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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on October 24, 2025 has been entered.
Claim Objections
Claims 8 and 16 are objected to because of the following informalities: “conditions-of-interest” should read “anomalies”. 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 1, 9 and 17 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.
The term “improving operations of the monitored system” in claims 1, 9 and 17 is a relative term which renders the claim indefinite. The term “improving operations of the monitored system” 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. The "operations of the monitored system" limitation has been rendered indefinite by the use of the term "improving".
Claims 3-8, 11-16, and 19-23 are further rejected for dependency on claims 1, 9, and 17.
Examiner’s Note: For the purposes of examination, “improving operations of the monitored system” will be interpreted as using the trained SVM model to detect the one or more anomalies of the monitored system.
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, 3-9, 11-17, and 19-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method for improving operation of a monitored system, which is directed to a method, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“selectively discarding points from the training data set based on an inverse distance to a separating hyperplane for the SVM model to obtain a reduced training data set”
“making approximations by generating an active set of support vectors based on the reduced training data set, wherein making the approximations reduces computing costs”
“iteratively performing the following operations under a condition that SVM misclassifications continue to decrease by more than a minimum amount”
“selecting additional points among the discarded points based on an inverse distance to the separating hyperplane for the SVM model”
“if the new active set of support vectors produces fewer misclassifications than the active set of support vectors, updating the active set of support vectors with the new active set of support vectors”
“detect one or more anomalies within the monitored system based on monitored data points received from the monitored system”
“when the one or anomalies are detected, performing an action to remedy the detected one or more anomalies
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim corresponds to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
The limitations:
“generating a new active set of support vectors based at least on the active set of support vectors and the selected additional points, wherein generating the new active set of support vectors includes solving a nonlinear kernel for the SVM model”
As drafted, under their broadest reasonable interpretations, cover mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. The above limitations in the context of this claim encompass mathematical calculations.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“during a training mode of a support vector machine (SVM) model for detecting one or more anomalies within the monitored system, wherein the one or more anomalies include an impending failure of the monitored system”
“training the SVM model using the training data set”
“during a surveillance mode: using the trained SVM model to…”
“wherein using the trained SVM model to detect the one or more anomalies produces fewer misclassifications of the monitored data points, thereby improving operations of the monitored system”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“receiving a training data set comprising labeled data points received from the monitored system”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply” and “insignificant extra-solution activity”. Specifically, receiving limitation recites the well-understood, routine, and conventional activity of receiving and transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 3,
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method for improving operation of a monitored system, which is directed to a method, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein while selecting the additional points, the method selects an additional point x from the discarded points with a probability
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27
133
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Greyscale
,
where d(x) represents a distance from x to the separating hyperplane, and µ, v, and β represent associated parameters; and µ, v, and β have positive values”
As drafted, under the broadest reasonable interpretation, covers a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations). The above limitation in the context of this claim corresponds to a mathematical calculation and/or mathematical formula.
Step 2A Prong Two Analysis: See corresponding analysis of claim 1.
Step 2B Analysis: See corresponding analysis of claim 1.
Regarding Claim 4,
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a method for improving operation of a monitored system, which is directed to a method, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the SVM model is formulated based on one of the following types of kernels: a linear kernel; a polynomial kernel; a hyperbolic tangent kernel; and a radial basis function kernel”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 5,
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method for improving operation of a monitored system, which is directed to a method, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the monitored system comprises one of the following: a computer system; a database system; a website; an online customer-support system; a vehicle; an aircraft; a utility system asset; and a piece of machinery”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 6,
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a method for improving operation of a monitored system, which is directed to a method, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“wherein data points received from the monitored system include one or more of the following: time-series sensor signals; computer parameters; textual data; numerical data; and image data”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “insignificant extra-solution activity”. Further, the receiving limitation recites the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 7,
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a method for improving operation of a monitored system, which is directed to a method, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein detecting the one or more anomalies further comprises detecting one or more of the following: a malicious-intrusion event in the monitored system; a preventive-maintenance condition for the monitored system; a fraud condition for the monitored system; a product-purchasing condition for the monitored system; and a consumer-attrition condition for the monitored system”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim corresponds to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: See corresponding analysis of claim 1.
Step 2B Analysis: See corresponding analysis of claim 1.
