without significantly more (Step-2A: Prong One). The applicant's claim limitations under broadest reasonable interpretation covers activities classified under mental processes - concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection Ill) and the 2019 PEG. As evaluated below:
Claims 1, 6, 11:
“identifying, from among a plurality of pieces of training data, a first plurality of pieces of training data, a second plurality of pieces of training data, and a third plurality of pieces of training data” (mental process of judgement)
“selecting first training data from among the second plurality of pieces of training data or the third plurality of pieces of the training data based on a specific probability” (mental process of judgement)
If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is
reasonable to conclude that the claim(s) recites an abstract idea in Step 2A Prong One.
Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim(s) as a whole integrates the recited judicial exception into a practical application of the exception. As evaluated below:
“generating third training data having the label of the first value and the first attribute of the second value by using second training data of the first plurality of pieces of training data and the first training data”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“each of the first plurality of pieces of training data having a label of a first value and a first attribute of a second value”
“each of the second plurality of pieces of training data having the label of the first value and the first attribute of a third value”
“each of the third plurality of pieces of training data having the label of a fourth value and the first attribute of the second value”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B: This part of the eligibility analysis evaluates whether the claim, as a whole, amounts to
significantly more than the recited exception, i.e., whether any additional element, or combination of
additional elements, adds an inventive concept to the claim. MPEP 2106.05.
First, the additional elements considered as part of the preamble and the additional elements
directed to the use of computer technology are deemed insufficient to transform the judicial exception
to a patentable invention to a patentable invention because they generally link the judicial exception to
the technology environment, see MPEP 2106.05(h).
Second, the additional elements directed to mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Third, the claims are directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception. The courts have found these types of limitations insufficient to transform the judicial exception to a patentable invention, see MPEP 2106.05(g).
Lastly, the claims directed to data gathering activity as noted above, are deemed directed to an insignificant extra-solution activity. The courts have found these types of limitations insufficient to
qualify as "significantly more", see MPEP 2106.05(g).
Furthermore, when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018). Examiner notes Berkheimer: Option 2 - A citation to one or more of the court decisions discussed in MPEP § 2106.05(d}(II} as noting the well understood, routine, conventional nature of the additional element (s) (e.g., limitations directed to mere data gathering):
The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d).
The additional limitations, as analyzed, failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above. Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible. Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole, claims 1, 6, 11 do not recite what the courts have identified as "significantly more".
Furthermore, regarding dependent claims 2-5, which depend from claim 1, claims 7-10, which depend from claim 6, the claims are directed to a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under the Step2A and 2B:
Claims 2, 7:
Incorporates the rejections of claims 1, 6, respectively.
“identifying, from among the plurality of pieces of training data, a plurality of pieces of neighbor training data for which distances from the second training data meet a specific criterion” (mental process of judgement)
“determining the specific probability based on number of pieces of training data having the labels of the first values among the plurality of pieces of neighbor training data” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 3, 8:
Incorporates the rejections of claims 2, 7, respectively.
“identifying, from among the plurality of pieces of training data, a plurality of pieces of first neighbor training data for which distances from the first plurality of pieces of training data meet the specific criterion” (mental process of judgement)
“identifying, from among the plurality of pieces of training data, a plurality of pieces of second neighbor training data for which distances from the second plurality of pieces of training data meet the specific criterion” (mental process of judgement)
“determining the specific probability based on number of pieces of training data having the labels of the first values among the plurality of pieces of first neighbor training data and based on number of pieces of training data having the labels of the first values among the plurality of pieces of second neighbor training data” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 4, 9:
Incorporates the rejections of claims 1, 6, respectively.
“identifying, from among the plurality of pieces of training data, a plurality of pieces of first neighbor training data for which distances from the first plurality of pieces of training data meet the specific criterion” (mental process of judgement)
“identifying, from among the plurality of pieces of training data, a plurality of pieces of second neighbor training data for which distances from the third plurality of pieces of training data meet the specific criterion” (mental process of judgement)
“identifying, from among the plurality of pieces of training data, a plurality of pieces of third neighbor training data for which distances from the second plurality of pieces of training data meet the specific criterion” (mental process of judgement)
“identifying, from among the plurality of pieces of training data, a plurality of pieces of fourth neighbor training data for which distances from a fourth plurality of piece of training data having the labels of the fourth values and the first attributes of the third values meet the specific criterion” (mental process of judgement)
“determining the specific probability based on number of pieces of data located at a boundary with data having the label of the fourth value among the pieces of training data having the labels of the first values included in the pieces of first neighbor training data” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“number of pieces of data located at a boundary with data having the label of the first value among the pieces of training data having the labels of the fourth values included in the second neighbor training data”
“number of pieces of data located at a boundary with data having the label of the fourth value among the pieces of training data having the labels of the first values included in the third neighbor training data”
“number of pieces of data located at a boundary with data having the label of the first value among the pieces of training data having the labels of the fourth values included in the fourth neighbor training data”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 5, 10:
Incorporates the rejections of claims 1, 6, respectively.
“identifying, from among the plurality of pieces of training data, a plurality of pieces of neighbor training data for which distances from the first training data meet a specific criterion” (mental process of judgement) “determining a weight based on a distance of each piece of the training data from each piece of the first plurality of pieces of training data” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“generating the third training data by using the first training data, the second training data, and the weight”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
Limitations directed to instructions for mere data gathering or data output or directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The dependent claims as analyzed above, do not recite limitations that integrated the judicial exception into a practical application. In addition, the claim limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step-2B). Therefore, the claims do not recite any limitations, when considered individually or as a whole, that recite what have the courts have identified as "significantly more", see MPEP 2106.05; and therefore, as a whole the claims are not patent eligible. As shown above, the dependent claims do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified. Therefore, as a whole, the dependent claims do not recite what have the courts have identified as "significantly more" than the recited judicial exception. Therefore, claims 2-5, 7-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception and does not recite, when claim elements are examined individually and as a whole, elements that the courts have identified as "significantly more" than the recited judicial exception.
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.
Claims 1-11 are rejected under 35 U.S.C. 103 as being unpatentable over Salazar et al. (NPL: "FAWOS: Fairness-Aware Oversampling Algorithm Based on Distributions of Sensitive Attributes", hereinafter ‘Salazar'), in view of Yang et al. (NPL: "Oversampling Methods Combined Clustering and Data Cleaning for Imbalanced Network Data", hereinafter 'Yang').
Regarding claim 1 and analogous claims 6, 11, Salazar teaches A non-transitory computer-readable recording medium having stored therein a training data generation program executable by one or more computers, the training data generation program comprising:
an instruction for identifying, from among a plurality of pieces of training data, a first plurality of pieces of training data, a second plurality of pieces of training data, and a third plurality of pieces of training data, each of the first plurality of pieces of training data having a label of a first value and a first attribute of a second value, each of the second plurality of pieces of training data having the label of the first value and the first attribute of a third value, each of the third plurality of pieces of training data having the label of a fourth value and the first attribute of the second value (III. FAWOS, pg. 81372] In this section, we describe our an instruction for identifying, from among a plurality of pieces of training data proposed Fairness-Aware algorithm: FAWOS. We assume that a first plurality of pieces of training data, a second plurality of pieces of training data, and a third plurality of pieces of training data each training dataset, D, contains:
• S- the set of sensitive attributes (e.g. gender and race) with size M where each Si (e.g. race) contains privileged attributes (e.g. White, male) represented as 1 and unprivileged attributes (e.g. Black, Hispanic, female) represented as 0
• CS-the set of combination of sensitive attributes where each combination contains at least one unprivileged attribute (e.g. Black male)
• Y- a target class where 1 is the positive class and 0 is the negative class (e.g. receiving credit or not)
• ˆY- the predicted class where 1 is the positive class and 0 is the negative class;
[FIGURE 1., pg. 81372] Diagram of typology and sensitive labels. The datapoint in the center of the neighbourhood region is Borderline since it has 2 datapoints (green) of the same sensitive and target classes against 3 (yellow) of different sensitive attribute but same target class.; (As described and taught by Salazar, Figure 1. shows a set of training data, each data point has an assigned label (class of receiving credit or not) and an assigned attribute (gender and race), similar to the labels and attributes of the multiple training data points of the claimed invention.);
Salazar fails to teach an instruction for selecting first training data from among the second plurality of pieces of training data or the third plurality of pieces of the training data based on a specific probability; and an instruction for generating third training data having the label of the first value and the first attribute of the second value by using second training data of the first plurality of pieces of training data and the first training data.