Regarding Claim 8,
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a method for improving operation of a monitored system, which is directed to a method, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“performing an action to stop a malicious-intrusion event in the monitored system”
“scheduling a maintenance operation for the monitored system”
“performing an action to stop an instance of fraud associated with the monitored system”
“performing an action to make offers to customers associated with the monitored system”
“performing an action to resolve dissatisfaction of a customer associated with the monitored system”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim corresponds to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“sending a notification to an administrator of the monitored system”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “insignificant extra-solution activity”. Further, the sending limitation recites the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 9,
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a non-transitory computer-readable storage medium storing instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“selectively discarding points from the training data set based on an inverse distance to a separating hyperplane for the SVM model to obtain a reduced training data set”
“making approximations by generating an active set of support vectors based on the reduced training data set, wherein making the approximations reduces computing costs”
“iteratively performing the following operations under a condition that SVM misclassifications continue to decrease by more than a minimum amount”
“selecting additional points among the discarded points based on an inverse distance to the separating hyperplane for the SVM model”
“if the new active set of support vectors produces fewer misclassifications than the active set of support vectors, updating the active set of support vectors with the new active set of support vectors”
“detect one or more anomalies within the monitored system based on monitored data points received from the monitored system”
“when the one or anomalies are detected, performing an action to remedy the detected one or more anomalies
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim corresponds to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
The limitations:
“generating a new active set of support vectors based at least on the active set of support vectors and the selected additional points, wherein generating the new active set of support vectors includes solving a nonlinear kernel for the SVM model”
As drafted, under their broadest reasonable interpretations, cover mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. The above limitations in the context of this claim encompass mathematical calculations.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for improving operation of a monitored system”
“during a training mode of a support vector machine (SVM) model for detecting one or more anomalies within the monitored system, wherein the one or more anomalies include an impending failure of the monitored system”
“training the SVM model using the training data set”
“during a surveillance mode: using the trained SVM model to…”
“wherein using the trained SVM model to detect the one or more anomalies produces fewer misclassifications of the monitored data points, thereby improving operations of the monitored system”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“receiving a training data set comprising labeled data points received from the monitored system”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply” and “insignificant extra-solution activity”. Specifically, receiving limitation recites the well-understood, routine, and conventional activity of receiving and transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 11,
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a non-transitory computer-readable storage medium storing instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein while selecting the additional points, the method selects an additional point x from the discarded points with a probability
PNG
media_image1.png
27
133
media_image1.png
Greyscale
,
where d(x) represents a distance from x to the separating hyperplane; and µ, v, and β represent associated parameters; and µ, v, and β have positive values”
As drafted, under the broadest reasonable interpretation, covers a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations). The above limitation in the context of this claim corresponds to a mathematical calculation.
Step 2A Prong Two Analysis: See corresponding analysis of claim 9.
Step 2B Analysis: See corresponding analysis of claim 9.
Regarding Claim 12,
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a non-transitory computer-readable storage medium storing instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 9.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the SVM model is formulated based on one of the following types of kernels: a linear kernel; a polynomial kernel; a hyperbolic tangent kernel; and a radial basis function kernel”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 13,
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a non-transitory computer-readable storage medium storing instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 9.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the monitored system comprises one of the following: a computer system; a database system; a website; an online customer-support system; a vehicle; an aircraft; a utility system asset; and a piece of machinery”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 14,
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a non-transitory computer-readable storage medium storing instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 9.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“wherein data points received from the monitored system include one or more of the following: time-series sensor signals; computer parameters; textual data; numerical data; and image data”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “insignificant extra-solution activity”. Further, the receiving limitation recites the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 15,
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a non-transitory computer-readable storage medium storing instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein detecting the one or more anomalies further comprises detecting one or more of the following: a malicious-intrusion event in the monitored system; a preventive-maintenance condition for the monitored system; a fraud condition for the monitored system; a product-purchasing condition for the monitored system; and a consumer-attrition condition for the monitored system”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim corresponds to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: See corresponding analysis of claim 9.
Step 2B Analysis: See corresponding analysis of claim 9.