Yang teaches an instruction for selecting first training data from among the second plurality of pieces of training data or the third plurality of pieces of the training data based on a specific probability; and an instruction for generating third training data having the label of the first value and the first attribute of the second value by using second training data of the first plurality of pieces of training data and the first training data ([3.3 Synthetic Sample Size Allocation Based on the Cluster Sparsity and Sample Size, pg. 1145-1146] 1) For each filtered cluster fc, calculate the Euclidean distance matrix for the minority samples in it. 2) Calculate the average distance of minority samples per filtered cluster, i.e., add all non-diagonal elements in the distance matrix and divide by the number of non-diagonal elements. 3) Calculate the sparsity factor for minority samples in a filtered cluster: Sparsity factor fc Average minority distance fc ð Þ2 ð Þ¼ minority count fc ð Þ (4) 4) The sparsity weight for each filtered cluster is the sum of the sparsity factors of the filtered clusters divided by the sparsity factors of all filtered clusters. 5) The quantitative weight for each filtered cluster is the sum of the number of minority classes in the filtered cluster divided by the number of minority classes in all filtered clusters. 6) The final an instruction for selecting first training data from among the second plurality of pieces of training data or the third plurality of pieces of the training data based on a specific probability sampling weights for each filtered cluster are determined by a combination of the sparsity and the quantity weights. 7) an instruction for generating third training data having the label of the first value and the first attribute of the second value by using second training data of the first plurality of pieces of training data and the first training data Multiply the sampling weights by the total number of samples to be synthesized to obtain the number of samples to be synthesized for each filtered cluster.).
Salazar and Yang are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Salazar, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Yang to Salazar before the effective filing date of the claimed invention in order to combine the SMOTE algorithm and FINCH clustering algorithm to filter out minority sample clusters, allocating the number of synthetic samples per cluster according to the clustering sparsity and sample weight (cf. Yang, [Abstract, pg. 1139] In network anomaly detection, network traffic data are often imbalanced, that is, certain classes of network traffic data have a large sample data volume while other classes have few, resulting in reduced overall network traffic anomaly detection on a minority class of samples. For imbalanced data, researchers have proposed the use of oversampling techniques to balance data sets; in particular, an oversampling method called the SMOTE provides a simple and effective solution for balancing data sets. However, current oversampling methods suffer from the generation of noisy samples and poor information quality. Hence, this study proposes an oversampling method for imbalanced network traffic data that combines the SMOTE algorithm and FINCH clustering algorithm to filter out minority sample clusters, proposes a scheme to allocate the number of synthetic samples per cluster according to the clustering sparsity and sample weight, and finally uses multi-layer sensors for noisy sample cleaning during sampling. We compare the proposed method with other oversampling methods, verifying that a data set processed using this method works better in network traffic anomaly detection.).
Regarding claim 2 and analogous claim 7, Salazar, as modified by Yang, teaches The non-transitory computer-readable recording medium of claim 1 and The computer-implemented training data generation method of claim 6, respectively.
Yang teaches the process further including: an instruction for identifying, from among the plurality of pieces of training data, a plurality of pieces of neighbor training data for which distances from the second training data meet a specific criterion ([3.2 Data Pre-processing Based on FINCH Clustering, pg. 1144] Given an integer index for the first neighbor of each data point, identifying, from among the plurality of pieces of training data, a plurality of pieces of neighbor training data for which distances from the second training data meet a specific criterion FINCH directly defines the neighbor link matrix according to Eq. (3). k1 i symbolizes the first neighbor of point i. The adjacency matrix links each point i to its first neighbor via j ¼ k1 i , enforces symmetry via k1 j ¼ i, and links points (i, j) that have the same neighbor by using k1 i ¼ k1 j . Eq. (3) returns a symmetric sparse matrix directly specifying a graph whose connected components are the clusters. With its amazing simplicity, the clustering equations transfer clusters in the data without relying on any threshold or further analysis.); and
the instruction for determining the specific probability based on number of pieces of training data having the labels of the first values among the plurality of pieces of neighbor training data ([3.3 Synthetic Sample Size Allocation Based on the Cluster Sparsity and Sample Size, pg. 1145-1146] 1) For each filtered cluster fc, calculate the Euclidean distance matrix for the minority samples in it. 2) Calculate the average distance of minority samples per filtered cluster, i.e., add all non-diagonal elements in the distance matrix and divide by the number of non-diagonal elements. 3) the instruction for determining the specific probability based on number of pieces of training data having the labels of the first values among the plurality of pieces of neighbor training data Calculate the sparsity factor for minority samples in a filtered cluster: Sparsity factor fc Average minority distance fc ð Þ2 ð Þ¼ minority count fc ð Þ (4) 4) The sparsity weight for each filtered cluster is the sum of the sparsity factors of the filtered clusters divided by the sparsity factors of all filtered clusters. 5) The quantitative weight for each filtered cluster is the sum of the number of minority classes in the filtered cluster divided by the number of minority classes in all filtered clusters. 6) The final sampling weights for each filtered cluster are determined by a combination of the sparsity and the quantity weights. 7) Multiply the sampling weights by the total number of samples to be synthesized to obtain the number of samples to be synthesized for each filtered cluster.).
Salazar and Yang are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 3 and analogous claim 8, Salazar, as modified by Yang, teaches The non-transitory computer-readable recording medium of claim 2 and The computer-implemented training data generation method of claim 7, respectively.
Yang teaches the process further including: an instruction for identifying, from among the plurality of pieces of training data, a plurality of pieces of first neighbor training data for which distances from the first plurality of pieces of training data meet the specific criterion ([3.2 Data Pre-processing Based on FINCH Clustering, pg. 1144] Given an integer index for the first neighbor of each data point, identifying, from among the plurality of pieces of training data, a plurality of pieces of first neighbor training data for which distances from the first plurality of pieces of training data meet the specific criterion FINCH directly defines the neighbor link matrix according to Eq. (3). k1 i symbolizes the first neighbor of point i. The adjacency matrix links each point i to its first neighbor via j ¼ k1 i , enforces symmetry via k1 j ¼ i, and links points (i, j) that have the same neighbor by using k1 i ¼ k1 j . Eq. (3) returns a symmetric sparse matrix directly specifying a graph whose connected components are the clusters. With its amazing simplicity, the clustering equations transfer clusters in the data without relying on any threshold or further analysis.);
an instruction for identifying, from among the plurality of pieces of training data, a plurality of pieces of second neighbor training data for which distances from the second plurality of pieces of training data meet the specific criterion ([3.2 Data Pre-processing Based on FINCH Clustering, pg. 1144] Given an integer index for the first neighbor of each data point, identifying, from among the plurality of pieces of training data, a plurality of pieces of second neighbor training data for which distances from the second plurality of pieces of training data meet the specific criterion FINCH directly defines the neighbor link matrix according to Eq. (3). k1 i symbolizes the first neighbor of point i. The adjacency matrix links each point i to its first neighbor via j ¼ k1 i , enforces symmetry via k1 j ¼ i, and links points (i, j) that have the same neighbor by using k1 i ¼ k1 j . Eq. (3) returns a symmetric sparse matrix directly specifying a graph whose connected components are the clusters. With its amazing simplicity, the clustering equations transfer clusters in the data without relying on any threshold or further analysis.); and
the instruction for determining the specific probability based on number of pieces of training data having the labels of the first values among the plurality of pieces of first neighbor training data and based on number of pieces of training data having the labels of the first values among the plurality of pieces of second neighbor training data ([3.3 Synthetic Sample Size Allocation Based on the Cluster Sparsity and Sample Size, pg. 1145-1146] 1) For each filtered cluster fc, calculate the Euclidean distance matrix for the minority samples in it. 2) Calculate the average distance of minority samples per filtered cluster, i.e., add all non-diagonal elements in the distance matrix and divide by the number of non-diagonal elements. 3) Calculate the determining the specific probability based on number of pieces of training data having the labels of the first values among the plurality of pieces of first neighbor training data and based on number of pieces of training data having the labels of the first values among the plurality of pieces of second neighbor training data sparsity factor for minority samples in a filtered cluster: Sparsity factor fc Average minority distance fc ð Þ2 ð Þ¼ minority count fc ð Þ (4) 4) The sparsity weight for each filtered cluster is the sum of the sparsity factors of the filtered clusters divided by the sparsity factors of all filtered clusters. 5) The quantitative weight for each filtered cluster is the sum of the number of minority classes in the filtered cluster divided by the number of minority classes in all filtered clusters. 6) The final sampling weights for each filtered cluster are determined by a combination of the sparsity and the quantity weights. 7) Multiply the sampling weights by the total number of samples to be synthesized to obtain the number of samples to be synthesized for each filtered cluster.).
Salazar and Yang are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 4 and analogous claim 9, Salazar, as modified by Yang, teaches The non-transitory computer-readable recording medium of claim 1 and The computer-implemented training data generation method of claim 6, respectively.