Regarding Claim 16,
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a non-transitory computer-readable storage medium storing instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“performing an action to stop a malicious-intrusion event in the monitored system”
“scheduling a maintenance operation for the monitored system”
“performing an action to stop an instance of fraud associated with the monitored system”
“performing an action to make offers to customers associated with the monitored system”
“performing an action to resolve dissatisfaction of a customer associated with the monitored system”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim corresponds to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“sending a notification to an administrator of the monitored system”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “insignificant extra-solution activity”. Further, the sending limitation recites the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 17,
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a system that improves operation of a monitored system, comprising: at least one processor and at least one associated memory, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“selectively discarding points from the training data set based on an inverse distance to a separating hyperplane for the SVM model to obtain a reduced training data set”
“making approximations by generating an active set of support vectors based on the reduced training data set, and wherein making the approximations reduce computing costs”
“iteratively performing the following operations under a condition that SVM misclassifications continue to decrease by more than a minimum amount”
“selecting additional points among the discarded points based on an inverse distance to the separating hyperplane for the SVM model”
“if the new active set of support vectors produces fewer misclassifications than the active set of support vectors, updating the active set of support vectors with the new active set of support vectors”
“detect the one or more anomalies within the monitored system based on monitored data points received from the monitored system”
“when the one or more anomalies are detected, performs an action to remedy the detected one or more anomalies”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim corresponds to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
The limitations:
“generating a new active set of support vectors based at least on the active set of support vectors and the selected additional points, wherein generating the new active set of support vectors includes solving a nonlinear kernel for the SVM model”
As drafted, under their broadest reasonable interpretations, cover mathematical
concepts, i.e., mathematical relationships, mathematical formulas or equations, and
mathematical calculations. The above limitations in the context of this claim encompass
mathematical calculations.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“A system that improves operation of a monitored system, comprising: at least one processor and at least one associated memory”
“an optimization mechanism that executes on the at least one processor”
“wherein during a training mode of a support vector machine (SVM) model for detecting anomalies within the monitored system, wherein the one or more anomalies include an impending failure of the monitored system, the optimization mechanism…”
“trains the SVM model, using the training data set”
“wherein during a surveillance mode, the optimization mechanism: uses the trained SVM model to…”
“wherein using the trained SVM model to detect the one or more anomalies produces fewer misclassifications of the monitored data points, thereby improving operations of the monitored system”
As drafted, are additional elements that amount to no more than mere instructions to
apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“receives a training data set comprising labeled data points received from the monitored system”
As drafted, are additional elements that amount to no more than insignificant extra-
solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical
application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to
amount to significantly more than the judicial exception. As discussed above with
respect to the integration of the abstract ideas into a practical application, all of the
additional elements are “mere instructions to apply” and “insignificant extra-solution
activity”. Specifically, receiving limitation recites the well-understood, routine, and
conventional activity of receiving and transmitting data over a network. MPEP
2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115
USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply and insignificant extra-solution activity cannot provide an
inventive concept. The claim is not patent eligible.
Regarding Claim 19,
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a system that improves operation of a monitored system, comprising: at least one processor and at least one associated memory, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein while selecting the additional points, the optimization mechanism selects an additional point x from the discarded points with a probability
PNG
media_image1.png
27
133
media_image1.png
Greyscale
,
where d(x) represents a distance from x to the separating hyperplane, and µ, v, and β represent associated parameters; and µ, v, and β have positive values”
As drafted, under the broadest reasonable interpretation, covers a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations). The above limitation in the context of this claim corresponds to a mathematical calculation.
Step 2A Prong Two Analysis: See corresponding analysis of claim 17.
Step 2B Analysis: See corresponding analysis of claim 17.
Regarding Claim 20,
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a system that improves operation of a monitored system, comprising: at least one processor and at least one associated memory, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 17.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the SVM model is formulated based on one of the following types of kernels: a linear kernel; a polynomial kernel; a hyperbolic tangent kernel; and a radial basis function kernel”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 21,
Claim 21 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 21 is directed to a method for improving operation of a monitored system, which is directed to a method, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein making the approximations includes using a block-diagonal approximation”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 22,
Claim 22 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 22 is directed to a non-transitory computer-readable storage medium storing instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 9.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein making the approximations includes using a block-diagonal approximation”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 23,
Claim 23 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 23 is directed to a system that improves operation of a monitored system, comprising: at least one processor and at least one associated memory, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 17.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein making the approximations includes using a block-diagonal approximation”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim 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.
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, 4-9, 12-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Glassman et al. (U.S. Patent Publication No. 2021/0342652) (“Glassman”) in view of Wang et al. (Training Data Selection for Support Vector Machines) (“Wang”).