Yang teaches the process further including: an instruction for identifying, from among the plurality of pieces of training data, a plurality of pieces of first neighbor training data for which distances from the first plurality of pieces of training data meet the specific criterion ([3.2 Data Pre-processing Based on FINCH Clustering, pg. 1144] Given an integer index for the first neighbor of each data point, identifying, from among the plurality of pieces of training data, a plurality of pieces of first neighbor training data for which distances from the first plurality of pieces of training data meet the specific criterion FINCH directly defines the neighbor link matrix according to Eq. (3). k1 i symbolizes the first neighbor of point i. The adjacency matrix links each point i to its first neighbor via j ¼ k1 i , enforces symmetry via k1 j ¼ i, and links points (i, j) that have the same neighbor by using k1 i ¼ k1 j . Eq. (3) returns a symmetric sparse matrix directly specifying a graph whose connected components are the clusters. With its amazing simplicity, the clustering equations transfer clusters in the data without relying on any threshold or further analysis.);
an instruction for identifying, from among the plurality of pieces of training data, a plurality of pieces of second neighbor training data for which distances from the third plurality of pieces of training data meet the specific criterion ([3.2 Data Pre-processing Based on FINCH Clustering, pg. 1144] Given an integer index for the first neighbor of each data point, an instruction for identifying, from among the plurality of pieces of training data, a plurality of pieces of second neighbor training data for which distances from the third plurality of pieces of training data meet the specific criterion FINCH directly defines the neighbor link matrix according to Eq. (3). k1 i symbolizes the first neighbor of point i. The adjacency matrix links each point i to its first neighbor via j ¼ k1 i , enforces symmetry via k1 j ¼ i, and links points (i, j) that have the same neighbor by using k1 i ¼ k1 j . Eq. (3) returns a symmetric sparse matrix directly specifying a graph whose connected components are the clusters. With its amazing simplicity, the clustering equations transfer clusters in the data without relying on any threshold or further analysis.);
an instruction for identifying, from among the plurality of pieces of training data, a plurality of pieces of third neighbor training data for which distances from the second plurality of pieces of training data meet the specific criterion ([3.2 Data Pre-processing Based on FINCH Clustering, pg. 1144] Given an integer index for the first neighbor of each data point, an instruction for identifying, from among the plurality of pieces of training data, a plurality of pieces of third neighbor training data for which distances from the second plurality of pieces of training data meet the specific criterion FINCH directly defines the neighbor link matrix according to Eq. (3). k1 i symbolizes the first neighbor of point i. The adjacency matrix links each point i to its first neighbor via j ¼ k1 i , enforces symmetry via k1 j ¼ i, and links points (i, j) that have the same neighbor by using k1 i ¼ k1 j . Eq. (3) returns a symmetric sparse matrix directly specifying a graph whose connected components are the clusters. With its amazing simplicity, the clustering equations transfer clusters in the data without relying on any threshold or further analysis.);
an instruction for identifying, from among the plurality of pieces of training data, a plurality of pieces of fourth neighbor training data for which distances from a fourth plurality of piece of training data having the labels of the fourth values and the first attributes of the third values meet the specific criterion ([3.3 Synthetic Sample Size Allocation Based on the Cluster Sparsity and Sample Size, pg. 1145-1146] 1) For each filtered cluster fc, calculate the Euclidean distance matrix for the minority samples in it. 2) an instruction for identifying, from among the plurality of pieces of training data, a plurality of pieces of fourth neighbor training data for which distances from a fourth plurality of piece of training data having the labels of the fourth values and the first attributes of the third values meet the specific criterion Calculate the average distance of minority samples per filtered cluster, i.e., add all non-diagonal elements in the distance matrix and divide by the number of non-diagonal elements. 3) Calculate the sparsity factor for minority samples in a filtered cluster: Sparsity factor fc Average minority distance fc ð Þ2 ð Þ¼ minority count fc ð Þ (4) 4) The sparsity weight for each filtered cluster is the sum of the sparsity factors of the filtered clusters divided by the sparsity factors of all filtered clusters. 5) The quantitative weight for each filtered cluster is the sum of the number of minority classes in the filtered cluster divided by the number of minority classes in all filtered clusters. 6) The final sampling weights for each filtered cluster are determined by a combination of the sparsity and the quantity weights. 7) Multiply the sampling weights by the total number of samples to be synthesized to obtain the number of samples to be synthesized for each filtered cluster.); and