Regarding claim 1, Glassman teaches a method for improving operation of a monitored system, comprising: during a training mode of a support vector machine (SVM) model for detecting one or more anomalies within the monitored system (Glassman [0038] “The feature values 115, 116, 117, and 118 are then subjected to at least two different, independent, support vector machines, also known as support vector machine techniques, Each support vector machine technique (SVM) is independently looking for an anomaly. The support vector machines, 120 and 125, can be the of the same kernel type, trained with different parameters or independently sampled training data examples, or different kernel types for each feature value 115, 116, 117, and 118.”; [0074] “Method 500 includes testing a manufactured part for anomalies with a two layer arrangement discussed herein, which is shown generally at 502, wherein the testing 502 comprises receiving at least one set of feature values data from at least one sensor or the manufactured part, which is shown generally at 504.” Glassman provides training support vector machines to detect anomalies in manufactured parts with sensor data, corresponding to a training mode of a support vector machine (SVM) model for detecting one or more anomalies within a monitored system.), wherein the one or more anomalies include an impending failure of the monitored system (Glassman [0075] “The method 500, may further comprise determining a severity level of an anomaly wherein the anomaly detection indicates the presence of an anomaly, and wherein the severity level is indicated by the real valued output of the network.” Glassman provides determining severity level of anomalies in a manufactured part, corresponding to the one or more anomalies include an impending failure of the monitored system.): receiving a training data set comprising labeled data points from the monitored system (Glassman [0038] “The feature values 115, 116, 117, and 118 are then subjected to at least two different, independent, support vector machines, also known as support vector machine techniques, Each support vector machine technique (SVM) is independently looking for an anomaly. The support vector machines, 120 and 125, can be the of the same kernel type, trained with different parameters or independently sampled training data examples, or different kernel types for each feature value 115, 116, 117, and 118.”; [0074] “Method 500 includes testing a manufactured part for anomalies with a two layer arrangement discussed herein, which is shown generally at 502, wherein the testing 502 comprises receiving at least one set of feature values data from at least one sensor or the manufactured part, which is shown generally at 504. The testing 502 includes extracting at least two sets of refined data, wherein the at least two sets of refined data are sets of numbers representing the feature values data extracted from the sensors or manufactured part using feature extraction operators, which is shown generally at 506. The testing 502 includes classifying the at least two sets of refined data as a representation of a property or output of the part and storing the at least two sets of refined data as stored data, which is shown generally at 508” Glassman provides receiving feature values from sensors monitoring manufactured parts and using the feature values to train support vector machines, corresponding to receiving a training data set comprising labeled data points from the monitored system, wherein the received feature values are classified and correspond to the received training dataset comprising labeled points.);
training the SVM model using the training data set (Glassman [0038] “The feature values 115, 116, 117, and 118 are then subjected to at least two different, independent, support vector machines, also known as support vector machine techniques, Each support vector machine technique (SVM) is independently looking for an anomaly. The support vector machines, 120 and 125, can be the of the same kernel type, trained with different parameters or independently sampled training data examples, or different kernel types for each feature value 115, 116, 117, and 118.” Glassman provides training the SVM with different kernel types for each feature value 115, 116, 117, and 118, corresponding to training the SVM model using the training data set.) …and during a surveillance mode: using the trained SVM model to detect the one or more anomalies within the monitored system based on monitored data points received from the monitored system (Glassman [0076] “The testing 502 includes performing, via a first layer of at least two support vector machines (120 and 125), anomaly detection of the manufactured part using the stored data and performing, via a second layer of at least two distinct types of support vector machine, anomaly detection of the manufactured part using the stored data, which is shown generally at 510. Method 500 uses the second layer of support vector machine outputs to compare or combine the results to determine the presence of an anomaly, which is shown generally at 512. Method 500 can further provide that the feature values data represents data from a scan, image, test, or output of the sensor or manufactured part.” Glassman provides using a trained support vector machine to determine the presence of an anomaly in a manufactured part based on a sensor of the manufactured part, corresponding to using the trained SVM model to detect the one or more anomalies within the monitored system based on monitored data points received from the monitored system.); and when one or more anomalies are detected, performing an action to remedy the detected one or more anomalies (Glassman [0076] “The testing 502 includes performing, via a first layer of at least two support vector machines (120 and 125), anomaly detection of the manufactured part using the stored data and performing, via a second layer of at least two distinct types of support vector machine, anomaly detection of the manufactured part using the stored data, which is shown generally at 510”; [0087] “These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output.” Glassman provides audible presentation of output if the quality of a part is unacceptable based on detected anomalies, which corresponds to when one or more anomalies are detected, performing an action to remedy the detected one or more anomalies.) wherein using the trained SVM model to detect the one or more anomalies produces fewer misclassifications of the monitored data points, thereby improving operations of the monitored system (Glassman [0012] “Since the operations of the manufactured part anomaly analysis technology provided by the present invention starts from analyzing the manufactured part feature values comprised in all the collected part data, it is suitable for various manufactured part environments. Moreover, the manufactured part anomaly analysis technology provided by the present invention trains the classification model using the optimization technique of linear discriminant margin maximization so the overfitting phenomenon caused by less important manufactured part feature values in the training process can be minimized based on the theory of large margin classifiers. Thereby, the accuracy rate regarding classifying manufactured part anomalies can be increased with the result that detection of manufactured part anomalies becomes more accurate.”; [0076] “Method 500 uses the second layer of support vector machine outputs to compare or combine the results to determine the presence of an anomaly, which is shown generally at 512. Method 500 can further provide that the feature values data represents data from a scan, image, test, or output of the sensor or manufactured part.” Glassman provides using support vector machines to determine anomalies in a monitored system, wherein the detection of manufactured part anomalies becomes more accurate using the trained SVM, corresponding to wherein using the trained SVM model to detect the one or more anomalies produces fewer misclassifications of the monitored data points, thereby improving operations of the monitored system.).