the instruction for determining the specific probability based on number of pieces of data located at a boundary with data having the label of the fourth value among the pieces of training data having the labels of the first values included in the pieces of first neighbor training data ([3.3 Synthetic Sample Size Allocation Based on the Cluster Sparsity and Sample Size, pg. 1145-1146] 1) For each filtered cluster fc, calculate the Euclidean distance matrix for the minority samples in it. 2) Calculate the average distance of minority samples per filtered cluster, i.e., add all non-diagonal elements in the distance matrix and divide by the number of non-diagonal elements. 3) Calculate the determining the specific probability based on number of pieces of data located at a boundary with data having the label of the fourth value among the pieces of training data having the labels of the first values included in the pieces of first neighbor training data sparsity factor for minority samples in a filtered cluster: Sparsity factor fc Average minority distance fc ð Þ2 ð Þ¼ minority count fc ð Þ (4) 4) The sparsity weight for each filtered cluster is the sum of the sparsity factors of the filtered clusters divided by the sparsity factors of all filtered clusters. 5) The quantitative weight for each filtered cluster is the sum of the number of minority classes in the filtered cluster divided by the number of minority classes in all filtered clusters. 6) The final sampling weights for each filtered cluster are determined by a combination of the sparsity and the quantity weights. 7) Multiply the sampling weights by the total number of samples to be synthesized to obtain the number of samples to be synthesized for each filtered cluster.),
number of pieces of data located at a boundary with data having the label of the first value among the pieces of training data having the labels of the fourth values included in the second neighbor training data ([3.3 Synthetic Sample Size Allocation Based on the Cluster Sparsity and Sample Size, pg. 1145-1146] 1) For each filtered cluster fc, calculate the Euclidean distance matrix for the minority samples in it. 2) Calculate the average distance of minority samples per filtered cluster, i.e., add all non-diagonal elements in the distance matrix and divide by the number of non-diagonal elements. 3) Calculate the sparsity factor for number of pieces of data located at a boundary with data having the label of the first value among the pieces of training data having the labels of the fourth values included in the second neighbor training data minority samples in a filtered cluster: Sparsity factor fc Average minority distance fc ð Þ2 ð Þ¼ minority count fc ð Þ (4) 4) The sparsity weight for each filtered cluster is the sum of the sparsity factors of the filtered clusters divided by the sparsity factors of all filtered clusters. 5) The quantitative weight for each filtered cluster is the sum of the number of minority classes in the filtered cluster divided by the number of minority classes in all filtered clusters. 6) The final sampling weights for each filtered cluster are determined by a combination of the sparsity and the quantity weights. 7) Multiply the sampling weights by the total number of samples to be synthesized to obtain the number of samples to be synthesized for each filtered cluster.),
number of pieces of data located at a boundary with data having the label of the fourth value among the pieces of training data having the labels of the first values included in the third neighbor training data ([3.3 Synthetic Sample Size Allocation Based on the Cluster Sparsity and Sample Size, pg. 1145-1146] 1) For each filtered cluster fc, calculate the Euclidean distance matrix for the minority samples in it. 2) Calculate the average distance of minority samples per filtered cluster, i.e., add all non-diagonal elements in the distance matrix and divide by the number of non-diagonal elements. 3) Calculate the sparsity factor for number of pieces of data located at a boundary with data having the label of the fourth value among the pieces of training data having the labels of the first values included in the third neighbor training data minority samples in a filtered cluster: Sparsity factor fc Average minority distance fc ð Þ2 ð Þ¼ minority count fc ð Þ (4) 4) The sparsity weight for each filtered cluster is the sum of the sparsity factors of the filtered clusters divided by the sparsity factors of all filtered clusters. 5) The quantitative weight for each filtered cluster is the sum of the number of minority classes in the filtered cluster divided by the number of minority classes in all filtered clusters. 6) The final sampling weights for each filtered cluster are determined by a combination of the sparsity and the quantity weights. 7) Multiply the sampling weights by the total number of samples to be synthesized to obtain the number of samples to be synthesized for each filtered cluster.), and
number of pieces of data located at a boundary with data having the label of the first value among the pieces of training data having the labels of the fourth values included in the fourth neighbor training data ([3.3 Synthetic Sample Size Allocation Based on the Cluster Sparsity and Sample Size, pg. 1145-1146] 1) For each filtered cluster fc, calculate the Euclidean distance matrix for the minority samples in it. 2) Calculate the average distance of minority samples per filtered cluster, i.e., add all non-diagonal elements in the distance matrix and divide by the number of non-diagonal elements. 3) Calculate the sparsity factor for number of pieces of data located at a boundary with data having the label of the first value among the pieces of training data having the labels of the fourth values included in the fourth neighbor training data minority samples in a filtered cluster: Sparsity factor fc Average minority distance fc ð Þ2 ð Þ¼ minority count fc ð Þ (4) 4) The sparsity weight for each filtered cluster is the sum of the sparsity factors of the filtered clusters divided by the sparsity factors of all filtered clusters. 5) The quantitative weight for each filtered cluster is the sum of the number of minority classes in the filtered cluster divided by the number of minority classes in all filtered clusters. 6) The final sampling weights for each filtered cluster are determined by a combination of the sparsity and the quantity weights. 7) Multiply the sampling weights by the total number of samples to be synthesized to obtain the number of samples to be synthesized for each filtered cluster.).