Glassman fails to teach …performing the operations of: selectively discarding points from the training data set based on an inverse distance to a separating hyperplane for the SVM model to obtain a reduced training data set; making approximations by generating an active set of support vectors based on the reduced training data set wherein making the approximations reduce computing costs; iteratively performing the following operations under a condition that SVM misclassifications continue to decrease by more than a minimum amount: selecting additional points among the discarded points based on an inverse distance to the separating hyperplane for the SVM model; generating a new active set of support vectors based at least on the active set of support vectors and the selected additional points, wherein generating the new active set of support vectors includes solving a nonlinear kernel for the SVM model; and if the new active set of support vectors produces fewer misclassifications than the active set of support vectors, updating the active set of support vectors with the new active set of support vectors...
However, Wang teaches …training the SVM model using the training data set (Wang Section 4 Results and Discussion “The SVM training algorithm was implemented based on the SMO method. For all datasets, Gaussian kernels were used and the generalization error of the SVMs was estimated using the 5-fold cross-validation method. For each training set, according to the data selection method used, a portion of the training set (ranging from 10 to 100 percent) was selected as the reduced training set to train the SVM classifier.” Wang providing training a SVM model using a training dataset.), which includes performing the operations of: selectively discarding points from the training data set based on an inverse distance to a separating hyperplane for the SVM model to obtain a reduced training data set (Wang Section 3.3 Data Selection Based on Random Sampling and Desired SVM Outputs “We sort the training examples according to their distances to the separating hyperplane and select a subset of training examples with the smallest distances as the reduced training set.” Wang provides selecting a subset of training examples based on the distance to the separating hyperplane to obtain a reduced training set, wherein the training examples with the smallest distance to the hyperplane (inverse distance) are selected as the reduced subset, corresponding to selectively discarding points from the training data set based on an inverse distance to a separating hyperplane for the SVM model to obtain a reduced training data set.); making approximations by generating an active set of support vectors based on the reduced training data set (Wang Section 4 Results and Discussion “For each training set, according to the data selection method used, a portion of the training set (ranging from 10 to 100 percent) was selected as the reduced training set to train the SVM classifier. The error rate reported is the average error rate of the resulting SVM classifiers on the test sets over the 5 iterations… Then a portion of the training examples in the training set is selected to form the reduced training set based on their distances to the desired separating hyperplane (see Eq. (10)). The second time a SVM classifier is trained with the reduced training set.” Wang provides training the support vector machines using the reduced training data set, corresponding to making approximations by generating an active set of support vectors based on the reduced training data set, wherein the SVM trained with the reduced dataset corresponds to the active set of support vectors and wherein the training of the SVMs correspond to the “making approximations”.), wherein making the approximations reduce computing costs (Wang Section 2 Related Background “To train a SVM classifier, one therefore needs to solve the dual quadratic programming problem (3) under the constraints (4). For a small training set, standard QP solvers, such as CPLEX, LOQO, MINOS and Matlab QP routines, can be readily used to obtain the solution. However, for a large training set, they quickly become intractable because of the large memory requirements and the enormous amounts of training time involved.”; Section 3.3 Data Selection Based on Random Sampling and Desired SVM Outputs “The random sampling strategy simply selects a small portion of the training data to form the reduced training set uniformly at random. This method is straightforward to implement and requires no extra computation.” Wang provides training an SVM with a reduced dataset to generate “active” support vectors, wherein the reduced dataset reduces memory requirements and training time, corresponding to making the approximations) reduces computing costs.); iteratively performing the following operations under a condition that SVM misclassifications continue to decrease by more than a minimum amount (Wang Section 4 Results and Discussion “For each training set, according to the data selection method used, a portion of the training set (ranging from 10 to 100 percent) was selected as the reduced training set to train the SVM classifier. The error rate reported is the average error rate of the resulting SVM classifiers on the test sets over the 5 iterations. Due to the space limit, only results on three datasets will be presented. Note that when the data selection method is based on the desired SVM outputs, the SVM training procedure has to be run twice in each iteration… It was found in our experiments that selecting training examples from each class separately often improves the classification accuracy of the resulting SVM classifiers.” Wang provides iterative operations for training an SVM to improve the classification accuracy of the resulting SVM classifiers, corresponding to iteratively performing operations under a condition that SVM misclassifications continue to decrease by more than a minimum amount.): selecting additional points among the discarded points based on an inverse distance to the separating hyperplane for the SVM model (Wang Section 4 Results and Discussion “For each training set, according to the data selection method used, a portion of the training set (ranging from 10 to 100 percent) was selected as the reduced training set to train the SVM classifier… Then a portion of the training examples in the training set is selected to form the reduced training set based on their distances to the desired separating hyperplane (see Eq. (10)). The second time a SVM classifier is trained with the reduced training set… From Table 3 we see that the data selection method based on the desired SVM outputs gives the best results when more than 20% of the data is selected. When more than 50% of the data is selected, the results of the confidence-based method are very close to the best achievable results.” Wang provides increasing the percentage of data points used for the reduced training set based on their distance to the separating hyperplane, which includes data points which were previously discarded, since the percentage of data points used was changed from 20% of data points to 50% of data points, corresponding to selecting additional points among the discarded points based on an inverse distance to the separating hyperplane for the SVM model.); generating a new active set of support vectors based at least on the active set of support vectors and the selected additional points (Wang Section 4 Results and Discussion “For each training set, according to the data selection method used, a portion of the training set (ranging from 10 to 100 percent) was selected as the reduced training set to train the SVM classifier… Then a portion of the training examples in the training set is selected to form the reduced training set based on their distances to the desired separating hyperplane (see Eq. (10)). The second time a SVM classifier is trained with the reduced training set” Wang provides training the SVM using the reduced training set, including data points which were previously excluded as the range of included data points for training ranges from 10% to 100% of data points, corresponding to generating a new active set of support vectors based at least on the active set of support vectors and the selected additional points, wherein the “active set” of support vectors correspond to the trained SVM.), wherein generating the new active set of support vectors includes solving a nonlinear kernel for the SVM model (Wang Section 4 Results and Discussion “The SVM training algorithm was implemented based on the SMO method. For all datasets, Gaussian kernels were used and the generalization error of the SVMs was estimated using the 5-fold cross-validation method.” Wang provides using a Gaussian kernel for SVM training, corresponding to generating the new active set of support vectors includes solving a nonlinear kernel for the SVM model, wherein the Gaussian kernel is nonlinear.); and if the new active set of support vectors produces fewer misclassifications than the active set of support vectors, updating the active set of support vectors with the new active set of support vectors (Wang Section 4 Results and Discussion “It was found in our experiments that selecting training examples from each class separately often improves the classification accuracy of the resulting SVM classifiers… The sizes of the training and test sets in each iteration are 281 and 70, respectively. The average number of support vectors is 159.8, which is 56.87% of the size of the training sets. From Table 3 we see that the data selection method based on the desired SVM outputs gives the best results when more than 20% of the data is selected. When more than 50% of the data is selected, the results of the confidence-based method are very close to the best achievable results.” Wang provides the best achievable results when more than 50% of the data is selected, which provides an improved SVM classification accuracy, corresponding to if the new active set of support vectors produces fewer misclassifications than the active set of support vectors, updating the active set of support vectors with the new active set of support vectors.)…
Glassman and Wang are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to support vector machines. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Glassman with the above teachings of Wang. Doing so would remove irrelevant training data for SVMs (Wang Abstract “In contrast, the SVM decision function is fully determined by a small subset of the training data, called support vectors. Therefore, it is desirable to remove from the training set the data that is irrelevant to the final decision function”.).
Regarding claim 4, Glassman in view of Wang teaches the method of claim 1 as discussed above in the rejection of claim 1, wherein the SVM model is formulated based on one of the following types of kernels: a linear kernel; a polynomial kernel; a hyperbolic tangent kernel; and a radial basis function kernel (Glassman [0038] “The support vector machines, 120 and 125, can be the of the same kernel type, trained with different parameters or independently sampled training data examples, or different kernel types for each feature value 115, 116, 117, and 118. The support vector machines can be linear, radial basis function (RBF), polynomial, or any other type of nonlinear kernel SVM” Glassman provides an SVM formulated on polynomial kernels, or radial basis function kernels.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Glassman in view of Wang for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 5, Glassman in view of Wang teaches the method of claim 1 as discussed above in the rejection of claim 1, wherein the monitored system comprises one of the following: a computer system; a database system; a website; an online customer-support system; a vehicle; an aircraft; a utility system assist; and a piece of machinery (Glassman [0044] “For each SVM in the manufactured part anomaly detection system, then, the set of features and associated hyperparameters is chosen based on minimum error over the RFE process. The trained SVM associated with that feature set associated with the minimum error point is then saved as the SVM that will be used for that hidden unit or node in the manufactured part classifier in processing new sensor data, in deployment of the classifier.” Glassman provides monitoring of manufactured parts for anomalies, which corresponds to monitoring a piece of machinery.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Glassman in view of Wang for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 6, Glassman in view of Wang teaches the method of claim 1 as discussed above in the rejection of claim 1, wherein data points received from the monitored system include one or more of the following: time-series sensor signals; computer parameters; textual data; numerical data; and image data (Glassman [0035] “The current disclosure takes the approach that each view is defined as the set of feature vectors output from each of domain appropriate distinct feature extraction operators, using the same raw image data from a sensor as input.” Glassman provides received data points from sensors that include image data.