Salazar and Yang are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 5 and analogous claim 10, Salazar, as modified by Yang, teaches The non-transitory computer-readable recording medium of claim 1 and The computer-implemented training data generation method of claim 6, respectively.
Yang teaches the process further including: an instruction for identifying, from among the plurality of pieces of training data, a plurality of pieces of neighbor training data for which distances from the first training data meet a specific criterion ([3.2 Data Pre-processing Based on FINCH Clustering, pg. 1144] Given an integer index for the first neighbor of each data point, identifying, from among the plurality of pieces of training data, a plurality of pieces of neighbor training data for which distances from the first training data meet a specific criterion FINCH directly defines the neighbor link matrix according to Eq. (3). k1 i symbolizes the first neighbor of point i. The adjacency matrix links each point i to its first neighbor via j ¼ k1 i , enforces symmetry via k1 j ¼ i, and links points (i, j) that have the same neighbor by using k1 i ¼ k1 j . Eq. (3) returns a symmetric sparse matrix directly specifying a graph whose connected components are the clusters. With its amazing simplicity, the clustering equations transfer clusters in the data without relying on any threshold or further analysis.);
an instruction for determining a weight based on a distance of each piece of the training data from each piece of the first plurality of pieces of training data; and an instruction for generating the third training data by using the first training data, the second training data, and the weight ([3.3 Synthetic Sample Size Allocation Based on the Cluster Sparsity and Sample Size, pg. 1145-1146] 1) For each filtered cluster fc, calculate the Euclidean distance matrix for the minority samples in it. 2) Calculate the average distance of minority samples per filtered cluster, i.e., add all non-diagonal elements in the distance matrix and divide by the number of non-diagonal elements. 3) Calculate the sparsity factor for minority samples in a filtered cluster: Sparsity factor fc Average minority distance fc ð Þ2 ð Þ¼ minority count fc ð Þ (4) 4) The sparsity weight for each filtered cluster is the sum of the sparsity factors of the filtered clusters divided by the sparsity factors of all filtered clusters. 5) The quantitative weight for each filtered cluster is the sum of the number of minority classes in the filtered cluster divided by the number of minority classes in all filtered clusters. 6) The an instruction for determining a weight based on a distance of each piece of the training data from each piece of the first plurality of pieces of training data final sampling weights for each filtered cluster are determined by a combination of the sparsity and the quantity weights. 7) an instruction for generating the third training data by using the first training data, the second training data, and the weight Multiply the sampling weights by the total number of samples to be synthesized to obtain the number of samples to be synthesized for each filtered cluster.).
Salazar and Yang are combinable for the same rationale as set forth above with respect to claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hagen et al. (U.S. Pre-Grant Publication No. 20200387755) teaches training a machine learning model with a full training data set, the full training data set comprising a plurality of data points, to generate a first model state of the machine learning model, generating respective embeddings for the data points in the full TRAINING data set with the first model state of the machine learning model, applying a clustering algorithm to the respective embeddings to generate one or more clusters of the embeddings, identifying outlier embeddings from the one or more clusters of the embeddings, generating a reduced training data set comprising the full training data set less the data points associated with the outlier embeddings, training the machine learning model with the reduced training data set to a second model state, and applying the second model state to one or more data sets to classify the one or more data sets.
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/MM/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129