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Glassman in view of Wang for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 7, Glassman in view of Wang teaches the method of claim 1 as discussed above in the rejection of claim 1, wherein detecting the one or more anomalies further comprises detecting one or more of the following: a malicious-intrusion event in the monitored system; a preventive-maintenance condition for the monitored system; a fraud condition for the monitored system; a product-purchasing condition for the monitored system; and a consumer-attrition condition for the monitored system (Glassman [0075] “The method 500, may further comprise determining a severity level of an anomaly wherein the anomaly detection indicates the presence of an anomaly, and wherein the severity level is indicated by the real valued output of the network.” Glassman provides determination of a severity level of an anomaly in a manufactured part, which also corresponds to further detecting a preventive-maintenance condition for the monitored system.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Glassman in view of Wang for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 8, Glassman in view of Wang teaches the method of claim 1 as discussed above in the rejection of claim 1, wherein performing the action to remedy the detected one or more conditions-of-interest comprises one or more of the following: sending a notification to an administrator of the monitored system; performing an action to stop a malicious-intrusion event in the monitored system; scheduling a maintenance operation for the monitored system; performing an action to stop an instance of fraud associated with the monitored system; performing an action to make offers to customers associated with the monitored system; and performing an action to resolve dissatisfaction of a customer associated with the monitored system (Glassman [0087] “These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output” Glassman provides an audible presentation of output when a severe anomaly of a manufactured part is detected, which corresponds to notifying/sending a notification to an administrator of a monitored system.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Glassman in view of Wang for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 9, it is the non-transitory computer-readable storage medium storing instructions embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found above in the rejection of claim 1. Further, Glassman teaches a non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for improving operation of a monitored system (Glassman [0086] “When implemented in software, the software code or instructions can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers. Furthermore, the instructions or software code can be stored in at least one non-transitory computer readable storage medium.” Glassman provides a non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for improving operation of a monitored system.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Glassman in view of Wang for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 12, the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Glassman in view of Wang for the same reasons disclosed above in the rejection of claim 4.
Regarding claim 13, the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Glassman in view of Wang for the same reasons disclosed above in the rejection of claim 5.
Regarding claim 14, the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Glassman in view of Wang for the same reasons disclosed above in the rejection of claim 6.
Regarding claim 15, the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Glassman in view of Wang for the same reasons disclosed above in the rejection of claim 7.
Regarding claim 16, the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Glassman in view of Wang for the same reasons disclosed above in the rejection of claim 8.
Regarding claim 17, it is the system embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found above in the rejection of claim 1. Further, Glassman teaches a system that improves operation of a monitored system, comprising: at least one processor and at least one associated memory (Glassman [0086] “When implemented in software, the software code or instructions can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers. Furthermore, the instructions or software code can be stored in at least one non-transitory computer readable storage medium.” Glassman provides a non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method, corresponding to a system comprising: at least one processor and at least one associated memory) and an optimization mechanism that executes on the at least one processor (Glassman [0029] “By maximizing the width of the margin through an optimization process operating on the training examples, there is a theoretical guarantee of best performance in generalizing to new data unseen during the training process.”; [0086] “When implemented in software, the software code or instructions can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers. Glassman provides an optimization process executed on a processor, corresponding to an optimization mechanism that executes on the at least one processor.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Glassman in view of Wang for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 20, the rejection of claim 17 is incorporated herein. Further, the limitations in this claim are taught by Glassman in view of Wang for the same reasons disclosed above in the rejection of claim 4.
Claims 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over Glassman (U.S. Patent Publication No. 2021/0342652) (“Glassman”) in view of Wang et al. (Training Data Selection for Support Vector Machines) (“Wang”) in further view of Dong et al. (A Fast Parallel Optimization for Training Support Vector Machine) (“Dong”).
Regarding claim 21, Glassman in view of Wang teaches the method of claim 1 as discussed above in the rejection of claim 1, but fails to teach wherein making the approximations includes using a block-diagonal approximation.
However, Dong teaches wherein making the approximations includes using a block-diagonal approximation (Dong Section 1, Introduction: “In this paper, we propose efficient solutions to the above problems. Two steps are designed to train support vector machines. The first step is called parallel optimization, in which the kernel matrix of support vector machines is approximated by block diagonal matrices so that the original optimization problem can be rewritten into hundreds of sub-problems which are easily solved. This step removes most non-support vectors quickly and collects training sets for the next step: sequential working set algorithm”; Section 2 “Training will speed up if we can quickly remove most non-support vectors. Since kernel matrix Q is symmetric and semidefinite, its block diagonal matrices are semidefinite, which are written as (Equation (4))” Dong provides using block diagonal approximation to initialize support vector machines and remove non-support vectors, corresponding to making the approximations includes using a block-diagonal approximation.).
Glassman, Wang, and Dong are all considered analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically support-vector-machines. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Glassman in view of Wang with the above teaching of Dong. Doing so would quickly remove most non-support vectors (Dong Section 1, Introduction: “The first step is called parallel optimization, in which the kernel matrix of support vector machines is approximated by block diagonal matrices so that the original optimization problem can be rewritten into hundreds of sub-problems which are easily solved. This step removes most non-support vectors quickly and collects training sets for the next step”).
Regarding claim 22, the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Glassman in view of Wang in further view of Dong for the same reasons disclosed above in the rejection of claim 21.
Regarding claim 23, the rejection of claim 17 is incorporated herein. Further, the limitations in this claim are taught by Glassman in view of Wang in further view of Dong for the same reasons disclosed above in the rejection of claim 21.
Response to Arguments
Regarding the rejection applied under 35 U.S.C. 103, Applicant asserts that neither Glassman nor Wang discloses or suggests an iterative process of selecting additional training data points among discarded training data points to iteratively improve SVM classification accuracy by more than a minimum amount (“Remarks”, Page 14). Applicant further asserts Wang specifically does not disclose “selecting additional points among the discarded points based on an inverse distance to the separating hyperplane for the SVM model” (“Remarks”, Page 14). Applicant further asserts Wang does not disclose iterative processes, and therefore does not teach iteratively performing operations to improve SVM classification accuracy (“Remarks”, Pages 14-15).
However, Wang does teach an iterative process of selecting additional training data points among discarded training data points to iteratively improve SVM classification accuracy, as discussed in the 35 U.S.C. 103 rejection of claim 1 above. As discussed in Section 4, Wang provides recording error rates of SVM classifiers over 5 iterations, which, as also discussed in Section 4, provides improved SVM classification accuracy. Therefore, Wang does teach iterations and iteratively improving SVM classification accuracy by more than a minimum amount. Further, as part of the iterative processing, Wang also teaches selecting additional data points among discarded data points based on an inverse distance to the separating hyperplane for the SVM model. As discussed in Section 4, Wang provides a data selection method based on a distance to a separating hyperplane. Wang further discloses that 20% of the data points closest to the hyperplane may be selected as the reduced dataset. Wang subsequently discloses additional iterations where 50% of the data points closest to the hyperplane may be selected as the reduced data set. Therefore, Wang teaches “selecting additional points among the discarded points based on an inverse distance to the separating hyperplane for the SVM model”.
Regarding the rejection applied under 35 U.S.C. 101, Applicant firstly asserts that detecting one or more anomalies in the monitored system, including an impending failures of the monitored system based on monitored data points from the monitored system is not an abstract idea (“Remarks”, Page 17). Applicant further asserts that using a statistical model to monitor, detect and, if necessary, mitigate one or more anomalies in a monitored system, including an impending failure of the monitored system, based on monitored data extracted from the monitored system, is not an abstract idea (“Remarks”, Page 17). Applicant further asserts that the use of a trained SVM model produces fewer misclassifications on the monitored data, which results in lower false alarm rates when using the SVM model to detect anomalies, thereby improving the health of the monitored system and therefore integrating any alleged abstract ideas into a practical application (“Remarks”, Page 18).
However, as discussed in the 35 U.S.C. 101 rejection of claim 1 above, “detect one or more anomalies within the monitored system based on monitored data points received from the monitored system” is an abstract idea (mental process e.g., evaluation and judgement with assistance of pen and paper). For example, one could review of plurality of data points, and through evaluation and judgement, determine anomalous data points, including data points received from a monitored system. Therefore, the claim recites at least an abstract idea. The use of a model (i.e., an SVM) to “monitor, detect and, if necessary, mitigate one or more anomalies in a monitored system” amounts to no more than mere instructions to apply an exception for the abstract ideas, as the model is deployed as a tool to perform the abstract idea of detecting anomalies in data points. Further, regarding the improved detection accuracy, even if the claims did recite an improvement, it would an improvement in the abstract idea of detecting anomalies (i.e., more accurate detections). The MPEP notes that it is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology. MPEP 2106.05(a)(II). Therefore, the claims, as written, remain rejected under 35 U.S.C.101 and 35 U.S.C. 103
Conclusion
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/KURT NICHOLAS PRESSLY/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125